湿地植物物种多样性无人机高光谱遥感反演研究  

Inversion of Wetland Plant Species Diversity Using UAV Hyperspectral Data

作　　者：唐希颖 李化哲 崔丽娟[1,2,3] 赵欣胜 翟夏杰[1,2,3] 雷茵茹 李晶 王金枝 李伟 TANG Xiying;LI Huazhe;CUI Lijuan;ZHAO Xinsheng;ZHAI Xiajie;LEI Yinru;LI Jing;WANG Jinzhi;LI Wei(Institute of Wetland Research,Chinese Academy of Forestry,Beijing Key Laboratory of Wetland Services and Restoration,Beijing 100091,China;Institute of Ecological Conservation and Restoration,Chinese Academy of Forestry,Beijing 100091,China;Beijing Hanshiqiao National Wetland Ecosystem Research Station,Beijing 101399,China)

机构地区：[1]中国林业科学研究院湿地研究所湿地生态功能与恢复北京市重点实验室,北京100091 [2]中国林业科学研究院生态保护与修复研究所,北京100091 [3]北京汉石桥湿地生态系统国家定位观测研究站,北京101399

出　　处：《地球信息科学学报》2024年第8期1954-1974,共21页Journal of Geo-information Science

基　　金：中央级公益性科研院所基本科研业务费专项资金(CAFYBB2021ZB003);国家自然科学基金项目(42101308);国家林业和草原局重点课题(20212DKT005-3)。

摘　　要：湿地植物物种多样性可以反映湿地生态系统的群落组织水平和稳定性,评价湿地健康、退化程度以及修复状况,快速量化物种多样性对保护湿地生物多样性至关重要,然而传统的实地调查方法费时费力,存在时间成本上的局限性,而高光谱技术的发展为实现这一目的提供了契机。为探究如何通过高光谱技术实现湿地植物物种多样性的精确反演,本研究在陕西汉中朱鹮国家级自然保护区对湿地植物展开调查并同步获取植物冠层的高光谱影像,使用Simpson(DS)、Margalef(DM)、Shannon-Weiner(H')和Pielou(J) 4种指标表征物种多样性,通过随机森林(Random Forest,RF)、BP神经网络(Back Propagation Neural Network,BPNN)和偏最小二乘(Partial Least Squares,PLS) 3种方法建立反演模型,最终实现对区域物种多样性的反演推算。结果表明:一阶微分变换比二阶微分变换能提取出更多的敏感波段,而通过组合任意波段植被指数,可以提高与物种多样性指数的相关性;基于原始光谱数据与基于多特征组合的反演精度接近,且都是RF模型取得较高精度(R2> 0.40);RF模型对H'和J的反演精度较好,R2高于0.6,DS的R2高于0.5,表明模型有一定预测能力,而DM的R2均低于0.5,模型预测能力并不理想。本研究展示了无人机高光谱技术在湿地植物物种多样性精确反演方面的有效性,证实了通过光谱微分变换和特征变量的提取结合随机森林模型实现无人机尺度的物种多样性反演方法的可靠性。该技术对于可为湿地生物多样性的大尺度检测提供技术支撑,为相关管理部门决策提供参考。Wetland plant species diversity,as a quantifiable indicator reflecting the level of organization in an ecosystem's community,can reveal the community organization and stability of wetland ecosystems.Accurate assessments of wetland health,degradation,and restoration status are crucial for effective wetland management and protection.Therefore,timely understanding of the current status of wetland plant community species diversity is of great importance.However,traditional field survey methods are time-consuming and labor-intensive,limited by temporal costs,and cannot achieve large-scale synchronous observation.Meanwhile,hyperspectral technology,with its high resolution,can capture more abundant spectral information,providing an opportunity for the realization of this goal.To investigate how to accurately invert wetland plant species diversity using hyperspectral technology,we investigated the wetland plants in Hanzhong Crested Ibis National Nature Reserve in Shaanxi Province and simultaneously acquired hyperspectral images of the plant canopy.Species diversity was characterized by four indicators:Simpson(D_S),Margalef(D_M),Shannon-Weiner(H'),and Pielou(J).The inverse model was established using three methods:Random Forest(RF),Back Propagation Neural Network(BPNN),and Partial Least Squares(PLS).Finally,the inverse projection of regional species diversity was realized.The outcomes indicate that spectral differentiation complicates the association between spectra and species diversity indices,producing a range of sensitive bands.Notably,the first-order differential transform is superior in extracting sensitive bands compared to the second-order differential transform.Furthermore,correlating species diversity indices can be enhanced through the integration of vegetation indices from various bands.When applying the RF model to analyze differential spectra and vegetation indices,it was found that both using original features and combinations of features,the model's inversion results demonstrated similar and high accuracy(

关 键 词：湿地植物 物种多样性 无人机 高光谱 反演 机器学习 特征选择 植被指数 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] Q948[自动化与计算机技术—控制科学与工程] TP181[生物学—植物学]