Pinned 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.
DSAT
Drought Severity Assessment Tool (formerly Drought Severity Assessment - Decision Support Tool)
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.
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.
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.
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.
VOCAL
Visualization of CALIPSO (VOCAL). A CALIPSO Cross Cutting tool for visualizing data
hello-world
Jumpy_Shooty2
Prototype_shooty_jumpy_blocks