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Chao Yang
Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen 518060, China

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Journal article
Published: 24 November 2020 in Environmental Pollution
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Due to rapid urbanization in China, lead (Pb) continues to accumulate in urban topsoil, resulting in soil degradation and increased public exposure. Mapping Pb concentrations in urban topsoil is therefore vital for the evaluation and control of this exposure risk. This study developed spatial models to map Pb concentrations in urban topsoil using proximal and remote sensing data. Proximal sensing reflectance spectra (350–2500 nm) of soils were pre-processed and used to calculate the principal components as landscape factors to represent the soil properties. Other landscape factors, including vegetation and land-use factors, were extracted from time-sequential Landsat images. Two hybrid statistical approaches, regression kriging (RK) and geographically weighted regression (GWR), were adopted to establish prediction models using the landscape factors. The results indicated that the use of landscape factors derived from combined remote and proximal sensing data improved the prediction of Pb concentrations compared with useing these data individually. GWR obtained better results than RK for predicting soil Pb concentration. Thus, joint proximal and remote sensing provides timely, easily accessible, and suitable data for extracting landscape factors.

ACS Style

Tiezhu Shi; Chao Yang; Huizeng Liu; Chao Wu; Zhihua Wang; He Li; Huifang Zhang; Long Guo; Guofeng Wu; Fenzhen Su. Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches. Environmental Pollution 2020, 272, 116041 .

AMA Style

Tiezhu Shi, Chao Yang, Huizeng Liu, Chao Wu, Zhihua Wang, He Li, Huifang Zhang, Long Guo, Guofeng Wu, Fenzhen Su. Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches. Environmental Pollution. 2020; 272 ():116041.

Chicago/Turabian Style

Tiezhu Shi; Chao Yang; Huizeng Liu; Chao Wu; Zhihua Wang; He Li; Huifang Zhang; Long Guo; Guofeng Wu; Fenzhen Su. 2020. "Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches." Environmental Pollution 272, no. : 116041.

Conference paper
Published: 11 November 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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In this paper, we develop a classification method of land cover based on support vector machines. As a case study, we choose five Landsat images to retrieve land cover maps in Shenzhen, China from 1979 to 2005. The classification method is based on support vector machines with assistance from visual interpretation. And then we take use of the complex network approach to analyze the character of land use-cover change from an overall perspective. The result shows that the main changes of land use-cover are different over time. The medium of bare land during the urban construction can hardly be witnessed, even though the time intervals are shorter than the two periods before. It reveals the transformation from vegetation to urban becomes faster. The transformation from vegetation to bare land is hard to be witnessed in the late stage. As bare land is the medium for transforming vegetation to urban land in Shenzhen during the past years from 1979 to 2005.

ACS Style

Kai Ding; Chisheng Wang; Ming Tao; Huijuan Xiao; Chao Yang; Peican Huang. A Classification Method of Land Cover Based on Support Vector Machines. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 48 -54.

AMA Style

Kai Ding, Chisheng Wang, Ming Tao, Huijuan Xiao, Chao Yang, Peican Huang. A Classification Method of Land Cover Based on Support Vector Machines. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():48-54.

Chicago/Turabian Style

Kai Ding; Chisheng Wang; Ming Tao; Huijuan Xiao; Chao Yang; Peican Huang. 2020. "A Classification Method of Land Cover Based on Support Vector Machines." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 48-54.

Journal article
Published: 13 August 2020 in Remote Sensing
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The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is one of the largest bay areas in the world. However, the spatiotemporal characteristics and driving mechanisms of urban expansions in this region are poorly understood. Here we used the annual remote sensing images, Geographic Information System (GIS) techniques, and geographical detector method to characterize the spatiotemporal patterns of urban expansion in the GBA and investigate their driving factors during 1986–2017 on regional and city scales. The results showed that: the GBA experienced an unprecedented urban expansion over the past 32 years. The total urban area expanded from 652.74 km2 to 8137.09 km2 from 1986 to 2017 (approximately 13 times). The annual growth rate during 1986–2017 was 8.20% and the annual growth rate from 1986 to 1990 was the highest (16.89%). Guangzhou, Foshan, Dongguan, and Shenzhen experienced the highest urban expansion rate, with the annual increase of urban areas in 51.51, 45.54, 36.76, and 23.26 km2 y−1, respectively, during 1986–2017. Gross Domestic Product (GDP), income, road length, and population were the most important driving factors of the urban expansions in the GBA. We also found the driving factors of the urban expansions varied with spatial and temporal scales, suggesting the general understanding from the regional level may not reveal detailed urban dynamics. Detailed urban management and planning policies should be made considering the spatial and internal heterogeneity. These findings can enhance the comprehensive understanding of this bay area and help policymakers to promote sustainable development in the future.

