Remote sensing ecological index dataset of the Loess Plateau from 2000 to 2024

The Loess Plateau, as a critical ecological security barrier in China, exhibits significant sensitivity and vulnerability in its ecosystem. Over the past few decades, ecological engineering projects such as the “Grain for Green Program” have significantly improved vegetation coverage in the region, effectively curbing soil erosion. However, due to the combined effects of frequent extreme climate events and human activities, the area remains a typical region for studying ecological vulnerability. Based on the Google Earth Engine (GEE) cloud platform, this study utilizes the MODIS dataset series and employs the Principal Component Analysis (PCA) method to construct a 1 km spatial resolution remote sensing based ecological index (RSEI) dataset of the Loess Plateau from 2000 to 2024. By integrating land use/land cover datasets, the study confirms that the spatial distribution of RSEI grades aligns closely with regional ecosystem types. This dataset provides critical data support for optimizing the ecological security pattern and formulating sustainable development policies for the Loess Plateau. It is of great significance for promoting ecological conservation and high-quality development strategies in the Yellow River Basin.

黄土高原作为中国至关重要的生态安全屏障，其生态系统具有显著的敏感性与脆弱性。近数十年来，以“退耕还林还草工程（Grain for Green Program）”为代表的生态工程项目显著提升了该区域植被覆盖度，有效遏制了水土流失。然而，受极端气候事件频发与人类活动的共同影响，该区域仍是研究生态脆弱性的典型区域。本研究基于谷歌地球引擎（Google Earth Engine, GEE）云平台，依托MODIS数据集序列，采用主成分分析（Principal Component Analysis, PCA）方法，构建了2000年至2024年黄土高原空间分辨率为1千米的遥感生态指数（Remote Sensing-based Ecological Index, RSEI）数据集。本研究结合土地利用/土地覆被数据集，验证了RSEI等级的空间分布与区域生态系统类型高度吻合。该数据集为优化黄土高原生态安全格局、制定可持续发展政策提供了关键数据支撑，其对于推动黄河流域生态保护与高质量发展战略具有重要意义。

搜集汇总

背景与挑战

背景概述

该数据集提供了2000年至2024年黄土高原的遥感生态指数数据，空间分辨率为1公里，基于Google Earth Engine云平台和MODIS数据，采用主成分分析方法构建。数据集整合了土地利用/覆盖信息，用于评估区域生态质量变化，为黄土高原生态安全格局优化和可持续发展政策制定提供关键数据支持，尤其适用于研究生态脆弱性和植被恢复效果。