NASA-DEVELOP/PrIME

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dnppy

DEVELOP National Program Python package for use with NASA data and GIS!

Python

81

2

DRIP-SLIP

DRIP and SLIP Landslide Detection Package

Python

64

3

METRIC

For estimating daily evapotranspiration from Landsat data

Python

47

4

VOCAL

Visualization of CALIPSO (VOCAL). A CALIPSO Cross Cutting tool for visualizing data

Python

38

5

M-SRM

Modified Snowmelt Runoff model for forecasting snowmelt in central northern Chile (M-SRM).

MATLAB

22

6

CCROP

Cover Crop Remotely Observed Performance (CCROP): The Maryland Department of Agriculture (MDA) is interested in verifying winter cover crop implementation and analyzing cover crop productivity using satellite imagery. As they do not have the expertise on-site to automate the process, we used a combination of scripting using JavaScript in Google Earth Engine (GEE) and ArcGIS to identify suitable Landsat and Sentinel images, extract individual farm field characters (such as values for various bands, NDVI, and red-edge) to a table, and export this table. Subsequently, this table will be incorporated in the MDA agronomic database where crop and farm productivity reports can be created as needed.

18

7

DSAT

Drought Severity Assessment Tool (formerly Drought Severity Assessment - Decision Support Tool)

R

15

8

LaSTMoV

Land Surface Temperature MODIS Visualization (LaSTMoV)

Python

14

9

ESVIA

Elkhorn Slough Vegetation Imagery Assessment (ESVIA). This suite of Google Earth Engine Javascript code analyzes vegetation change in a historical time-series using Landsat, as well as current-day vegetation productivity using Sentinel 2A imagery. Included in the scripts are operations such as image acquisition, image processing, and applications of vegetation indices with band combinations highlighting vegetation. The purpose of the software is to apply vegetation indices to Landsat 5, Landsat 8, and Sentinel 2A images, conduct a simply NDVI change detection, create histograms showing data distribution, and export the results.

JavaScript

14

10

ORCAA

The Optical Reef and Coastal Area Assessment (ORCAA) tool in Google Earth Engine allows users to monitor, track, and evaluate water parameters in the Belize and Honduras Barrier Reefs from January 2013 to present using Landsat 8, Sentinel-2, and Aqua/Terra MODIS imagery.

13

11

SET

Beta version of the Skyglow Estimation Toolbox. Documentation ->

Python

11

12

LCD

The Normalized Difference Vegetation Index (NDVI) for the study time period is calculated and then compared to the maximum and minimum NDVI from a baseline range of years in order to calculate Relative Greenness (RG). The change in RG from the previous year is found, and this allows the user to identify abrupt change in vegetation. Normalized Burn Ratio (NBR) and USDA Croplands Dataset have been added as additional datasets that can help establish if the change was caused by a fire or by a change in crop type. Recent available NAIP imagery for the study area is also included, as an example of what is available for high resolution imagery within GEE. Based on a date input by the user, the map viewer displays the RG, the change in RG, the percent change in RG, and the NBR, along with the Cropland layer from that year and NAIP imagery taken closest in time to the requested display date.

11

13

SAVeTrEE

SAVeTrEE is a script within Google Earth Engine for classifying areas of vegetation mortality. It prompts the user for a year, duration, and spectral index for which a mortality map should be produced, then fits a trend line to an imagery time sequence of vegetative spectral index values calculated from Landsat multispectral data. The slope of the trend line, as well as the spectral index values, are used in determining the final classification of each pixel within the study area. Classification categories are: 1) Growing 2) Mortality (declining) 3) Stable Vegetation and 4) Stable Barren.

9

14

GATER

Geographic Applications for Transitioning Everglades Regions (GATER). Meant for running within the Google Earth Engine API, this JavaScript code provides an algorithm for cloud removal from Landsat scenes, and runs a classification scheme which classifies mangrove extent within Everglades National Park.

