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

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

Accurate vegetation destruction detection using remote sensing imagery based on the three-band difference vegetation index (TBDVI) and dual-temporal detection method

Satellite remote sensing, as an important tool for Earth observation, has been widely used to monitor various vegetation destruction events (VDEs), such as logging, wildfires and insect infestations. However, due to the spectral diversity of VDE and the complexity of background environments (BE), achieving accurate VDE detection remains a challenge. To overcome this limitation, this study developed a novel index, called the three-band difference vegetation index (TBDVI), which fully considered the spectral characteristics of both various BEs and multiple VDEs, for the accurate detection of vegetation destruction in complex scenarios. Three experiments were chosen to prove the performance of TBDVI, including (1) various possible vegetation changes; (2) various possible background changes; and (3) multiple real vegetation destruction events. The results showed that TBDVI was suitable for various vegetation change scenarios and complex background conditions, with F1 scores of 0.906–0.979. Moreover, TBDVI accurately identified the extent of VDE caused by logging, insect infestation, landslides, wildfires, and floods, with F1 scores of 0.922–0.965. Compared with existing spectral indices (VIs) (i.e., normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and normalized burn ratio (NBR)), TBDVI has obvious advantages in reducing the impact of the background environment. In addition, TBDVI exhibits cross-sensor applicability and has potential for large-scale and high-frequency vegetation monitoring. In conclusion, TBDVI is an effective and robust spectral metric that is important for the conservation and management of vegetation resources