WELCOME TO THE GERS LABORATORY!
Viewing the Earth from space is a breathtaking experience. In the daytime, the green and brown masses of earth blend into the deep blue ocean, covered by curling wisps of clouds. At night, the globe is peppered with constellations of golden lights. The images we take of space have more than an aesthetic value; they facilitate research about how the Earth is changing by creating the big picture we cannot get from the ground. A central research theme of the Global Environmental Remote Sensing (GERS) laboratory is to understand how the world is changing based on quantitative remote sensing. We are interested in using a variety of remote sensing sensors, such as drones, small satellites, Landsat, Sentinel-2, MODIS, VIIRS, LIDAR, and Radar to monitor environmental change at regional to global scales. The photos from the upper row showing the 'GERS' letters are Landsat images and the GIFs in the lower row are time series of Landsat data that illustrate the land-water dynamics, forest change, urban disturbance, and agricultural practice in the past 40 years.
GERS LabX News
Ghost forests are creeping into North Carolina. https://earthobservatory.nasa.gov/images/153497/ghost-forests-creep-into-north-carolina 👻
Do you want to use Google Earth Engine and ArcMap to learn Salinity Index mapping?🤔 As soon as possible registration for our upcoming online training program, Class will start on 15th November. Check all details from the below 👇
https://www.studyhacksgeospatial.com/google-earth-engine/
#googleearthengine #GEE
Registration is Open for 7-day Online Live Training on Google Earth Engine for Remote Sensing & GIS Analysis.
Registration Info: https://www.studyhacksgeospatial.com/google-earth-engine/
Class Start: 15th November 2024
Admission Last Date: 14th November 2024
#googleearthengine #GEE #EarthEngine #gee
(New paper alert!)
Forecasting models typically rely on numerical historical data. However, in many cases, numerical data is insufficient and context is key.
E.g., In the series below, would you have predicted the drop? Even the best models do not (forecast in blue).