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Forest canopy density and height are used as variables in a number of environmental applications, including the estimation of biomass, forest extent and condition, and biodiversity. The airborne Light Detection and Ranging (LiDAR) is very useful to estimate forest canopy parameters according to the generated canopy height models (CHMs). The purpose of this work is to introduce an algorithm to delineate crown parameters, e.g. tree height and crown radii based on the generated rasterized CHMs. And accuracy assessment for the extraction of volumetric parameters of a single tree is also performed via manual measurement using corresponding aerial photo pairs. A LiDAR dataset of a golf course acquired by Leica ALS70-HP is used in this study. Two algorithms, i.e. a traditional one with the subtraction of a digital elevation model (DEM) from a digital surface model (DSM), and a pit-free approach are conducted to generate the CHMs firstly. Then two algorithms, a multilevel morphological active-contour (MMAC) and a variable window filter (VWF), are implemented and used in this study for individual tree delineation. Finally, experimental results of two automatic estimation methods for individual trees can be evaluated with manually measured stand-level parameters, i.e. tree height and crown diameter. The resulting CHM generated by a simple subtraction is full of empty pixels (called "pits") that will give vital impact on subsequent analysis for individual tree delineation. The experimental results indicated that if more individual trees can be extracted, tree crown shape will became more completely in the CHM data after the pit-free process.
K. T Chang; C. Lin; Y. C. Lin; J. K. Liu. ACCURACY ASSESSMENT OF CROWN DELINEATION METHODS FOR THE INDIVIDUAL TREES USING LIDAR DATA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B8, 585 -588.
AMA StyleK. T Chang, C. Lin, Y. C. Lin, J. K. Liu. ACCURACY ASSESSMENT OF CROWN DELINEATION METHODS FOR THE INDIVIDUAL TREES USING LIDAR DATA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B8 ():585-588.
Chicago/Turabian StyleK. T Chang; C. Lin; Y. C. Lin; J. K. Liu. 2016. "ACCURACY ASSESSMENT OF CROWN DELINEATION METHODS FOR THE INDIVIDUAL TREES USING LIDAR DATA." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8, no. : 585-588.
The purposes of this study are to identify the maximum number of correlated factors for landslide susceptibility mapping and to evaluate landslide susceptibility at Sihjhong river catchment in the southern Taiwan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN). The landslide inventory data of the Central Geological Survey (CGS, MOEA) in 2004-2014 and two digital elevation model (DEM) datasets including a 5-meter LiDAR DEM and a 30-meter Aster DEM were prepared. We collected thirteen possible landslide-conditioning factors. Considering the multi-collinearity and factor redundancy, we applied the CF approach to optimize these thirteen conditioning factors. We hypothesize that if the CF values of the thematic factor layers are positive, it implies that these conditioning factors have a positive relationship with the landslide occurrence. Therefore, based on this assumption and positive CF values, seven conditioning factors including slope angle, slope aspect, elevation, terrain roughness index (TRI), terrain position index (TPI), total curvature, and lithology have been selected for further analysis. The results showed that the optimized-factors model provides a better accuracy for predicting landslide susceptibility in the study area. In conclusion, the optimized-factors model is suggested for selecting relative factors of landslide occurrence.
K. T. Chang; J. Dou; Y. Chang; C. P. Kuo; K. M. Xu; J. K. Liu. SPATIAL RESOLUTION EFFECTS OF DIGITAL TERRAIN MODELS ON LANDSLIDE SUSCEPTIBILITY ANALYSIS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B8, 33 -36.
AMA StyleK. T. Chang, J. Dou, Y. Chang, C. P. Kuo, K. M. Xu, J. K. Liu. SPATIAL RESOLUTION EFFECTS OF DIGITAL TERRAIN MODELS ON LANDSLIDE SUSCEPTIBILITY ANALYSIS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B8 ():33-36.
Chicago/Turabian StyleK. T. Chang; J. Dou; Y. Chang; C. P. Kuo; K. M. Xu; J. K. Liu. 2016. "SPATIAL RESOLUTION EFFECTS OF DIGITAL TERRAIN MODELS ON LANDSLIDE SUSCEPTIBILITY ANALYSIS." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8, no. : 33-36.
Assessment of outdoor thermal comfort is becoming increasingly important due to the urban heat island effect, which strongly affects the urban thermal environment. The mean radiant temperature (Tmrt) quantifies the effect of the radiation environment on humans, but it can only be estimated based on influencing parameters and factors. Knowledge of Tmrt is important for quantifying the heat load on human beings, especially during heat waves. This study estimates Tmrt using several methods, which are based on climatic data from a traditional weather station, microscale ground surface measurements, land surface temperature (LST) and light detection and ranging (LIDAR) data measured using airborne devices. Analytical results reveal that the best means of estimating Tmrt combines information about LST and surface elevation information with meteorological data from the closest weather station. The application in this method can eliminate the inconvenience of executing a wide range ground surface measurement, the insufficient resolution of satellite data and the incomplete data of current urban built environments. This method can be used to map a whole city to identify hot spots, and can be contributed to understanding human biometeorological conditions quickly and accurately.
