【作者】 凌成星；

【导师】 鞠洪波；

【作者基本信息】 中国林业科学研究院 ， 森林经理学， 2013， 博士

【摘要】 目前，湿地资源作为一种与森林、海洋生态系统同样地位的重要自然资源，它的变化和可持续保护利用是地球科学核心内容的重要部分。利用遥感技术在湿地区域进行监测成为越来越重要的手段，可以解决湿地研究中一些科学问题，如湿地类型信息、湿地景观信息、湿地变化特征等。通过这些研究可以推动湿地区域生态资源保护和发展，从而保障政府对湿地保护工程和保护区建设的科学、正确的决策。本研究选取湖南省东洞庭湖湿地自然保护区核心区为研究对象，为了准确掌握东洞庭湖湿地核心区域的湿地资源情况，利用高分辨率影像WORLDVIEW-2数据，对湿地类型和湿地植被类型信息进行准确分类，并探索了湿地植被信息参数反演的研究方法，以经验模型和基于植被信息参数的湿地地上生物量估算模型来科学获取整个研究区的湿地资源状况。研究的主要工作如下：（1）湿地类型提取研究首先建立了研究区湿地类型信息提取分类的系统：河流湿地、湖泊湿地、草滩地（芦苇滩地、苔草滩地、辣蓼滩地、泥蒿滩地）、光滩地共四大类七小类。然后对WORLDVIEW-2数据进行了大气校正和几何精校正，分析该数据特有的光谱特征和波段特征，构建了四个改进遥感特征指数（改进归一化水体指数（NDWI*）、改进归一化植被指数（NDVI*）、改进归一化土壤指数（NDSI*）、非均匀差异指数（NHFD*）），采用这四个改进特征指数对湿地类型进行分层提取。精度验证结果表明湿地主要类型提取的精度达到92.24%，Kappa系数为0.902，比传统植被指数分层提取的结果提高了8%。针对湿地植被区域进行辅以波谱分析的Worldview-2影像面向对象湿地植被类型信息提取研究，利用光谱角阈值进行植被类型的阈值分割，在面向对象信息提取出湿地植被的细分类型，总体精度达到85%以上。（2）湿地植被信息反演研究对植被指数与叶面积指数（VI-LAI）的相关性进行了分析，然后选择了归一化植被指数（NDVI）、比值植被指数（RVI）、差值植被指数（DVI）、土壤调节植被指数（SAVI）、修正的土壤调节植被指数（MSAVI）、增强型植被指数（EVI）、重归一化植被指数（RDVI）七个植被指数作为VI-LAI的模型因子，然后采用多种回归模型（包括一元线性、二次多项式、三次多项式、指数模型、对数模型、幂函数模型）与LAI实测数据进行拟合分析，选取出这六种回归模型的最优因子，然后利用23组实测数据作为检验样本，最终确立了以NDVI为模型变量的指数模型是用于LAI估测的最优模型，精度达到了74.34%。利用WORLDVIEW-2数据特有的八波段光谱特性，以构建的改进归一化植被指数（NDVI*）进行NDVI-VFC植被盖度像元二分模型的构建，通过外业实测的数据得到模型估算精度为87.8%，并计算出研究区植被盖度为64.3％，生成了0-20％、20％-40％、40％-60％、60％-80％、80％-100％5级湿地植被覆盖分级图。实验证明，利用改进归一化植被指数（NDVI*）可以很好的估测湿地植被的盖度特征。为了更科学合理的计算湿地植被生物量特征，引入了跟植被生物量密切相关的植株结构参数的概念，包括了湿地植被的植株平均密度、平均高度、叶冠平均半径，选取了十八个遥感因子与植株参数这三个因子进行相关性分析，选取与苔草、辣蓼、芦苇、泥蒿的植株参数相关性较强的前6个因子参与到下一步的多元线性逐步回归过程，使用0.05≥P≥0.01作为阈值选择变量因子，当模型在出现了异方差性，影响了回归方程的准确性时，把该变量因子剔除，并最终得到了各植被类型指数参数的9组多元回归估测模型。数据表明估测总体精度都达到75%以上。构建的9组估测模型的R2和平均相对误差都在比较理想。（3）湿地地上生物量估算在进行LAI-AGBionmass模型拟建的过程中，分析叶面积指数与地上生物量的相关关系，将实地测量的35组地上生物量数据与LAI实测数据进行拟合，构建形如相对生长的模型形式（Y=aXb）的地上生物量估测模型。最终得到基于相对生长模型特征的LAI-地上生物量估算模型为：AGBiomass48.018LAI1.0278，研究结果表明，湿地植被的叶面积指数（LAI）与地上生物量具有高概率性规律，呈现出很强的稳定性和相关性，可以很好的估算出湿地植被地上生物量。在研建植被信息参数参与下的地上生物量估算模型时，将与地上生物量关系最密切的叶面积指数LAI、植被盖度（VFC）、植株平均密度（PD）、平均高度（PH）、叶冠平均半径（PLCR）引入，通过分析植被信息参数与地上生物量之间的相关性分析，建立了典型的苔草、辣蓼、芦苇、泥蒿的植被信息参数与地上生物量估算的最优模型，研究区范围的模型总体精度都达到了70%以上，最好的是芦苇地上生物量估算模型，估测值和实测值的评价模型判定系数达到0.7288，均方根误差为0.5121，平均相对误差为22.54%，即总体估测精度为77.46%。将研究的基于相对生长模型特征的LAI-AGBionmass模型地上生物量估算模型和植被信息参数参与下的地上生物量估算模型与运用最广泛的NDVI-Bionmass模型（即归一化植被指数方法）进行了生物量估算结果比较。结果表明，植被信息参数参与的地上生物量估算模型更加接近实测的结果，模型估算得到研究区地上生物量结果为12440.5294吨。通过上述研究，技术方法的创新点主要在以下几个方面体现：（1）构建了四种改进的遥感指数（NDWI*、NDVI*、NDSI*、NHFD*），实现了湿地类型有效区分，其结果比利用传统分类方法获取的结果精度大大提高。（2）提出了多指数特征和光谱特性为基础的多特征植株结构（PD、PH、PLCR）参数反演思路，采用了多元逐步线性回归分析法将十八个的特征因子进行模型拟合，确立了典型植被类型（苔草、辣蓼、芦苇、泥蒿）覆盖下的植株结构参数反演的敏感因子和最优模型。（3）构建了与生物量机理相关的相对生长模型形式（Y=aXb）的地上生物量估算模型。将与生物量关系最为密切的植被信息参数叶面积指数LAI、植被盖度（VFC）、植株平均密度（PD）、平均高度（PH）、叶冠平均半径（PLCR）引入到地上生物量估算中，构建了典型湿地植被信息参数参与的地上生物量估算模型。综上，利用WORLDVIEW-2高空间分辨率卫星影像对湿地区域进行类型信息提取、植被信息参数反演和生物量估算是可行的、准确的。通过研究得到的技术方法很好的拓宽了湿地遥感研究的内容。更多还原

【Abstract】 Currently wetland resource is considered to be important the same as forest and marineecosystems and thus its dynamics and sustainable protection and utilization is of a majorconcern. Using remote sensing technology to monitor wetlands has become a more and morepowerful tool, which can solve some scientific problems we are facing in wetland research,such as wetland classification, wetland landscape mapping, wetland change detection and so on.Without doubt, the wetland study will promote the development of wetland ecological resourceprotection and therefore provide useful information for government’s scientific and correctdecision making in wetlant