NASA-DEVELOP's repositories
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.
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).
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
MHEST
The MHEST tool created by the 2021 Spring ID Southern Idaho HAQ II team, takes CALIPSO and MODIS data, calculates mixing heights, and stages them for comparison with NWS Fire Weather Forecasts (and /or Spot Forecasts). The Fire Weather Forecasts are scrapes from an online archive, while CALIPSO and MODIS data for desired dates must be downloaded.