The conceptual framework of EHSP
EHSP connected to a powerful cloud-based computing service provided by Google Earth Engine (earthengine.google.com) to acquire diverse satellite remote sensing images under Python environment. Multiple environmental parameters related public health interests can be derived from different product of satellite images. The end-users can retrieve customized environmental parameters from EHSP and help them to develop models for analysis. The next generation of EHSP will integrate different machine learning algorithms/AI templets for end-users to fit and visualize their data.
Remote Sensing Satellite Products
At current stage, multiple remote sensing products have been selected in the EHSP. The characteristics of these products have been described below briefly.
[The Moderate Resolution Imaging Spectroradiometer, MODIS]
MODIS is a key optical sensor operated on the Aqua and Terra satellites. EHSP uses different products derived from MODIS to generate Land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI).
Landsat program has the longest history in the earth observation. The images have been used for land use/cover classifications and landscape characteristics. EHSP use Landsat 8 to generate NDVI, EVI, and NDWI which are similar the product of MODIS; however, the 30 meters spatial resolution provide more detail landscape information for the study focus on smaller areas.
Sentinel-2 is the new generation satellite launched by European Space Agency (ESA) in 2015, The highe spatial resolution (10 meter~60 meters) allow users to study at finer scale of study areas. EHSP also generate NDVI, EVI, and NDWI from Sentinel-2 data.
WorldCover is the latest global land cover/use products generated by ESA. The product is based by Sentnel-1 and Sentinel-2 images to produce global LCLU in 2020. EHSP applied zonal statistic to customized the area sizes and percentages of 11 LCLU types in the region provided by the users. The 11 LCLU types includes tree cover, shrubland, grassland, cropland, built-up, bare / sparse vegetation, snow and ice, permanent water bodies, herbaceous wetland, mangroves, and moss and lichen
ERA-5 is a reanalysis dataset generated by European Centre for Medium-Range Weather Forecasts (ECMWF). The climate dataset integrates information from satellite images and ground measurements to estimate multiple weather station-based parameters, including air temperature, precipitation, humidity, surface pressure, and windspeed. Although the 30KM spatial resolution is relatively coarse, the long data availability (1979-2020) is appropriate for studies focus on long term changes at large scale.
Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is another product integrates satellite images and in-situ station data. CHIRPS provides daily rainfall estimations at 5 KM spatial resolution.
[The Shuttle Radar Topography Mission, SRTM]
SRTM is global digital elevation dataset at 30-meter spatial resolution.
Eco Health 2022