Using Remote Sensing Data at Google Earth Engine to Analyze the Deforestation at the State of Mato Grosso in Twenty Years

Abstract:

In this study, we used Google Earth Engine (GEE) platform to explore deforestation at the State of Mato Grosso on the edge of the Amazon rainforest of Brazil in recent years. We used accessible datasets such as Defense Meteorological Satellite Program (DMSP), Visible Infrared Imaging Radiometer Suite (VIIRS), and Moderate Resolution Imaging Spectroradiometer(MODIS) land use and land cover. By the GEE Platform, we analyzed the variation of the DMSP data in the average visible band of nighttime lights from 1992 to 2013 and found it increased by approximately 19.6%, reflecting on the exploitation of forest land. The VIIRS data showed an overall increase of day/night bands per month after 2014 with a slope of 9.3E-5. The change of MODIS land cover data corresponds to the nighttime lights data, as there is more visible light the area of Evergreen Broadleaf forest decreased with a slope of -40.5, while the area of savannas and grassland both increased with a positive slope, represents many forest areas were replaced by farmlands in early 21st century. The trend in these data corresponds to the real-world event in the late 1990s through 2014. During this phase, the price of soy and beef spiked, and the production of soy reached approximately to 5.5 millions of metric tons and the production of beef reached to 2 millions of metric tons, while more than half of the deforestation was in the State of Mato Grosso, the largest agricultural region in Brazil. Deforestation in Brazil highlights a complex issue involved in analyzing geographical, social, and economical data while uncovering the various uses of online-intuitive-platforms for scientific researchers across the globe. As remote platforms become more accessible for researchers, this reflects the narrow gap between isolated research and at-home high-level science.