JavaScript

9

15

WET2.0

The 2020 Spring Great Lakes Water Resources II adapted the Wetland Extent Tool (WET) to create WET 2.0, which is a tool with a Graphical User Interface (GUI) that automates wetland classification for the entire Great Lakes Basin using Sentinel-1 C-SAR, Landsat 8 OLI, Sentinel-2 MSI, and Dynamic Surface Water Extent (DSWE).

8

16

LUCT

We used the Google Earth Engine Code interface to create a classification of land use on the United States Virgin Islands (USVI). We used six classes: water, low density residential, high-density residential, forest/shrub, agriculture and barren. We included DEM, classification points, and landsat imagery bands to analyze the imagery. Our final product is at a 30 meter spatial scale.

JavaScript

8

17

POPP

Palm Oil Plantation Predictor (POPP). This Python software automates the process of downloading, atmospherically correcting, and processing raster data in order to identify potential palm oil plantations.

Python

7

18

WET

The Wetland Extent Tool (WET) was developed by the 2019 Spring JPL Great Lakes Water Resources team for wetland mapping in Minnesota using Sentinel-1 C-SAR, Landsat 8 OLI, and a LiDAR-derived Topographic Wetness Index (TWI) in Google Earth Engine.

6

19

FiSSH

Finding Suitable Spawning Habitats: iSSH uses a compilation of data products during the study range 2003-2018, and includes Grunion Greeters citizen science data, in situ measurements, and NASA Earth Observations. The Grunion is a fish endemic to California with a range historically in Southern California (San Diego to Santa Barbara), and a more recent expansion northward to Monterey and San Francisco in the past three decades. During a "grunion run", the fish spawns on the beach, riding the waves onshore to lay its eggs in the sand. This MATLAB app matches user input of chlorophyll-a levels, ocean temperature, and upwelling indices, to the most the similar conditions at a recorded grunion run from available data. It may be used to get an idea of the potential size of future grunion runs based on the conditions during past runs. The application was created using MATLAB's App Designer.

MATLAB

6

20

HAE

Using the cloud-based computing power of Google Earth Engine (GEE), the Hydrologic Anomaly Index (HAE) is capable of uploading and analyzing large amounts of Earth observation climate data for the purpose of hydrologic analysis and monitoring. The end-user will be able to pull from and modify a library of scripts that are stored in Earth Engine, as well as upload and access data stored on a private data catalog. The final stage of development of the tool will include a more user-friendly application built using Google’s App Engine, in which users will be able to display data products and interactive maps.

JavaScript

6

21

HIVE-OS

Honeybee Informatics Via Earth Observations - 2018 Summer - The software was motivated by a collaborator desire to take beehive health data that has traditionally been used aspatially and apply it in a spatial format in conjunction with NASA Earth observations in order to determine what correlations exist between the health data and local landscape, environmental, and atmospheric phenomena. This software addresses this desire at two points. It directs the user to shape their data into a compatible format and then ingests the raw recorded data, converts it to a GeoJSON using Python, and then provides documentation in order for the user to upload their data to Google Earth Engine in order to utilize the scripts generated that access the Earth observations data. These scripts summarize as well as provide statistics for download for the users based on a point or polygon typology.

JavaScript

6

22

COVER

This code develops calibration models using linear regression models with in-situ field data. The calibration models are then used to predict biomass (log), nitrogen percent, and nitrogen content for Landsat images from 2006-2016. Model results and data tables are output as separate files for each field season (i.e. winter and spring seasons).

R

5

23

CMAT

The Coal Mining Assessment Tool (CMAT) in Google Earth Engine (GEE) monitors the impacts and reclamation efforts of coal mines in the basin. The tool incorporates Earth observations from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI), and utilizes the LandTrendr change detection algorithm to assess land disturbance. CMAT outputs include land disturbance maps and charts showing how land cover, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and tasseled cap transformations have changed from 1985 to 2018.

JavaScript

5

24

STFC

The Short-term Forest Change Tool (STFC) is a Google Earth Engine script created by the Spring 2020 Costa Rica and Panama Ecological Forecasting team. The main scope of the software is to display changes in vegetation of forested areas and identify regions of possible deforestation.