protection and reservation development.This research selected a core area of the wetland nature reserve of Dongting Lake inHunan provice as the study area. In order to accurately obtain wetland resource information ofthe core wetland region, accurate and detailed wetland type and vegetation classification wasconducted using high resolution image WORLDVIEW-2images. Moreover, the methods toextract wetland vegetation biophysical parameters were explored. An empirical model and avegetation structure parameter based above-ground wetland biomass estimation model weredeveloped to obtain the information of the wetland resources. The important finding were asfollows:(1)Wetland lassificationIn this study, a refined wetland classification system was first developed, consisting offour classes at the first level and eight classes at the second level. At the first level, thewetlands were classified into river, lake, grassland, and mudflat. At the secodn level, thegrassland was divided into Reed, Sedge, Polygonum hydropiper l and Mud artemisia. Afteratmospheric and geometric corrections, the spectral and wavelength reflectance characteristicsof WORLDVIEW-2images were then analyzed and four modified spectral indices includingmodified NDWI, modified NDVI, modified NDSI and NHFD were developed. Using theindices, the wetland classification was made. The obtained accuracy of the wetland classification was92.24%with Kappa coefficient of0.902. Moreover, object-orientedclassification for the detailed wetland vegetation types was carried out based on segmentationof spectral angle threshold and the obtained overal classification accuracy was higher than85%.(2)Wetland vegetation structure parameter modelingPearson product moment correlation between vegetation indices and leaf area index (LAI)was first analyzed. A total of seven vegetation indices including NDVI, RVI, DVI, SAVI,MSAVI, EVI and RDVI were selected as predictor variables of LAI. Various regression modelsincluding univariate linear regression model, polynomial (quadratic and cubic) models,exponential model, logarithmic model, and power regression model were used to fit the LAIdata and develop VI-LAI models. A sample consisting of23observations were employed forvalidating the models. The obtained optimal model to predict LAI was the NDVI basedexponential model with an accuracy of74.34%.After analyzing eight bands of WORLDVIEW-2images, a modified NDVI*basedvegetation fraction cover (VFC), NDVI*-VFC, was developed and this model was validatedusing the collected field observations. The obtained accuracy was87.8%. The overallvegetation fraction cover of the wetland region was64.3%. The VFC map was displayed withfive classes:0-20%,20-40%,40-60%,60-80%,80-100%. The results showed that the VFC ofthe study area could be accurately predicted using the modified NDVI*.In order to scientifically and reasonably predict vegetation biomass of the wetland region,the concept of plant structure parameters that have close relationship with vegetation biomasswas introduced, including average density PD, average height PHand average radius PLCRof theplants. In addition, the correlation analysis between each of eighteen image derived spectralvariables and each of the vegetation structure parameters were conducted and the first sixspectral variables having the highest correlation with vegetation structure parameters of