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Received：29 March 2022，
Published：07 June 2023
移动端阅览
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（
<math id="M1"><msup><mrow><mi>R</mi></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
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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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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。
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 (
R
2
) 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
BP
ELM
DELM
and PSO-DELM
R
2
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.
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