未来不同气候变化情景下青藏高原植被动态变化图集

本数据集包含当前时期（1993-2022）与未来时期（2071-2100）三个排放情景（SSP119，SSP245，SSP585）下青藏高原潜在自然植被空间分布。包含16种植被功能型，空间分辨率为0.25度（采用Albers投影）。研究使用LPJ-GUESS模拟考虑水分循环下的潜在自然植被状态（叶面积指数，蒸散发，碳储量等）模拟结果，采用随机森林模型建立顶级自然植被与如上模拟结果及自然环境变量（气候，土壤，地形）之间的关系。该模型基于中国区构建，使用210,897条记录对模型进行训练，经验证具有较高的分类精度（总体精度达0.89，Kappa系数为0.87），后应用于青藏高原地区。该数据集可为评估未来气候变化背景下青藏高原植被分布及生态服务功能研究提供数据支持。

This dataset contains spatial distributions of potential natural vegetation on the Qinghai-Tibet Plateau during the historical period (1993–2022) and future periods (2071–2100) under three emission scenarios (SSP119, SSP245, and SSP585). It includes 16 plant functional types, with a spatial resolution of 0.25° and projected using the Albers projection.

This study utilized simulation outputs of potential natural vegetation states (including leaf area index, evapotranspiration, carbon stocks, and other related variables) under consideration of the water cycle, generated by the LPJ-GUESS model, and established a predictive relationship between climax natural vegetation and these simulation outputs as well as natural environmental covariates (climate, soil, and topography) using a Random Forest model.

The model was developed based on data from China, trained on 210,897 sample records, and validated to exhibit high classification accuracy, with an overall accuracy of 0.89 and a Cohen's Kappa coefficient of 0.87, before being applied to the Qinghai-Tibet Plateau region.

This dataset can provide robust data support for research assessing vegetation distribution and ecosystem service functions on the Qinghai-Tibet Plateau under the context of future climate change.

搜集汇总

背景与挑战

背景概述

该数据集提供了青藏高原在当前和未来不同气候变化情景下的植被动态变化信息，包含16种植被功能型，空间分辨率为0.25度，数据格式为TIF，适用于地理信息系统分析。数据集可用于评估未来气候变化对青藏高原植被分布及生态服务功能的影响。