The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 865–872, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-865-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 865–872, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-865-2021

  29 Jun 2021

29 Jun 2021

DEFORESTATION MAPPING USING SENTINEL-1 AND OBJECT-BASED RANDOM FOREST CLASSIFICATION ON GOOGLE EARTH ENGINE

V. Yordanov1,2 and M. A. Brovelli1,3 V. Yordanov and M. A. Brovelli
  • 1Department of Civil and Environmental Engineering (DICA) Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, Italy
  • 2Vasil Levski National Military University, Veliko Tarnovo, Bulgaria
  • 3Istituto per il Rilevamento Elettromagnetico dell’Ambiente, CNR-IREA, via Bassini 15, 20133 Milano, Italy

Keywords: Deforestation, Change detection, Google Earth Engine, SAR, Sentinel-1, Segmentation, SNIC, Random forest

Abstract. Deforestation can be defined as the conversion of forest land cover to another type. It is a process that has massively accelerated its rate and extent in the last several decades. Mainly due to human activities related to socio-economic processes as population growth, expansion of agricultural land, wood extraction, etc. In the meantime, there are great efforts by governments and agencies to reduce these deforestation processes by implementing regulations, which cannot always be properly monitored whether are followed or not. In this work is proposed an approach that can provide forest loss estimations for a short period of time, by using Synthetic Aperture Radar imagery for an area in the Brazilian Amazon. SAR are providing data with almost no alteration due to weather conditions, however they may present other limitations. To mitigate the speckle effect, here was applied the dry coefficient, which is the mean of image values under the first quartile while preserving the spatial resolution. While for obtaining land cover maps containing only forest and non-forest areas an object-based machine learning classification on the Google Earth Engine platform was applied. The preliminary tests were carried out in a bitemporal manner between 2015 and 2019, followed by applying the approach monthly for the year of 2020. The outputs yielded very satisfactory and accurate results, allowing to estimate the forest dynamics for the area under consideration for each month.