基于Sentinel⁃1和Sentinel⁃2数据的杉木林地上生物量估算

潘磊; 孙玉军; 王轶夫; 陈丽萍; 曹元帅

doi:10.3969/j.issn.1000-2006.201811012

潘磊, 孙玉军, 王轶夫, 陈丽萍, 曹元帅

南京林业大学学报（自然科学版） ›› 2020, Vol. 44 ›› Issue (3) : 149-156.

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南京林业大学学报（自然科学版） ›› 2020, Vol. 44 ›› Issue (3) : 149-156. DOI: 10.3969/j.issn.1000-2006.201811012

研究论文

基于Sentinel⁃1和Sentinel⁃2数据的杉木林地上生物量估算

作者信息 +

Estimation of aboveground biomass in a Chinese fir (Cunninghamia lanceolata)forest combining data of Sentinel⁃1 and Sentinel⁃2

Author information +

文章历史 +

摘要

目的

雷达和光学遥感数据可以提供不同方面的信息，利用Sentinel?1与Sentinel?2联合估算亚热带地区森林地上生物量，探索光学数据与合成孔径雷达（SAR）数据结合对于提高森林地上生物量估测的优势。

方法

以福建省将乐国有林场杉木林为研究对象，以Sentinel?1 SAR数据和Sentinel?2光学数据为数据源，采用多元线性逐步回归方法进行建模，以决定系数（R2）、调整决定系数（R2_adj）、均方根误差（RMSE）、方差膨胀因子（VIF）为模型评价指标，对比分析Sentinel?2光学数据与Sentinel?2结合Sentinel?1 SAR数据估算森林地上生物量的能力。

结果

基于Sentinel?2光学数据的森林地上生物量估算模型，其调整决定系数（R2_adj）达到0.501、均方根误差（RMSE）为64.04 Mg/hm²；Sentinel?2光学数据结合Sentinel?1 SAR数据的森林地上生物量估算模型，其调整决定系数（R2_adj）达到0.575、均方根误差（RMSE）为59.13 Mg/hm²，对比Sentinel?2估算模型，该模型精度有明显提高。

结论

Sentinel?2卫星的多光谱数据能够作为估算亚热带地区森林地上生物量的有效数据，加入Sentinel?1 SAR影像的极化纹理信息后，利用Sentinel?1雷达传感器的全天候获取数据能力与Sentinel?2多光谱传感器丰富的光谱波段信息特点，以及两者短重访周期的能力，能够有效提高估算森林地上生物量模型的精度。

Abstract

Objective

Forest above-ground biomass (AGB) is the main storage site of carbon, which plays an important role in the regional and global carbon cycle. In studies on AGB estimation based on remote sensing, saturation of optical and radar data is a well-known phenomenon, however, it is unclear so far as to how this problem may be reduced in order to improve AGB estimations. Optical and radar data offer different advantages for recording forest stand structures, thus optimized use of their features may help improve AGB estimation. This study was conducted to assess whether forest AGB estimations can be produced in a subtropical area based on data produced using Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral instruments.

Method

A stand of Chinese fir (Cunninghamia lanceolata) in a state-owned forest farm in Jiangle, Fujian Province, China, and Sentinel-1 SAR data and Sentinel-2 multispectral optical data were used. Backscatter coefficients of Sentinel-1 images in different polarization modes (VH, VV, VH/VV) and second-order texture measures of a gray level co-occurrence matrix in different window sizes (3×3, 5×5, 7×7 and 9×9) were recorded, and band values, vegetation indices, and second-order texture measures in 3 × 3 windows of Sentinel-2 image were extracted as remote sensing factors. Models were fitted using multiple linear stepwise regression. AGB estimation models based on Sentinel-2 remote sensing factors only and on cooperative Sentinel-2 and Sentinel-1 remote sensing were established. Determination coefficient (R2), adjusted determination coefficient (R $a d j 2$ ), root mean square error (RMSE), and variance inflation factor were selected for assessing the models. The ability of Sentinel-2 optical data and Sentinel-2 optical data combined with Sentinel-1 SAR data to estimate forest AGB was compared and analyzed.

Result

The results showed that when Sentinel-2 band values, vegetation indices and texture measures were used as dependent variables to predict AGB, the estimation effect was inadequate, and the R $a d j 2$ values of the models were 0.319, 0.303 and 0.455, respectively; an AGB estimation model based on all remote sensing factors of Sentinel-2 optical data (Model-1) produced an R $a d j 2$ value of 0.501, while RMSE was 64.04 Mg/hm². When the Sentinel-1 remote sensing factor was added, the AGB estimation model based on Sentinel-2 optical data and Sentinel-1 SAR data was improved, with a model adjustment coefficient (R $a d j 2$ ) and RMSE were 0.575 and 59.13 Mg/hm². Compared with Model-1, the accuracy of Model-2 was considerably improved, thus the second-order texture measures of Sentinel-1 VH polarimetric imaging plays an important role.

Conclusion

Our study demonstrates that multispectral optical data of the Sentinel-2 satellite can be used as reliable data source for estimating forest AGB in subtropical areas, and the combination of Sentinel-1 SAR data and Sentinel-2 multispectral optical data can substantially improve accuracy of estimating forest AGB. Estimations of forest ABG by combining multispectral optical remote sensing images and space-borne polarimetric SAR data offers considerable advantages. The results of the present study may help improve accuracy of forest AGB estimation, and it provides a reference basis for future studies on forest AGB using different remote sensing data sources.

导出引用

潘磊, 孙玉军, 王轶夫, 陈丽萍, 曹元帅. 基于Sentinel⁃1和Sentinel⁃2数据的杉木林地上生物量估算[J]. 南京林业大学学报（自然科学版）. 2020, 44(3): 149-156 https://doi.org/10.3969/j.issn.1000-2006.201811012

PAN Lei, SUN Yujun, WANG Yifu, CHEN Liping, CAO Yuanshuai. Estimation of aboveground biomass in a Chinese fir (Cunninghamia lanceolata)forest combining data of Sentinel⁃1 and Sentinel⁃2[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2020, 44(3): 149-156 https://doi.org/10.3969/j.issn.1000-2006.201811012

中图分类号： S758.5

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