Yu Cheng Chen; Chih-Yu Chen; Andreas Matzarakis; Jin-King Liu; Tzu-Ping Lin. Modeling of mean radiant temperature based on comparison of airborne remote sensing data with surface measured data. Atmospheric Research 2016, 174-175, 151 -159.
AMA StyleYu Cheng Chen, Chih-Yu Chen, Andreas Matzarakis, Jin-King Liu, Tzu-Ping Lin. Modeling of mean radiant temperature based on comparison of airborne remote sensing data with surface measured data. Atmospheric Research. 2016; 174-175 ():151-159.
Chicago/Turabian StyleYu Cheng Chen; Chih-Yu Chen; Andreas Matzarakis; Jin-King Liu; Tzu-Ping Lin. 2016. "Modeling of mean radiant temperature based on comparison of airborne remote sensing data with surface measured data." Atmospheric Research 174-175, no. : 151-159.
Land subsidence is a worldwide problem that is typically caused by human activities, primarily the removal of groundwater. In Western Taiwan, groundwater has been pumped for industrial, residential, agricultural, and aquacultural uses for over 40 years. In this study, a multisensor monitoring system comprising GPS stations, leveling surveys, monitoring wells, and Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) was employed to monitor land subsidence in Western Taiwan. The results indicate that land subsidence in Yunlin County was mainly affected by the compaction of subsurface soils and over-pumping of groundwater from deep soils. The study area comprised western foothills, characterized by sediments containing predominantly gravel, and coastal areas, where clay was predominant. The subsidence in coastal areas was more severe than that in the western foothills, as a result of groundwater removal. An additional factor affecting subsidence was the compaction of deep layers caused by deep groundwater removal and the deep-layer compaction was difficult to recover. Based on multisensor monitoring results, severe subsidence is mainly affected by compaction of subsurface soils, over-pumping of groundwater from deep soils, and deep soil compaction.
Wei-Chen Hsu; Hung-Cheng Chang; Kuan-Tsung Chang; En-Kai Lin; Jin-King Liu; Yuei-An Liou. Observing Land Subsidence and Revealing the Factors That Influence It Using a Multi-Sensor Approach in Yunlin County, Taiwan. Remote Sensing 2015, 7, 8202 -8223.
AMA StyleWei-Chen Hsu, Hung-Cheng Chang, Kuan-Tsung Chang, En-Kai Lin, Jin-King Liu, Yuei-An Liou. Observing Land Subsidence and Revealing the Factors That Influence It Using a Multi-Sensor Approach in Yunlin County, Taiwan. Remote Sensing. 2015; 7 (6):8202-8223.
Chicago/Turabian StyleWei-Chen Hsu; Hung-Cheng Chang; Kuan-Tsung Chang; En-Kai Lin; Jin-King Liu; Yuei-An Liou. 2015. "Observing Land Subsidence and Revealing the Factors That Influence It Using a Multi-Sensor Approach in Yunlin County, Taiwan." Remote Sensing 7, no. 6: 8202-8223.
This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes.
Jie Dou; Kuan-Tsung Chang; Shuisen Chen; Ali P. Yunus; Jin-King Liu; Huan Xia; Zhongfan Zhu. Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm. Remote Sensing 2015, 7, 4318 -4342.
AMA StyleJie Dou, Kuan-Tsung Chang, Shuisen Chen, Ali P. Yunus, Jin-King Liu, Huan Xia, Zhongfan Zhu. Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm. Remote Sensing. 2015; 7 (4):4318-4342.
Chicago/Turabian StyleJie Dou; Kuan-Tsung Chang; Shuisen Chen; Ali P. Yunus; Jin-King Liu; Huan Xia; Zhongfan Zhu. 2015. "Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm." Remote Sensing 7, no. 4: 4318-4342.
Rainfall intensity plays an important role in landslide prediction especially in mountain areas. However, the rainfall intensity of a location is usually interpolated from rainfall recorded at nearby gauges without considering any possible effects of topographic slopes. In order to obtain reliable rainfall intensity for disaster mitigation, this study proposes a rainfall-vector projection method for topographic-corrected rainfall. The topographic-corrected rainfall is derived from wind speed, terminal velocity of raindrops, and topographical factors from digital terrain model. In addition, scatter plot was used to present landslide distribution with two triggering factors and kernel density analysis is adopted to enhance the perception of the distribution. Numerical analysis is conducted for a historic event, typhoon Mindulle, which occurred in 2004, in a location in central Taiwan. The largest correction reaches 11%, which indicates that topographic correction is significant. The corrected rainfall distribution is then applied to the analysis of landslide triggering factors. The result with corrected rainfall distribution provides better agreement with the actual landslide occurrence than the result without correction.
