Long-term Satellite Image Time Series for the Assessment of Land Use/Cover Change in the Brazilian Amazon Rainforest

Abstract

The regular acquisition of Earth Observations by remote sensing satellites provides long-term Satellite Image Time Series (SITS). Land surface spectral variability provides the capacity for the assessment of Land Use/Cover Change (LUCC) information through SITS. As the reduction of deforestation rates is a matter of global concern, we selected a test area in the Brazilian Amazon Rainforest to assess LUCC information through long-term SITS. Top of Atmosphere reflectance images acquired from Landsat satellites between 1984 and 2017 were downloaded. A filtering process was carried out through the analysis of cloud and shadow masks. A total of 279 images were used to build a long-term Normalized Difference Vegetation Index (NDVI) SITS for every pixel. Images from across 7 years were used to identify SITS for the classes No Change, Anthropic Change, and Natural Change in order to define reference SITS. The Fast-Dynamic Time Warping (FastDTW) algorithm was used to compute the similarity between the reference SITS and the SITS to be labeled. The K-Nearest Neighbor algorithm was applied to classify the SITS based on the similarity measurements. Two different values of the FastDTW radius parameter were used to build two LUCC maps. The overall accuracies of the LUCC maps were 58.06% and 55.02%, for the radius parameter of one and 20, respectively. It was observed that atmospheric effects, clouds, cloud shadows, smoke, among other noisy agents, can modify the real SITS shape. As a result, the use of raw SITS can lead to a reduction in the accuracy of LUCC maps. Furthermore, high cloud coverage in the Brazilian Amazon Rainforest results in high-frequency irregularity in the SITS, which further reduces the accuracy of the LUCC maps. However, the study showed that the long-term NDVI SITS can describe land cover types and the classes defined despite the constraints mentioned.