NASA ARSET Introduction to the GEDI Mission and its Derived Products

0:02 Speaker 1: Welcome to our training series Spaceborne lightar for Monitoring vegetation,

0:07 Speaker 1: structure and biomass using Jedi. My name is Savannah Cooley.

0:13 Speaker 1: I'm a researcher at NASA Ames Research Center with the

0:15 Speaker 1: Bay Area Environmental Research Institute in Mountain View, California, and

0:20 Speaker 1: I'm a trainer with the RSET Ecological Conservation Team. There

0:25 Speaker 1: are three parts in this training. In Part two, today

0:29 Speaker 1: will further introduce the Jedi mission and its data products

0:33 Speaker 1: with the hands on exercise for accessing and analyzing data

0:37 Speaker 1: from Jedi's level to b HDF five files. Part one

0:42 Speaker 1: covered full waveform lightar foundations, and Part three will cover

0:47 Speaker 1: estimating biomass change with the OBI one application. Remember that

0:53 Speaker 1: the homework opens on the last day of the training

0:56 Speaker 1: on November six, twenty five. It is due two weeks

0:59 Speaker 1: later on November twentieth. A certificate of completion will be

1:03 Speaker 1: awarded to participants who attend all live sessions and complete

1:07 Speaker 1: the homework assignment before the given due date. Let's dive

1:12 Speaker 1: into Part two of the series, introduction to the Jedi

1:15 Speaker 1: mission and its derived products. We have one guest instructor

1:22 Speaker 1: for Part two. Stephanie Humenis is a research associate with

1:26 Speaker 1: the Satellite Needs Working Group Management Office based out of

1:30 Speaker 1: the National Space Science and Technology Center with NASA Marshall's

1:33 Speaker 1: Space Flight Center and the University of Alabama and Huntsville

1:37 Speaker 1: Earth System Science Center. Additional support for this training came

1:42 Speaker 1: from NASA earth Rise, an Earth Action program at Marshall's

1:45 Speaker 1: Space Flight Center. I would like to acknowledge our fellow

1:51 Speaker 1: colleagues at NASA earth Rise who supported the development of

1:54 Speaker 1: this training. There are three learning objectives for this session.

2:03 Speaker 1: By the end of the session, participants will be able

2:05 Speaker 1: to first identify the general characteristics, strengths, and limitations of

2:10 Speaker 1: the Jedi mission and its data. Second, identify Jedi data

2:15 Speaker 1: products and their characteristics for elevation, canopy, height, vegetation structure,

2:19 Speaker 1: and biomass estimation. Third, access and visualized Jedi elevation, height, vegetation, structure,

2:28 Speaker 1: and biomass data sets for an area of interest using

2:31 Speaker 1: openly available tools. If you have questions during today's training,

2:39 Speaker 1: you can put them in the Q and A box

2:41 Speaker 1: within WebEx at any time. At the end of the training,

2:44 Speaker 1: we will address as many questions as we can, and

2:48 Speaker 1: we will post these questions and written answers to the

2:51 Speaker 1: training page within a week after the training. With that,

2:55 Speaker 1: I will pass to Stephanie.

3:02 Speaker 2: All right, and with that, let's dive into Section one,

3:05 Speaker 2: which reviews what the JEDI mission is and how its

3:08 Speaker 2: data for ecosystem structure can be used. JEDI is a

3:13 Speaker 2: joint project between the University of Maryland and NASA Goddard

3:17 Speaker 2: Spaceflight Center and is a NASA Ventures instrument housed aboard

3:21 Speaker 2: the International Space Station. The sensor produces high resolution laser

3:26 Speaker 2: ranging observations of three dimensional structures on Earth's surface. The

3:31 Speaker 2: use of near infrared wavelength and the science team's waveform

3:35 Speaker 2: processing algorithms enable JEDI to optimally capture detail in the

3:40 Speaker 2: structure and density of vegetation. The mission was launched in

3:44 Speaker 2: twenty eighteen and began collecting data from April twenty nineteen

3:48 Speaker 2: through March twenty twenty three, but from March twenty twenty

3:51 Speaker 2: three to April twenty twenty four the instrument went into hibernation,

3:57 Speaker 2: but has since returned return of the JEDI and will

4:01 Speaker 2: collect data through this decade. So the purpose of this

4:06 Speaker 2: mission is to assess the current state of Earth's forest structure.

4:11 Speaker 2: From these measurements, models can be tested to observe how

4:15 Speaker 2: forest dynamics have changed historically and predict how they may

4:19 Speaker 2: change over time. From each JEDI sample or observation, we

4:24 Speaker 2: gain a better understanding of how ecosystems are structured dynamically.

4:29 Speaker 2: These novel perspectives of ecosystem structure can better inform the

4:33 Speaker 2: status of habitat quality and other biodiversity indicators when combining

4:38 Speaker 2: the data with other ecosystem information. JEDI not only observes forests,

4:43 Speaker 2: but it can summarize topography below vegetated canopies, track surface deformation,

4:50 Speaker 2: assess continental and coastal water resources, and even predict weather impacts.

4:55 Speaker 2: JEDI can inform human to landscape interactions, such as when

4:59 Speaker 2: managing e ecosystems, modeling fires, and assessing the carbon cycle

5:03 Speaker 2: with its data. So the next fuel Slides will offer

5:08 Speaker 2: a brief overview of possible applications of JEDI data. Biomass

5:13 Speaker 2: estimations are essential for quantifying carbon storage inflexes in terrestrial ecosystems.

5:19 Speaker 2: Information on biomass can inform models of the carbon cycle

5:23 Speaker 2: by constraining estimates of carbon sources and sinks. Improved models

5:28 Speaker 2: enable more accurate assessments of how forests contribute to climate regulation.

5:33 Speaker 2: Biomass can be modeled from jedi's data since it captures

5:37 Speaker 2: vegetation height and structure. Jedi's biomass estimations at the footprint

5:43 Speaker 2: and graded scales provide high resolution and globally consistent measurements

5:47 Speaker 2: to track global carbon budgets. Jedi's height metrics and its

5:53 Speaker 2: ability to measure structural complexity make it possible to assess

5:56 Speaker 2: dynamics in vegetation strata, ages, types, in competition, etc. As

6:02 Speaker 2: shown in the leftmost image. From this more in depth

6:06 Speaker 2: understanding of vegetation, professionals can better inform their efforts in conservation, restoration,

6:13 Speaker 2: habitat modeling, and fire management. An example data flow in

6:17 Speaker 2: the rightmost figure demonstrates how local measurements are matched to

6:21 Speaker 2: landscape scale measurements like those from JEDI or other remote

6:25 Speaker 2: sensing satellite based sources. Integrating data at this scale over

6:30 Speaker 2: time enables greater spatial extent to monitor fuels or vegetation

6:34 Speaker 2: regrowth or ecosystem disturbances. JEDI data provide detail measurements of

6:42 Speaker 2: the canopy, structure and ground elevation, which enhance land surface

6:46 Speaker 2: models used in weather prediction by improving the representation of vegetation, height,

6:51 Speaker 2: density and distribution. JEDI data can help refine simulations of

6:56 Speaker 2: surface energy balance, evapotransporation, and n and even wind dynamics.

7:01 Speaker 2: Each of these are diversely influenced by different ecosystem structures.

7:06 Speaker 2: These atmospheric effects are key processes influencing local and regional

7:11 Speaker 2: atmospheric conditions. LIGHTAR in general is commonly used to monitor

7:17 Speaker 2: water volume changes, wetland water levels, river reach slopes, stages,

7:22 Speaker 2: or discharge across inland water surfaces or coastal regions. JEDI

7:27 Speaker 2: can support these same hydrological analyzes with its sub kilometer

7:31 Speaker 2: sampling of elevation, including that of water and water under

7:35 Speaker 2: highly vegetated areas such as in mangrove forests. Accurate elevation data,

7:42 Speaker 2: including those beneath vegetation, are essential for mapping topography and

7:46 Speaker 2: detecting surface deformation, such as in landslides or subsidence. Understanding

7:51 Speaker 2: elevation and slope changes, especially below forest canopies, can be

7:55 Speaker 2: crucial for disaster risk management, such as when managing and

7:58 Speaker 2: responding to flooding, fire spread, or landslides, by capturing precise

8:04 Speaker 2: ground elevations, even in densely forested areas. JEDI enhances elevation

8:09 Speaker 2: modeling across diverse land cover types, so the mission specifications

8:15 Speaker 2: could potentially impact your ability to apply its datas products

8:19 Speaker 2: for your study area. This is a reference table for

8:22 Speaker 2: recognizing systematic considerations which may impact data availability. Given that

8:28 Speaker 2: JEDI is on the International Space Station, it only samples

8:31 Speaker 2: between fifty one point six degrees north and south, leaving

8:35 Speaker 2: out the far southern and northern study areas or regions

8:38 Speaker 2: of the globe, which are actually shaded in blue gray

8:41 Speaker 2: on the map below. It is important to recognize the

8:44 Speaker 2: sampling density of the sensor itself, where each of the

8:48 Speaker 2: eight beams lie six hundred meters apart and each sample

8:52 Speaker 2: or footprint is located sixty meters from the other within beam.

8:57 Speaker 2: Temporal availability is important as well, those samples may be

9:01 Speaker 2: available Spatially available records only fall between April twenty nineteen

9:06 Speaker 2: to March twenty twenty three, and then again from April

9:09 Speaker 2: twenty twenty four until now. Where data is available, the

9:13 Speaker 2: resolution and geolocation accuracy may be a big contender in

9:17 Speaker 2: how representative that data is of your desired application. Updated

9:22 Speaker 2: versioning and improved data products such as Fusing JEDI with

9:26 Speaker 2: other full coverage data sets like optical or SAR can

9:29 Speaker 2: improve accuracies, but may change resolutions. The twenty five meter

9:34 Speaker 2: footprints are valuable for their higher resolution and customizability, while

9:38 Speaker 2: the one kilometer or more grids and fused products may

9:42 Speaker 2: provide more reliable information for landscape scale studies. Spaceborne lightar

9:50 Speaker 2: is an incredible novel technology advancing the field of light ar,

9:54 Speaker 2: which has been a common and trusted remote sensing technique

9:57 Speaker 2: used to manage and monitor land and water across available

10:02 Speaker 2: light our systems, whether free and open sources or commercial.

10:06 Speaker 2: There are several major differences and or trade offs, as

10:10 Speaker 2: shown in the figure. Various methods for measuring vegetation with

10:13 Speaker 2: light ar or field studies occur at different spatial sampling

10:17 Speaker 2: patterns has shown on the bottom, or sampling resolutions as

10:20 Speaker 2: shown in the middle, and by the size of the beams,

10:23 Speaker 2: which are part in part dictated by the light our

10:25 Speaker 2: system itself, as you can visualize by the altitude of

10:29 Speaker 2: the sensors. As with many applications, some of the most

10:33 Speaker 2: trusted data are with field plots or human light observation.

10:37 Speaker 2: While highly accurate field surveys can be costly and time consuming,

10:40 Speaker 2: involving a lot of manual input. Because of this, these

10:44 Speaker 2: campaigns tend to cover small areas and are collected inconsistently,

10:48 Speaker 2: making it difficult to pair timely relevant data with other

10:52 Speaker 2: remote sensing products. Terrestrial light OAR systems or TLS are

10:57 Speaker 2: similarly reliable since they are placed on the ground in location.

11:02 Speaker 2: TLS can be costly, costly instruments and require specialized software, computation, storage,

11:09 Speaker 2: or skills to process and interpret the results, while quicker

11:13 Speaker 2: and incredibly detailed repeat coverage can also be costly. Airborne

11:18 Speaker 2: light our systems like NASA ELVIS, which is the land

11:22 Speaker 2: vegetation and ice system based out of the NASA Jet

11:25 Speaker 2: Propulsion Laboratory, can be used to calibrate and validate spaceborn

11:29 Speaker 2: light our missions like the ISATS and JEDI, because they

11:32 Speaker 2: cover large areas while collecting high density and accurate observations.

11:37 Speaker 2: Unmanned aerial vehicles like drones are similarly flexible. Greater spatial

11:42 Speaker 2: coverage can promote increased frequency of observation and coverage when funded,

11:47 Speaker 2: yet also require specialized software, computation, storage, and skills to

11:52 Speaker 2: process and interpret large amounts of data. Space based lighter

11:57 Speaker 2: though a great technological feat introduce trade off in accuracy

12:00 Speaker 2: and resolution compared to airborne UAV, terrestrial light our and

12:04 Speaker 2: field plot data. The density of the data may vary

12:08 Speaker 2: though repake coverage over a particular landscape. Globally, it is

12:12 Speaker 2: highly advantageous. While NASA works to improve accuracy, accessibility, and

12:17 Speaker 2: ease users skills to interpret the data, the ISAT missions

12:21 Speaker 2: and JEDI remain free and open source on public repositories.