ACS Style

Jie Zhang; Le Yu; Xuecao Li; Chenchen Zhang; Tiezhu Shi; Xiangyin Wu; Chao Yang; Wenxiu Gao; Qingquan Li; Guofeng Wu. Exploring Annual Urban Expansions in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal Features and Driving Factors in 1986–2017. Remote Sensing 2020, 12, 2615 .

AMA Style

Jie Zhang, Le Yu, Xuecao Li, Chenchen Zhang, Tiezhu Shi, Xiangyin Wu, Chao Yang, Wenxiu Gao, Qingquan Li, Guofeng Wu. Exploring Annual Urban Expansions in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal Features and Driving Factors in 1986–2017. Remote Sensing. 2020; 12 (16):2615.

Chicago/Turabian Style

Jie Zhang; Le Yu; Xuecao Li; Chenchen Zhang; Tiezhu Shi; Xiangyin Wu; Chao Yang; Wenxiu Gao; Qingquan Li; Guofeng Wu. 2020. "Exploring Annual Urban Expansions in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal Features and Driving Factors in 1986–2017." Remote Sensing 12, no. 16: 2615.

Research article
Published: 11 April 2020 in Land Degradation & Development
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Fuxian Lake is one of the most important freshwater supply lakes of China; however, its water quality is affected by the land degradation in its basin inevitably. This study aimed to monitor and simulate the spatiotemporal characteristics of land degradation in Fuxian Lake Basin. A comprehensive land degradation index (LDI), which integrates the indices for land use/land cover (LULC), vegetation coverage (VC), water loss and soil erosion (WLSE), wind erosion(WE) and soil moisture content (SMC), was proposed to describe the spatiotemporal characteristics of land degradation in 1990–2015; a CA‐Markov model was used to simulate and forecast the land degradation in 2020 and 2025; and the mechanisms behind land degradation were analysed. The results showed that: (1) the degree of land degradation from 1990 to 2015 generally decreased, the severe years of land degradation occurred in 1990, 2000 and 2005, and the total area of degraded land remained steady at around 250 km2 from 2005 to 2015; (2) the land degradation in different years was primarily moderate, with few severe or extreme degradation; (3) the simulated land degradation would remain relatively stable in 2020 and 2025, and the degraded area was slight fluctuation compared with that in 2015; and (4) the land degradation in this region was driven by both human factors and natural drivers. Therefore, some land degradation control measures should be set in advance for protecting this important supply area for freshwater. This article is protected by copyright. All rights reserved.

ACS Style

Chao Yang; Qingquan Li; Junyi Chen; Junjie Wang; Tiezhu Shi; Zhongwen Hu; Kai Ding; Guihua Wang; Guofeng Wu. Spatiotemporal characteristics of land degradation in the Fuxian Lake Basin, China: Past and future. Land Degradation & Development 2020, 31, 1 .

AMA Style

Chao Yang, Qingquan Li, Junyi Chen, Junjie Wang, Tiezhu Shi, Zhongwen Hu, Kai Ding, Guihua Wang, Guofeng Wu. Spatiotemporal characteristics of land degradation in the Fuxian Lake Basin, China: Past and future. Land Degradation & Development. 2020; 31 (16):1.

Chicago/Turabian Style

Chao Yang; Qingquan Li; Junyi Chen; Junjie Wang; Tiezhu Shi; Zhongwen Hu; Kai Ding; Guihua Wang; Guofeng Wu. 2020. "Spatiotemporal characteristics of land degradation in the Fuxian Lake Basin, China: Past and future." Land Degradation & Development 31, no. 16: 1.

