雅鲁藏布江中游地区人工林种植时间空间分布（1988-2020）

本数据集提供了雅鲁藏布江流域中段1988-2020年人工林（AF）种植时间的空间分布信息，旨在弥补当前人工林种植时间量化记录的不足，为森林管理和荒漠化治理提供支持。基于长期归档的Landsat影像（TM、ETM+和OLI），结合归一化植被指数（NDVI）时间序列，构建了一种基于Google Earth Engine (GEE) 的趋势变化点（TCP）检测算法，用于自动提取人工林种植时间信息。该算法利用生长季（5月至9月）的NDVI最大值时间序列，通过平滑和分段方法计算子空间拟合斜率差值（Sdiff），以此指示种植时间的显著变化点。经野外实地调查的222个样本验证，种植时间检测结果的皮尔逊相关系数达到0.93，均方根误差为2.95年。此外，不同年份的种植面积分布与生态重建项目实施的时间进程具有高度一致性。本数据集生成的种植时间信息不仅在人工林的可持续管理方面具有重要应用价值，还为后续评估人工林对荒漠化治理的影响提供了基础。

This dataset provides spatial distribution information of afforestation (AF) planting time in the middle reaches of the Yarlung Zangbo River Basin from 1988 to 2020, aiming to address the gap in current quantitative records of afforestation planting time and support forest management and desertification control. Based on long-term archived Landsat imagery (TM, ETM+ and OLI) combined with Normalized Difference Vegetation Index (NDVI) time series, a trend change point (TCP) detection algorithm built on Google Earth Engine (GEE) was developed to automatically extract afforestation planting time information. This algorithm uses the time series of maximum NDVI during the growing season (May to September), and calculates the subspace fitting slope difference (Sdiff) via smoothing and segmentation methods to indicate the significant change points of planting time. Validated by 222 field survey samples, the Pearson correlation coefficient of the planting time detection results reached 0.93, with a root mean square error (RMSE) of 2.95 years. In addition, the spatial distribution of afforestation area in different years is highly consistent with the implementation timeline of ecological restoration projects. The planting time information generated by this dataset not only has great application value in the sustainable management of afforestation, but also provides a foundation for subsequent assessments of the impacts of afforestation on desertification control.

搜集汇总

背景与挑战

背景概述

该数据集提供了雅鲁藏布江中游地区1988-2020年人工林种植时间的空间分布信息，基于Landsat影像和NDVI时间序列，采用Google Earth Engine的TCP检测算法自动提取，经实地样本验证精度高（相关系数0.93，误差2.95年），旨在支持森林管理和荒漠化治理，具有重要的可持续管理应用价值。