12:27 Speaker 2: Many field inventory plots or other field data plots were

12:30 Speaker 2: used to derive jedimetrics based on alemetric equations that relate

12:34 Speaker 2: what is observed and measured on the ground with what

12:37 Speaker 2: JEDI and other remote sensors capture. The field inventory and

12:41 Speaker 2: analysis plots can also be used for local calibration or

12:44 Speaker 2: validating results. NASA has committed the past few decades to

12:50 Speaker 2: advancing space based light our systems. Here are the Earth

12:54 Speaker 2: observing satellites collecting light our perspectives of Earth from ISAT

12:58 Speaker 2: to ISAT, iiO to JEDI, and the future EDGE and

13:01 Speaker 2: STV missions. Earth observing lighter capabilities have undergone multiple iterations

13:08 Speaker 2: across time, starting with full waveform observations over predominantly water

13:12 Speaker 2: and ice landscapes. Full waveform LIGHTAR was then adapted to

13:17 Speaker 2: measure vegetation with increased density and resolution. A photon counting

13:22 Speaker 2: lighter for ice, water and terrestrial area with iat IW

13:26 Speaker 2: also greatly improve the spatial resolution. Features from each of

13:31 Speaker 2: these systems have contributed to the design of future missions

13:34 Speaker 2: like EDGE who will increase sampling density with additional beams

13:38 Speaker 2: and swath mapping for improved change detection capabilities and repeat coverage.

13:45 Speaker 2: Now let's dive into section two to overview Jedi's data products,

13:48 Speaker 2: including the waveform metrics and estimations and derived products for vegetation, structure,

13:54 Speaker 2: and biomass. When JEDI samples data, which occurus milis millions

14:00 Speaker 2: of times per week, several data products can be derived.

14:05 Speaker 2: The samples are collected as received waveforms, which are geolocated

14:08 Speaker 2: to Earth's surface and presented as the Level one B product.

14:12 Speaker 2: These waveforms capture the height structure of the surface features

14:16 Speaker 2: within the waveform. Several algorithms that are developed by the

14:19 Speaker 2: Jedi science team make sense of the waveform shape and

14:22 Speaker 2: its intensity based on its geographic location, the land cover type,

14:26 Speaker 2: and more to generate each of the other data products.

14:31 Speaker 2: These interpretations result in elevation and height data sets for

14:34 Speaker 2: bare earth elevation, top canopy height, and relative heights across

14:38 Speaker 2: the vertical profile as found in the Level two A product,

14:42 Speaker 2: which are footprints, Level three and the other derived products

14:47 Speaker 2: aggregate and reformat those elevation and height metrics at different

14:50 Speaker 2: resolutions and with certain quality checks applied. Additional measured variables

14:55 Speaker 2: for vegetation structure include canopy cover, plant area index, plant

15:00 Speaker 2: area volume density, and folio height diversity in the Level

15:04 Speaker 2: two B footprint data set. The waveform structural complexity index

15:08 Speaker 2: in Level four captures the complexity of the canopy within

15:12 Speaker 2: the entire lightar waveform return. Several of these metrics are

15:16 Speaker 2: total summaries, or they are calculated across the vertical profile

15:20 Speaker 2: along the height of the surface feature. From these different

15:24 Speaker 2: types of metrics, biomass can then be modeled either the

15:27 Speaker 2: density or the total biomass with associated uncertainty metrics that

15:31 Speaker 2: are included in jedi's Level four A footprint and Level

15:35 Speaker 2: four B or other derived biomass data products. The footprint

15:41 Speaker 2: level data sets are publicly available as HDF five files,

15:45 Speaker 2: But what is an HDF five file? These files organize

15:49 Speaker 2: data sets of multiple formats into hierarchies or groups along

15:53 Speaker 2: alongside their associated metadata. The root group stores all data

15:58 Speaker 2: within the file under which additional groups are housed. The

16:02 Speaker 2: groups direct to several stored data sets of metrics and

16:04 Speaker 2: metadata for each observation. An example of the fire files

16:09 Speaker 2: oh my god, Okay, let me repeat that. An example

16:14 Speaker 2: of the file structure is shown on the right. The

16:17 Speaker 2: level one B waveform, Level two A heights, Level two

16:21 Speaker 2: B structure, Level four A biomass, and level four C

16:24 Speaker 2: waveform Structural Complexity Index are accessible as HDF five formats.

16:30 Speaker 2: To date, opening and handling the HDF five files, including

16:34 Speaker 2: converting from HDF five to say CSV, shape file or

16:38 Speaker 2: in array, requires either using coding solutions in Python R

16:43 Speaker 2: or in Google Earth Engine, and there are some non

16:47 Speaker 2: coding oriented existing tools with great visualization capabilities with some

16:51 Speaker 2: limitations to data sets available over a certain study area

16:55 Speaker 2: with some size constraints. And by the way, the Google

16:58 Speaker 2: Earth Engine Jedi FI foot prints are formatted into monthly rosters,

17:02 Speaker 2: which is a bit more convenient to use when managing

17:07 Speaker 2: the HDF five file itself. Knowing this file structure will

17:10 Speaker 2: be really important for opening, subsetting and visualizing the data themselves.

17:15 Speaker 2: Each product has a unique setup, so the data dictionaries

17:19 Speaker 2: and specific path names become particularly important when working with

17:22 Speaker 2: a file. Beam xxxx indicates that the same groups of

17:27 Speaker 2: data sets are hosted eight times, meaning that they were

17:31 Speaker 2: collected and stored separately by for each beam. For example,

17:35 Speaker 2: if you want to find the latitude of the lowest

17:38 Speaker 2: mode of the waveform, you'll need to follow the path

17:41 Speaker 2: from the root group down to the data set. So

17:44 Speaker 2: if you're working with the root group, which would be

17:47 Speaker 2: for example, JEDI Level two A, you'll have to navigate

17:51 Speaker 2: to one of the beams, say Beam one thousand, then

17:55 Speaker 2: go to the geolocation group where the latitude lowest mode

17:59 Speaker 2: group is housed and all of the data values for

18:04 Speaker 2: each observation are there. If you wanted to get lat

18:07 Speaker 2: lowest mode for all of the beams, you'd have to

18:09 Speaker 2: select and go through this process eight times. Note that

18:14 Speaker 2: programs like RCGIS or QGIS cannot read the Jedi HDF

18:18 Speaker 2: five files directly, so they will need to be opened

18:21 Speaker 2: or transformed by alternative methods, which we can talk about

18:26 Speaker 2: later in this section. So all the other data products

18:31 Speaker 2: are provided in a more familiar GEOTIP format. This includes

18:34 Speaker 2: the Level three, Level four B and all the other

18:37 Speaker 2: gridded or derived products. These, however, may have predetermined quality

18:42 Speaker 2: filters and be at lower resolution, but could be more

18:46 Speaker 2: accessible and applicable to larger scale studies. Each product will

18:54 Speaker 2: include some combination of the listed conventions for identification and

18:58 Speaker 2: definition that could be with a particular time period, version number,

19:03 Speaker 2: spatial resolution or instrument as listed on the slide. For

19:09 Speaker 2: the rest of the section, we will share Jedi products

19:12 Speaker 2: reference tables with important information for choosing a Jedi data

19:16 Speaker 2: product to work with. For more detailed information on each

19:19 Speaker 2: of these products, I encourage you to review the linked

19:21 Speaker 2: reference flyers. So the waveforms, as we detailed in Part

19:26 Speaker 2: one are the basis for all of the other Jedi

19:29 Speaker 2: metrics and estimations. The original transmitted and received waveform data,

19:34 Speaker 2: which are shown as TX and RX and not publicly available,

19:38 Speaker 2: are geolocated to Earth's surface and made available as a

19:41 Speaker 2: Level one B product. We went over how to access

19:44 Speaker 2: this in Part one. Level two A is the next

19:48 Speaker 2: product level built from those geolocated waveforms. It includes data

19:53 Speaker 2: for the detected ground elevation, top canopy height, and relative

19:57 Speaker 2: heights along the vertical profiles that are zero to one

20:01 Speaker 2: hundred from interpreting the waveform itself with the Level two

20:05 Speaker 2: A processing algorithms. The Level three gridded surface metrics pictured

20:10 Speaker 2: on the bottom here offers a gridded approach to the

20:13 Speaker 2: elevation and height metrics in TIF image format rather than

20:16 Speaker 2: as footprints within the one kilometer by one kilometer grids globally,

20:21 Speaker 2: the JEDI data footprint counts are recorded within each grid

20:24 Speaker 2: to calculate the mean canopy height specifically with R one

20:28 Speaker 2: hundred and the mean ground elevation along with the standard

20:32 Speaker 2: deviations for each The calculated elevation and height variables are

20:36 Speaker 2: produced for a set of five different time periods of

20:39 Speaker 2: the mission in order to incorporate more of the footprint

20:42 Speaker 2: level data that's been accumulated as the mission continues. Now

20:45 Speaker 2: a lot of the other gridded products and derived products

20:50 Speaker 2: follow a similar method of aggregating over different time periods

20:54 Speaker 2: to generate those data sets. Level two B is also

20:59 Speaker 2: based on the geolocated waveform as well as the derived

21:02 Speaker 2: elevation and canopy top height from Level to A, but

21:06 Speaker 2: it mostly relies on the probability of complete laser penetration

21:10 Speaker 2: through the canopy and to the ground. In order to

21:13 Speaker 2: accurately calculate the structural metrics. The vegetation metrics from level

21:18 Speaker 2: to B are all built off one another along the

21:21 Speaker 2: vertical profile of the surface feature, canopy cover and plant

21:25 Speaker 2: area index are calculated from the vertical metrics. A total

21:29 Speaker 2: value for canopy cover fraction and the total plant area

21:33 Speaker 2: index can also be calculated. From those plant area index results.

21:38 Speaker 2: The vertical profile plant area of volume density is also calculated,

21:43 Speaker 2: and from that the foliage height diversity is derived. Each

21:47 Speaker 2: of these data sets offers a unique perspective of the

21:50 Speaker 2: vegetation characteristics, though they are closely linked algorithmically. The figure

21:56 Speaker 2: shows an example subset of the waveform structural complexity index

21:59 Speaker 2: prediction from level four C over the Eastern Amazon. Level

22:05 Speaker 2: four C provides an index for the structural complexity of

22:07 Speaker 2: the waveform and is estimated with models that are specified

22:11 Speaker 2: over four plant functional types. The index values for high

22:15 Speaker 2: and low complexity are available as a total for the

22:18 Speaker 2: entire footprint or as multiple values along the vertical profile

22:22 Speaker 2: like those in Level two B. The index combines the

22:25 Speaker 2: information from the vertical and horizontal canopy layers from the

22:29 Speaker 2: waveforms in relation to available airborne line or data into

22:33 Speaker 2: a single interpretable metric. The figure on the right shows

22:39 Speaker 2: above ground biomass density over the US with cloud free

22:43 Speaker 2: data from February twenty twenty three. The biomass level data

22:47 Speaker 2: sets are available in footprint and gridded formats, offering related

22:51 Speaker 2: information at multiple resolutions or aggregated mean over a one

22:55 Speaker 2: kilometer grid. The biomass is modeled in each case with

22:59 Speaker 2: environmental conditions in mind, such as leaf status whether it's

23:02 Speaker 2: leaf on or leaf off depending on the land cover type,

23:06 Speaker 2: the plant functional type, and geographic region considered respective to

23:10 Speaker 2: the algorithm that's applied to each footprint or guid. Since

23:15 Speaker 2: JEDI is a sampling mission and not an imaging or

23:18 Speaker 2: wall to wall mapping sensor, many workflows may need to

23:21 Speaker 2: combine JEDI with other data to fully map a landscape

23:25 Speaker 2: and measure change over time. An example of this is

23:28 Speaker 2: in the derived product put out by the JEDI mission.

23:31 Speaker 2: In many of the derived products put out by the

23:33 Speaker 2: JEDI mission that help increase the quality and density of

23:36 Speaker 2: the information. For example, a simultaneous spaceborne light our mission

23:41 Speaker 2: is SAT two collects photon point cloud data over vegetation

23:46 Speaker 2: in terrestrial areas as well that can be fused with

23:49 Speaker 2: JEDI for increasing the three D sampling density and temporal

23:52 Speaker 2: coverage of the observations, and it's provided as a fuse

23:56 Speaker 2: product at lower resolutions. For select pantropical forests. Shown on

24:01 Speaker 2: the leftmost map, height and biomass are calculated with a

24:04 Speaker 2: combination of JEDI and InSAR from the TANDEMX mission by

24:08 Speaker 2: the European Space Agency. These are tropically optimized wall to

24:13 Speaker 2: wall maps of height and biomass information. The figure on

24:16 Speaker 2: the bottom left presents canopy height from that TANNEMAX fusion

24:20 Speaker 2: over Gabon, Mexico, French Guiana and the Amazon Basin. Two

24:26 Speaker 2: other available products from the mission are highlighted here. The

24:30 Speaker 2: gridded version of all the Level two B vegetation structure

24:33 Speaker 2: metrics are presented at multiple resolutions and have been processed

24:37 Speaker 2: with stricter quality filters applied. For example, the mean foliage

24:42 Speaker 2: high diversity in the leftmost map was generated with JEDI

24:45 Speaker 2: shots acquired between April twenty nineteen to March twenty twenty

24:48 Speaker 2: three and aggregated in six kilometer grid cells. The Level

24:53 Speaker 2: four B country level summaries for above ground biomass are

24:57 Speaker 2: also completed at multiple time periods throughout the Jedi emission

25:00 Speaker 2: to help countries meet reporting standards. The figure on the

25:04 Speaker 2: right demonstrates country wide estimates of total above ground biomass

25:07 Speaker 2: in pedagrams created using the level four two point one version.

