Abstract 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”.

中文翻译：

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可持续发展目标 15 的报告框架：使用 MODIS、Landsat 和 Sentinel 数据对森林退化进行多尺度监测

摘要 可持续发展目标 (SDG) 指标 15.1.1 建议量化“森林面积占土地总面积的比例”，以实现 SDG 目标 15.1。虽然森林覆盖面积可以提供有关森林覆盖的离散变化的有用信息，但它不能提供任何关于广泛植被类别内细微变化的洞察，例如森林退化。由于森林退化的精细时空特征，大陆或国家级研究（主要利用粗尺度卫星数据）可能无法捕捉到这些变化。然而，这些长期变化会影响森林结构、组成和功能，从而最终限制可持续发展目标的成功实施。使用多尺度、基于卫星的监测方法，我们的目标是为南亚森林生态系统提供一个易于实施的报告框架。我们系统地分析了谷歌地球引擎上免费可用的遥感资产，以监测退化并评估具有不同空间分辨率的多个卫星数据报告森林退化的潜力。在步骤 1 中采用粗略的方法，我们使用中分辨率成像光谱仪 (MODIS) 归一化差异植被指数 (NDVI) 计算六个南亚国家（孟加拉国、不丹、印度、尼泊尔、巴基斯坦和斯里兰卡）的植被趋势2000-2016 年期间。我们还使用气候危害组红外降水与站点数据 (CHIRPS) 降雨数据计算这些国家的降雨趋势，并进一步计算显示降雨变化背景下植被趋势的降雨利用效率 (RUE)。在第 2 步中，我们重点关注来自印度和斯里兰卡的两个保护区测试案例，以评估与 MODIS（即 Landsat 8 和 Sentinel-2 数据）相比更高分辨率卫星数据的潜力，用于捕获森林退化信号，最终将为可持续发展目标指标 15.1.1 和 15.1.2 做出贡献。我们发现大多数国家多年来植被状况呈现波动趋势，伴随着局部绿化和褐变。步骤 2 中使用的随机森林 (RF) 分类器能够生成保护区内非完整森林的准确地图（印度和斯里兰卡测试案例的总体准确度分别为 87% 和 91%）。我们发现几乎三分之一的印度测试案例是退化森林，尽管按照粗刷方法显示整体绿化。这一发现证实了我们的论点，即利用比通常用于国家级研究的数据更高分辨率的卫星数据（例如 10 米）对于报告可持续发展目标指标 15.2.1：“实现可持续森林管理的进展”至关重要。