JavaScript

5

25

RIG

Rainfall Intensity Graphs (RIG) v.1.0 - 2018 Spring - A Google Earth Engine Tool developed for end-user the National Park Service at Navajo National Monument (NAVA) in northern Arizona. Quickly visualizes GPM or TRMM in space and gives rainfall intensity values for time.

JavaScript

5

26

CaVE

This program can be used with future Landsat 8 satellite data to determine land change in Chalatenango, El Salvador. The results this tool produces will identify land classes and allow data analysis of land change.

4

27

SWIM

This tool allows users to identify surface water from the most current imagery available, or to monitor temporal changes of surface water in their management area.

JavaScript

4

28

CBCHI

Chesapeake Bay Chlorophyll Hotspot Identifier (CBCHI) takes in raw Landsat 8 surface reflectance products and produces two maps to be opened in ArcMap that can be used to identify chlorophyll hotspots. It also creates a true color image.

Python

4

29

RIFRAF

This software was created to address concerns of fire managers in the Missouri River Basin. Previously, fire managers inferred a regional fire risk from separate sources, (i.e. temperature maps, drought indices, and precipitation averages). This software combined partner specified variables that have historically lead to large, complex fire conditions in a single map.

4

30

B-FED

The Beaver-Flood Event Detector (B-FED) is a Google Earth Engine script created by the Spring 2020 MA Massachusetts Water Resources team. It uses NASA Earth Observations, a MassGIS wetland polygon layer, citizen science Global Biodiversity Information Facility (GBIF) Data and remote sensing methodology to detect flooding events that are likely caused by beavers in Massachusetts, USA. The objective of this kit is to have an algorithm with conditional statements to determine for a given year if flooding, based on spectral signatures, caused by beavers has occurred. This is then filtered for a wetland layer and then inlaid with citizen science data of beaver observations from GBIF. The correlation of having flooding, along with reported beaver observations acts as a validation for the tool. B-FED is divided into three scripts: preprocessing, analysis, and visualization.

4

31

UHEAT

Tool to determine priority areas of concern due to the urban heat island effect for the City of Tempe, AZ.

3

32

CORAL-TT

Calculating Oscillations in Regional Aquatic Locations - Temperature and Turbidity

3

33

RAiN

This software can be used to understand precipitation and its influence on resiliency. It will analyze and visualize data as maps and charts.

3

34

SLaCC

The Supervised Land Cover Classification (SLaCC) tool is a Google Earth Engine script created by the Summer 2019 Southern Maine Health and Air Quality Team. It uses NASA Earth observations, the National Land Cover Database, land cover classification training data, and a shapefile of Cumberland County, Maine, USA. The goal of the project was to evaluate land cover and tick habitat suitability in southern Maine. The SLaCC script occurs in two parts. Part 1 of the script allows users to create a supervised land cover map over a region using a Classification And Regression Tree (CART) model. Part 2 of the script allows users to create a map that displays the "edges" of chosen land covers.

JavaScript

3

35

PiCo

Pixelwise Correlation-Based Landscape Classification (PiCo). PiCo, an R script, automates and regionalizes the Climate Landscape Response (CLaRe) metrics developed by Wallace et al 2016. This script performs a pixel-wise regression analysis to produce rasters whose correlation values can be evaluated to target buffelgrass.

3

36

MARSHe

The software will be used locally and possibly region-wide around the Chesapeake Bay to create maps illustrating changes in Chesapeake Bay marsh health from the year 2000 to 2017. It will run analyses on imported imagery to determine changes in features and project results within Google Earth Engine. Once it is shared with project partners, they will be able to use the software to perform their own analyses using the same methodology on a scale of their choosing.

3

37

ASIT

The software will load in image collections from Google Earth Engine from different sensors (Landsat 5, 7, and 8, Sentinel 2a, 2b) and the code will identify pixels meeting certain criteria as snow. Then the area of snow can be calculated for a region or for an individual watershed. Then a chart can be generated which would create a visual that shows the change in area covered by snow across images as a percentage of the whole area. As the sky islands stretch across much of Southeastern Arizona, the software also eliminates the need to download multiple images from differing rows/paths.

