典型喀斯特山区的森林蓄积量遥感估算

郑佳佳; 周忠发; 朱孟; 黄登红; 吴小飘; 刘荣萍; 龙洋洋

doi:10.13961/j.cnki.stbctb.2024.02.019

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典型喀斯特山区的森林蓄积量遥感估算

试验研究

- 典型喀斯特山区的森林蓄积量遥感估算
- Remote Sensing Estimation of Forest Volume in Typical Karst Mountainous Areas
- 水土保持通报 2024年44卷第2期页码：176-186
- 作者机构：
  
  1. 贵州师范大学喀斯特研究院/地理与环境科学学院,贵州,贵阳,550001
  2. 贵州喀斯特山地生态环境国家重点实验室培育基地,贵州,贵阳,550001
  3. 国家喀斯特石漠化防治工程技术研究中心,贵州,贵阳,550001
- 作者简介：
- 基金信息：
  
  国家自然科学基金地区项目“喀斯特石漠化地区生态资产与区域贫困耦合机制研究”(41661088);贵州省高层次创新型人才培养计划“百”层次人才(黔科合平台人才(2016)5674号);贵州省研究生科研基金立项课题(黔教合YJSKYJJ〔2021〕090)
- DOI：10.13961/j.cnki.stbctb.2024.02.019
  中图分类号： S758.4
- 纸质出版：2024
- 稿件说明：
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郑佳佳, 周忠发, 朱孟, 等. 典型喀斯特山区的森林蓄积量遥感估算[J]. 水土保持通报, 2024,44(2):176-186.

ZhengJiajia, Zhou Zhongfa, Zhu Meng, et al. Remote Sensing Estimation of Forest Volume in Typical Karst Mountainous Areas[J]. Bulletin of Soiland Water Conservation, 2024, 44(2): 176-186.
郑佳佳, 周忠发, 朱孟, 等. 典型喀斯特山区的森林蓄积量遥感估算[J]. 水土保持通报, 2024,44(2):176-186. DOI： 10.13961/j.cnki.stbctb.2024.02.019.

ZhengJiajia, Zhou Zhongfa, Zhu Meng, et al. Remote Sensing Estimation of Forest Volume in Typical Karst Mountainous Areas[J]. Bulletin of Soiland Water Conservation, 2024, 44(2): 176-186. DOI： 10.13961/j.cnki.stbctb.2024.02.019.

摘要

[目的] 通过森林蓄积量的遥感监测了解喀斯特地区森林生态系统的健康状况和生态功能，为该地区碳汇监测与评估以及森林管理与决策提供理论依据。 [方法] 本研究选取典型喀斯特山区为研究对象，基于Sentinel-2A影像和样地调查数据，结合随机森林(RF)、K近邻回归(KNN)和BP神经网络3种机器学习模型，在山地坡度条件约束下开展森林蓄积量反演研究。 [结果] ①单波段反射率、植被指数和纹理特征等遥感因子在不同地形约束条件下的表现不同，建立模型的最优子集均不同，不同立地条件下建立蓄积量估测模型均有差异。 ②在喀斯特山区森林蓄积量估算中，RF相比KNN和BP模型鲁棒性和适应性最强。在缓坡、斜坡、陡坡立地条件下，RF模型精度分别达到80.1%，79.0%，80.5%。 [结论] 喀斯特山区空间异质性强，不同坡度立地条件下参与蓄积量遥感估测的建模自变量因子均不相同。坡度的划分可以细化复杂场景遥感估算模型，提高蓄积量遥感估算精度。

Abstract

[Objective] The health status and ecological functions of forest ecosystems were studied in karst areas through remote sensing monitoring of forest volume in order to provide theoretical basis for carbon sink monitoring and assessment

as well as forest management and decision-making in the region. [Methods] Sentinel-2A images and sample plot survey data were acquired for typical karst mountainous areas. Three machine learning models

including random forest (RF)

K-nearest neighbor (KNN)

and back propagation (BP) neural network

were combined to conduct a study on forest volume inversion under mountain slope conditions. [Results] ① The performance of single-band reflectance

vegetation index

and texture features varied under different topographic constraints

and the optimal subsets of models established were different. There were differences in the establishment of forest volume estimation models under different site conditions. ② For forest volume estimation in the karst mountainous area

RF had the strongest robustness and adaptability compared with KNN and BP. For gentle slope

inclined slope

and steep slope conditions

the accuracy of RF reached 80.1%

79.0% and 80.5%

respectively. [Conclusion] Karst mountainous areas have strong spatial heterogeneity

and the modeling independent variables involved in the remote sensing estimation of storage volume are not the same under different slope site conditions. Categorizing slope conditions can refine the remote sensing estimation model of complex scenes and improve the accuracy of remote sensing estimation of forest volume.

关键词

Keywords

references

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