Jin-King Liu; Peter T.Y. Shih. Topographic Correction of Wind-Driven Rainfall for Landslide Analysis in Central Taiwan with Validation from Aerial and Satellite Optical Images. Remote Sensing 2013, 5, 2571 -2589.
AMA StyleJin-King Liu, Peter T.Y. Shih. Topographic Correction of Wind-Driven Rainfall for Landslide Analysis in Central Taiwan with Validation from Aerial and Satellite Optical Images. Remote Sensing. 2013; 5 (6):2571-2589.
Chicago/Turabian StyleJin-King Liu; Peter T.Y. Shih. 2013. "Topographic Correction of Wind-Driven Rainfall for Landslide Analysis in Central Taiwan with Validation from Aerial and Satellite Optical Images." Remote Sensing 5, no. 6: 2571-2589.
Assessment and inventory of natural hazards such as landslides are essential for effective watershed management and sustainable development. In Taiwan, a typhoon (tropical cyclone) or earthquake event can trigger hundreds to thousands of shallow landslides in mountainous areas with steep slopes and rapid streams. Therefore, how to improve the efficiency and accuracy of landslide mapping by means of GIS (geographic information system) and remote sensing techniques is an important research issue. This study proposes a novel, semiautomatic method for mapping and editing landslides at a watershed level. Data sources include airborne laser scanner (ALS) data and color/near infrared ortho-imagery: the ALS data provide topographic features such as elevation, slope, surface roughness, and object height, and the ortho-imagery furnishes the radiometric characteristic of land cover such as greenness index or NDVI (normalized difference vegetation index) for identifying bare grounds. Based on the derived topographic and radiometric parameters, the method first uses a global, automatic algorithm to interpret and delineate landslides. Then it uses a local region growing algorithm and a 3D eraser to edit and compile landslide maps. To explore the causes of mass movement, these landslide maps can also be registered with other geospatial data in a GIS for data visualization and analysis. Experimental results indicate that the method is highly efficient and accurate compared with results of human interpretation from the stereo pairs of aerial photographs. Because Taiwan experiences an average of four or five typhoons every year, this new, semiautomatic method is expected to provide a useful tool for watershed management.
Jiann-Yeou Rau; Kang-Tsung Chang; Chi-Chung Lau; Liang-Chien Chen; Yi-Chen Shao; Jin-King Liu. Application of GIS and RS for Mapping Landslides at the Watershed Level. Terrigenous Mass Movements 2012, 171 -192.
AMA StyleJiann-Yeou Rau, Kang-Tsung Chang, Chi-Chung Lau, Liang-Chien Chen, Yi-Chen Shao, Jin-King Liu. Application of GIS and RS for Mapping Landslides at the Watershed Level. Terrigenous Mass Movements. 2012; ():171-192.
Chicago/Turabian StyleJiann-Yeou Rau; Kang-Tsung Chang; Chi-Chung Lau; Liang-Chien Chen; Yi-Chen Shao; Jin-King Liu. 2012. "Application of GIS and RS for Mapping Landslides at the Watershed Level." Terrigenous Mass Movements , no. : 171-192.
Jin-King Liu; Kuan-Tsung Chang; Jiann-Yeou Rau; Wei-Cheng Hsu; Zu-Yi Liao; Chi-Chung Lau; Tian-Yuan Shih. The Geomorphometry of Rainfall-Induced Landslides in Taiwan Obtained by Airborne Lidar and Digital Photography. Geoscience and Remote Sensing 2009, 1 .
AMA StyleJin-King Liu, Kuan-Tsung Chang, Jiann-Yeou Rau, Wei-Cheng Hsu, Zu-Yi Liao, Chi-Chung Lau, Tian-Yuan Shih. The Geomorphometry of Rainfall-Induced Landslides in Taiwan Obtained by Airborne Lidar and Digital Photography. Geoscience and Remote Sensing. 2009; ():1.
Chicago/Turabian StyleJin-King Liu; Kuan-Tsung Chang; Jiann-Yeou Rau; Wei-Cheng Hsu; Zu-Yi Liao; Chi-Chung Lau; Tian-Yuan Shih. 2009. "The Geomorphometry of Rainfall-Induced Landslides in Taiwan Obtained by Airborne Lidar and Digital Photography." Geoscience and Remote Sensing , no. : 1.