基于随机森林协同克里金法的区域森林地上生物量制图——以粤北森林为例

周友锋; 谢秉楼; 李明诗

doi:10.12302/j.issn.1000-2006.202202015

周友锋, 谢秉楼, 李明诗

南京林业大学学报（自然科学版） ›› 2024, Vol. 48 ›› Issue (1) : 169-178.

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南京林业大学学报（自然科学版） ›› 2024, Vol. 48 ›› Issue (1) : 169-178. DOI: 10.12302/j.issn.1000-2006.202202015

研究论文

基于随机森林协同克里金法的区域森林地上生物量制图——以粤北森林为例

周友锋 ¹ ,
谢秉楼 ²^,^* ,
李明诗 ¹

作者信息 +

Mapping regional forest aboveground biomass from random forest Co-Kriging approach: a case study from north Guangdong

Author information +

文章历史 +

摘要

【目的】森林地上生物量(aboveground biomass,AGB)是森林生态系统健康状态和碳汇潜力评估的重要指标,本研究提出了一种快速、准确地获取区域尺度 AGB 信息的制图方法。【方法】基于 Landsat 5 TM、ALOS-1 PALSAR-1、STRM DEM和国家森林资源清查数据,提取光谱特征、纹理指数、后向散射系数和地形因子等特征因子,采用随机森林进行AGB建模。考虑到AGB是典型的具有空间自相关特性的生物物理参量,针对模型残差,使用以高程为协变量的克里金空间插值法分离残差项中的结构化成分,并将其叠加到随机森林模型预测值上形成最终的AGB预测值,从而提高区域尺度的AGB制图精度。【结果】协同克里金法将高程数据作为协变量,在预测AGB残差结构化成分方面的性能优于普通克里金法,协同克里金法与随机森林协同的AGB预测性能明显提升。经独立AGB数据的模型验证表明,随机森林模型预测的AGB与实际观测的AGB间的决定系数 R²为0.46,随机森林结合普通克里金的验证 R²提高到了0.51,而随机森林结合协同克里金模型的验证R²为0.57。相应的均方根误差(RMSE)分别从32.48 t/hm²降低到31.58、29.80 t/hm²,平均绝对误差(MAE)从27.28 t/hm² 降低到26.63、25.12 t/hm²,相对改进指数为0.03和0.08。【结论】总体而言,本研究提出的随机森林协同克里金模型提供了一种更准确、可靠地进行复杂地形区域的亚热带森林AGB制图新方法,所生成的AGB专题图有助于发展针对固碳效能的森林经营方法,为全球气候变化背景下的森林碳增汇和森林可持续经营提供参考。

Abstract

【Objective】 Forest aboveground biomass (AGB) is an important indicator for evaluating forest ecosystem health status and carbon sink potential. Accurate and quick mapping regional forest AGB has become intensively researched in forest ecosystem status assessment and global climate change studies in recent years. The major objective of this study was to develop a framework for improving the mapping accuracy of AGB in a subtropical forested area with complex terrain. 【Method】 Spectral features, textural indices, backscattering coefficients, and topographical variables were derived from Landsat 5 TM, ALOS-1 PALSAR-1 data and STRM DEM. Next, in tandem with national forest inventory plot measurements, a random forest/Co-Kriging framework that combines the advantages of random forest (RF) and a geostatistical approach was proposed to map AGB in northern Guangdong Province. 【Result】 The experimental results showed that the ordinary Kriging (OK) and Co-Kriging (CK) were able to predict the distribution of the RF-predicted AGB residuals. The predicted structured components of the residuals adding onto the RF predictions could improve the mapping accuracy of AGB to some extent. After the validation of the independent 20% dataset, the determination coefficient between the predictions and the observations increased from 0.46 (RF) to 0.51 (RFOK) and to 0.57 (RFCK). The root mean square error decreased from 32.48 to 31.58 and to 29.80 t/hm² accordingly. The mean absolute error decreased from 27.28 to 26.63 and to 25.12 t/hm². Overall, co-Kriging, which considers elevation as a co-variable, was better than ordinary Kriging in predicting AGB residuals. 【Conclusion】 The RFCK framework provides an accurate and reliable method to map subtropical AGB with complex topography. The resulting AGB maps contribute to targeted forest resource management and promote forest carbon sequestration and sustainable forest management under global warming scenarios.

导出引用

周友锋, 谢秉楼, 李明诗. 基于随机森林协同克里金法的区域森林地上生物量制图——以粤北森林为例[J]. 南京林业大学学报（自然科学版）. 2024, 48(1): 169-178 https://doi.org/10.12302/j.issn.1000-2006.202202015

ZHOU Youfeng, XIE Binglou, LI Mingshi. Mapping regional forest aboveground biomass from random forest Co-Kriging approach: a case study from north Guangdong[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2024, 48(1): 169-178 https://doi.org/10.12302/j.issn.1000-2006.202202015

中图分类号： S771.8

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