EXPLORING NASA’S HARMONIZED LANDSAT AND SENTINEL-2 (HLS) DATASET TO MONITOR DEFORESTATION IN THE AMAZON RAINFOREST
- 1Institute for Geoinformatics, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany
- 2General Coordination of Earth Observation - OBT National Institute for Space Research (INPE), Av. dos Astronautas, 1758, São José dos Campos - SP, Brazil
Keywords: HLS dataset, BFAST monitor, Random Forest, Brazilian Legal Amazon, Deforestation
Abstract. Deforestation is a threat to biodiversity and the world’s climate. As agriculture and mining areas grow, forest loss becomes unbearable for the environment. Consequently, monitoring deforestation is crucial for decision makers to create polices. The most reliable deforestation data about the Amazon forest is generated by the Brazil’s National Institute for Space Research (INPE) through its PRODES project. This effort is labor and time intensive because it depends on visual interpretation from experts. Additionally, frequent Amazon’s atmospheric phenomena, such as clouds, difficult image analysis which induces alternative approaches such as time series analysis. One way to increase the number of images of an area consists of using images from different satellites. NASA provides the Harmonized Landsat and Sentinel-2 (HLS) dataset solving spectral dissimilarities of satellite sensors. In this paper, the possibilities of HLS for forest monitoring are explored by applying two deforestation detection methods, Break Detection for Additive Season and Trend (BFAST) monitor and Random Forest, over four different vegetation indices, NDVI, EVI, GEMI and SAVI. The SAVI index used as input for BFAST monitor performed the best in this data setup with 95.23% for deforested pixel, 53.69% for non-deforested pixels. Although the HLS data is described as analysis ready, further pre-processing can enhance the outcome of the analysis. Especially, since the cloud and cirrus cover in the Amazon causes gaps in the dataset, a best pixel method is recommended to create patched images and thus a continuous time series as input for any land cover and land use classification.