Sustainable Development Goal (SDG) indicator 15.1.1 proposes to quantify “Forest area as a proportion of total land area” in order to achieve SDG target 15.1. While area under forest cover can provide useful information regarding discrete changes in forest cover, it does not provide any insight on subtle changes within the broad vegetation class, e.g. forest degradation. Continental or national-level studies, mostly utilizing coarse-scale satellite data, are likely to fail in capturing these changes due to the fine spatial and long temporal characteristics of forest degradation. Yet, these long-term changes affect forest structure, composition and function, thus ultimately limiting successful implementation of SDG targets. Using a multi-scale, satellite-based monitoring approach, our goal is to provide an easy-to-implement reporting framework for South Asian forest ecosystems. We systematically analyze freely available remote sensing assets on Google Earth Engine for monitoring degradation and evaluate the potential of multiple satellite data with different spatial resolutions for reporting forest degradation. Taking a broad-brush approach in step 1, we calculate vegetation trends in six south Asian countries (Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka) using the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) during 2000–2016. We also calculate rainfall trends in these countries using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall data, and further calculate Rain-Use Efficiency (RUE) that shows vegetation trends in the context of rainfall variability. In step 2, we focus on two protected area test cases from India and Sri Lanka for evaluating the potential of finer-resolution satellite data compared to MODIS, i.e. Landsat 8, and Sentinel-2 data, for capturing forest degradation signals, which will ultimately contribute towards SDG indicators 15.1.1 and 15.1.2. We find that most countries show a fluctuating trend in vegetation condition over the years, along with localized greening and browning. The Random Forest (RF) classifier utilized in step 2 was able to generate accurate maps (87% and 91% overall accuracy for Indian and Sri Lankan test cases, respectively) of non-intact forest within the protected areas. We find that almost one-third of the Indian test case is degraded forest, even though it shows overall greening as per the broad-brush approach. This finding corroborates our argument that utilizing higher-resolution satellite data (e.g. 10-m) than those normally used for national-level studies will be crucial for reporting SDG indicator 15.2.1: “progress towards sustainable forest management”.
Remote Sensing of Environment, Volume 237, February 2020,