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遥感技术与应用  2021, Vol. 36 Issue (1): 229-236    DOI: 10.11873/j.issn.1004-0323.2021.1.0229
遥感应用     
基于GF-1遥感数据的若尔盖高寒沼泽湿地地上生物量与土壤有机碳密度估算
曹霸1(),凌成星2()
1.贵州省林业调查规划院,贵州 贵阳 550000
2.中国林业科学研究院资源信息研究所,北京 100091
Estimation of Aboveground Biomass and Soil Organic Carbon Density of Zoige Alpine Wetland based on GF-1 Remote Sensing Data
Ba Cao1(),Chengxing Ling2()
1.Guizhou Provincial Forestry Investigation and Planning Institute,Guiyang 550000,China
2.Institute of Forest Resource Information Techniques CAF,Beijing 100091
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摘要:

随着我国遥感技术迅速的发展,国产系列卫星数据越来越多的应用到各个行业中。在湿地遥感监测方面,湿地生物量和碳储量的遥感估算研究是研究人员非常关注的研究问题,我国自主研制的高分(GF)系列卫星为湿地生态系统的资源监测提供新的途径和方法。提出了基于GF-1卫星的若尔盖高寒沼泽湿地地上生物量与土壤有机碳密度估算方法,通过选取GF-1遥感数据单波段信息,计算植被指数信息、纹理特征、地形特征等27个遥感因子,采用逐步回归法确定建模因子,构建了若尔盖湿地地上生物量和有机碳密度估算模型。研究结果表明:整个若尔盖湿地地上生物量为109.93万t,0~30 cm的土壤有机碳密度为18.99 kg/m2。经地面调查数据验证,地上生物量估算精度为86.44%,有机碳密度估算精度为81.56%;并且,地上生物量和土壤有机碳密度与研究区的湿地植被分布主要集中在中部和西北部范围的空间特征一致,模型估算出的研究结果具有较好的可靠性和合理性。

关键词: 高分卫星高寒沼泽湿地地上生物量土壤有机碳遥感估算    
Abstract:

With the rapid development of remote sensing technology in China, more and more domestic satellite data are applied to various industries. In the field of wetland remote sensing monitoring, remote sensing estimation of wetland biomass and carbon storage is a research area of great concern to researchers In particular, the use of our own developed GF series of satellites for wetland ecosystem resources monitoring to provide a new way and method. In this paper, GF-1 satellite-based method was developed to estimate the aboveground biomass and soil organic carbon density of Alpine marsh wetlands in Ruo'ergai By selecting and calculating 27 remote sensing factors such as single band information, vegetation index information, texture feature and terrain feature of GF-1 remote sensing data, the modeling factors are determined by stepwise regression method an estimation model of aboveground biomass and organic carbon density in zoige wetland was constructed. The results showed that the aboveground biomass of the whole zoige wetland was 1.09 million tons, the soil organic carbon density of 0~30 cm soil was 18.99 kg/m2.Through field investigation, the estimation accuracy of aboveground biomass was 86.44%, and the estimation accuracy of organic carbon density was 81.56%. Moreover, the aboveground biomass and soil organic carbon density are consistent with the spatial characteristics of the wetland vegetation distribution in the study area, which is mainly concentrated in the middle and northwest. The research results estimated by the model are of good reliability and rationality.

Key words: High resolution satellite    Alpine wetland    Aboveground Biomass    Soil Organic Carbon(SOC)    Remote Sensing estimation
收稿日期: 2019-11-11 出版日期: 2021-04-13
ZTFLH:  S54  
基金资助: 高分湿地资源监测应用子系统(二期)(21?Y30B02?9001?19/22?2);贵州省林业数据验收平台研发项目(黔林科合[2018]15号)
通讯作者: 凌成星     E-mail: 1039974695@qq.com;lingcx@ifrit.ac.cn
作者简介: 曹霸(1989-),男,安徽宿州人,高级工程师,主要从事林业“3S”技术应用研究。E?mail:1039974695@qq.com
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引用本文:

曹霸,凌成星. 基于GF-1遥感数据的若尔盖高寒沼泽湿地地上生物量与土壤有机碳密度估算[J]. 遥感技术与应用, 2021, 36(1): 229-236.

Ba Cao,Chengxing Ling. Estimation of Aboveground Biomass and Soil Organic Carbon Density of Zoige Alpine Wetland based on GF-1 Remote Sensing Data. Remote Sensing Technology and Application, 2021, 36(1): 229-236.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.1.0229        http://www.rsta.ac.cn/CN/Y2021/V36/I1/229

图1  研究区样地设置
植被指数计算公式备注说明

NDVI

归一化植被指数

NDVI=ρNIR-ρREDρNIR+ρRED式中ρNIRρRED分别代表GF-1卫星红外波段和红光波段的反射率

SR

比值植被指数

SR=ρNIRρRED式中ρNIRρRED分别代表GF-1卫星红外波段和红光波段的反射率

DVI

差值植被指数

DVI=ρNIR-ρRED式中ρNIRρRED分别代表GF-1卫星红外波段和红光波段的反射率

SAVI

土壤调节植被指数

SAVI=ρNIR-ρREDρNIR+ρRED+L(1+L)式中ρNIRρRED分别代表GF-1卫星红外波段和红光波段的反射率。L取值0.5

NDWI

归一化水体植树

NDWI=ρGREEN-ρNIRρGREEN+ρNIR式中ρNIRρGREEN分别代表GF-1卫星红外波段和绿光波段的反射率

EVI

增强型植被指数

EVI=2.5×(ρNIR-ρRED)(ρNIR+6×ρRED-7×ρBLU+1)式中ρNIRρRED分别代表GF-1卫星红外波段和红光波段的反射率
表1  本文所用主要植被指数计算公式
图2  参与生物量模型估算的遥感因子图像
图3  参与土壤有机碳模型估算的遥感因子图像
参数模型(1)模型(2)模型(3)
a1403.826 0484.763 05.937 3
a293.841 4-2.021 90.288 3
a3-0.354 1-0.648 8-0.001 2
a4-0.023 00.002 4-0.000 2
a572.672 50.045 10.286 8
a60.110 8
表2  地上生物量估测模型参数
模型eˉσ2MSER (相关系数)AIC
模型(1)0.000 217 452.130 0132.106 50.566 6389.135 8
模型(2)1.050 518 858.480 0137.330 20.495 1393.462 6
模型(3)1.015 716 706.000 0129.255 70.802 0387.826 9
表3  估测模型评价指标
参数模型(1)模型(2)模型(3)
a1223.145 0196.546 71.326 5
a241.841 4-0.879 70.104 7
a3-0.178 8-0.648 8-0.005 4
a4-0.014 10.017 6-0.010 1
a531.225 40.099 40.974 2
a60.443 2
表4  土壤有机碳估测模型参数
模型eˉσ2MSER (相关系数)AIC
模型(4)0.000 512 333.542 299.157 50.604 7242.245 7
模型(5)1.011 111 326.547 1105.781 20.500 1251.369 8
模型(6)1.021 910 883.002 2100.125 40.814 7232.257 9
表5  模型的评价指标
图4  模型估算结果精度验证
估测指标

实测值与估测值

拟合方程

检验指标
R2RMSE相对误差/%
地上生物量AGBy=0.735 4x + 68.0710.740 80.487 713.56
土壤有机碳密度SOC densityy=0.860 2x + 2.969 80.755 20.591 118.44
表6  模型估算结果精度检验
图5  若尔盖湿地区域地上生物量和0~30 cm土壤有机碳密度估算结果
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