25:12 Speaker 2: This data set also includes biomass estimated by FAO for comparison.

25:19 Speaker 2: More details on how these metrics and data sets are

25:21 Speaker 2: calculated or derived will not be reviewed in this training,

25:25 Speaker 2: but there are additional trainings and resources in the NASA dacks.

25:29 Speaker 2: You can reference the data dictionaries, the algorithm theoretical based documents,

25:35 Speaker 2: or reference some additional trainings like the one we'll discuss

25:37 Speaker 2: at the end by NASA earth Rise for data preparation

25:41 Speaker 2: techniques for applied users and will that will dive into

25:47 Speaker 2: section three to discuss resources and tools for accessing, visualizing,

25:51 Speaker 2: and analyzing JEDI data products. So there are many considerations

25:57 Speaker 2: when choosing a data product to work with. Of course,

26:01 Speaker 2: as previously mentioned, ensuring the coverage and spatial and tempore

26:04 Speaker 2: resolution is primary. Your choice in using Jedi may depend

26:08 Speaker 2: on those mission specifications that we showed earlier. If you're

26:12 Speaker 2: looking to select data from specific timestamps. You may need

26:16 Speaker 2: to work with the footprint level samples since the gridded

26:19 Speaker 2: or other derived products aggregate annually or over larger time

26:23 Speaker 2: periods across the lifetime of the JEDI mission. Working with

26:27 Speaker 2: the footprint level data sets will be at twenty five

26:30 Speaker 2: meter resolution, which could be useful for localized applications or

26:34 Speaker 2: for use as calibration and validation or as training data themselves. However,

26:40 Speaker 2: these data sets are not represented analysis ready. This means

26:44 Speaker 2: that the user will need to deploy footprint level specific

26:47 Speaker 2: means of access, preparation and visualization strategies to work with

26:51 Speaker 2: the files, and will go over some open source tools

26:54 Speaker 2: and a demonstration with a Python notebook to exemplify this.

27:00 Speaker 2: If you are applying to a landscape, country, regional, or

27:04 Speaker 2: global scale application, using the gridded or derived products that

27:09 Speaker 2: are already quality filtered and aggregated over certain time periods

27:12 Speaker 2: could be really flexible options. These data sets are easy

27:15 Speaker 2: to work with since they're in GEOTIPH, but it's recommended

27:19 Speaker 2: to refer to the data preparation descriptions in order to

27:22 Speaker 2: better understand the assumptions that were made when generating those

27:25 Speaker 2: derived data sets and review whether those assumptions are optimal

27:29 Speaker 2: for your particular ecosystem's characteristics. So this reference table can

27:36 Speaker 2: be used to help you decide on how to handle

27:38 Speaker 2: your desired Jedi data product. So we will review the

27:43 Speaker 2: Earth Data Guy, tes Viz, slide rule, and Harmony Api

27:47 Speaker 2: in part two, while part three will review accessing Jedi

27:50 Speaker 2: data sets and Google Earth Engine, and the rest of

27:53 Speaker 2: the options are up here for your exploration. So the

27:57 Speaker 2: takeaway here is that not every product is available for

27:59 Speaker 2: as access or able to be processed across every platform.

28:03 Speaker 2: Some platforms like Earth Data Search are really only for

28:07 Speaker 2: data access and basic boundary overlap or temporal filtering, while

28:12 Speaker 2: platforms like tesviz, the Harmony API, slide rule client go

28:16 Speaker 2: beyond access and allow for more customized data processing, download, formatting,

28:21 Speaker 2: and visualization. For the greatest customization capabilities over large study areas,

28:27 Speaker 2: the user may need to deploy programming oriented approaches, especially

28:31 Speaker 2: when handling the footprint data. So platforms like and other

28:35 Speaker 2: packages in our Jedi Google Earth Engine or other Python

28:39 Speaker 2: workarounds like JEDIDB and other ones that will present in

28:43 Speaker 2: our demonstration today are good options that can enable you

28:47 Speaker 2: to have within platform data storage processing or link to

28:51 Speaker 2: other APIs or platforms. Any Jedi data product can be

28:58 Speaker 2: accessed via the Earth Data Search. It can be single

29:02 Speaker 2: download or bulk or cloud optimized methods. There are already

29:06 Speaker 2: many tutorials that exist to help you navigate the Earth

29:09 Speaker 2: Data system and interface that we will not replicate here today,

29:13 Speaker 2: and the same goes for coding related bulk access with

29:16 Speaker 2: the LP and ournl dax that provide command line and

29:19 Speaker 2: Python based guides to download from the catalog. With specific

29:24 Speaker 2: spatial and temporal subsetting linked here, you can acquire the

29:29 Speaker 2: footprints and download the HDF five files that roughly overlap

29:34 Speaker 2: with your location in time period from Earth Data Search,

29:37 Speaker 2: but further spatial clipping, variable subsetting, and any type of

29:41 Speaker 2: processing that will need to be done would have to

29:43 Speaker 2: be done on an alternative platform once you've acquired those

29:47 Speaker 2: files from the platform. So let's take a quick look

29:52 Speaker 2: at how the Jedi data products show up on the

29:55 Speaker 2: Earth Data Search interface. On the top left I typed

30:00 Speaker 2: Jedi into the search bar to get all the possible

30:03 Speaker 2: data product collections available to be filtered by the temporal

30:07 Speaker 2: and spatial extent specifications on the top left panel. There

30:11 Speaker 2: are the main options for temporal and spatial filtering. This

30:15 Speaker 2: is what controls the search results of the data products

30:17 Speaker 2: that can then be further opened or visualized. The geotips

30:21 Speaker 2: and HGF five products that will meet the filter criteria

30:24 Speaker 2: will appear and you can download the data. They might

30:27 Speaker 2: not be necessarily clipped or subset to the exact spatial

30:30 Speaker 2: extent we defined here with the polygon tool, so you

30:34 Speaker 2: input the time period, use the polygon tool to get

30:38 Speaker 2: a rough area of interest and as you can see,

30:41 Speaker 2: the entire swath, which includes the trajectories of all the

30:44 Speaker 2: eight of the beams are shown overlapping with the study area.

30:49 Speaker 2: The individual beams and samples are not visualized here. You

30:53 Speaker 2: can highlight the overlapping orbit to see its location and

30:56 Speaker 2: download the h five file for example. Other tutorials can

31:01 Speaker 2: go over AWS access for bulk download. But the level

31:06 Speaker 2: two B and Level four A, Level one B and

31:09 Speaker 2: Level four C waveform footprint products, so basically all the

31:13 Speaker 2: HDA five data sets show up in the same way

31:18 Speaker 2: where you're just seeing the outline and going back to

31:22 Speaker 2: the main results. Level three gridded elevation and height metric

31:25 Speaker 2: are actually visualized on the map where you can see

31:28 Speaker 2: the values and the spatial extents of each of those

31:32 Speaker 2: grids with a particular color legend and can be downloaded

31:37 Speaker 2: in a similar way. The Level four B biomass data

31:43 Speaker 2: values are also conveniently visualized with the color scheme and

31:48 Speaker 2: demonstrated with the grids. Some of the derived products are

31:54 Speaker 2: global TIFFs and will have associated p and gs for

31:59 Speaker 2: your convenience to visualize the data set, but other products

32:05 Speaker 2: the data will have to be downloaded to properly visualize.

32:09 Speaker 2: So for here we see the outline, the global outline

32:12 Speaker 2: of the data set, but there's no visualization. When we

32:16 Speaker 2: zoom back in we see that it just highlights that

32:18 Speaker 2: it overlaps. The Terrestrial Ecology Subsetting and Visualization Services platform

32:29 Speaker 2: is exclusively for the Level three gridded Height and Biomass

32:33 Speaker 2: and Level four A and Level four B biomass products.

32:37 Speaker 2: You can use this platform for acquiring data from conveniently

32:41 Speaker 2: pre processed locations or use the API tool for customization

32:46 Speaker 2: or subset by a user defined location. So let's walk

32:52 Speaker 2: through how to select a location for data processing of,

32:55 Speaker 2: for example, the gridded Level three product. Though you can

33:01 Speaker 2: acquire the prepared data sets or use the web service tool.

33:04 Speaker 2: We are going to demonstrate the user defined subsets tool.

33:08 Speaker 2: From here, you can define a location point or customize

33:12 Speaker 2: by manual input to grab generally overlapping data. This could

33:16 Speaker 2: pose an issue if you're looking to a subset by

33:18 Speaker 2: larger areas. Scrolling down the sensor and the data product

33:22 Speaker 2: can be selected. You can select multiple products at a

33:25 Speaker 2: time or type them in. You can also select specific

33:28 Speaker 2: variables if you desire. The next step is to input

33:31 Speaker 2: the desired time period to filter by, followed by optionally

33:35 Speaker 2: choosing to generate geotifs and choose between the projections. The

33:40 Speaker 2: data requests will be submitted and take some time to

33:42 Speaker 2: deliver depending on the demand of the request. The resulting

33:50 Speaker 2: subset data sets get sent to your email. When they

33:53 Speaker 2: are ready for download in multiple formats, the user is

34:00 Speaker 2: directed to a temporary page where the data in multiple

34:02 Speaker 2: formats can be downloaded. The order summary is also available

34:06 Speaker 2: with auxiliary maps and statistical summaries for modus and land

34:09 Speaker 2: cover and phonology, and other resources for visualization, as well

34:13 Speaker 2: as some statistics. In addition, the citation is generated and

34:18 Speaker 2: the location, subset and projection are summarized. If you wish

34:22 Speaker 2: to download the data. You can click the link and

34:26 Speaker 2: a local directory page pops up to save the files

34:29 Speaker 2: on your local computer. The same process can be applied

34:33 Speaker 2: to each of the available download links. Here you can

34:36 Speaker 2: see that each of the bands are available separately and

34:40 Speaker 2: in multiple formats like GEOTIP or as CSVS. Finally, the

34:46 Speaker 2: visualization tool can be accessed under the tab for order summary.

34:49 Speaker 2: From here you can run the visualization where each band

34:53 Speaker 2: from the Level three data set is visualized in a

34:56 Speaker 2: uniform way and a comparative table, so with the same

35:00 Speaker 2: color scheme you're getting each of those bands for the

35:04 Speaker 2: Level three product and statistics associated. Next, we have slide

35:13 Speaker 2: real client. It's a platform made in collaboration with the

35:16 Speaker 2: University of Washington and NASA Goddard's Face Flight Center. It's

35:21 Speaker 2: an open source framework for on demand processing of science

35:24 Speaker 2: data in the cloud with AWS. It also has access

35:27 Speaker 2: to ISAT two, Jedi, Lancelot, Arctic deem Rima, and a

35:32 Speaker 2: growing list of other data sets that are stored in

35:34 Speaker 2: AWSS three. This solution includes an API for customization and

35:40 Speaker 2: is well documented. Customizing the API may allow for more

35:44 Speaker 2: flexible use over larger areas, but this user interface tool

35:48 Speaker 2: that will go over today. Is also built for the

35:50 Speaker 2: ISAD data sets, which is a plus if you're looking

35:53 Speaker 2: to fuse these data sets in your application, but it

35:57 Speaker 2: is currently only developed for three of the footprint level

36:00 Speaker 2: data sets, which includes the Level one B waveform, Level

36:04 Speaker 2: two A heights, and Level four A biomass data. Let's

36:09 Speaker 2: take a look at how to interact with JEDI data

36:11 Speaker 2: using slide roll clients functionalities on the left panel, the

36:18 Speaker 2: user can select the data set they would like to visualize,

36:20 Speaker 2: process and download one at a time. On the map,

36:24 Speaker 2: use the polygon tool to define a relatively small study

36:26 Speaker 2: area to work with. This can be a limitation for

36:28 Speaker 2: users looking to use slide roll for larger studies. I

36:31 Speaker 2: can check the request parameters on the top left and

36:35 Speaker 2: using the advanced settings, additional parameters to what is predetermined

36:38 Speaker 2: for the selected data set can be applied. You can

36:40 Speaker 2: control timeout checks, select specific data variables, only specific beams

36:45 Speaker 2: or quality flags that you'd like to include in the

36:47 Speaker 2: final data set. And there's another parameter for data sets

36:51 Speaker 2: that we won't use. And as you can see, the

36:53 Speaker 2: request parameters are updated with these customizations. You click run

36:57 Speaker 2: to process the data. You may take a little while

37:00 Speaker 2: and then the user will be automatically directed to additional

37:03 Speaker 2: processing and visualization tools once the acquisition is run. There's

37:06 Speaker 2: a map that visualizes the beam transects and has advanced

37:09 Speaker 2: filter controls that allow you to specialize the visualization layers

37:13 Speaker 2: by individual beams, ground tracks, or select a particular orbital path.

