结合多模态数据的滨海湿地碱蓬叶面积指数无人机高光谱反演

何爽; 张森; 田家; 卢霞

doi:10.11834/jrs.20222136

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结合多模态数据的滨海湿地碱蓬叶面积指数无人机高光谱反演
UAV hyperspectral inversion of Suaeda Salsa leaf area index in coastal wetlands combined with multimodal data
2023年27卷第6期页码：1441-1453
纸质出版日期： 2023-06-07 ，
DOI： 10.11834/jrs.20222136

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何爽，张森，田家，卢霞.2023.结合多模态数据的滨海湿地碱蓬叶面积指数无人机高光谱反演.遥感学报，27（6）： 1441-1453

He S，Zhang S，Tian J and Lu X. 2023. UAV hyperspectral inversion of Suaeda Salsa leaf area index in coastal wetlands combined with multimodal data. National Remote Sensing Bulletin， 27（6）：1441-1453
何爽，张森，田家，卢霞.2023.结合多模态数据的滨海湿地碱蓬叶面积指数无人机高光谱反演.遥感学报，27（6）： 1441-1453 DOI： 10.11834/jrs.20222136.

He S，Zhang S，Tian J and Lu X. 2023. UAV hyperspectral inversion of Suaeda Salsa leaf area index in coastal wetlands combined with multimodal data. National Remote Sensing Bulletin， 27（6）：1441-1453 DOI： 10.11834/jrs.20222136.

摘要

叶面积指数（LAI）是表征植被对光合辐射的吸收和拦截以及植被生长状况的重要参数。为准确、快速地提高叶面积指数估算精度，选取黄河三角洲碱蓬滩湿地，以中国土著植物碱蓬为研究对象，获取无人机高光谱遥感影像和测定地面光谱，结合区域土壤因子、植被光谱特征、高光谱影像纹理特征和植被覆盖度构建多模态数据，发展随机森林RF（Random Forest）和粒子群PSO（Particle Swarm Optimization）双优化策略的深度极限学习机DELM（Deep Extreme Learning Machine）算法构建滨海湿地碱蓬叶面积指数反演模型，决定系数（

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2.96333337

2.53999996

）和均方根误差（RMSE）分别为0.9546，0.1341。与基于支持向量机SVM（Support Vector Machine）、BP神经网络BP（Back Propagation Neural Network）、极限学习机ELM（Extreme Learning Machine）、深度极限学习机DELM（Deep Extreme Learning Machine）、粒子群优化的深度极限学习机（PSO-DELM）5种算法构建的碱蓬叶面积指数反演模型精度相比，决定系数

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http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=45630703&type=

2.96333337

2.53999996

最高提高了0.2654，均方根误差RMSE最大降低了0.0828。与传统的反演模型SVM相比，RF-PSO-DELM模型具有更好的泛化性，融合多模态数据则可以有效提高反演模型精度。该研究进一步丰富了基于无人机高光谱遥感技术实现盐沼植被精准监测的理论和技术。

Abstract

Salt marsh vegetation is an important part of the blue carbon ecosystem

which has strong carbon sequestration and carbon storage capacity. The Leaf Area Index (LAI) determines its growth

photosynthetic active radiation absorption ratio

and biomass. LAI is often used to simulate the photosynthesis

respiration

and transpiration of vegetation

thus playing a crucial role in improving the yield of

Suaeda salsa

. An accurate estimation of

Suaeda salsa

can provide an important basis for judging the growth status of alkali ponies and thus provide an effective aid for monitoring salt marsh wetlands.

To improve the accuracy of LAI estimation accurately and rapidly

the Yellow River Delta

Suaeda salsa

shoal wetland was selected

and the indigenous plant

Suaeda salsa

was used as the research object. A UAV hyperspectral remote sensing image was obtained

and the ground spectrum was measured in combination with regional soil factors

vegetation spectral characteristics

hyperspectral image texture characteristics

and vegetation coverage. Multimodal data

through Random Forest (RF) feature selection for multimodal data

and the RF-PSO-DELM algorithm with a dual optimization strategy was developed to construct an inversion model of the LAI of

Suaeda salsa

in coastal wetlands.

The coefficient of determination (

) and the Root Mean Square Error (RMSE) were 0.9546 and 0.1341

respectively. Compared with the inversion model accuracy of

Suaeda salsa

LAI constructed on the basis of the five algorithms of SVM

ELM

DELM

and PSO-DELM

was increased by 0.2654 at most

and the RMSE was reduced by 0.0828 at most.

Compared with the traditional inversion model (SVM)

the RF-PSO-DELM model had better generalization; moreover

the fusion of multimodal data could effectively improve the accuracy of the inversion model. This study further enriched the theory and technology for the accurate monitoring of salt marsh vegetation based on UAV hyperspectral remote sensing technology. Multisource modal data

such as soil factors

texture features

spectral features

and vegetation cover affecting the growth of alkali ponies in coastal wetlands

were comprehensively considered

and the important influencing factors sensitive to the LAI of alkali ponies’ LAI were extracted by the random forest feature preference algorithm

which effectively reduced the complexity of model inversion and greatly improved the accuracy of model prediction.

关键词

无人机多模态数据碱蓬叶面积指数随机森林粒子群算法深度极限学习机黄河三角洲

Keywords

UAVmultimodal dataSuaeda salsaleaf area indexrandom forestparticle swarm optimizationdeep extreme learning machineYellow River Delta

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