Monitoring of deforestation events in the tropics using multidimensional features of Sentinel 1 radar data

Chuanwu Zhao¹ Yaozhong Pan^{1, 2*} Xiufang Zhu¹ Le Li³ Xingsheng Xia² Shoujia Ren¹ Yuan Gao¹

• ¹Beijing Normal University, China
• ²Qinghai Normal University, China
• ³Guangdong University of Technology, China

Many countries and regions are currently developing new forest strategies to better address the challenges facing forest ecosystems. Timely and accurate monitoring of deforestation events is necessary to guide tropical forest management activities. Synthetic aperture radar (SAR) is less susceptible to weather conditions and plays an important role in high-frequency monitoring in cloudy regions. Currently, most SAR image-based deforestation identification uses manually supervised methods, which rely on high quality and sufficient samples. In this study, we aim to explore radar features that are sensitive to deforestation, focusing on developing a method (named 3DC) to automatically extract deforestation events using radar multidimensional features. First, we analyzed the effectiveness of radar backscatter intensity (BI), vegetation index (VI), and polarization feature (PF) in distinguishing deforestation areas from the background environment. Second, we selected the best-performing radar features to construct a multidimensional feature space model and used an unsupervised K-mean clustering method to identify deforestation areas. Finally, qualitative and quantitative methods were used to validate the performance of the proposed method. The results in Paraguay, Brazil, and Mexico showed that 1) the overall accuracy (OA) and F1 score (F1) of 3DC were 88.1%-98.3% and 90.2%-98.5%, respectively. 2) 3DC achieved similar accuracy to supervised methods without the need for samples. 3) 3DC matched well with Global Forest Change (GFC) maps and provided more detailed spatial information. Furthermore, we applied the 3DC to deforestation mapping in Paraguay and found that deforestation events occurred mainly in the second half of the year. To conclude, 3DC is a simple and efficient method for monitoring tropical deforestation events, which is expected to serve the restoration of forests after deforestation. This study is also valuable for the development and implementation of forest management policies in the tropics.