| Carbon, water, and energy ecological exchanged processes between forest and atmosphereare influenced by forest canopy and stand structure. Therefore, exploring the technique andmethod of quantizing canopy structure and stand structure to monitor the change of foreststructural parameters is very significant. Vegetation canopy is the important interface of theatmosphere and terrestrial biosphere. Since leaf area index (LAI) is very often a criticalparameter in process-based models of vegetation canopy response to global environmentalchange, for numerous studies of interaction of atmosphere and vegetation, rapid, reliable andobjective estimations of LAI are essential.Explaining vegetation cover of earth surface from the view of optical remote sensing,vegetation indices are the most valuable. However, when retrieving LAI by vegetation indices(VIs) derived from remotely sensed data, the main problem encountered is the saturation athigh levels of LAI. That is to say, optical remote sensing data only detect horizontal structureinformation of vegetation canopy. At high levels of LAI, the VIs related to LAI reachsaturation and the LAI doesn't increase linearly with the VI, which will cause theunderestimation of LAI. Therefore, extraction LAI by optical remotely sensed data has acertain limitation and the accuracy of LAI inversion will be affected to some extent. How toimprove the accuracy of forest LAI inversion and make the inversion method simple and easyto use is a very important study area at home and abroad.Light Detection And Ranging (LiDAR) is a new emerging active remote sensingtechnology in recent years, which has developed very rapidly in the world. LiDAR canmeasure both the vertical and horizontal structure of forested areas effectively with highprecision and it can accurately estimate tree height, canopy closure and above-groundbiomass.The main purpose of this dissertation is to investigate the potential and feasibility ofderiving forest LAI from LiDAR data. The specific objectives of this study were:(1) Exploringtheories and methods of using airborne and spaceborne LiDAR data to extract forest LAI;(2)Establishing the forest LAI estimation model from LiDAR data and validating its accuracy;(3)Mapping forest LAI of the study area based on LiDAR data LAI inversion model;(4) Usingoptical remotely sensed data to extract forest LAI, and the comparison and analysis being carried out between the result of LAI LiDAR-derived and based on optical remotely senseddata. And specifically, this dissertation mainly conducted some research as follows:(1) Classifed the airborne LiDAR data and calculated the laser penetration index (LPI) ofdifferent spatial scales.(2) Studied the theory and method of using airborne LiDAR data to extract forest LAI,and inversed the forest LAI by the LPI.(3) Compared the accuracy of the airborne LiDAR-derived LAI inversion model withdifferent spatial scales. And then, the optimum model of LiDAR-derived LAI wasobtained and the determination coefficient (R2) was 0.77, where the samplingradius was 10 m. Finally, Mapped LAI of the study area based on the model.(4) Studied deeply processing methods of spaceborne LiDAR waveform data and the rawGLAS waveform data were decomposed into Gaussian peaks. And then, the ratiosof ground to the entire waveform return energy of satellite borne LiDAR.(5) Explored the method of LAI retrieval based on satellite borne LiDAR, andestablished the model (R2=0.80). Finally, Mapped LAI of the study area based onthe model.(6) Introduced the theory of optical remote sensing retrieving LAI and established theoptimum model of LAI inversion in the study area. And then, mapped LAI of thestudy area using the model.(7) Compared and analyzed the results between airborne LiDAR-derived LAI and basedon optical remotely sensed data in the Dayekou study area. The R2of based onoptical remote sensing was 0.63, while the R2of airborne attained to 0.77. Thisshowed that airborne LiDAR data could improve accuracy of LAI retrieval.(8) Compared and analyzed the results between satellite borne LiDAR-derived LAI andbased on optical remotely sensed data in the study area of Linzhi, Tibet. Themaximum R2of based on optical remote sensing was 0.65, while the R2ofGLAS-derived LAI attained to 0.80.The result showed that satellite borne LiDARdata could improve accuracy of LAI inversion.The main innovations in this dissertation included as followings:(1) The method of GLAS waveform data inversing forest LAI was put forward. (2) Reversed and mapped forest LAI of the study area by integrating GLAS and TM data.(3) The simple method of inversing LAI was developed based on airborne LiDAR data.By the research above, the dissertation found that LiDAR data offer a new way toaccurately estimate forest LAI. Especially, satellite borne LiDAR data open the possibility ofglobal forest LAI estimation with high accuracy. |
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