37:18 Speaker 2: The map plotting can also be controlled where the data

37:20 Speaker 2: values for each of the shown attributes get displayed. When

37:23 Speaker 2: you hover over the data point, the user can interact

37:26 Speaker 2: with the map and retrieve the data values for every

37:29 Speaker 2: included JEDI variable. Next, the right most feature generates an

37:33 Speaker 2: elevation plotter for the data along the latitude. This is

37:36 Speaker 2: most relevant for the elevation and high products. There is

37:40 Speaker 2: also a three D viewer that displays the values along

37:42 Speaker 2: the latitude and longitude with a particular legend to demonstrate

37:45 Speaker 2: the range along the z access and then much of

37:48 Speaker 2: the parameters below are to control the interactive three D visualizations.

37:53 Speaker 2: Mostly Finally, there is a table tab that allows you

37:59 Speaker 2: to run an SQI querry to visualize the data sets

38:02 Speaker 2: that can be exported as a CSV. Okay, so now

38:10 Speaker 2: for our demonstration, we'll give an example of how to

38:13 Speaker 2: use the Harmony API, there's going to be a focus

38:17 Speaker 2: on accessing, downloading, visualizing, and analyzing the footprint level data.

38:24 Speaker 2: So really the question that we're trying to address is

38:27 Speaker 2: what do you do once you have the hd of

38:29 Speaker 2: five files in you know, whatever format that you selected,

38:33 Speaker 2: If you want to work with a larger area or

38:36 Speaker 2: specify with a particular shape file, you know, how can

38:40 Speaker 2: you do that? So when you download the h five

38:45 Speaker 2: files from Earth Data Access, they're not necessarily clipped directly

38:49 Speaker 2: to your spatial extent. So this solution will help you

38:52 Speaker 2: do that when you download the file. It also includes

38:56 Speaker 2: all of the variables that could be hundreds of variables,

39:00 Speaker 2: most of those you do not need. Maybe you want

39:02 Speaker 2: a handful about twenty two recommended So how can you

39:05 Speaker 2: down select those from the HDF five and then transform

39:09 Speaker 2: that data into a preferred format something that's maybe more

39:12 Speaker 2: compatible across multiple geospatial platforms like a shape file or

39:16 Speaker 2: a CSV. And just to know that Harmony API works

39:22 Speaker 2: with all of the waveform data. So that's level one

39:25 Speaker 2: B to A to B, level four A and four C.

39:30 Speaker 2: And so what you'll need is the NASA Earth Data

39:32 Speaker 2: Access account, the Google Drive account as well, when you'll

39:36 Speaker 2: need your credentials for the Earth Data Access ready to

39:39 Speaker 2: plug into the script and access to Google Collab. So

39:44 Speaker 2: this Python notebook will demonstrate how to download the data,

39:47 Speaker 2: select certain variables to subset that you'll want to analyze,

39:51 Speaker 2: generate some statistics and visualizations. We're going to use two

39:55 Speaker 2: example study areas. One of them is of Pinon Pine

39:59 Speaker 2: Forge around Albuquerque, New Mexico, as well as a paint

40:05 Speaker 2: Rock research forest in North Alabama, which is part of

40:08 Speaker 2: the geotrees Field Data Network and is largely managed by

40:12 Speaker 2: Alabama A and M University. So just as for your

40:16 Speaker 2: awareness the Google Collab notebook, if you do command or

40:20 Speaker 2: control question mark if you're looking to customize a notebook

40:24 Speaker 2: and plug in your own AOI or change the data products.

40:28 Speaker 2: So we'll be using an example with level two B

40:30 Speaker 2: vegetation structure. If you wanted to look at level one

40:33 Speaker 2: A or look at four A for biomass, you can

40:38 Speaker 2: command search for the question marks and follow the input

40:42 Speaker 2: prompts throughout the demonstration. Okay, so when you click the link,

40:54 Speaker 2: it will take you to this GitHub repot for our

40:57 Speaker 2: said too. This is where our script is held. The tutorial.

41:03 Speaker 2: There's a little read me here that gives an overview

41:06 Speaker 2: of what we'll be doing. So you're going to search

41:10 Speaker 2: and retrieve the Jedi footprints over particular areas of interest.

41:14 Speaker 2: You're going to filter them by specific metrics and other

41:17 Speaker 2: important variables which we'll talk a little bit about. We'll

41:20 Speaker 2: be able to visualize them with different figures, get some

41:23 Speaker 2: statistics as well as some three dimensional figures. You can

41:27 Speaker 2: export them as a CSV or a shape file, and

41:31 Speaker 2: everything is run in the Jupiter notebooks, so there's no

41:34 Speaker 2: set up on your local computer required. And just for

41:39 Speaker 2: a quick overview, this is all the steps that we'll

41:42 Speaker 2: be doing within the script. So we'll set up our

41:46 Speaker 2: environment by getting our libraries with PIP and stall import

41:51 Speaker 2: our core dependencies. Set up some directories, so that's using

41:56 Speaker 2: the temporary space which is housed here on the little

42:01 Speaker 2: folder tab that when you're connected to the run time,

42:04 Speaker 2: you're able to save all your files. Here. We'll get

42:09 Speaker 2: access to the NASA Harmony API capabilities. We'll set up

42:15 Speaker 2: our study area and I'll show you a couple ways

42:17 Speaker 2: to connect your study area data, establish your temporal period

42:23 Speaker 2: to filter Jedi by, and then we'll go ahead and

42:26 Speaker 2: execute the downloading the hd of five files with the

42:30 Speaker 2: Harmony API. But when you download those files, you're going

42:34 Speaker 2: to get a bunch of data that you probably don't want.

42:36 Speaker 2: There's in some of them hundreds of variables that you're

42:41 Speaker 2: probably not going to use, and we list the about

42:45 Speaker 2: twenty two recommended and not varies depending on the level product.

42:48 Speaker 2: So we're going to be looking at level two B

42:50 Speaker 2: the vegetation structure metrics, and we'll go ahead and subset

42:54 Speaker 2: the files greatly reduce how much data is being stored,

42:57 Speaker 2: which will really help with computation and when you're working

43:00 Speaker 2: within in other platforms. We'll take a quick look at

43:04 Speaker 2: those files to make sure that everything was good with them,

43:06 Speaker 2: and also just to demonstrate how to open the HDF

43:09 Speaker 2: five file in the Collab notebook, convert them to a

43:13 Speaker 2: geodata frame which is usable within the Jupiter Notebook in

43:17 Speaker 2: that temporary computational space, and then offer some options to

43:21 Speaker 2: export it to your drive as a shape file or

43:24 Speaker 2: a CSV. We'll talk a little bit about some quality

43:27 Speaker 2: filtering techniques that we're discussed in Part one, but if

43:33 Speaker 2: you would really like to go more in depth on

43:35 Speaker 2: preparing the data with more application specifications, in mind. The

43:40 Speaker 2: repo that this is housed in is a training that

43:43 Speaker 2: goes more in depth on that, and so there's several

43:46 Speaker 2: modules that talk a lot more about application specific. Quality

43:50 Speaker 2: filtering will just be applying a couple which includes getting

43:53 Speaker 2: rid of no data values and seeing how that changes

43:55 Speaker 2: the data set. We'll map those and take a look

43:58 Speaker 2: at what the data that looks like before and after

44:01 Speaker 2: that quality filtering, we'll do some basic statistics on counting

44:05 Speaker 2: the observations per year over the study area and generate

44:08 Speaker 2: our final filtered data set. We'll go ahead and plot

44:13 Speaker 2: and explore the data sets, so we'll have some violin

44:16 Speaker 2: plots that look at each variable from the vegetation structure

44:19 Speaker 2: metrics per year per study area, generate a pair plot

44:23 Speaker 2: that will show some relationships between those available data sets,

44:27 Speaker 2: and generate some three D visualizations. So when you're here,

44:32 Speaker 2: you have a couple options to access the file. So

44:35 Speaker 2: you can click this tab here and that will bring

44:39 Speaker 2: you to the script that you can run. So I'll

44:44 Speaker 2: open then in a new tab. Just for reference, you

44:47 Speaker 2: can go to the file itself here and click download.

44:52 Speaker 2: That's just a GitHub error that they're still fixing, but

44:55 Speaker 2: it's still a valid data script, so you can download

44:59 Speaker 2: it and then up load it to your Google Drive

45:01 Speaker 2: or Another neat trick is when you're open to this file,

45:05 Speaker 2: you can hit two colm right after GitHub and you'll

45:09 Speaker 2: see that the icon changes. Hit enter, and then you're

45:12 Speaker 2: taken to the script as well. So when you get

45:17 Speaker 2: to this area, you're probably going to need to connect here,

45:21 Speaker 2: so you hit connect and it'll set up. And just

45:26 Speaker 2: two important tabs here to note are this table of contents,

45:30 Speaker 2: which is essentially that outline that I just went over,

45:33 Speaker 2: which when you click somewhere it jumps you to the

45:36 Speaker 2: section where you can start executing code, and the folder here.

45:41 Speaker 2: So this when you don't have it connected, will not

45:45 Speaker 2: have these two folders. You'll just be connected to this

45:48 Speaker 2: temporary space as you can see here in this disc

45:50 Speaker 2: This is what you're computing under. But I'll show you

45:55 Speaker 2: when we start off, we connect to your drive, so

45:57 Speaker 2: here is your personal drive that you can and connect

46:02 Speaker 2: to data or upload data sets to, which will show.

46:05 Speaker 2: And then when you connect to the temporary space and

46:08 Speaker 2: we start running through the code, it will generate this

46:11 Speaker 2: folder here that only exists in this when you're working

46:15 Speaker 2: through this script live. So when you start, we have

46:24 Speaker 2: again a little overview and just a note here, so

46:27 Speaker 2: you can follow along with this script and run sell

46:31 Speaker 2: by sell. That's just kind of at your own pace.

46:35 Speaker 2: But you can also hit run all and follow the

46:39 Speaker 2: input prompts which will show in this tutorial in order

46:44 Speaker 2: to run through what's written in this script. If you

46:47 Speaker 2: want to do further customization and change, so we're working

46:50 Speaker 2: with level two B. If you want to go to

46:52 Speaker 2: level two A, level one B, level four A biomass,

46:57 Speaker 2: you will be prompted to input certain variables and or

47:02 Speaker 2: you can search, so you can do command F for

47:05 Speaker 2: a question mark and you'll see all of these areas

47:08 Speaker 2: here where you might want to customize. So, oh, I

47:11 Speaker 2: want to work with level two A instead, that's somewhere

47:14 Speaker 2: where I would type in the code to change that.

47:16 Speaker 2: If you want to input a different AOI and say

47:20 Speaker 2: you're working California or something like that, you would need

47:23 Speaker 2: to rename these And just for the sake of how

47:26 Speaker 2: this tutorial is written, if you want to customize it,

47:29 Speaker 2: it is written in dictionaries, so it's going to iterate

47:32 Speaker 2: over multiple aois, so just a reference on you'll have

47:37 Speaker 2: to modify some of the script if you want to

47:39 Speaker 2: look at just one area. So that's just a little

47:43 Speaker 2: thing to work around. So when you start, you can

47:49 Speaker 2: either hit run all follow the inputs, or go sell

47:52 Speaker 2: by Sell. Here, we're going to go sell by Sell.

47:55 Speaker 2: So to set up your collab, you're going to install,

47:58 Speaker 2: So I'm actually gonna head over to some pre run

48:01 Speaker 2: code here. It should take in total about thirty minutes

48:05 Speaker 2: to run the whole thing, depending on the size of

48:07 Speaker 2: your AOI, which isn't too bad. So when you do

48:10 Speaker 2: pip install, you get a bunch of these prompts. It

48:14 Speaker 2: may prompt you to restart the session. You don't need

48:18 Speaker 2: to do that. You can click cancel and as long

48:20 Speaker 2: as this green check mark comes up, you're good to go.

48:24 Speaker 2: And then from here you want to mount your drive.

48:27 Speaker 2: So when you mount your drive, a pop up will

48:30 Speaker 2: come up and it'll ask you to put in your

48:33 Speaker 2: Google Drive credentials, just like you're signing in to your

48:35 Speaker 2: email or whatnot, and then the folder will pop up

48:39 Speaker 2: here like I showed, where you can access all of

48:43 Speaker 2: your content. Now we're going to establish the directories. That

48:49 Speaker 2: is how we're going to save data and locate to

48:52 Speaker 2: our aois. So you can add more aois if you wanted. Again,

48:56 Speaker 2: be careful with some size. It's not unlimited. But if

49:01 Speaker 2: you're working with a product, you want to make sure

49:03 Speaker 2: that this variable is changed and make sure that your

49:05 Speaker 2: ais are set. So you can just click run for that.

49:10 Speaker 2: And I put some directions here on just clicking run,

49:13 Speaker 2: which means that it's just kind of automated. Everything is

49:15 Speaker 2: connected to the variables that you change, so you can

49:19 Speaker 2: just go ahead and click. And this sets the folders

49:23 Speaker 2: where everything is located. So you have paint Rock that's

49:26 Speaker 2: within that main directory and pinon. So we create this

49:33 Speaker 2: base path that we then change our directory to. So

49:36 Speaker 2: this is just telling our code, hey, we want to

49:38 Speaker 2: work out of this main folder, not anything else. So

49:41 Speaker 2: that helps us execute all of our code. And this

49:45 Speaker 2: may take a little while when you run the required packages.