Journal article
Published: 08 April 2020 in Ecological Indicators
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As one of the major bay areas in the world, the Guangdong-Hong Kong-Macau Greater Bay Area (GBA) has been experiencing a remarkable urbanization process, and its ecological quality also has been suffering from intense human activities and urbanization inevitably. This study aimed to reveal the spatiotemporal characteristics of ecological quality evolution under the pressure of urbanization in the GBA from 1987 to 2017. A comprehensive ecological evaluation index (CEEI) was proposed to describe the spatiotemporal characteristics change of ecological quality by integrating the remote sensing-based parameters of vegetative cover (VC), vegetative health index (VHI), normalized differential build-up and bare soil index (NDBSI), land surface moisture (LSM) and land surface temperature (LST). The results revealed that: (1) the evolution of ecological quality showed a trend of first improvement then degradation from 1987 to 2017 and the regions with poor and fair ecological quality gradually shifted from suburbs to urban areas; (2) the ecological quality was not optimistic in 1987, with the areas with poor and fair ecological quality reaching 3.45% and 30.36% of total area, respectively; (3) the ecological quality greatly improved in 1997 and 2007, and the areas with poor ecological quality only accounted for 0.41% and 0.70% of total area, respectively; and (4) the ecological quality degraded again in 2017, and the degraded area reached 52% and the areas with poor and fair ecological quality reached 4.3% and 17.35%, respectively. The changes of ecological quality were mainly driven by urbanization process and policy variation, and these results may provide helpful information for the ecological conservation and sustainable development of GBA.

ACS Style

Chao Yang; Chenchen Zhang; Qingquan Li; Huizeng Liu; Wenxiu Gao; Tiezhu Shi; Xu Liu; Guofeng Wu. Rapid urbanization and policy variation greatly drive ecological quality evolution in Guangdong-Hong Kong-Macau Greater Bay Area of China: A remote sensing perspective. Ecological Indicators 2020, 115, 106373 .

AMA Style

Chao Yang, Chenchen Zhang, Qingquan Li, Huizeng Liu, Wenxiu Gao, Tiezhu Shi, Xu Liu, Guofeng Wu. Rapid urbanization and policy variation greatly drive ecological quality evolution in Guangdong-Hong Kong-Macau Greater Bay Area of China: A remote sensing perspective. Ecological Indicators. 2020; 115 ():106373.

Chicago/Turabian Style

Chao Yang; Chenchen Zhang; Qingquan Li; Huizeng Liu; Wenxiu Gao; Tiezhu Shi; Xu Liu; Guofeng Wu. 2020. "Rapid urbanization and policy variation greatly drive ecological quality evolution in Guangdong-Hong Kong-Macau Greater Bay Area of China: A remote sensing perspective." Ecological Indicators 115, no. : 106373.

Research article
Published: 01 January 2020 in European Journal of Remote Sensing
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The accuracy and efficiency of impervious surface extraction using different algorithms vary greatly, and algorithm applicability depends on the study area. Therefore, it is necessary to carry out a comparative study of different algorithms across different study areas. This study compared six impervious surface extraction indices (i.e., normalized difference built-up index (NDBI), index-based built-up index (IBI), biophysical composition index (BCI), combinational build-up index (CBI), combinational biophysical composition index (CBCI), and enhanced normalized difference impervious surfaces index (ENDISI)) using Sentinel-2 imagery in Fuxian Lake Basin, Shenzhen City, and Nanjing City. Three study areas with different geographical locations, climatic conditions and altitudes can test spatial heterogeneity of different indices. The results show that: (1) All indices could be used to extract impervious surface, but BCI and CBI were greatly disturbed by water bodies; (2) CBCI, IBI, and NDBI were influenced by study area, while ENDISI could be used across all three study areas; (3) ENDISI algorithm was the best among the six algorithms with a much higher separability degree and an overall accuracy of more than 91.00%. ENDISI can extract impervious surface quickly and accurately from Sentinel-2 imagery across different study areas, and can be well applied in the field of impervious surface change monitoring.

ACS Style

Junyi Chen; Suozhong Chen; Chao Yang; Liang He; Manqing Hou; Tiezhu Shi. A comparative study of impervious surface extraction using Sentinel-2 imagery. European Journal of Remote Sensing 2020, 53, 274 -292.

