基于地统计学和多源遥感数据的森林碳密度估算

doi:10.11707/j.1001-7488.20170708

林业科学 ›› 2017, Vol. 53 ›› Issue (7): 72-84.doi: 10.11707/j.1001-7488.20170708

基于地统计学和多源遥感数据的森林碳密度估算

严恩萍^1,2, 赵运林^1,2, 林辉^1,2, 莫登奎^1,2, 王广兴^1,2,3

1. 中南林业科技大学林业遥感大数据与生态安全湖南省重点实验室长沙 410004;
2. 中南林业科技大学林学院长沙 410004;
3. 南伊利诺伊大学地理系卡本代尔 629012

收稿日期:2016-03-21 修回日期:2016-09-15 出版日期:2017-07-25 发布日期:2017-08-23
通讯作者: 林辉
基金资助:
国家"十二五"高技术发展研究计划项目（2012AA102001）；国家自然科学基金面上项目（31470643）；林学重点学科开放基金拟资助项目（2016YB08）。

Estimation of Forest Carbon Density Based on Geostatistics and Multi-Resource Remote Sensing Data

Yan Enping^1,2, Zhao Yunlin^1,2, Lin Hui^1,2, Mo Dengkui^1,2, Wang Guangxing^1,2,3

1. Key Laboratory of Forestry Remote Sensing Big Data & Ecological Security for Hunan Province Central South University of Forestry & Technology Changsha 410004;
2. College of Forestry, Central South University of Forestry & Technology Changsha 410004;
3. Department of Geography, Southern Illinois University Carbondale Il 629012 USA

Received:2016-03-21 Revised:2016-09-15 Online:2017-07-25 Published:2017-08-23

摘要/Abstract

摘要： [目的]基于遥感影像空间分辨率和地面样地大小不一致的现象，采用地统计学和多源遥感数据进行森林碳密度估算，为MODIS数据在区域森林碳密度估算领域的应用提供参考。[方法]以湖南省攸县为试验区，首先利用基于块的序列高斯协同模拟算法，将25.8 m×25.8 m的样地数据分别上推到250 m×250 m、500 m×500 m和1 000 m×1 000 m；然后将上推后的样地数据分别与MOD13Q1、MOD09A1、MOD15A2数据结合，利用序列高斯协同模拟算法开展区域森林碳密度估算研究；最后将最优结果用于湖南省森林碳密度估算。[结果]Landsat5和MODIS数据与森林碳密度的敏感因子具有高度相似性，排在前3位的分别为1/TM3、1/TM2、1/TM1和1/Band1、1/Band4、1/Band3；与植被指数产品MOD13Q1和MOD15A2相比，多光谱数据Landsat5和MOD09A1在攸县森林碳密度估算方面显示出巨大潜力，估算精度分别为82.02%和75.64%；基于MOD09A1的序列高斯协同模拟算法具有很好的适用性，可用于湖南省森林碳密度的空间模拟，估算精度为74.07%。[结论]采用基于块的序列高斯协同模拟算法，可以实现由地面样地到不同空间分辨率MODIS像元之间的转换；由于空间分辨率的限制，MOD09A1数据在刻画空间细节方面不如Landsat5精细。该研究方法适用于地面调查样地大小和遥感影像空间分辨率不一致的区域森林碳密度估算。

关键词: 林业遥感, 森林资源清查, 多源遥感, 基于块的序列高斯协同模拟, 森林碳密度

Abstract: [Objective] Due to the inconsistency of spatial resolutions between sample plots and image pixels, in this study the estimation of forest carbon density was conducted by using geostatistics method and multi-resource remote sensing data, which aims to provide reference for the application of MODIS data in the regional estimation of forest carbon density.[Method] Firstly, the spatial block co-simulation algorithm was employed to scale up the sample plots of forest carbon density in You county of Hunan Province from the spatial resolution of 25.8 m×25.8 m to the spatial resolutions of 250 m×250 m, 500 m×500 m and 1 000 m×1 000 m respectively. Then, MODIS images with three spatial resolutions corresponding to those mentioned above, were applied to map forest carbon density for this county using sequential Gaussian co-simulation algorithm. Finally, the best model was applied in the estimation of forest carbon density for Hunan Province.[Result] There were highly similarities for sensitive factors of forest carbon density between Landsat5 and MOD09A1 data, according to theresult of Pearson product moment correlations, the top three sensitive factors were 1/TM3, 1/TM2, 1/TM1 for Landsat5 and 1/Band1, 1/Band4, 1/Band3 for MOD09A1 respectively; compared to the vegetation product of MOD13Q1 and MOD15A2, multi-spectral data such as Landsat5 and MOD09A1 showed great potential in the simulation of forest carbon density with the accuracy of 82.02% and 75.64%, respectively; there is good application for the sequential Gaussian co-simulation algorithms based on the image of MOD09A1, which can be used in the spatial simulation of forest carbon density for Hunan Province with the accuracy of 74.07%.[Conclusion] The spatial block co-simulation algorithm could be used to realize the conversion of spatial resolutions from sample plots to the pixels of MODIS images. It was also found that the MODIS derived maps were more smoothed than those from Landsat5 due to the limitation of spatial resolutions, especially in the terms of capturing the spatial variability of forest carbon density. The adopted method was well suited for regional estimation of forest carbon density based on the combination of forest inventory sample plot data and remotely sensed images, especially for the areas that the plot sizes and images pixels were inconsistent.

Key words: forestry remote sensing, forest resource inventory, multi resource remote sensing, sequential Gaussian block co-simulation, forest carbon density

中图分类号:

严恩萍, 赵运林, 林辉, 莫登奎, 王广兴. 基于地统计学和多源遥感数据的森林碳密度估算[J]. 林业科学, 2017, 53(7): 72-84.

Yan Enping, Zhao Yunlin, Lin Hui, Mo Dengkui, Wang Guangxing. Estimation of Forest Carbon Density Based on Geostatistics and Multi-Resource Remote Sensing Data[J]. Scientia Silvae Sinicae, 2017, 53(7): 72-84.

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基于地统计学和多源遥感数据的森林碳密度估算

Estimation of Forest Carbon Density Based on Geostatistics and Multi-Resource Remote Sensing Data

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