49:51 Speaker 2: So we need all of these to plot, to use

49:54 Speaker 2: the widgets to map, to get Earth data access, to

50:00 Speaker 2: get Harmony, all that kind of stuff, and then we're

50:05 Speaker 2: ready for step two. So here this is where you're

50:09 Speaker 2: gonna need your credentials for Earth Data access. We're gonna

50:14 Speaker 2: basically set up the request for Harmony API, set up

50:19 Speaker 2: all of the requirements to make that call to Harmony

50:22 Speaker 2: to get the data. So first we need to input

50:27 Speaker 2: our credentials here and it'll prompt you with an enter

50:32 Speaker 2: to enter your your user name and your password this

50:37 Speaker 2: was also shown in part one. And then we go

50:41 Speaker 2: ahead and actually use that password to authenticate and connect

50:45 Speaker 2: to Harmony client. I might take a little moment, and

50:49 Speaker 2: then from here, this is where we define the Jedi

50:52 Speaker 2: product that we want to use. So when you hit

50:56 Speaker 2: this code, it'll ask you to enter something and so

51:00 Speaker 2: Harmony works with all the footprint level data. We're going

51:02 Speaker 2: to be working with Jedi two B, so you can

51:05 Speaker 2: either type it or copy it. So if you were

51:07 Speaker 2: plugging in another data set, you would copy any of

51:09 Speaker 2: these other names here and hit enter and there you go.

51:14 Speaker 2: So from here we want to make sure that the

51:17 Speaker 2: Harmony client is actually accessing the variables and all of

51:21 Speaker 2: the capabilities specific to the Jedi product that we just defined.

51:25 Speaker 2: So when you run that, it allows you to see

51:29 Speaker 2: that oh great. So for each beam this variable in

51:32 Speaker 2: RX processing called shot number, I'm able to access it.

51:35 Speaker 2: And so you see there's hundreds of different variables which

51:39 Speaker 2: we will show you how to choose from and get

51:44 Speaker 2: So now we go ahead and get the collection ID.

51:48 Speaker 2: This is what's going to be used in the Harmony

51:50 Speaker 2: API request. So really we're just telling it to give

51:54 Speaker 2: us these in a variable that we input later. I

52:00 Speaker 2: want to set up a variable that allows us to

52:06 Speaker 2: connect to the path, the area, the folder that is

52:10 Speaker 2: connected to our AOI that will use in the later functions. Again,

52:15 Speaker 2: when you change these above, these should change if you're

52:17 Speaker 2: plugging in your own AOI, and now we want to

52:21 Speaker 2: import our AOI data, So this is you're going to

52:26 Speaker 2: have to have them in geojason format, so that's quite important.

52:30 Speaker 2: What we're using here is the gethub a git hub geojason,

52:36 Speaker 2: so you can do this as well in your own repo.

52:39 Speaker 2: You would be able to upload wherever. It doesn't have

52:43 Speaker 2: to be in the same exact folder, but you could

52:46 Speaker 2: upload a GeoJSON. You go ahead and click it and

52:51 Speaker 2: get hub is this nice thing where you can visualize.

52:54 Speaker 2: But you want to click raw over here, which gives

52:57 Speaker 2: you the raw file and then you just copy this

52:59 Speaker 2: you here and paste it into your code here. So

53:05 Speaker 2: for here, we're just using what's openly available in our

53:07 Speaker 2: own rebel, so you can run through this tutorial, but

53:10 Speaker 2: you can change that yourself, or you can connect to

53:13 Speaker 2: a geodjason that is located in your own drive. So

53:18 Speaker 2: a new trick here is when you connect to drive

53:22 Speaker 2: and let's say I just pick a random I'll just

53:25 Speaker 2: pick a random file here. So say we are looking

53:29 Speaker 2: at this video and we want to copy the path here,

53:34 Speaker 2: So we click those three dots copy the path. That's

53:37 Speaker 2: what gives us this path right here. You would paste that,

53:41 Speaker 2: you would need to uncomment, so you would need to

53:44 Speaker 2: get rid of these hashtags in front. If you were

53:46 Speaker 2: to run with the plugging in your AOI to the

53:48 Speaker 2: drive instead, and that should work. Then you go over

53:53 Speaker 2: here and you want to convert those geojasons into a

53:56 Speaker 2: geodata frame. So that's basically just reading the file in

53:59 Speaker 2: this collab notebook. So when we do that, it makes

54:04 Speaker 2: it available to be mapped. And so we click this

54:07 Speaker 2: here too, and it'll display your AOI sites. So we're

54:12 Speaker 2: looking at pinon pine forage areas, so really yummy pine

54:18 Speaker 2: nuts here outside of Albuquerque, New Mexico. So that's quite

54:22 Speaker 2: a large area. And then we're looking at a very

54:28 Speaker 2: small area of a conservation forest here in northern Alabama.

54:37 Speaker 2: Not much has happened here, not much you know, in

54:40 Speaker 2: any logging fires and what have you. Super beautiful site.

54:46 Speaker 2: So those are what it's great we have those aois.

54:50 Speaker 2: They look great. And notice that, well this one was

54:54 Speaker 2: a rectangle, but this one had as a more complex polygon.

54:59 Speaker 2: So you're able to upload your GeoJSON that's more complex.

55:02 Speaker 2: But when you actually run the Harmony API, it takes

55:05 Speaker 2: a bounding box, so you may need to clip your

55:10 Speaker 2: data afterward in a different like within collab or whatever

55:15 Speaker 2: platform you're comfortable with. But when it makes the request

55:18 Speaker 2: to NASA the DAS, it needs just a really simple polygon.

55:24 Speaker 2: So this is what's taking the shape file that you

55:27 Speaker 2: input and making a box around that instead of using

55:30 Speaker 2: a complex polygon. Then we define these this variable that

55:37 Speaker 2: is just configuring the names of the sites that we're

55:43 Speaker 2: using for the AOI bounding boxes that we've created to

55:48 Speaker 2: the folder that it's housed in. So that's over here.

55:53 Speaker 2: These folders to help us with later functions. So now

55:59 Speaker 2: we've established AOI, So where are we here? So we've

56:06 Speaker 2: gone through and we've gotten Harmony. We've defined our Jedi product,

56:10 Speaker 2: we've defined our aois. Now we want to establish our

56:13 Speaker 2: temporal filter. So remember just a render here. This is

56:17 Speaker 2: when the data is available, and you're gonna get to

56:22 Speaker 2: input that yourself. So here, when you run this, it

56:27 Speaker 2: will allow you to hit enter. So I'm gonna try

56:34 Speaker 2: this over here just so I don't okay, So we

56:45 Speaker 2: can hit enter here and we can type in two

56:52 Speaker 2: January first hit enter, then we can do to the

56:56 Speaker 2: end of the year. Let's see that you don't have

57:02 Speaker 2: to use the zeros in front it. It takes regular

57:06 Speaker 2: single digit numbers as well, so you can have this

57:09 Speaker 2: enter or if you want just for something static, you

57:12 Speaker 2: can uncomment this and use a static variable. So now

57:18 Speaker 2: we're going to actually prepare for downloading the data with

57:23 Speaker 2: the Harmony and API and request it. So we're just

57:26 Speaker 2: going to define some tracking functions that you'll see in

57:30 Speaker 2: the code that's printed out. These are really helpful for

57:33 Speaker 2: understanding the size of the data. You know, how many

57:38 Speaker 2: files did you download, and you know, just other tracking information,

57:43 Speaker 2: how long did it take things like that, and so

57:47 Speaker 2: we'll run this download script. This can take a while

57:50 Speaker 2: sometimes if I did the whole time period here, the

57:53 Speaker 2: whole time period of all the data that's available as

57:55 Speaker 2: you can see for the most part, and it takes

57:59 Speaker 2: about ten minutes to run over these aois that can

58:03 Speaker 2: differ by the size of your AOI as well, so

58:07 Speaker 2: you don't need to know much about this code. But

58:09 Speaker 2: I did just want to show what the actual Harmony

58:12 Speaker 2: API request looks like. So remember, we define our concept ID,

58:17 Speaker 2: so that's what's specific to our level to be product.

58:21 Speaker 2: Then we have our bounding box and this is our

58:23 Speaker 2: dictionary of data day that we input for our two aois,

58:28 Speaker 2: and then we've defined our temporal range and so that's

58:31 Speaker 2: what's actually getting the file. So there's no variable subset.

58:35 Speaker 2: You can do variable subset with Harmony and I, you know,

58:40 Speaker 2: refer you to the documentation to do that. The reason

58:43 Speaker 2: why we chose this alternative is because there were really

58:48 Speaker 2: big limitations on the size of the AOI that you

58:50 Speaker 2: can use, and we wanted to enable a solution that

58:53 Speaker 2: would allow us to look at a larger area or

58:56 Speaker 2: look at multiple areas to compare over any time period

59:00 Speaker 2: that we'd like. There are still limitations here. You can't

59:03 Speaker 2: do the whole globe, you can't do a whole country

59:05 Speaker 2: that you would have to customize coding for. But it's

59:08 Speaker 2: a lot more flexible than having Harmony itself parsed through

59:12 Speaker 2: the HGA five files to get the specific variables. So

59:15 Speaker 2: instead we download the files in the temporary space and

59:18 Speaker 2: not onto your local folder and then move on to

59:21 Speaker 2: step three to actually subset the data to the variables

59:24 Speaker 2: we want. And so here this is what it looks like.

59:28 Speaker 2: It's a lot of coding, but it reminds you of

59:32 Speaker 2: what your temporal range is. It lets you know which

59:35 Speaker 2: AOI it's working on, where it's at. You know, is

59:40 Speaker 2: what's the percentage of processing that it goes through. And

59:44 Speaker 2: then it actually lists out all of the files that

59:47 Speaker 2: it downloaded successfully, and it does that for both of

59:52 Speaker 2: the aois, and then at the end you get a

59:54 Speaker 2: nice summary of how many aois were processed, how many

59:57 Speaker 2: raw files were there was gigabytes, and the average size

1:00:03 Speaker 2: per the files that you understand what's going on and

1:00:06 Speaker 2: where they're located. So you see, wow, the Pinon area

1:00:08 Speaker 2: we knew it was larger and had one hundred and

1:00:10 Speaker 2: six files. Paint Rock only had six files, even though

1:00:14 Speaker 2: we're looking at the whole range of years in twenty

1:00:16 Speaker 2: nineteen to twenty twenty five. So I wanted to show

1:00:18 Speaker 2: these really big differences, and we'll see that throughout the figures.

1:00:22 Speaker 2: You can have you know, Jedi. You could be wanting

1:00:25 Speaker 2: to use Jedi over an area, but it really depends

1:00:28 Speaker 2: on what your area looks like and how Jedi happens

1:00:33 Speaker 2: to collect over that region. So now we're going to

1:00:38 Speaker 2: move on to actually subsetting these variables. So we don't want,

1:00:41 Speaker 2: you know, six gigabytes of data, and we don't need

1:00:45 Speaker 2: all of those. We don't need hundreds of variables. So

1:00:48 Speaker 2: from here we're going to use this text file. This

1:00:52 Speaker 2: is located in the repo that I showed before. It's

1:00:56 Speaker 2: basically just a text file of all of the variables.

1:01:01 Speaker 2: It's like a dictionary of all of the variables that

1:01:04 Speaker 2: are available in the Level two products. So you can

1:01:08 Speaker 2: see hundreds and hundreds of variables, and we don't need

1:01:12 Speaker 2: all those. So we're going to use this text file

1:01:17 Speaker 2: that will just load in by running this code and

1:01:19 Speaker 2: see here, Great, it gets all the data in order

1:01:24 Speaker 2: to then plug in. Hey, I only want Level to

1:01:27 Speaker 2: BE data from this, so this will prompt us to

1:01:31 Speaker 2: put in and from here you can I'm working with

1:01:35 Speaker 2: a level to be, so I can copy this or

1:01:38 Speaker 2: you put in the product that you're working with, enter

1:01:41 Speaker 2: it here, great, and then it gets me only the

1:01:45 Speaker 2: variables that are available for that product. That's still many

1:01:49 Speaker 2: more than I might need depending on my application and

1:01:53 Speaker 2: what I'm doing, the research that I'm doing, and so

1:01:58 Speaker 2: from here we actually want to select those variables. So

1:02:02 Speaker 2: what's important about this list here and you can also

1:02:04 Speaker 2: visit the NASA Data dictionaries, is that we need this

1:02:07 Speaker 2: full filepath name. So even if I just want to

1:02:09 Speaker 2: get the leaf off flag, I can't just type leaf off.

1:02:12 Speaker 2: I need the folder or the group that it's in

1:02:15 Speaker 2: in the HDA five file. So this changes across the product.

1:02:20 Speaker 2: So that's why this step of visualizing and getting all

1:02:22 Speaker 2: of the data or looking at the NASUB Data dictionary

1:02:25 Speaker 2: is really important to get what that real total value is.