3

38

GC-ReDI

Grand Canyon Regions of Drought Impact (GC-ReDI). This Google Earth Engine software quantifies the decreasing water surface area in Lake Mead and the lower Grand Canyon and assesses the resulting changes in land cover –specifically, riparian vegetation and riparian sediment. The software provides images, statistics, and graphs to understand the drought-induced changes.

JavaScript

3

39

OPLC

Osa Peninsula Land Cover, 2018 Summer GA Osa Water Resources OPLC

JavaScript

2

40

FAULT

Flood Analysis Utilizing Landsat and ArcMap Tools (FAULT): This product was created in an effort to automate flood analysis throughout the Mississippi River Basin. The objective was to reduce the strenuous research efforts being conducted by relief organizations such as the Federal Emergency Management Agency (FEMA) and United States Geological Survey Hazards Data Distribution Systems (USGS HDDS), allowing them to focus their energy on recovery efforts and future policy.

Python

2

41

PSTP

Provisional Surface Temperature Processing (PSTP) script applies cloud- and water-masking techniques to specific raster layers of the USGS Landsat Provisional Surface Temperature product to make imagery analysis-ready.

R

1

42

PHOENIX

The Plume Hazards and Observations of Emissions by Navigating an Interactive Explorer (PHOENIX) is a Google Earth Engine script created by the Summer 2020 Pacific Northwest Health & Air Quality team.

JavaScript

1

43

SDCI

Scaled Drought Condition Index Tool

R

1

44

Texas-NAC

The NAC code takes collections of Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI data in a specified boundary and from 1986 to 2016 and combines it into one merged collection.

1

45

MASC

Move Away Superfluous Clouds (MASC). This code removes clouds, cloud shadow, water, and snow pixels from Landsat scenes using the cloud mask layer that is provided with Landsat data.

R

1

46

iMMOD

iMMOD: An Interactive Model of Mosquito Distribution | This Google Earth Engine (GEE) code visualizes NASA Earth observations, citizen science and public health data relevant to mosquito habitat suitability. The code also implements a model to predict habitat suitability for mosquitoes in Western Europe.

1

47

FoCIS

Eastern Hemlock Distribution Model: Four codes scripted to run in Google Earth Engine to compute predicted habitat distribution of Eastern Hemlock in (1) Adirondack Park and (2) New York State.

1

48

MIPDA

MIPDA (Mapping Insect and Pathogen Disturbance Automation) - LaRC 2017 Spring - ArcMap processing with a Landsat time series that was automated in Python for studying climate of Glacier National Park.

Python

1

49

TAOW

Turbidity Assessment Over Water - 2017 Summer - The Chesapeake Bay Automation Master Script provides automation for processing atmosperhically corrected satellite imagery. This script specifically pre-processes Landsat 8 and Sentinel-2 datasets that were atmospherically corrected by ACOLITE.

Python

1

50

ADIM

The Asian Disaster Preparedness Center (ADPC) was established in 1986 to provide technical services and capabilities to national governments in the region. Together with NASA and the United States Agency for International Development (USAID), ADPC is able to use satellite imagery and other geospatial decision-support tools to aid in the prediction and management of environmental events, as well as help communities build resilience to the negative effects of natural hazards in this area of the world. In August 2015, NASA, USAID, and ADPC officially launched the SERVIR-Mekong Hub at the ADPC in Bangkok, Thailand. This hub is in place to support and provide publicly available satellite data on the Lower Mekong region in order to address pressing environmental concerns in Cambodia, Laos, Myanmar, Thailand, and Vietnam. It is a five-year regional project aimed at increasing the use of geospatial analysis in addressing common or urgent policy and planning needs. The project partners at the ADPC, SERVIR-Mekong Hub, and the NASA SERVIR Coordination Office expressed the need for a script that automates the downloading and processing of data in order to better monitor agricultural drought in the Lower Mekong River Basin.

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