AMA Style

Junyi Chen, Suozhong Chen, Chao Yang, Liang He, Manqing Hou, Tiezhu Shi. A comparative study of impervious surface extraction using Sentinel-2 imagery. European Journal of Remote Sensing. 2020; 53 (1):274-292.

Chicago/Turabian Style

Junyi Chen; Suozhong Chen; Chao Yang; Liang He; Manqing Hou; Tiezhu Shi. 2020. "A comparative study of impervious surface extraction using Sentinel-2 imagery." European Journal of Remote Sensing 53, no. 1: 274-292.

Journal article
Published: 23 September 2019 in Remote Sensing
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The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is one of the major bay areas in the world. However, the spatiotemporal characteristics and rationalities of urban expansions within this region over a relatively long period of time are not well-understood. This study explored the spatiotemporal evolution of 11 cities within the GBA in 1987–2017 by integrating remote sensing, landscape analysis, and geographic information system (GIS) techniques, and further evaluated the rationalities of their expansion using the urban area population elastic coefficient (UPEC) and the urban area gross domestic product (GDP) elastic coefficient (UGEC). The results showed the following: (1) Guangzhou, Shenzhen, Foshan, Dongguan, Zhongshan, and Zhuhai experienced unprecedented urbanization compared with the other cities, and from 1987 to 2017, their urban areas expanded by 10.12, 11.48, 14.21, 24.90, 37.07, and 30.15 times, respectively; (2) several expansion patterns were observed in the 11 cities, including a mononuclear polygon radiation pattern (Guangzhou and Foshan), a double-nucleated polygon pattern (Macau and Zhongshan), and a multi-nuclear urbanization pattern (Shenzhen, Hong Kong, Dongguan, Jiangmen, Huizhou, Zhaoqing, and Zhuhai); (3) with regard to the proportion of area, the edge-expansion and outlying growth types were the predominant types for all 11 cities, and the infilling growth type was the one of the important types during 2007–2017 for Shenzhen, Hong Kong, Dongguan, Zhongshan, and Foshan; (4) the expansion of most cities took on an urban-to-rural landscape gradient, especially for Guangzhou, Shenzhen, Foshan, Zhongshan, Dongguan, and Zhuhai; and (5) the rationalities of expansion in several time periods were rational for Guangzhou (1997–2007), Hong Kong (2007–2017), Foshan (1987–2007), Huizhou (1987–1997), and Dongguan (1997–2007), and the rationalities of expansion in the other cities and time periods were found to be irrational. These findings may help policy- and decision-makers to maintain the sustainable development of the Guangdong–Hong Kong–Macau Greater Bay Area.

ACS Style

Chao Yang; Qingquan Li; Tianhong Zhao; Huizeng Liu; Wenxiu Gao; Tiezhu Shi; Minglei Guan; Guofeng Wu. Detecting Spatiotemporal Features and Rationalities of Urban Expansions within the Guangdong–Hong Kong–Macau Greater Bay Area of China from 1987 to 2017 Using Time-Series Landsat Images and Socioeconomic Data. Remote Sensing 2019, 11, 2215 .

AMA Style

Chao Yang, Qingquan Li, Tianhong Zhao, Huizeng Liu, Wenxiu Gao, Tiezhu Shi, Minglei Guan, Guofeng Wu. Detecting Spatiotemporal Features and Rationalities of Urban Expansions within the Guangdong–Hong Kong–Macau Greater Bay Area of China from 1987 to 2017 Using Time-Series Landsat Images and Socioeconomic Data. Remote Sensing. 2019; 11 (19):2215.

Chicago/Turabian Style

Chao Yang; Qingquan Li; Tianhong Zhao; Huizeng Liu; Wenxiu Gao; Tiezhu Shi; Minglei Guan; Guofeng Wu. 2019. "Detecting Spatiotemporal Features and Rationalities of Urban Expansions within the Guangdong–Hong Kong–Macau Greater Bay Area of China from 1987 to 2017 Using Time-Series Landsat Images and Socioeconomic Data." Remote Sensing 11, no. 19: 2215.

