中国东北天然林保护工程区森林变化数据集（1986-2018）

森林变化（包含森林损失和恢复）是受自然和人类活动影响的复杂生态过程，对全球物质循环和能量流动具有重要的影响。基于长时间序列树冠覆盖度（tree-canopy cover, TCC）数据，采用双时相类概率模型对森林变化进行检测，得到1986-2018年中国东北天然林保护工程区森林变化数据集（空间分辨率为30米，时间分辨率为1年）。使用分层随机采样方法在保护区范围内选取1000样点并进行目视解译，对森林变化提取结果进行精度评价，结果显示森林损失（Producer’s accuracy = 85.21%；User’s accuracy = 84.26%）和森林恢复（Producer’s accuracy = 87.74%；User’s accuracy = 88.31%）精度均较高，可以有效反映保护区森林变化状态。

Forest change (including forest loss and recovery) is a complex ecological process influenced by natural and human activities, which exerts a substantial impact on global material cycling and energy flow. Based on long-time series tree-canopy cover (TCC) data, forest change detection was implemented using a dual-temporal class probability model, and the forest change dataset of the Natural Forest Protection Project area in Northeast China spanning 1986 to 2018 was developed, with a spatial resolution of 30 meters and a temporal resolution of 1 year. A stratified random sampling approach was employed to select 1000 sample points within the protected area and perform visual interpretation, followed by an accuracy assessment of the forest change extraction results. The findings revealed that both forest loss (Producer’s accuracy = 85.21%; User’s accuracy = 84.26%) and forest recovery (Producer’s accuracy = 87.74%; User’s accuracy = 88.31%) achieved high accuracies, which can effectively reflect the forest change status of the protected area.

搜集汇总

背景与挑战

背景概述

该数据集提供了1986-2018年中国东北天然林保护工程区的森林变化信息，包括森林损失和恢复，空间分辨率为30米，时间分辨率为1年。数据集基于长时间序列树冠覆盖度数据，采用双时相类概率模型检测森林变化，精度评价显示森林损失和恢复的精度均较高。