1:02:28 Speaker 2: So I want latitude of the highest return, well, it

1:02:31 Speaker 2: needs to be written as if it's from the geolocation folder.

1:02:35 Speaker 2: So from here this is going to ask you to input.

1:02:38 Speaker 2: You can either enter or you can uncomment here and

1:02:43 Speaker 2: create this list yourself, depending on the product. So something

1:02:47 Speaker 2: to notice, because we're working with Level two B, these

1:02:50 Speaker 2: products are specific to level to be for the most part,

1:02:53 Speaker 2: and these ones are just we recommend these. These are

1:02:55 Speaker 2: pretty commonly used if you're working with any Jedi data

1:03:00 Speaker 2: set that are important for quality filtering, for getting your

1:03:04 Speaker 2: location coordinates, for being able to understand the behavior or

1:03:10 Speaker 2: penetrative ability of each of the shots. So I highly

1:03:14 Speaker 2: recommend keeping these, although the prefixes might be different, so

1:03:19 Speaker 2: in like a different product, you know, maybe the degrade

1:03:24 Speaker 2: flag is in a different path folder here, might not

1:03:27 Speaker 2: be in geolocation, might be somewhere else, so you might

1:03:29 Speaker 2: need to double check that with when you're changing your

1:03:32 Speaker 2: folder to that. And so from here, I'm just I

1:03:35 Speaker 2: already wrote this out. So you just have the full

1:03:38 Speaker 2: path names separated by commas in a space, copy that

1:03:42 Speaker 2: and input it into your enter here. Or you can

1:03:46 Speaker 2: put a bunch of apostrophes like this and use this

1:03:49 Speaker 2: variable instead. So great, it loaded those in and it

1:03:53 Speaker 2: formatted it the way that it needs to be. And

1:03:56 Speaker 2: now we're going to define some functions and help us

1:03:58 Speaker 2: select the variable itself. So you just click run here

1:04:02 Speaker 2: to subset. Now you can also specify how many beams

1:04:06 Speaker 2: that you want, So again, subsetting the variables and by

1:04:09 Speaker 2: beams is something that's available in Harmony API, but it

1:04:12 Speaker 2: greatly reduces your computational ability, so we're doing it here

1:04:15 Speaker 2: post download. So a really common thing to do is

1:04:19 Speaker 2: to use only the power beams. So power means that

1:04:21 Speaker 2: the laser is that full power. Coverage means it was

1:04:24 Speaker 2: about reduced to about half of the amount of power,

1:04:27 Speaker 2: which means that maybe it wasn't able to penetrate denser

1:04:31 Speaker 2: forests all the way through. That depends on the AOI,

1:04:34 Speaker 2: and a lot of people are still researching that. So

1:04:36 Speaker 2: if you want to include all the beams and look

1:04:38 Speaker 2: at a comparison, and we'll show you a little bit

1:04:41 Speaker 2: of how to do that, that could be really really interesting.

1:04:46 Speaker 2: Or if you already know that you're working in a

1:04:49 Speaker 2: really dense tropical forest or something like that, you should

1:04:51 Speaker 2: probably just use the power beam. You can just select

1:04:54 Speaker 2: these four beams instead. But for this we want to

1:04:58 Speaker 2: look at all of them because I know that we

1:05:00 Speaker 2: don't have as dense forests in the US, and I

1:05:02 Speaker 2: would like the ability to compare them if I really

1:05:05 Speaker 2: wanted to. And also, when you separate those beams, you're

1:05:10 Speaker 2: greatly reducing the amount of data that you have available,

1:05:12 Speaker 2: and as you'll see, that can be a huge barrier

1:05:15 Speaker 2: to using Jedi. When you actually apply these quality filters,

1:05:19 Speaker 2: how much data is left. So now we're going to

1:05:23 Speaker 2: find some helper functions to actually download the HDA five file.

1:05:27 Speaker 2: So we'll just click run on these. They're just some

1:05:30 Speaker 2: functions that help us do stuff. And this is just

1:05:35 Speaker 2: basically opening the HDF five file, getting the data sets,

1:05:40 Speaker 2: putting them in the right place in the right order,

1:05:43 Speaker 2: reformatting them into the way that we want, and then

1:05:48 Speaker 2: we'll execute that function here. So you just click run

1:05:50 Speaker 2: on all of these, and what it'll look like is

1:05:54 Speaker 2: so I have it print out each of these things

1:05:57 Speaker 2: just to keep track of what's going on. So for

1:06:01 Speaker 2: paint Rock, AI are really small AOI we're making. We're

1:06:05 Speaker 2: creating a folder that's going to be our raw downloads,

1:06:09 Speaker 2: or where we're getting the raw downloads from is from

1:06:13 Speaker 2: this folder. But we're going to create a new folder

1:06:16 Speaker 2: that's for variables selected. And actually you could see that here.

1:06:21 Speaker 2: So we're in paint Rock Great, we created a folder

1:06:24 Speaker 2: here where we got all of our subset variables in.

1:06:29 Speaker 2: So as we do that, we're opening each of those

1:06:32 Speaker 2: files and it's you know, going through and finding each

1:06:36 Speaker 2: of the twenty two variables that we specified from each

1:06:40 Speaker 2: of the eight beams that we want and processes each

1:06:43 Speaker 2: file so it find defines these summaries of how many

1:06:47 Speaker 2: variables we're getting. But from this first file that we

1:06:50 Speaker 2: see here this from twenty to twenty two whatever, you

1:06:56 Speaker 2: see that there's no beams here, so it's skipping them

1:06:59 Speaker 2: and that's just because of the spatial clipping that we got.

1:07:02 Speaker 2: So even if you had the full file available, because

1:07:08 Speaker 2: of the spatial subset, it did not include all of

1:07:11 Speaker 2: the beams. So even though you're requesting for all the

1:07:15 Speaker 2: beams to be selected, that may not be the case

1:07:17 Speaker 2: depending on where they fall over the earth and over

1:07:20 Speaker 2: your shape all that you defined. So this is where

1:07:23 Speaker 2: we really get into and something that you know earth data.

1:07:26 Speaker 2: And until you convert the HDA five into something more

1:07:29 Speaker 2: readable and are able to look at this kind of information,

1:07:32 Speaker 2: you don't really know exactly how many shots within each

1:07:36 Speaker 2: of the beams are available over your aoy. So this

1:07:38 Speaker 2: is really useful for tracking what's actually going on. And

1:07:42 Speaker 2: then you realize that, oh, you know, I only I

1:07:44 Speaker 2: don't actually have coverage beams for this area. So we'll

1:07:48 Speaker 2: actually visualize this and make it a little easier to read.

1:07:51 Speaker 2: But this just goes through tracking, it defines the size

1:07:55 Speaker 2: and then you get to see, wow, we reduced that

1:07:57 Speaker 2: file ninety eight percent, which is great because we did

1:08:01 Speaker 2: not need all of that other data. So now we

1:08:04 Speaker 2: just do a quick check are they where we want

1:08:06 Speaker 2: them to be? It's just for security, and then we're

1:08:13 Speaker 2: going to go ahead and look at those HDA five files,

1:08:15 Speaker 2: so just something interesting to look at. We're not really

1:08:18 Speaker 2: going to work with them anymore since we're going to

1:08:20 Speaker 2: convert it. But if you want to look at the

1:08:22 Speaker 2: raw files, so let's take a look at a file

1:08:25 Speaker 2: in Pignon and we're looking at the raw files here,

1:08:29 Speaker 2: so you can choose from any of these that you want.

1:08:32 Speaker 2: You can say, okay, cool. So all of these beams

1:08:36 Speaker 2: are potentially available as well as these other groups. And

1:08:42 Speaker 2: these are all of the variables that are there from

1:08:45 Speaker 2: each of these beams in the raw data set. We

1:08:49 Speaker 2: don't really need all of them, but just to see great,

1:08:51 Speaker 2: they're all there. And then if you want to look

1:08:53 Speaker 2: at look at the variables selected, see what happens after

1:08:58 Speaker 2: you subset them. You get to see, oh, okay, great,

1:09:01 Speaker 2: So we actually only have these two beams available after

1:09:04 Speaker 2: we subset, and looks like we got all of the

1:09:07 Speaker 2: variables that we selected. So that's pretty good. And you

1:09:11 Speaker 2: can go ahead and look at the other files and

1:09:14 Speaker 2: see how these change specific to each file. So now

1:09:19 Speaker 2: we're gonna actually look at those variable characteristics across the

1:09:24 Speaker 2: HGA five file. So this is another example of opening up.

1:09:28 Speaker 2: So you hit run on that and you run this,

1:09:31 Speaker 2: and we're just going to look at first the raw

1:09:34 Speaker 2: download just to be able to see what's going on

1:09:37 Speaker 2: with this data set. And so something that we learned

1:09:40 Speaker 2: about in the lecture part of part two was that

1:09:44 Speaker 2: the HGF five files are holding data sets in each group.

1:09:49 Speaker 2: So if you have ancillary or the beam group that

1:09:52 Speaker 2: are of different types. And so when you look at

1:09:54 Speaker 2: this file here, and it's also in the Data dictionary

1:09:56 Speaker 2: that's an online page through the NASA, but you get

1:10:02 Speaker 2: to see the shape of the data set. Is it

1:10:04 Speaker 2: you know, one dimensional, two dimensional? How many points are

1:10:09 Speaker 2: in it? And what type of data is it? So

1:10:12 Speaker 2: is it integer? Is it float? Is it this or that?

1:10:16 Speaker 2: So if we want to look at okay, so let's

1:10:18 Speaker 2: look at this. We have cover here from this file,

1:10:21 Speaker 2: it looks like there's only one shot available and the

1:10:27 Speaker 2: cover is afloat. So this is just really to check

1:10:30 Speaker 2: what's going on if you needed to do some extra

1:10:33 Speaker 2: research with that and understand what's going on with each

1:10:36 Speaker 2: of those files. So these are again all the data sets,

1:10:40 Speaker 2: but if you want to look at something that you

1:10:42 Speaker 2: subset and see what's going on. Okay, So there's nine

1:10:46 Speaker 2: shots that are available here in this here you'll see

1:10:50 Speaker 2: that pa Z the Z profile. So we won't talk

1:10:54 Speaker 2: as much about this, and there's another training with earth

1:10:57 Speaker 2: Rise that talks a little more in detail about this,

1:10:59 Speaker 2: but this is just how it's a two dimensional data

1:11:04 Speaker 2: set of how the PAV values are stored along the

1:11:06 Speaker 2: vertical access so you know, along the heights you get

1:11:11 Speaker 2: a PIAI plant area index value. And so there's thirty

1:11:16 Speaker 2: different value possible values. Not all of them may be

1:11:18 Speaker 2: valid data for each of the shots, so it's a

1:11:22 Speaker 2: different format. It can't be opened the same way. We

1:11:25 Speaker 2: won't go over that in this training, but just to

1:11:26 Speaker 2: recognize that there are data sets of different sizes the

1:11:29 Speaker 2: same thing here, there's just different ways that they're formatted

1:11:33 Speaker 2: within the HD of five files. So if you were

1:11:35 Speaker 2: to customize, you need to pay attention to that and

1:11:39 Speaker 2: then we get a summary of those information. So here

1:11:42 Speaker 2: this is an optional step. If you're working on your

1:11:45 Speaker 2: disc is full and something's going on, you might want

1:11:49 Speaker 2: to go ahead and delete the raw downloads folder to

1:11:53 Speaker 2: get if you're not needing that and you just want

1:11:55 Speaker 2: to work with your final data set. So when you

1:11:58 Speaker 2: click run here and it's on false, it'll tell you, hey,

1:12:01 Speaker 2: set it to true in order to rerun and actually

1:12:04 Speaker 2: delete your files. So that means come over here and

1:12:08 Speaker 2: again there's a question mark to find that, and you'll

1:12:10 Speaker 2: just change that to true, hit run again, and then

1:12:14 Speaker 2: it will delete this entire folder. And if you do

1:12:18 Speaker 2: that again, if you needed that later, you're going to

1:12:20 Speaker 2: have to rerun the download to get those back. So

1:12:25 Speaker 2: now we're going to actually create the geodata frame with

1:12:28 Speaker 2: the subset of HDF five files. So this is what's

1:12:30 Speaker 2: going to help us start thinking about visualizing the data

1:12:36 Speaker 2: and being able to convert it to another data format.

1:12:39 Speaker 2: So we're going to define some functions that help us

1:12:41 Speaker 2: do this, lots of documentation throughout. If you're looking to customize,

1:12:46 Speaker 2: we try to make it easy and understandable. So now

1:12:53 Speaker 2: we're going to go ahead and actually execute converting those

1:12:58 Speaker 2: HTA five files for each AOI, and we're going to

1:13:00 Speaker 2: create a dictionary of geodata frames, so that's one geodata

1:13:05 Speaker 2: frame per AOI that we'll use to plot later. And

1:13:11 Speaker 2: again it's going to be using the data that was

1:13:13 Speaker 2: subset in order to do so. So when we do this,

1:13:19 Speaker 2: it goes through and it tells us what's going on

1:13:21 Speaker 2: with each of these again just to help us track.

