SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Department of Integrative Biology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Glob Chang Biol. 2023 Aug;29(16):4620-4637. doi: 10.1111/gcb.16800. Epub 2023 May 30.
Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long-term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modeling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002-2020 time series, and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non-photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote sensing-based monitoring of trends in grasslands so that underlying processes can be discerned.
草原生态系统覆盖了全球土地面积的 40%，为全球 10 多亿人口的生计直接提供了多种生态系统服务。监测草原的长期变化对粮食安全、生物多样性保护、实现土地退化中性目标以及模拟全球碳预算至关重要。尽管利用遥感进行长期草原监测已经很广泛，但它通常基于单一的植被指数，并且没有考虑时间和空间自相关，这意味着一些趋势被错误地识别，而另一些则被忽略。我们的目标是分析欧亚大陆（地球上最大的连续草原生态系统）草原的变化趋势。为此，我们计算了 MODIS 2002-2020 时间序列的累积端元分数（每月地面覆盖分数的年度总和），并应用了一种新的统计方法 PARTS，该方法明确考虑了趋势中的时间和空间自相关。我们研究了绿色植被、非光合植被和土壤地面覆盖分数的变化趋势，考虑了它们独立的变化轨迹和随时间的分数之间的关系。我们得出了时间上不相关的基于像素的趋势图，并统计检验了观测到的趋势是否可以用海拔、土地覆盖、SPEI3、气候、国家及其组合来解释，同时考虑到空间自相关。我们没有发现欧亚大陆草原植被覆盖减少的统计证据。相反，整个地区的非光合植被显著增加，而绿色植被局部增加，土壤部分相应减少。独立的环境变量对趋势有显著影响，但影响因地区而异。总体而言，我们的分析以统计上稳健的方式表明，过去二十年来，欧亚大陆的草原发生了相当大的变化。我们的方法增强了基于遥感的草原趋势监测，以便能够辨别潜在的过程。
