摘要
为提高对荒漠草原的监测水平和利用效率,实现无破坏性、准确估算荒漠草原地上生物量(aboveground biomass,AGB),避免过度放牧加速荒漠化,该研究选取内蒙古荒漠草原为研究区域,利用无人机采集研究区域高光谱数据,铺设样方采集地上生物量数据。对高光谱数据进行预处理并去除噪声,利用主成分分析法降维,将主成分、植被指数、全波段反射率数据与AGB进行相关性分析,并利用遗传算法进行特征选择。基于机器学习算法建立荒漠草原AGB估算模型,对比特征选择前后模型性能,选择最佳估算模型。结果表明:利用遗传算法筛选出的光谱特征构建机器学习模型可以提高模型的性能,最佳模型的决定系数为0.81,均方根误差为0.04 kg/m^(2),平均绝对误差为0.04 kg/m^(2),归一化均方根误差为0.11,模型可用于荒漠草原AGB的高精度估算制图。研究结果可为无人机高光谱的荒漠草原AGB精准估算提供数据支撑和理论依据。
Grassland can provide the abundant forage and feed resources for the livestock industry,in order to maintain the ecological balance and biodiversity.One special types of the grassland,desert steppe is located at the transitional zone between grasslands and deserts,particularly with the relatively fragile ecosystem at risk of desertification.Aboveground biomass is one of the most important indicators to monitor the community structure and function in the grassland.However,the traditional estimation of aboveground biomass cannot fully meet the monitoring needs of desert grassland in a large area,due to the timeconsuming,destructiveness and laborious.Satellite multi-spectral remote sensing can be expected to serve as such application against the clouds and resolution.In this study,non-destructive and accurate estimation was performed on the aboveground biomass in the desert grassland,in order to improve the monitoring level and utilization efficiency.The study area was taken from the desert grassland at Wulan Town,Etuoke Banner,Ordos City,Inner Mongolia,China.A drone was utilized to capture the hyperspectral data in the study area.Sample plots were established to collect the aboveground biomass data.A series of preprocessing was carried out on the hyperspectral datasets.Firstly,the average reflectance of each sample plot was precisely calculated to standardize the data.Subsequently,the advanced techniques of noise reduction were applied to eliminate any potential noise interference,particularly for the integrity and reliability of data.Principal component analysis(PCA)was then utilized to reduce the dimensionality.Three principal components were extracted successfully,including PC1,PC2,and PC3.These principal components were effectively condensed to highlight the key spectral features in the high-dimensional hyperspectral data.In parallel,16 vegetation indices were calculated using the reflectance data.These indices were widely recognized in the field of vegetation research,in order to characterize the physiological and ecological status of the vegetation.After that,the correlation analysis was conducted among the principal components,vegetation indices,full-band reflectance data,and AGB.The relationships among these variables were then obtained to identify the most significant influencing factors on the AGB.A genetic algorithm was employed to further optimize the feature set.This algorithm was inspired by the principles of natural evolution,indicating the strong global searching.Finally,an optimal combination of optimal features was selected after continuous iteration and evaluation,including PC1,PC3,NDVI,NDRE.Among them,the specific bands were in the ranges of 536-557 nm,673-690 nm,and 703-715 nm,respectively.According to these selected features,different estimation models of aboveground biomass were developed for the desert grasslands using Random Forest,BP Neural Network,and LASSO regression.A ten-fold cross-validation was applied to evaluate the performance of the improved model.The results demonstrate that the better performance of the improved model was achieved in the machine learning with the spectral features that selected by the genetic algorithm.Among them,the LASSO model exhibited the greatest performance,indicating R^(2)=0.76,an increase of 65.2%,RMSE=0.05 kg/m^(2),an improvement of 28.6%,MAE(mean absolute error)=0.04 kg/m^(2),an increase of 20%,NRMSE(normalized root mean square error)=0.12,and an improvement of 36.8%.The BP Neural Network model performed the best,with R^(2)=0.81,RMSE=0.04 kg/m^(2),MAE=0.04 kg/m^(2),and NRMSE=0.11.A consistence was found between the estimation and mapping of desert grassland AGB with the BPNN model and the actual distribution of vegetation.Therefore,the UAV-based hyperspectral data can be expected to construct an AGB estimation model for the desert grasslands.The finding can provide a scientific basis to formulate the grazing plans,in order prevent the overgrazing from the desertification in sustainable ranches.
作者
宋耀邦
宣传忠
唐朝辉
张涛
李琦
SONG Yaobang;XUAN Chuanzhong;TANG Zhaohui;ZHANG Tao;LI Qi(College of Mechanical and Electrical Engineering,Inner Mongolia Agricultural University,Hohhot 010018,China;Inner Mongolia Engineering Research Center for Intelligent Facilities in Prataculture and Livestock Breeding,Hohhot 010018,China;Inner Mongolia Engineering Research Center of Intelligent Equipment for the Entire Process of Forage and Feed Production,Hohhot 010018,China)
出处
《农业工程学报》
北大核心
2025年第4期135-143,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金项目(31860666)
内蒙古直属高校基本科研业务费(BR221314,BR221032)
一流学科科研专项项目(YLXKZXNND-009)。
关键词
高光谱
机器学习
荒漠草原
地上生物量
遗传算法
hyperspectral
machine learning
desert steppe
aboveground biomass
genetic algorithm
