内蒙古中部草地地上生物量时空变化及其驱动因素

doi:10.13287/j.1001-9332.202511.007

应用生态学报 ›› 2025, Vol. 36 ›› Issue (11): 3315-3326.doi: 10.13287/j.1001-9332.202511.007

内蒙古中部草地地上生物量时空变化及其驱动因素

张深林¹, 吴田军^1,2,3, 韩玲^1,2,3, 王刘华^1*, 孙海莲⁴

¹长安大学土地工程学院, 西安 710054;
²陕西省土地整治重点实验室, 西安 710054;
³西安市国土空间信息重点实验室, 西安 710054;
⁴包头师范学院生态环境学院, 内蒙古包头 014030

收稿日期:2025-06-21 接受日期:2025-09-22 出版日期:2025-11-18 发布日期:2026-06-18
通讯作者: * E-mail: 806484382@qq.com
作者简介:张深林, 男, 1999年生, 硕士研究生。主要从事智能遥感分析与地理信息应用研究。E-mail: 2022127010@chd.edu.cn
基金资助:
国家自然科学基金项目(42471394)、陕西省自然科学基础研究计划项目(2025JC-QYCX-035)、中国科学院空天信息创新研究院自主部署项目(E4Z202021F)和河北省中央引导地方科技发展资金项目(236Z0104G)

Spatial and temporal variations in grassland aboveground biomass and their drivers in central Inner Mongolia, China

ZHANG Shenlin¹, WU Tianjun^1,2,3, HAN Ling^1,2,3, WANG Liuhua^1*, SUN Hailian⁴

¹School of Land Engineering, Chang'an University, Xi'an 710054, China;
²Key Laboratory of Land Consolidation of Shaanxi Province, Xi'an 710054, China;
³Xi'an Key Laboratory of Territorial Spatial Information, Xi'an 710054, China;
⁴College of Ecology and Environment, Baotou Teacher's College, Baotou 014030, Inner Mongolia, China

Received:2025-06-21 Accepted:2025-09-22 Online:2025-11-18 Published:2026-06-18

摘要/Abstract

摘要： 内蒙古中部草原生态系统是中国北方生态安全屏障的重要组成部分。本研究以内蒙古中部草地为对象,整合草地样方实测数据、遥感影像及环境变量,基于机器学习算法构建草地地上生物量估算模型,生成2000—2020年高分辨率草地地上生物量空间分布数据,并结合趋势分析与地理探测器方法,分析草地地上生物量的时空变化特征及驱动因素。结果表明: 在多种机器学习模型中,梯度提升机(GBM)算法表现最优,其决定系数(R²)、平均绝对误差(MAE)及均方根误差(RMSE)分别为0.58、42.40 g·m^-2和56.99 g·m^-2。2000—2020年间,研究区草地地上生物量整体上呈波动上升趋势,多年平均值为148.72 g·m^-2,空间上呈现西北低、东南高的分布格局。77.9%的区域草地地上生物量有所增加,仅0.3%的区域显著退化。因子探测表明,年降水量、生长季降水量、土壤氮和土壤有机碳含量是草地地上生物量空间分异的主要驱动因子,且因子间交互作用均呈增强效应。研究结果可为该地区草地资源的管理及可持续发展提供科学依据。

关键词: 内蒙古中部, 草地地上生物量, 机器学习, 时空变化, 驱动因素

Abstract: Grasslands of central Inner Mongolia are a crucial component of ecological security barrier in northern China. By integrating field-measured quadrat data, remote sensing imagery, and environmental variables of grasslands in central Inner Mongolia, we developed aboveground biomass estimation models using machine learning algorithms, and generated high-resolution spatial distribution datasets for the period 2000-2020. We further analyzed the spatiotemporal variations and driving factors of aboveground biomass by the trend analysis and GeoDetector methods. The results showed that among multiple machine learning models, the gradient boosting machine (GBM) algorithm demonstrated optimal performance, with the coefficient of determination (R²), mean absolute error (MAE) and root mean square error (RMSE) being 0.58, 42.40 g·m^-2, and 56.99 g·m^-2, respectively. From 2000 to 2020, aboveground biomass showed a fluctuating upward trend, with a multi-year average value of 148.72 g·m^-2. Spatially, aboveground biomass displayed a pattern of low values in the northwest and high values in the southeast. Overall, 77.9% of the region experienced increases in aboveground biomass, while only 0.3% showed significant degradation. Factor detection revealed that annual precipitation, growing season precipitation, soil nitrogen, and soil organic carbon content were the primary drivers of spatial heterogeneity in aboveground biomass, and all interactions exhibiting enhanced effects. Our results could provide scientific basis for the management and sustainable development of grassland resources in central Inner Mongolia.

Key words: central Inner Mongolia, grassland aboveground biomass, machine learning, spatiotemporal change, driving factor

张深林, 吴田军, 韩玲, 王刘华, 孙海莲. 内蒙古中部草地地上生物量时空变化及其驱动因素[J]. 应用生态学报, 2025, 36(11): 3315-3326.

ZHANG Shenlin, WU Tianjun, HAN Ling, WANG Liuhua, SUN Hailian. Spatial and temporal variations in grassland aboveground biomass and their drivers in central Inner Mongolia, China[J]. Chinese Journal of Applied Ecology, 2025, 36(11): 3315-3326.

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内蒙古中部草地地上生物量时空变化及其驱动因素

Spatial and temporal variations in grassland aboveground biomass and their drivers in central Inner Mongolia, China

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