Forest LAI And Individual Trees Biomass Estimation Using Small-footprint Full-waveform LiDAR Data

The vertical structure of the forest is one of the most important parameter in terrestrialecosystem. It is of great significance for the monitoring forestry resources and global climatechange by improving its retrieval accuracy of the remote sensing. LiDAR (lighting detectionand ranging)is an rapidly developing active technology of the international remote sensing inin recent years. Especially for forest height and vertical structure detection, it has a specialadvantage comparing with traditional optical remote sensing data. Forest leaf area index andbiomass are two important parameters of the forestry ecosystem and their accurate estimateshave great significance. The main works and results are as follows:(1) Creating a Gaussian decomposition algorithm and relative radiometric calibrationmodel according to the characteristics of lidar waveform data.Full-waveform data needs for further processing because it is relatively inconvenient todirectly use. A non-linear least-squares method with the Levenberg-Marquardt algorithm wasused to fit the return waveforms by Gaussian function and Gaussian amplitude, standarddeviation and energy were extracted. Generally, different objects response to the emitted pulsediversely, which is incarnated in the waveform data. But acquired data is influenced by severalfactors, so it cannot be directly used in wide area before calibration. A relative calibrationmethod using the range between the sensor and target based on a radar equation was applied tocalibrate the amplitude and energy, and the change of transmit pulses energy was alsoconsidered in this process, which is to enhance the comparability of waveform data and toimprove the accuracy and precision of the classification results. Finally, a quantitative analysison the decomposition and relative radiometric calibration results was applied.(2) The inversion of forest LAI using the decomposition energy of full-waveform LiDARdata.Decomposed full-waveform result data was rasterized to classify the study area and theforest area was extracted. A method based on the Bill-Lambert law was proposed by using waveform data energy to esttimation Leaf Area Index. Some detailed information weredescribed including waveform data classification, data normalization,the principle of LAIestimation using full-waveform data,the best scale of LAI inversion and LAI mapping of thestudy area. The result showed that full-waveform data could effectivly estimate forest LAI.(3) Combining single tree segementation and parameters extrated from full-waveform datato identify the tree species.DEM, DSM and CHM were generated form the point groun basingon on the Gaussianwaveform decomposition. Then the morphology-controlled watershed algorithm was adoptedto separate single tree on the CHM filled with invalid value. The individual tree positions andtree crowns were acquired. The total number of return waveforms within a beam, the pulsewidth, the calibrated amplitude and energy in single tree bounds are counted as tree features todetect seven tree species by a SVM classifier. The overall classification accuracy for this studyarea was55.07%using calibrated data for seven tree species, which is5.1%higher than that ofadopting uncalibrated data. Limiting to the five main species accuracy was improved to66.15%and to conifers and broadleaved trees accuracy was85.72%using calibrated data,which are also5.75%and3.56%higher than that of using uncalibrated data. Calibration offull-waveform data is necessary for its application in tree species classification.(4) Biomass estimation of individual trees using full-waveform LiDAR data.First,regression analysis of measured tree height and crown diameter with the diameter atbreast height (DBH) was performed. The result showed that using the linear regressionequation could fit the DBH well. The collected allometric equations relating biomass could beexpressed by the tree height and crown diameter. Then combing the single tree speciesidentification result and the tree hight and crown diameter extracted from indivudual treesegementation, the biomass could be estimated with the equation.In a word, the entire workflow of forest LAI and single tree biomass inversion wasestablished in this article. The results showed that the high-density small-footprintfull-waveform LiDAR data could The results show that the high-density airborne small spot waveform lidar could get the detaile forest vertical structure information and estimate forestleaf area index and single wood biomass fast and accurately.