摘要
针对红树林叶面积指数(Leaf Area Index,LAI)实地测量难度大、无法快速大范围LAI估算的问题。该研究以广西北部湾红树林为研究对象,以无人机(Unmanned Aerial Vehicle,UAV)和哨兵二号(Sentinel-2A,S2)多光谱影像为数据源,整合原始光谱波段、植被指数和组合植被指数构建高维数据集,并进行数据降维和特征优选。定量评估6种机器学习算法(XGBoost、前馈反向传播神经网络(Back Propagation,BP)、支持向量机(SVM)、岭回归(Ridge)、Lasso和弹性网络(ElasticNet))对不同红树林树种LAI的估算能力;探究UAV和Sentinel-2A影像对红树林树种LAI估算的精度差异。研究结果表明:1)基于XGBoost算法构建的模型实现了红树林LAI高精度估算,R^(2)均高于0.70,RMSE均低于0.349;2)在UAV和Sentinel-2A影像下,XGBoost模型对不同红树林树种LAI的估算精度(R^(2))比其他5种模型分别提高了0.105~0.365和0.283~0.540,RMSE降低了0.100~0.392和0.102~0.518;3)UAV影像数据与XGBoost算法构建的模型对海榄雌LAI的估算精度优于其他组合(R^(2)=0.821、RMSE=0.288),Sentinel-2A影像数据与XGBoost算法构建的模型对秋茄和桐花树LAI的估算精度优于其他组合(R^(2)=0.940~0.979、RMSE=0.142~0.104),不同红树林树种LAI的估算精度依次为桐花树>秋茄>海榄雌;4)SNAP-SL2P算法整体性低估红树林LAI值,UAV影像红树林树种LAI的平均估算精度(R^(2)=0.677~0.713)均优于Sentinel-2A影像,实现了不同红树林树种LAI的高精度估算。
Leaf Area Index(LAI) can often be used to represent the growth status of vegetation.The absorption ratio of crop Photosynthetically Active Radiation(PAR) can then be determined to monitor the vegetation growth and crop yield estimation.Taking the mangrove as the research object,this study aims to realize the field measurement for the rapid and accurate estimation of large-scale LAI.The multi-spectral images were collected to serve as the data source using the Unmanned Aerial Vehicle(UAV) and Sentinel-2A(S2) in the North Gulf of Guangxi in South China.After that,the original spectral bands of UAV and S2 were used to calculate 16 vegetation indices and their combinations under various images.High-dimensional datasets of different mangrove species were constructed to integrate the original bands,vegetation indices,and combined vegetation indices of different images with the ground-truth LAI data.Some operations were then selected to process the datasets for the better normality of the data and the higher estimation accuracy of LAI models,including the data normalization,Box-Cox transformation,logarithmic transformation,model fitting,and the removal of outliers.The maximum correlation coefficient method was used to remove the redundant bands for the low dimensionality of the high dimensional datasets.The characteristic bands were then obtained to estimate the LAI of different mangrove species.Subsequently,the feature optimization was performed on the characteristic bands of forest species.The optimal feature dataset was established for the LAI estimation of mangrove tree species.Six machine learning algorithms(XGBoost,Feedforward Back Propagation(BP),Support Vector Machine(SVM),Ridge Regression(Ridge),Lasso) and Elastic Net were selected for the preferred feature bands of LAI of different mangrove species,according to the optimal characteristic band dataset.Model training and parameter tuning were also carried out for the different models at a ratio of 7:3 for the training set and test set.The performances of 6machine learning algorithms were evaluated for the LAI of mangrove species.The accuracy of LAI estimation on the mangrove species was compared to determine the optimal models and image combinations for the various tree species.The research results indicated that:1) The XGBoost model achieved the highest precision estimation of mangrove LAI under UAV and S2 image data,where the coefficient of determination(R^(2)) was higher than 0.70,and the Root Mean Square Error(RMSE)was lower than 0.349.2) The R^(2) values of the XGBoost model were improved by 0.105-0.365 and 0.283-0.540,respectively,and the RMSEs were reduced by 0.100-0.392 and 0.102-0.518,respectively,compared with the other five models.3) The XGBoost mangrove LAI model that was constructed by UAV image data performed a better estimation accuracy for the Avicennia marina(AM) LAI than the rest images and combinations(R^(2)=0.821,RMSE=0.288).The XGBoost mangrove LAI model constructed by S2 image data presented the excellent estimation accuracy(R^(2)=0.940-0.979,RMSE=0.104-0.142) for the LAI of Kandelia candel(KC) and Aegiceras corniculatum(AC).The R^(2) values of LAI models for the different mangrove tree species under UAV and S2 images were ranked in the order of the Aegiceras corniculatum > Kandelia candel > Avicennia marina.4) The R^(2) values of the S2 image for the mangrove LAI were better than those of UAV images.The SNAP-SL2P underestimated the mangrove LAI values as a whole.The UAV images presented a better estimation accuracy for the mangrove species LAI than the S2 images.
作者
付波霖
孙军
李雨阳
左萍萍
邓腾芳
何宏昌
范冬林
高二涛
Fu Bolin;Sun Jun;Li Yuyang;Zuo Pingping;Deng Tengfang;He Hongchang;Fan Donglin;Gao Ertao(College of Geomatics and Geoinformation,Guilin University of Technology,Guilin 541000,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2022年第7期218-228,共11页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金项目(41801071)
广西自然科学基金项目(2018GXNSFBA281015)
广西科技计划项目(桂科AD20159037)
桂林理工大学科研启动基金项目(GUTQDJJ2017096)
广西研究生教育创新计划项目(YCSW2022328)
广西八桂学者团队项目。
关键词
遥感
多光谱影像
红树林
叶面积指数
数据降维和特征优选
模型参数调优
机器学习算法
remote sensing
multispectral imagery
mangrove
leaf area index
data dimensionality reduction and feature optimization
model parameter tuning
machine learning algorithm
