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自然资源遥感  2025, Vol. 37 Issue (4): 88-98    DOI: 10.6046/zrzyyg.2024072
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
基于航空多参数遥感的滨海湿地植物固碳能力研究
赵国凤1,2(), 方彦奇1,2, 陈浩峰1,2, 严维兵1, 黄岩1,2,3(), 陈伟1,2
1.江苏省地质勘查技术院,南京 210049
2.江苏省航空对地探测与智能感知工程研究中心,南京 210049
3.南京理工大学,南京 210094
Exploring carbon sequestration capacities of coastal wetland plants based on multi-parameter airborne remote sensing
ZHAO Guofeng1,2(), FANG Yanqi1,2, CHEN Haofeng1,2, YAN Weibing1, HUANG Yan1,2,3(), CHEN Wei1,2
1. Geological Exploration Technology Institute of Jiangsu Province, Nanjing 210049, China
2. Jiangsu Province Engineering Research Center of Airborne Detecting and Intelligent Perceptive Technology, Nanjing 210049, China
3. Nanjing University of Science & Technology, Nanjing 210094, China
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摘要 文章以江苏省滨海湿地为例,通过卫星遥感、航空多参数遥感等方法估算滨海湿地主要植被生物量,并计算其固碳能力。结果表明: ①基于航空高光谱数据完成研究区地物精细分类,提取了地表覆盖分类信息共11类,植被覆盖率约76%,人类活动区域面积占比约1.5%; ②基于航空多参数方法反演植被生物量模型精度高于卫星遥感方法,其决定系数大于0.8,均方根误差为0.25; ③通过航空多参数遥感方法计算得到研究区内互花米草与芦苇的地上固碳能力分别为0.41 kg/m2和0.58 kg/m2。研究显示,航空多参数遥感方法能准确获取湿地植被种类及固碳能力,可为研究湿地生态系统的碳循环和生境现状提供重要评价参数,精准服务湿地资源调查。
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赵国凤
方彦奇
陈浩峰
严维兵
黄岩
陈伟
关键词 航空高光谱遥感航空多参数遥感固碳能力植被地上生物量遥感    
Abstract

This study investigated the coastal wetland of Jiangsu Province. Using methods such as satellite remote sensing and airborne multi-parameter remote sensing, this study estimated the biomass of dominant plants and estimated their carbon sequestration capacities. Based on fine-scale classification of surface features achieved using airborne hyperspectral data, this study extracted 11 land cover types. The vegetation cover was approximately 76%, and zones with human activities accounted for about 1.5% of the study area. The model for vegetation biomass inversion using the multi-parameter airborne remote sensing demonstrated higher accuracy than that based on satellite remote sensing, with a coefficient of determination (R2) greater than 0.8 and a root mean square error (RMSE) of 0.25. As calculated using the multi-parameter airborne remote sensing, Spartina alterniflora and reed within the study area exhibited aboveground carbon sequestration capacities of 0.41 kg/m2 and 0.58 kg/m2, respectively. This study demonstrates that the multi-parameter airborne remote sensing method can accurately determine vegetation types in wetlands and carbon sequestration capacity, thus providing crucial assessment parameters for research on the carbon cycle of the ecosystem and the current status of habitats within wetlands and precisely serving wetland resource surveys.

Key wordsairborne hyperspectral remote sensing    airborne multi-parameter remote sensing    carbon sequestration capacity    aboveground biomass    remote sensing
收稿日期: 2024-02-22      出版日期: 2025-09-03
ZTFLH:  TP79  
基金资助:江苏省地质局科技创新项目“基于多参数遥感开展滨海湿地植物固碳能力快速调查研究”(2021KY14);江苏省自然发展专项资金(海洋科技创新)项目“黄海湿地生态岸线调查与保护关键技术研究”(JSZRHYKJ202115)
作者简介: 赵国凤(1985-),女,高级工程师,主要从事航空对地探测技术研究。Email: xiaozhao0428@126.com
引用本文:   
赵国凤, 方彦奇, 陈浩峰, 严维兵, 黄岩, 陈伟. 基于航空多参数遥感的滨海湿地植物固碳能力研究[J]. 自然资源遥感, 2025, 37(4): 88-98.
