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Owing to the complex forest structure and large variation in crown size, individual tree detection in subtropical mixed broadleaf forests in urban scenes is a great challenge. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) is a powerful tool for individual tree detection due to its ability to acquire high density point cloud that can reveal three-dimensional crown structure. Tree detection based on a local maximum (LM) filter, which is applied on a canopy height model (CHM) generated from LiDAR data, is a popular method due to its simplicity. However, it is difficult to determine the optimal LM filter window size and prior knowledge is usually needed to estimate the window size. In this paper, a novel tree detection approach based on crown morphology information is proposed. In the approach, LMs are firstly extracted using a LM filter whose window size is determined by the minimum crown size and then the crown morphology is identified based on local Gi * statistics to filter out LMs caused by surface irregularities contained in CHM. The LMs retained in the final results represent treetops. The approach was applied on two test sites characterized by different forest structures using UAV LiDAR data. The sensitivity of the approach to parameter setting was analyzed and rules for parameter setting were proposed. On the first test site characterized by irregular tree distribution and large variation in crown size, the detection rate and F-score derived by using the optimal combination of parameter values were 72.9% and 73.7%, respectively. On the second test site characterized by regular tree distribution and relatively small variation in crown size, the detection rate and F-score were 87.2% and 93.2%, respectively. In comparison with a variable-size window tree detection algorithm, both detection rates and F-score values of the proposed approach were higher.
Wenbing Xu; Susu Deng; Dan Liang; Xiaojun Cheng. A Crown Morphology-Based Approach to Individual Tree Detection in Subtropical Mixed Broadleaf Urban Forests Using UAV LiDAR Data. Remote Sensing 2021, 13, 1278 .
AMA StyleWenbing Xu, Susu Deng, Dan Liang, Xiaojun Cheng. A Crown Morphology-Based Approach to Individual Tree Detection in Subtropical Mixed Broadleaf Urban Forests Using UAV LiDAR Data. Remote Sensing. 2021; 13 (7):1278.
Chicago/Turabian StyleWenbing Xu; Susu Deng; Dan Liang; Xiaojun Cheng. 2021. "A Crown Morphology-Based Approach to Individual Tree Detection in Subtropical Mixed Broadleaf Urban Forests Using UAV LiDAR Data." Remote Sensing 13, no. 7: 1278.
Logistic regression methods have been widely used for landslide research. However, previous studies have seldom paid attention to the frequent occurrence of spatial autocorrelated residuals in regression models, which indicate a model misspecification problem and unreliable results. This study accounts for spatial autocorrelation by implementing eigenvector spatial filtering (ESF) into logistic regression for landslide susceptibility assessment. Based on a landslide inventory map and 11 landslide predisposing factors, we developed the eigenvector spatial filtering-based logistic regression (ESFLR) model, as well as a conventional logistic regression (LR) model and an autologistic regression (ALR) model for comparison. The three models were evaluated and compared in terms of their prediction capability and model fit. The ESFLR model performed better than the other two models. The overall predictive accuracy of the ESFLR model was 90.53%, followed by the ALR model (76.21%) and the LR model (74.76%), and the areas under the ROC curves for the ESFLR, ALR and LR models were 0.957, 0.828 and 0.818, respectively. The ESFLR model adequately addressed the spatial autocorrelation of residuals by reducing the Moran’s I value of the residuals to 0.0270. In conclusion, the ESFLR model is an effective and flexible method for landslide analysis.
Huifang Li; Yumin Chen; Susu Deng; Meijie Chen; Tao Fang; Huangyuan Tan. Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment. ISPRS International Journal of Geo-Information 2019, 8, 332 .
AMA StyleHuifang Li, Yumin Chen, Susu Deng, Meijie Chen, Tao Fang, Huangyuan Tan. Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment. ISPRS International Journal of Geo-Information. 2019; 8 (8):332.
Chicago/Turabian StyleHuifang Li; Yumin Chen; Susu Deng; Meijie Chen; Tao Fang; Huangyuan Tan. 2019. "Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment." ISPRS International Journal of Geo-Information 8, no. 8: 332.
Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.
Qianqian Ba; Yumin Chen; Susu Deng; Qianjiao Wu; Jiaxin Yang; Jingyi Zhang. An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping. ISPRS International Journal of Geo-Information 2017, 6, 18 .
AMA StyleQianqian Ba, Yumin Chen, Susu Deng, Qianjiao Wu, Jiaxin Yang, Jingyi Zhang. An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping. ISPRS International Journal of Geo-Information. 2017; 6 (1):18.
Chicago/Turabian StyleQianqian Ba; Yumin Chen; Susu Deng; Qianjiao Wu; Jiaxin Yang; Jingyi Zhang. 2017. "An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping." ISPRS International Journal of Geo-Information 6, no. 1: 18.