Journal article
Published: 12 March 2019 in Science of The Total Environment
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As major urban agglomerations with strong urbanization, global bay areas are seldom detected and compared in detail regarding the spatiotemporal evolution of their urban expansion. In this work, a framework was applied for detecting and comparing the spatiotemporal evolution of urban agglomerations in four major bay areas: the San Francisco Bay Area and the New York Bay Area in the US, the Tokyo Bay Area in Japan, and the Guangdong-Hong Kong-Macau (GHM) Bay Area in China. Landsat images from 1987, 1997, 2007 and 2017 were employed to derive the four urban bay areas using the object-oriented support vector machine (O-SVM) classification method, and a multi-scale spatial analysis method was applied to detect the landscape characteristics and types of growth in the urban expansions. The results showed that: (1) the O-SVM classification method exhibited a high accuracy in urban area extraction, especially for classifying large-scale images; (2) the urban areas of the San Francisco Bay Area, the New York Bay Area, the Tokyo Bay Area and the GHM Bay Area from 1987 to 2017 expanded from 1686.82, 5315.93, 3765.09 and 605.71 km2 to 2714.7, 8359.18, 5351.06 and 7568.19 km2, respectively, with a corresponding annual average increase of 1.60%, 1.52%, 1.18% and 8.82%; (3) the GHM Bay Area had the largest expansion area and rate among the four bay areas; (4) both the San Francisco Bay Area and the New York Bay Area successively formed a multi-nuclei ribbon model, and the Tokyo Bay Area and the GHM Bay Area formed a multinuclear fan-shaped model and a triangle zonal expansion pattern, respectively; and (5) the spatial patterns of urban expansions in these bay areas shifted from outlying to edge-expansion and infilling, in which the Tokyo Bay Area and the New York Bay Area experienced the largest infilling growth, and the San Francisco Bay Area followed closely thereafter; all were ahead of the GHM Bay Area. These results will be helpful for the understanding and sustainable development of these bay areas.

ACS Style

Chao Yang; Qingquan Li; Zhongwen Hu; Junyi Chen; Tiezhu Shi; Kai Ding; Guofeng Wu. Spatiotemporal evolution of urban agglomerations in four major bay areas of US, China and Japan from 1987 to 2017: Evidence from remote sensing images. Science of The Total Environment 2019, 671, 232 -247.

AMA Style

Chao Yang, Qingquan Li, Zhongwen Hu, Junyi Chen, Tiezhu Shi, Kai Ding, Guofeng Wu. Spatiotemporal evolution of urban agglomerations in four major bay areas of US, China and Japan from 1987 to 2017: Evidence from remote sensing images. Science of The Total Environment. 2019; 671 ():232-247.

Chicago/Turabian Style

Chao Yang; Qingquan Li; Zhongwen Hu; Junyi Chen; Tiezhu Shi; Kai Ding; Guofeng Wu. 2019. "Spatiotemporal evolution of urban agglomerations in four major bay areas of US, China and Japan from 1987 to 2017: Evidence from remote sensing images." Science of The Total Environment 671, no. : 232-247.

Articles
Published: 01 January 2019 in European Journal of Remote Sensing
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The land-use/cover (LUC) change (LUCC) in small-scale basins can strongly impact regional environments and developments, and understanding the spatio-temporal characteristics of LUCC is important for sustainable development planning. This study explored the spatio-temporal characteristics of China’s Fuxian Lake Basin in 2017–2067, which may provide decision-making support for ecological and environmental protection measures. Landsat-7 ETM+ and Landsat-8 OLI images combined with a cellular automata–Markov model were used to simulate and forecast LUCC, and expand analysis and reduce analysis methods were applied to explore the spatio-temporal patterns of LUCC in this basin. The results showed that in 2017–2067: (1) arable land, forestland and water would be expected to expand; (2) garden, grassland, building region, road, structure, artificial piling and digging land, and desert and bare surface would contract; (3) the rate of arable land expansion in 2032–2037 and the rate of grassland loss in 2057–2062 would be the largest changes, reaching 1.670 and 0.856 km2/year, respectively and (4) the expansion and contraction of different LUCC classes were closely related to the “Four Retire Three Return” policy implemented by the local government. Therefore, the protection measures for environment should be set in advance according to the forecasting results of this study.