1:13:25 Speaker 2: So here we have that it went for each beam.

1:13:29 Speaker 2: It's getting all of the records. So those are the

1:13:31 Speaker 2: individuals the sixteen shots here, thirty six shots, fifty eight

1:13:34 Speaker 2: shots in each of these beams for that file. What

1:13:37 Speaker 2: did it do to each of those data sets? So

1:13:40 Speaker 2: PEGAP datasy we don't really work it with this, but

1:13:43 Speaker 2: this is something that it truncated the information that might

1:13:46 Speaker 2: be something that needs to be investigated and fixed if

1:13:50 Speaker 2: you're using that data. But here it just lets you know, hey,

1:13:55 Speaker 2: I wasn't able to find this in these beams because

1:13:57 Speaker 2: those beams don't exist or how many records were actually processed.

1:14:03 Speaker 2: So for the most part, everything was successfully processed, but

1:14:06 Speaker 2: it might need to you know, be changed if you're

1:14:13 Speaker 2: working with these data sets. And you find that they're

1:14:14 Speaker 2: not correctly downloaded. And so here also the reference length

1:14:20 Speaker 2: that we're using, where did that go? The reference length

1:14:25 Speaker 2: is essentially just looking at lat lowest mode, so that's

1:14:28 Speaker 2: we know that we're looking at the latitude and longitude

1:14:32 Speaker 2: of each shot. We know that that shot exists. So

1:14:36 Speaker 2: if there's thirty six latitude points, then we're just going

1:14:39 Speaker 2: to assume that those are the thirty two shots that

1:14:42 Speaker 2: we want to be able to generate our geodata frame from.

1:14:50 Speaker 2: So that's just a bunch of tracking records for that,

1:14:53 Speaker 2: and then we're able to say, great, we have this available.

1:14:57 Speaker 2: We want to create a variable that allows us to

1:15:03 Speaker 2: access the individual geodata frame for each product or for

1:15:07 Speaker 2: each AOI for that product, so that you can look

1:15:10 Speaker 2: at the data sets. So great, we have the lat

1:15:12 Speaker 2: long Okay, everything looks great, it's in it's it's in order.

1:15:16 Speaker 2: You can see every every data set here some of

1:15:19 Speaker 2: the values and so this is what we're more familiar with.

1:15:25 Speaker 2: So now this is an optional step. So what we

1:15:28 Speaker 2: downloaded here was the so we had the raw file

1:15:32 Speaker 2: that was subset to the variables that we wanted, and

1:15:35 Speaker 2: then we converted that to a geodata frame, but no

1:15:40 Speaker 2: quality processing, no getting rid of, no data values, no

1:15:44 Speaker 2: look into the actual data sets themselves and their quality

1:15:49 Speaker 2: has been done at this point. So if you were

1:15:51 Speaker 2: looking at a study that was you know, comparing different

1:15:54 Speaker 2: quality filters that you wanted to apply, or looking at

1:15:58 Speaker 2: you know, validated the diferens in different validation of the

1:16:02 Speaker 2: results that you get, you might want to save the

1:16:04 Speaker 2: original data set that has had nothing done to it itself.

1:16:11 Speaker 2: So this is an option to do that here before

1:16:13 Speaker 2: you start applying different filters. And this might help you

1:16:16 Speaker 2: compare if you're if you're doing that, or maybe you

1:16:19 Speaker 2: found some literature that you think is really applicable to

1:16:22 Speaker 2: your ecosystem. You don't really care about the raw files

1:16:24 Speaker 2: you've already you know, you know, you're just going to

1:16:27 Speaker 2: apply the final filter data set and that's what you're

1:16:29 Speaker 2: going to work with, and you can skip this step.

1:16:31 Speaker 2: But when you do this, uh, it connects to your

1:16:34 Speaker 2: drive and it creates a folder for shape files or

1:16:41 Speaker 2: csvs where the data is actually saved. So you have

1:16:46 Speaker 2: that's just so it should look like this piinone with

1:16:50 Speaker 2: the product and then you have your shape file files

1:16:55 Speaker 2: all there and again that's the original nothing done to

1:17:01 Speaker 2: the data set, and now we're going to start thinking

1:17:05 Speaker 2: about exploring the quality filtering techniques. So we just click

1:17:09 Speaker 2: run on these cells to establish This just helps us

1:17:12 Speaker 2: visualize and then so we may have defined the beams

1:17:20 Speaker 2: by listing when we wanted to subset, but it's kind

1:17:24 Speaker 2: of hard to remember what each one of those numbers

1:17:27 Speaker 2: is corresponding to. You'd have to you know, memorize that.

1:17:32 Speaker 2: So instead we want to actually label them as power

1:17:35 Speaker 2: or coverage themselves. So we're going to define this function

1:17:39 Speaker 2: that allows us to create a column that tells us

1:17:42 Speaker 2: whether it's coverage or power instead of the individual beam name.

1:17:46 Speaker 2: So that'll help us generate some later figures. So now

1:17:50 Speaker 2: we have our original data set and we're really interested

1:17:53 Speaker 2: in seeing well some basic statistics of what's available when

1:17:58 Speaker 2: this could be make or break for your application of Jedi,

1:18:02 Speaker 2: and so we're going to define some of the functions

1:18:06 Speaker 2: that help us actually get this figure going plot it.

1:18:13 Speaker 2: So again you just click run on all of these.

1:18:15 Speaker 2: There's not much else to do. If you were changing

1:18:20 Speaker 2: some of the aois, you might want to change some

1:18:23 Speaker 2: of the colors, or if you added you might need

1:18:25 Speaker 2: to add some specifications to colors here and we go

1:18:32 Speaker 2: ahead and click run here. And so this is an

1:18:34 Speaker 2: example of what the output would look like. So here

1:18:38 Speaker 2: we have Wow, we have two hundred and fifty five

1:18:42 Speaker 2: total shots that are total observations that were available between

1:18:48 Speaker 2: twenty nineteen and twenty twenty five. Four pinon for Jedi

1:18:51 Speaker 2: level to be. And now we get a little summary

1:18:54 Speaker 2: chart here that tells US across the years, how many

1:18:57 Speaker 2: of those shots are associated with those U what was

1:19:01 Speaker 2: the peak month of how many shots they're worse? You

1:19:03 Speaker 2: see that can vary by what is that thousands across

1:19:10 Speaker 2: across each of these months per year, So it can

1:19:13 Speaker 2: be really important if you're looking at seasonal, seasonally specific applications.

1:19:19 Speaker 2: So I think I have this actually pulled up here.

1:19:22 Speaker 2: So what this does is it puts all the aois

1:19:25 Speaker 2: on one subfigure for each year, so you can do

1:19:28 Speaker 2: a comparison between them. And as you saw, paint Rock

1:19:32 Speaker 2: has very very few. It only has total of sixty

1:19:36 Speaker 2: four shots across all of these years. Now it's a

1:19:40 Speaker 2: small AOI. I don't know why it has that few,

1:19:43 Speaker 2: but this could be your point, you know, if you're looking, Hey,

1:19:47 Speaker 2: I want data from let's say twenty twenty two in

1:19:52 Speaker 2: April or May. For either of these, you can't do

1:19:56 Speaker 2: your study with data from that area because it's just

1:19:58 Speaker 2: not there. So that's what this code helps facilitate, is understanding.

1:20:03 Speaker 2: You know, even if they is potential, it really is variable.

1:20:07 Speaker 2: You just have to open the data in order to

1:20:08 Speaker 2: see how much is available for that time period. Or

1:20:12 Speaker 2: you see, you know, there's only nine ten shots here.

1:20:15 Speaker 2: There's no data from paint Rock in this time period,

1:20:19 Speaker 2: So yeah, it can be incredibly variable. So that's what

1:20:25 Speaker 2: this figure is doing, and then we get a nice

1:20:30 Speaker 2: other kind of descriptive comparison across those years to just

1:20:33 Speaker 2: help us understand. So if you're using it as trained data,

1:20:36 Speaker 2: this could be really really valuable for understanding the representativeness.

1:20:41 Speaker 2: Of course, this is total shots. This isn't even over

1:20:43 Speaker 2: individual land cover types. If you put a land cover mask,

1:20:46 Speaker 2: that could also greatly change the amount of available data

1:20:52 Speaker 2: depending on your application. So now we're going to make

1:20:56 Speaker 2: a bar plot that is similar to this, but now

1:21:00 Speaker 2: comparing beam types. Like I said, there could potentially be

1:21:04 Speaker 2: a very big difference between the power and the coverage beam.

1:21:07 Speaker 2: So when wee we just click run to find this

1:21:12 Speaker 2: function in order to generate our plot, and so we're

1:21:16 Speaker 2: going to get this monthly observations for one AOI. So

1:21:19 Speaker 2: we're just looking at payrock and we have color coded

1:21:22 Speaker 2: for each of the years, separated by power and coverage.

1:21:26 Speaker 2: So we see, okay, well, of all of the shots,

1:21:28 Speaker 2: they are all power, so maybe that's possibly more reliable shots.

1:21:33 Speaker 2: Really depends on the application, but this will help us

1:21:36 Speaker 2: better understand when we actually go and validate. And then

1:21:39 Speaker 2: for this one here, we have some coverage in twenty

1:21:42 Speaker 2: twenty one, but as you can see again, there's a

1:21:44 Speaker 2: very different distribution across the years, and of course we

1:21:49 Speaker 2: get a table that helps us visualize that. And I

1:21:52 Speaker 2: think I have yeah, and you can switch, So this

1:21:56 Speaker 2: widget here allows you to switch to the other. Ay,

1:22:00 Speaker 2: it might take a while depending on how much data

1:22:03 Speaker 2: there is that you're working with. And I kind of

1:22:09 Speaker 2: wanted to show jumping over here. Two. I only selected

1:22:23 Speaker 2: one year's worth of data from twenty twenty two. And

1:22:27 Speaker 2: again this is summarizing all of it, but if you

1:22:30 Speaker 2: just wanted to select, you can do some comparison there.

1:22:34 Speaker 2: So for pinon, we have a bunch of data that's

1:22:38 Speaker 2: distributed very differently across the coverage and power beams. This

1:22:44 Speaker 2: could really help you understand again if you're using it

1:22:47 Speaker 2: as training data, can help you understand the behavior of

1:22:53 Speaker 2: that information and summarize that across as a table. Okay,

1:22:59 Speaker 2: now we're going to look at us out a plot

1:23:00 Speaker 2: and see how many of these values are no data values.

1:23:06 Speaker 2: We want to get rid of those. So here we

1:23:09 Speaker 2: have before we separate. It's really difficult to see what's

1:23:12 Speaker 2: going on and just understand how many of those values

1:23:15 Speaker 2: are removed and what our information looks like across all

1:23:19 Speaker 2: the years, all the data for the AI when that

1:23:22 Speaker 2: no data is removed. And see, there's a lot more

1:23:24 Speaker 2: information that we're seeing here across the track of observations.

1:23:31 Speaker 2: So this is along each of the observations that get dealt.

1:23:35 Speaker 2: So see it's a lot again, there's only sixty four

1:23:37 Speaker 2: in paint Rock, but we understand the behavior a lot better.

1:23:42 Speaker 2: So now with that understanding of a little bit about

1:23:45 Speaker 2: the no data and how much is available, we want

1:23:48 Speaker 2: to go ahead and generate our filter data set. So again,

1:23:53 Speaker 2: if you don't have any data over the time period

1:23:55 Speaker 2: that you're looking at, then for the study area that

1:23:57 Speaker 2: you want, this might be the end of you know,

1:24:00 Speaker 2: you using this script where you'd have to change course

1:24:04 Speaker 2: if you want to use Jedi or if you have

1:24:06 Speaker 2: data and you want to make it higher quality, then

1:24:10 Speaker 2: you know, start applying these filters and see what happens.

1:24:14 Speaker 2: So we're going to remove the no data values. This

1:24:18 Speaker 2: will change when you're using a different product you're going

1:24:20 Speaker 2: to use you know, uh, this is for FH FH

1:24:25 Speaker 2: Foliage High Diversity plannary index and cover. This is what

1:24:29 Speaker 2: we're gonna be looking at in this level to be

1:24:31 Speaker 2: data set, but we also want to look at if

1:24:35 Speaker 2: you want to look at level two a elevation and height,

1:24:38 Speaker 2: you would change these here and get rid of those

1:24:39 Speaker 2: no data and then we have the quality flag and

1:24:42 Speaker 2: the degrade flag which we talked about in part one

1:24:45 Speaker 2: that will apply to remove to keep high quality flags

1:24:50 Speaker 2: and get rid of degraded flags. There are many other

1:24:53 Speaker 2: types of filtering that you can do that we won't

1:24:55 Speaker 2: cover in this training that you would add them to

1:24:57 Speaker 2: this section here if you wanted to apply those and compare.