ZHAO Guofeng, FANG Yanqi, CHEN Haofeng, YAN Weibing, HUANG Yan, CHEN Wei. Exploring carbon sequestration capacities of coastal wetland plants based on multi-parameter airborne remote sensing. Remote Sensing for Natural Resources, 2025, 37(4): 88-98.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2024072      或      https://www.gtzyyg.com/CN/Y2025/V37/I4/88
Fig.1  研究区位置图
Fig. 2  多参数航空遥感系统组成图
Fig.3  工作流程图
Fig.4  地物分类结果图
Fig.5  互花米草和芦苇在不同波长值下植物生物量相关系数图
序号 植被指数 公式
X1 归一化植被指数 NDVI NDVI=(R800-R670)/(R800+R670)
X2 改进叶绿素吸收指数 MCARI MCARI=[(R700-R670)-0.2(R700-R500)](R700/R670)
X3 转换叶绿素吸收指数 TCARI TCARI=3[(R700-R670)-0.2(R700-R500)](R700/R670)
X4 改进型土壤调整植被指数 MSAVI MSAVI=0.5[2R800+1- ( 2 R 800 + 1 ) 2 - 8 ( R 800 - R 670 )]
X5 增强型植被指数 EVI EVI=2.5(R800-R670)/(R870+6R670-7.5R440+1)
X6 土壤调整植被指数 SAVI SAVI=1.5(R800-R670)/(R800-R670+0.5)
X7 优化型土壤调整植被指数 OSAVI OSAVI=(1+0.16)(R800-R670)/(R800-R670+0.16)
X8 改进三角植被指数2 MTVI2 MTVI2=1.5[1.2(R800-R550)-2.5(R670-R550)]/
( 2 R 800 + 1 ) 2 - ( 6 R 800 - 5 R 670 ) - 0.5
X9 简单比值植被指数 SR SR=R800/R680
X10 陆地叶绿素指数 MTCI MTCI=(R750-R710)/(R710-R680)
X11 TCARI/OSAVI
X12 红边归一化植被指数 NDVI750 NDVI750=(R750-R705)/(R750+R705)
X13 改进红边比值植被指数 mSR750 mSR750=(R750-R445)/(R705-R445)
X14 改进红边归一化植被指数 mNDVI750 mNDVI750=(R750-R705)/(R750+R705-2R445)
X15 红边指数 VOGI VOGI=R740/R720
X16 结构不敏感指数 SIPI SIPI=(R800-R450)/(R800+R680)
X17 生理反射植被指数 PRI PRI=(R570-R531)/(R570+R531)
X18 改进叶绿素吸收指数 MCARI2 MCARI2=1.5[2.5(R800-R670)-1.3(R800-R550)]/
[ ( 2 R 800 + 1 ) 2 - ( 6 R 800 - 5 R 670 ) ] - 0.5
Tab.1  植被指数计算公式
模型 R2 RMSE
数据 方法 编号 训练 验证 训练 验证
基于高光谱相关波段 MLR-Enter S-M1 0.518 7 0.406 1 1.101 5 0.916 1
PLSR S-M2 0.526 9 0.354 3 0.658 4 0.649 7
SVR S-M3 0.619 9 0.520 9 0.524 6 0.505 2
基于多种植被指数 MLR-Enter S-M4 0.631 6 0.389 8 1.152 7 1.048 4
PLSR S-M5 0.636 9 0.461 6 0.576 8 0.653 9
SVR S-M6 0.643 8 0.512 0 0.490 5 0.529 5
基于高光谱全谱段二
阶导数
PLSR S-M7 0.673 0 0.504 1 0.547 4 0.587 8
SVR S-M8 0.929 3 0.825 4 0.223 6 0.306 9
Tab.2  互花米草生物量的建模结果精度评价
Fig.6  互花米草生物量模型实测值与预测值散点图
模型 R2 RMSE
数据 方法 编号 训练 验证 训练 验证
基于高光谱相关波段 MLR-Enter R-M1 0.656 5 0.155 7 1.243 9 1.803 5
PLSR R-M2 0.618 5 0.223 8 0.633 4 1.411 2
SVR R-M3 0.663 6 0.255 6 0.486 5 0.910 9
基于多种植被指数 MLR-Enter R-M4 0.623 3 0.315 8 1.228 3 2.523 4
PLSR R-M5 0.475 8 0.280 8 0.742 5 1.333 6
SVR R-M6 0.384 0 0.259 7 0.776 7 0.795 9
基于高光谱全谱段二
阶导数
PLSR R-M7 0.908 7 0.585 7 0.309 8 0.889 2
SVR R-M8 0.919 3 0.839 4 0.095 1 0.399 7
Tab.3  芦苇生物量的建模结果精度评价
Fig.7  芦苇生物量模型实测值与预测值散点图
Fig.8  航空多参数遥感方法的植被生物量反演结果
Fig.9  卫星遥感方法的植被生物量反演结果
Fig.10  航空多参数遥感与卫星遥感方法的样本反演结果散点图
Fig.11  2种方法反演的植被生物量结果统计图
Fig.12  研究区植被地上固碳能力反演结果统计图
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