基于基准样地法和国产高分数据的湖南省森林植被碳储量估测

doi:10.13287/j.1001-9332.201910.016

应用生态学报 ›› 2019, Vol. 30 ›› Issue (10): 3385-3394.doi: 10.13287/j.1001-9332.201910.016

基于基准样地法和国产高分数据的湖南省森林植被碳储量估测

张沁雨¹, 王海宾², 彭道黎^1*, 夏朝宗³, 陈健³, 柳文杰⁴

¹北京林业大学林学院, 北京 100083;
²国家林业和草原局林产工业规划设计院, 北京 100010;
³国家林业和草原局调查规划设计院, 北京 100714;
⁴内蒙古自治区大兴安岭森林调查规划院, 内蒙古牙克石 022150

收稿日期:2018-12-17 出版日期:2019-10-20 发布日期:2019-10-20
通讯作者: *E-mail: dlpeng@bjfu.edu.cn
作者简介:张沁雨, 女, 1995年生, 硕士研究生. 主要从事林业遥感与信息技术相关研究. E-mail: zqy421031368@163.com
基金资助:
“十三五”国家重点研发计划项目(2016YFD0600205)和高分林业遥感应用示范系统(二期)项目资助

Estimation of forest vegetation carbon storage in Hunan Province, China based on k-NN method and domestic high-resolution data

ZHANG Qin-yu¹, WANG Hai-bin², PENG Dao-li^1*, XIA Chao-zong³, CHEN Jian³, LIU Wen-jie⁴

¹College of Forestry, Beijing Forestry University, Beijing 100083, China;
²Academy of Forest Industry Planning and Design, National Forestry and Grassland Administration, Beijing 100010, China;
³Academy of Forest Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China;
⁴Inner Mongolia Daxing-anling Inventory and Planning Institute, Yakeshi 022150, Inner Mongolia, China

Received:2018-12-17 Online:2019-10-20 Published:2019-10-20
Contact: *E-mail: dlpeng@bjfu.edu.cn
Supported by:
This work was supported by the National Key R&D Program of China (2016YFD0600205) and the High-resolution Forestry Remote Sensing Application Demonstration System (Phase II) Project.

摘要/Abstract

摘要： 为推广国产高分数据在大尺度范围碳储量估测计量的应用,采用覆盖湖南省的206景高分辨率遥感影像,将估测的最小单元固定为由多个像元组合成的面积为0.06 hm²的正方格,通过解译标志的建立和提纯,在森林信息提取上,利用基于像元法和面向对象分类法进行比较;在乔木林碳储量估测上,利用稳健估计、偏最小二乘法和基准样地法(k-NN)估计进行比较,最后实现了对湖南省森林的碳储量估测,并生成了全省的碳密度等级分布图.结果表明: 基于样地自动提取的解译标志在经过提纯后,能进一步增加乔木林提取精度;对于大尺度范围森林植被碳储量估测,无论是在森林信息提取还是乔木林碳储量建模方面,k-NN算法都体现了较大优势,是最佳估测方法;206景影像的平均分类总精度为76.8%,平均均方根误差为8.95 t·hm^-2,平均相对均方根误差为19.1%,湖南省碳储量总量为22.28 Mt.本研究结果为省级及国家级尺度的森林植被碳储量估测计量与监测提供了有效参考.

Abstract: To promote the application of domestic high-resolution satellite data in large-scale carbon storage estimation and measurement, a total of 206 high-resolution remote sensing images covering Hunan Province were used as the data source, and the estimated minimum unit was fixed as a 0.06 hm² square composed of multiple pixels. Through the establishment and purification of the interpretation marks, in the extraction of forest information, the pixel-based method and object-oriented classification method were used to compare. In the estimation of carbon storage of arbor forest, the robust estimate, partial least squares method and k-NN estimate were used to compare. Finally, we estimated forest carbon storage in Hunan Province and generated the distribution map of carbon density levels. The results showed that the interpretation mark based on the automatic extraction of plots could increase the extraction accuracy of arbor forest after purification. For the estimation of forest carbon storage at large-scale, the k-NN algorithm embodied a large advantage in forest information extraction and arbor forest carbon storage modeling. The average classification accuracy of the 206 scene images was 76.8%, the average RMSE was 8.95 t·hm^-2, the average RRMSE was 19.1%, and the total carbon stock in Hunan Province was 22.28 Mt. The results provided effective reference for the estimation and measurement of forest carbon storage at the provincial and national scales.

张沁雨, 王海宾, 彭道黎, 夏朝宗, 陈健, 柳文杰. 基于基准样地法和国产高分数据的湖南省森林植被碳储量估测[J]. 应用生态学报, 2019, 30(10): 3385-3394.

ZHANG Qin-yu, WANG Hai-bin, PENG Dao-li, XIA Chao-zong, CHEN Jian, LIU Wen-jie. Estimation of forest vegetation carbon storage in Hunan Province, China based on k-NN method and domestic high-resolution data[J]. Chinese Journal of Applied Ecology, 2019, 30(10): 3385-3394.

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基于基准样地法和国产高分数据的湖南省森林植被碳储量估测

Estimation of forest vegetation carbon storage in Hunan Province, China based on k-NN method and domestic high-resolution data

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