1:25:00 Speaker 2: And now you see that the data sets reduced. So

1:25:02 Speaker 2: instead of sixty four, now we have forty two in

1:25:04 Speaker 2: paint rock and instead of two hundred fifty thousand, we

1:25:07 Speaker 2: have one hundred and twenty four thousand, So again that'll

1:25:10 Speaker 2: vary by the study area that you have. We can

1:25:16 Speaker 2: map the differences between that. So we have the original

1:25:22 Speaker 2: here and then we have the filtered data set, and

1:25:26 Speaker 2: we can see just how different those are. Again, the

1:25:30 Speaker 2: scale between the no data and the data values change

1:25:35 Speaker 2: from what's available, and we can check that for the

1:25:39 Speaker 2: different data products as well. Pai about a plannary index. Yeah,

1:25:47 Speaker 2: now just to show what happened after we filter, What

1:25:51 Speaker 2: does our distribution of the data across the months look

1:25:55 Speaker 2: like after we get rid of that data. So again

1:25:57 Speaker 2: that can be really variable. You just kind of have

1:25:59 Speaker 2: to see it to see what happens. Could stay largely

1:26:03 Speaker 2: the same paint rock, didn't have too many points that

1:26:07 Speaker 2: were taken out, but you'll see sometimes that could get

1:26:12 Speaker 2: rid of an entire month that you were hoping for

1:26:15 Speaker 2: by applying the filter, which can be really important. Okay,

1:26:20 Speaker 2: and then again an option to save it to shape

1:26:22 Speaker 2: file or CSV. This is your final filtered data set.

1:26:25 Speaker 2: This would be your analysis ready data would be saved

1:26:28 Speaker 2: in the same location with a different suffix specifying that

1:26:33 Speaker 2: it is filtered. So now we're just going to generate

1:26:37 Speaker 2: a few figures. We're going to make a violin plot

1:26:41 Speaker 2: that's gonna again help us summarize but get a little

1:26:44 Speaker 2: bit more information on some statistics of what's going on

1:26:47 Speaker 2: with each of these variables. So we have you know,

1:26:50 Speaker 2: the with the size of the violin plot is telling

1:26:53 Speaker 2: us how many of the data are there in that

1:26:57 Speaker 2: month that are contributing to these statistics across So as

1:27:04 Speaker 2: you can see, there's just different shapes that allow us

1:27:07 Speaker 2: to understand that. And I believe we can see a

1:27:12 Speaker 2: little bit more with pignone here. So you'll get a

1:27:16 Speaker 2: plot for each month to understand a little bit more

1:27:19 Speaker 2: about the distribution per you know, okay, what's going on

1:27:23 Speaker 2: here February not mentioned variability, So yeah, lots to look at,

1:27:29 Speaker 2: lots to interpret over your study area, and then you

1:27:36 Speaker 2: do that for each variable. So for level to be,

1:27:40 Speaker 2: we just looked at the foliage high diversity and you

1:27:44 Speaker 2: can do that for plant area index. So again you'll

1:27:47 Speaker 2: have the same time periods because all of these variables

1:27:50 Speaker 2: are built off of the same original shots, but they'll

1:27:54 Speaker 2: have different values and distributions. Now we'll generate a pairple

1:28:01 Speaker 2: This will include all of the variables that are included

1:28:07 Speaker 2: in that original subset that we copy and pasted everything,

1:28:10 Speaker 2: So we don't go over all of these. You might

1:28:14 Speaker 2: need to take an additional training or go to the

1:28:16 Speaker 2: algorithm theoretical based document for that, but this just helps

1:28:19 Speaker 2: you understand the behavior a little bit more. So we

1:28:22 Speaker 2: know plant area index against cover. We see that there's

1:28:26 Speaker 2: a really strong relationship here, and so this is just

1:28:31 Speaker 2: a nice way to start understanding a little bit more

1:28:33 Speaker 2: about the data sensitivity of the beam. How well is

1:28:37 Speaker 2: the the beam the shot able to penetrate through a canopy,

1:28:44 Speaker 2: and how does that relate to canopy cover. You get

1:28:47 Speaker 2: to see a little bit more about that information here.

1:28:50 Speaker 2: So that plots across all of the available data sets

1:28:52 Speaker 2: that are in your in your file that you subset,

1:28:58 Speaker 2: or you can select a few. And finally we'll go

1:29:02 Speaker 2: over some three dimensional plotting. So here we're going to

1:29:06 Speaker 2: make an interactive plot that allows us to play around

1:29:10 Speaker 2: with what's available. So these are our forty two shots.

1:29:13 Speaker 2: As we're looking at the filtered data set and how

1:29:16 Speaker 2: it looks, it's plotting the visual of the value of

1:29:24 Speaker 2: the variable itself. So right here we're looking at plant

1:29:28 Speaker 2: area index right, and the value of that plant area

1:29:33 Speaker 2: index is what's put on the z axis for each

1:29:36 Speaker 2: of the shots. So you can look at it in

1:29:37 Speaker 2: different ways. You get to see the tracks there and whatnot,

1:29:42 Speaker 2: so there's different ways to view. You can save it

1:29:46 Speaker 2: as a p ANDNG, you can pan, you can do

1:29:50 Speaker 2: different types of rotation across, make a video of yourself

1:29:54 Speaker 2: looking at it. Pretty cool, holo use interactive site and yeah,

1:30:00 Speaker 2: there's a lot more in that other one. We'll see

1:30:02 Speaker 2: if it loads. But then you can also create a

1:30:05 Speaker 2: static three D plots, so something that's a little bit

1:30:07 Speaker 2: easier to save and visualize. So here we have this

1:30:12 Speaker 2: example from Pignon that's you know, thousands of shots that

1:30:18 Speaker 2: we see in order to better understand each of these

1:30:20 Speaker 2: variables cover plant area index, foliage high diversity, get a

1:30:25 Speaker 2: sense for what our landscape looks like with all of

1:30:28 Speaker 2: these shots, and then again you see wow, you know,

1:30:34 Speaker 2: this might not be a lot of data. You'd have

1:30:38 Speaker 2: to think about what kind of value that plays in

1:30:41 Speaker 2: your study area. And again this is across all of

1:30:44 Speaker 2: the years, so it could be greatly reduced if you're

1:30:46 Speaker 2: just looking at a particular time period and not aggregating

1:30:49 Speaker 2: over and this is what it looks like when you

1:30:52 Speaker 2: have a lot of points. Okay, so thank you very much.

1:31:01 Speaker 2: If you have any issues, there is a discussion forum

1:31:04 Speaker 2: on the repo here having to do with these tutorials,

1:31:09 Speaker 2: and I hope you have fun exploring Jedi. So to

1:31:16 Speaker 2: conclude this part too, just wanted to leave with some

1:31:21 Speaker 2: recommendations on additional resources and how to stay updated. So,

1:31:25 Speaker 2: as we've mentioned before, a really great reference for understanding

1:31:29 Speaker 2: the basis and background of all of these data sets

1:31:32 Speaker 2: are there individual algorithm theoretical based documents as well as

1:31:37 Speaker 2: the individual user guides that are hosted by the docs

1:31:42 Speaker 2: and on Earth Data Catalog. The OURNL and lp doc

1:31:47 Speaker 2: sites are always processing updating products, uploading new versions and

1:31:54 Speaker 2: archiving older versions as well. So these are really the

1:31:57 Speaker 2: best sources of information and original and new data sets

1:32:02 Speaker 2: that Jedi and the Jedi Mission team are putting out

1:32:06 Speaker 2: Google Earth Engine are Jedi Jedi dB, which is a

1:32:10 Speaker 2: Python api that recently came out, as well as some

1:32:13 Speaker 2: of the other tools that we mentioned are continuously advancing

1:32:16 Speaker 2: their functionalities, so always revisiting their resources and their their

1:32:21 Speaker 2: main pages you know, will be updated with new functionalities

1:32:26 Speaker 2: to test and if you're interested in learning more application

1:32:31 Speaker 2: specific information on you know, once you have the data

1:32:35 Speaker 2: or you're looking at it, you finally mapped it with

1:32:37 Speaker 2: some of these example scripts or the tools that you've

1:32:41 Speaker 2: been able to access and run through, you know how

1:32:44 Speaker 2: to interpret the data, understand some quality checks, how to

1:32:48 Speaker 2: improve it or optimize it with other data sets. You

1:32:52 Speaker 2: can take the NASA earth Rise Jedi Applications focused training.

1:32:56 Speaker 2: It has another series of hands on tutorials UH in

1:33:00 Speaker 2: collaboration with some of the other collaborators who generated this

1:33:03 Speaker 2: training with our set that are specific for data preparation

1:33:08 Speaker 2: and analysis recommendations. And given that Jedi is a novel

1:33:12 Speaker 2: sensor it was launched in twenty nineteen, the research is

1:33:15 Speaker 2: still very active. So the literature is also a great

1:33:18 Speaker 2: place to go and keep up with in understanding new

1:33:21 Speaker 2: applications or recommendations of using Jedi, and the Jedi Mission

1:33:25 Speaker 2: Page conveniently hosts a Jedi related literature Zoto group that

1:33:31 Speaker 2: gets continuously updated with the latest literature. So thank you

1:33:35 Speaker 2: very much and I hope this training advances your use

1:33:39 Speaker 2: of Jedi.

1:33:45 Speaker 1: Thank you so much, Stephanie. Now I will provide a

1:33:50 Speaker 1: summary of Part two. Let's review the key concepts from

1:33:57 Speaker 1: Part two. First, we cover jedi's mission objectives and applications

1:34:03 Speaker 1: operating from the International Space Station with data from twenty

1:34:06 Speaker 1: nineteen to twenty twenty three and resumed in twenty twenty four.

1:34:10 Speaker 1: JETI quantifies three dimensional ecosystem structure for carbon cycle tracking,

1:34:15 Speaker 1: fire management, weather prediction, water resource monitoring beneath dense forests

1:34:20 Speaker 1: and topography mapping and vegetated areas. We explore jedi's data

1:34:25 Speaker 1: product hierarchy. Level one B products provide geolocated waveforms. Level

1:34:33 Speaker 1: two A extracts elevation and canopy height metrics. Level two

1:34:38 Speaker 1: B calculates canopy cover plant area index, plant area volume

1:34:42 Speaker 1: density and foliage height diversity. Level four A models footprint

1:34:48 Speaker 1: level biomass, while Level four C provides a structural complexity index,

1:34:53 Speaker 1: capturing three dimensional canopy complexity. Understanding file format it's crucial.

1:35:01 Speaker 1: Footprint level data sets at twenty five meter resolution use

1:35:05 Speaker 1: HDF five hierarchical format, requiring specialized tools or coding skills.

1:35:11 Speaker 1: Gridded products like L three and L four B use

1:35:15 Speaker 1: familiar GEOTIP format at one kilometer resolution, with quality filters

1:35:19 Speaker 1: already applied, making them more accessible. Data fusion products overcome

1:35:26 Speaker 1: Jedi's discrete sampling limitations in certain ways. The mission team

1:35:32 Speaker 1: developed fusion products combining Jedi with ISAT II for increased

1:35:36 Speaker 1: sampling density, Tandem x InSAR for wall to wall tropical maps,

1:35:41 Speaker 1: and landsat for continuous canopy height. Products leveraging Jedi's vertical

1:35:47 Speaker 1: precision alongside broader coverage sensors. Field inventory plots from networks

1:35:56 Speaker 1: like US Forest Service Inventory and Analysis established the aleometric

1:36:01 Speaker 1: equations relating ground measurements to Jedi observations. Future missions like

1:36:07 Speaker 1: Hedge will build on this with increased beam density and

1:36:11 Speaker 1: swath mapping for improved change detection. Finally, you learned about

1:36:18 Speaker 1: access tools. Earth Data Search provides downloads with spatial temporal filtering.

1:36:24 Speaker 1: Testfis offers subsetting for level three and level four B products.

1:36:31 Speaker 1: Slide Rule Client enables cloud based processing, Harmony API handles

1:36:37 Speaker 1: HDF five transformation.

1:36:40 Speaker 2: Coding.

1:36:40 Speaker 1: Platforms like our Jedi and Google Earth Engine provide custom

1:36:44 Speaker 1: analysis capabilities. Matching the right tool to your workflow is

1:36:50 Speaker 1: key to working efficiently with Jedi data. In Part three

1:36:55 Speaker 1: of this training, you will be able to utilize Jedi

1:36:58 Speaker 1: standard biomass products at both footprint and one kilometer scals.

1:37:02 Speaker 1: To map forest resources, You'll be able to access the

1:37:05 Speaker 1: open source obi wan API to generate estimates of biomass

1:37:10 Speaker 1: changing areas of interest. You'll also be able to identify

1:37:13 Speaker 1: how obi wan can be used to create baseline scenarios

1:37:17 Speaker 1: for forest carbon accounting projects. To receive a completion certificate

1:37:25 Speaker 1: for this training, you will need to submit the homework assignment,

1:37:31 Speaker 1: which opens on November six and closes on November twentyth.

1:37:40 Speaker 1: This is the contact information for our guest instructor, Stephanie Humanez,

1:37:45 Speaker 1: as well as for myself listed here. We're also including

1:37:50 Speaker 1: links to the r SET website and the r ST

1:37:53 Speaker 1: YouTube channel, where you can find many more trainings like

1:37:58 Speaker 1: this available for free online. Here is a list of

1:38:05 Speaker 1: references and additional resources to look into. Thank you so

1:38:13 Speaker 1: much for your participation in today's training.