Department of Land, Environment, Agriculture and Forestry, University of Padova, Padova, Veneto Region 35020, Italy; School of Geography and Remote Sensing, Guangzhou University, Guangzhou, Guangdong 510006, China.
School of Geography and Remote Sensing, Guangzhou University, Guangzhou, Guangdong 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, Guangdong 511458, China.
Sci Total Environ. 2022 Mar 25;814:152595. doi: 10.1016/j.scitotenv.2021.152595. Epub 2022 Jan 4.
With the rapid development of remote sensing technology, the monitoring of land surface ecological status (LSES) based on remote sensing has made remarkable progress, which has a positive contribution on improving the regional ecological environment and promoting the realization of Sustainable Development Goals (SDGs). Among them, the proposed Remote Sensing-based Ecological Index (RSEI) becomes the most widely used model in the current application of remote sensing-based LSES monitoring due to its complete derived from remote sensing images and no subjective intervention. RSEI is not flawless either, and it still suffers from some uncertainties in its application in multiple scenarios. However, compared to the extensive applied research, work on the instability assessment and improvement of RSEI is particularly scarce and urgently needed. Therefore, in this paper, we analyzed the possible instabilities in the RSEI calculation process and proposed various inversion models to evaluate their accuracy and stability in time-series LSES monitoring. The results indicated that the existing normalized RSEI is relatively stable for the characterization of single-phase LSES, however, there is a high risk in the time-series analysis or cross-regional comparison due to the interference of component extremes. The standard deviation discretized DRSEI proposed in this paper perform better in both single-phase and long-term dynamics LSES assessments and are more consistent with the real land cover changes. Also, compared with the approach that measures LSES dynamics using time-series regional RSEI mean values, the DRSEI change detection results can reveal the spatial heterogeneity of regional LSES dynamics more effectively and provide a finer reference for the formulation and implementation of ecological protection policies.
随着遥感技术的飞速发展，基于遥感的陆地表面生态状况监测（LSES）取得了显著进展，这对改善区域生态环境、促进可持续发展目标（SDGs）的实现具有积极贡献。其中，所提出的基于遥感的生态指数（RSEI）由于其完全源自遥感图像且没有主观干预，成为当前遥感 LSES 监测应用中使用最广泛的模型。RSEI 也并非完美无缺，它在多种场景中的应用仍然存在一些不确定性。然而，与广泛的应用研究相比，关于 RSEI 不稳定性评估和改进的工作尤其稀缺且迫切需要。因此，在本文中，我们分析了 RSEI 计算过程中可能存在的不稳定性，并提出了各种反演模型，以评估它们在时间序列 LSES 监测中的准确性和稳定性。结果表明，现有的归一化 RSEI 对于单相 LSES 的特征描述相对稳定，但由于成分极值的干扰，在时间序列分析或跨区域比较中存在较高的风险。本文提出的离散化标准偏差 DRSEI 在单相和长期动态 LSES 评估中表现更好，并且与真实的土地覆盖变化更一致。此外，与使用时间序列区域 RSEI 均值来衡量 LSES 动态的方法相比，DRSEI 的变化检测结果可以更有效地揭示区域 LSES 动态的空间异质性，并为生态保护政策的制定和实施提供更精细的参考。