sedge,red-knees herb, reed and artemisia selengensis were selected to develop linear stepwiseregression. The spectral variables were selected at a significant level of0.05. Whenheteroscedasticity appeared in a model and the quality of the regression model was dtramatically degraded, the spectral variable was removed. A total of9groups of multipleregression models were obtained. The results showed that the accuracy of all the models wereover75%. The obtained R2values and average relative errors of the estimates were reasonable.(3)Estimation of above-ground biomass for the wetland regionThe correlation between LAI and above-ground biomass was first analyzed. A total of35above-group biomass field observatons were then used to develop a relative growth model(Y=aXb) that accounts for the relationship of the above-ground biomass with LAI. The resultsshowed that the obtained:AGBiomass48.018LAI1.0278had a high correlation withabove-ground biomass. The relationship was stable and robust and can be used to accuratelyestimate above-ground biomass of the wetland vegetation.Moreover, vegetation structure parameters that were highly correlated with above-groundbiomass, including LAI, VFC, PD, PHand PLCR, were introduced into the above-groundbiomass regression models. Through correlation analysis of these predictor variables withabove-ground biomass, the optimal estimation models of biomass were obtained for typicalsedge, red-knees herb, reed and artemisia selengensis,respectively, with the accuracy of morethan70%. The reed above-ground biomass estimating model was the best, in which thecoefficint of determination was0.7288and root mean-square error was0.5121, averagerelative error was22.54%, and the overall accuracy was77.46%.In addition, the above vegetation structure parameter based biomass model was comparedwith the relative growth model and the widely used NDVI-based biomass model obtainedpreviously. The results show that the results from the vegetation structure parameter basedabove-ground biomass model were closer to the measurements and the estimate was12440.5294tons for the study region.Overall, this study led to the following innovations:(1) modifying four spectral indices(NDWI, NDVI, NDSI and NHFD) and greatly incraesing the accuracy of wetland classificationcompared with traditional classification methods;(2) putting forward a new idea with whhichvegetation structure parameters (PD、PHand PLCR) were introduced into estimation of wetland above-ground biomass and further using multivariate stepwise regression analysis method toseek the most important variables that were sensitive to the variation of wetland biomass and toobtain the optimal models for typical plant types (sedge, red-knees herb, reed and artemisiaselengensis); and (3) developing a relative growing model that is related to biophysicalmechanism and introducing LAI, VFC, PD, PHand PLCRwhich had close relationship withbiomass into biomass estimation to develop the vegetation structure parameter basedabove-ground biomass model. In a word, this study showed that using WORLDVIEW-2highspatial resolution imagery to perform wetland classification and information extraction,vegetation structure parameter inversion and biomass estimation was practical and the obtainedresults were accurate. The obtained methods through this research greatly enhanced the contentof wetland remote sensing research.更多还原