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Forest Parameters Inversion Using Airborne Lidar And Hyperspectral Data Fusion

Posted on:2012-10-11Degree:DoctorType:Dissertation
Country:ChinaCandidate:L J LiuFull Text:PDF
GTID:1113330335973112Subject:Forest management
Abstract/Summary:PDF Full Text Request
Forest is key resource for material circulation and energy exchange. It has functions of climate regulation, water conservation, windbreak and sand-fixation, pollution reduction and biodiversity conservation. It also plays a very important role in maintaining ecological balance, human survival, economic development and social progress. However, the long-term over-harvesting and destruction on forest resources have resulted in serious damage to global ecological environment. Protect and develop forest resources to sustainable use have been concerned by a number of countries in world wide. The traditional time-consuming work of large-scale ground surveys has been gradually replace by remote sensing technology which using the special spectral characteristics of ground objects reflected in remote sensing images for forest resource survey and monitoring. However, due to various constraints, the present study mainly focus on the application of low spatial resolution satellite remote sensing data which with single sensor. It is a hard work to survey and monitoring the Boreal forest for its complex horizontal and vertical structure. This study is based on airborne high spatial resolution data, high spectral resolution data and LiDAR data, combined with the characteristics of each of the two data to identify the forest tree species, and inversed the leaf area index (LAI) and canopy chlorophyll content. The main contents and results include:1) Separated the ground points and non-ground points from small footprint LiDAR with high-density point data, and created a canopy height model (CHM). Then removed the gap between trees by combined the statistics of tree height measured in field plots, it reduced the interference of non-forest spectra and improved matching of the image spectra and reference spectra of tree species, took preparation for the training samples extraction of classification.2) To reduce the impact of noise on the spectrum, using spectral derivative technique to processed both imaging spectra and reference spectra by first spectral derivative transform, then selected the range of features on behalf of characteristics and calculated the correlation coefficient between two spectra, extracted the high correlation spectroscopy pixels as reference samples to achieve the automatic extraction of training samples.3) For the pixels of shadow of high spatial resolution images, the traditional methods of shadow information compensation still have some problems. In this study we identified the block direction by calculating the solar radiation orientation and then filled the pixel of shadow from its neighboring pixel. This method is both scientific and simple in the work.4) Compared the SAM and SVM classification accuracies using hyperspectral data only and the integration data, and results showed that using SVM to classify the integration data of LiDAR and CASI, and filled the shadow pixels after classification had the highest overall accuracy of classification which reached 86.68%. It indicated that the tree species classification method in this study is feasible for the integration data of LiDAR and CASI.5) The inversion of LAI based on statistical model. Since established the correlation between vegetation index and LAI still have onesidedness. In this study, the vegetation index and the vertical structure parameters were both extracted based on the echo numbers and intensities of LiDAR. These parameters were input as statistical model variables. Then the measured effective LAIs were converted into real LAIs as the dependent variable according to the different forest types and stepwise regression was used for variables selection, and the inversion model was created and validated simultaneously.6) The physical model of forest canopy biochemical parameters inversion related the problem of scaling. For the masked forest area with the classified result of Chapter III, we choose PROSPECT, LIBERTY model as broadleaf and coniferous radiative transfer model, respectively. The SAIL model was used as canopy radiative transfer model. The scope of changes was determined by analysing the sensitivity, then, output the simulated canopy reflectance.7) The lookup table was established between input parameters and output reflectances. The leaf chlorophyll contents were derived by matching the coincident images and simulated canopy reflectance. The R2 between retrieved chlorophyll and measured data was 0.8379, which satisfied the requirements accuracy. And then the inversion of forest canopy chlorophyll contents were achieved by scaled the leaf chlorophyll contents up to canopy.
Keywords/Search Tags:Airborne LiDAR, Airborne Hyperspectral CASI, Tree Species Classification, LAI, Chlorophyll Content of Canopy, Inversion of Forest Parameters
PDF Full Text Request
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