ACS Style

Chao Yang; Guofeng Wu; Junyi Chen; Qingquan Li; Kai Ding; Guihua Wang; Chenchen Zhang. Simulating and forecasting spatio-temporal characteristic of land-use/cover change with numerical model and remote sensing: a case study in Fuxian Lake Basin, China. European Journal of Remote Sensing 2019, 52, 374 -384.

AMA Style

Chao Yang, Guofeng Wu, Junyi Chen, Qingquan Li, Kai Ding, Guihua Wang, Chenchen Zhang. Simulating and forecasting spatio-temporal characteristic of land-use/cover change with numerical model and remote sensing: a case study in Fuxian Lake Basin, China. European Journal of Remote Sensing. 2019; 52 (1):374-384.

Chicago/Turabian Style

Chao Yang; Guofeng Wu; Junyi Chen; Qingquan Li; Kai Ding; Guihua Wang; Chenchen Zhang. 2019. "Simulating and forecasting spatio-temporal characteristic of land-use/cover change with numerical model and remote sensing: a case study in Fuxian Lake Basin, China." European Journal of Remote Sensing 52, no. 1: 374-384.

Journal article
Published: 11 February 2018 in Sensors
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In this paper, an improved method based on a mixture of Gaussian and quadrilateral functions is presented to process airborne bathymetric LiDAR waveforms. In the presented method, the LiDAR waveform is fitted to a combination of three functions: one Gaussian function for the water surface contribution, another Gaussian function for the water bottom contribution, and a new quadrilateral function to fit the water column contribution. The proposed method was tested on a simulated dataset and a real dataset, with the focus being mainly on the performance of retrieving bottom response and water depths. We also investigated the influence of the parameter settings on the accuracy of the bathymetry estimates. The results demonstrate that the improved quadrilateral fitting algorithm shows a superior performance in terms of low RMSE and a high detection rate in the water depth and magnitude retrieval. What’s more, compared with the use of a triangular function or the existing quadrilateral function to fit the water column contribution, the presented method retrieved the least noise and the least number of unidentified waveforms, showed the best performance in fitting the return waveforms, and had consistent fitting goodness for all different water depths.

ACS Style

Kai Ding; Qingquan Li; Jiasong Zhu; Chisheng Wang; Minglei Guan; Zhipeng Chen; Chao Yang; Yang Cui; Jianghai Liao. An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms. Sensors 2018, 18, 552 .

AMA Style

Kai Ding, Qingquan Li, Jiasong Zhu, Chisheng Wang, Minglei Guan, Zhipeng Chen, Chao Yang, Yang Cui, Jianghai Liao. An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms. Sensors. 2018; 18 (2):552.

Chicago/Turabian Style

Kai Ding; Qingquan Li; Jiasong Zhu; Chisheng Wang; Minglei Guan; Zhipeng Chen; Chao Yang; Yang Cui; Jianghai Liao. 2018. "An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms." Sensors 18, no. 2: 552.

Journal article
Published: 27 November 2017 in Remote Sensing
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Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence. The abundance and minimum noise fraction (MNF) results that were obtained from mixed pixel decomposition were added to decision tree multi-features using a three-dimensional (3D) Terrain model, which was created using an image fusion digital elevation model (DEM), to select training samples (ROIs), and improve ROI separability. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the Kappa coefficient and the overall accuracy of integrated pixel unmixing and decision tree method increased by 0.093% and 10%, respectively, as compared with the original decision tree method. This proposed method could effectively eliminate the influence of mixed pixels and improve the accuracy in complex LULC classifications.

ACS Style

Chao Yang; Guofeng Wu; Kai Ding; Tiezhu Shi; Qingquan Li; Jinliang Wang. Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods. Remote Sensing 2017, 9, 1222 .

AMA Style

Chao Yang, Guofeng Wu, Kai Ding, Tiezhu Shi, Qingquan Li, Jinliang Wang. Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods. Remote Sensing. 2017; 9 (12):1222.

Chicago/Turabian Style

Chao Yang; Guofeng Wu; Kai Ding; Tiezhu Shi; Qingquan Li; Jinliang Wang. 2017. "Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods." Remote Sensing 9, no. 12: 1222.