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The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha−1. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R2 = 0.62 > 0.52, RMSE = 50.36 Mg ha−1<56.63 Mg ha−1). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R2 = 0.62, RMSE = 51.03 Mg ha−1). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests.
Dezhi Wang; Bo Wan; Jing Liu; Yanjun Su; Qinghua Guo; Penghua Qiu; Xincai Wu. Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery. International Journal of Applied Earth Observation and Geoinformation 2019, 85, 101986 .
AMA StyleDezhi Wang, Bo Wan, Jing Liu, Yanjun Su, Qinghua Guo, Penghua Qiu, Xincai Wu. Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery. International Journal of Applied Earth Observation and Geoinformation. 2019; 85 ():101986.
Chicago/Turabian StyleDezhi Wang; Bo Wan; Jing Liu; Yanjun Su; Qinghua Guo; Penghua Qiu; Xincai Wu. 2019. "Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery." International Journal of Applied Earth Observation and Geoinformation 85, no. : 101986.
Detailed hydrothermally altered mineral mapping is important for mineral exploration. ASTER data are commonly combined with Hyperion data to classify the hydrothermally altered minerals. However, when machine learning algorithms are applied to the shortwave infrared (SWIR) bands of an ASTER reflectance image (AST_07XT), misclassification of Mg-OH group minerals is the major source of errors. In this study, an ASTER emissivity image (AST_05) and the AST_07XT SWIR bands are integrated to map minerals in the Duolong area, Tibetan Plateau. The results show that ASTER thermal infrared (TIR) bands can successfully identify Mg-OH group minerals. To improve the performance of classification, a novel voting-based extreme learning machine (V-ELM) algorithm is introduced to map hydrothermally altered minerals. The classification based on the ASTER SWIR-TIR data gets good identification of Mg-OH group minerals, which is better than those acquired using SWIR and TIR data alone. Moreover, these results also show that the AST_05 TIR bands cannot discriminate Al-OH group minerals. Compared with the classification applied to AST_07XT SWIR data, the classification applied to the ASTER SWIR-TIR data can achieve a higher overall accuracy (99.01%). The ASTER data results are spatially consistent with those of the Hyperion data. In accordance with the image processing results, a new deposit associated with felsic intrusions has been validated by field investigations. These schemes are promising for mineral mapping in the Tibetan Plateau.
Bin Hu; Bo Wan; Yongyang Xu; Liufeng Tao; Xincai Wu; Qinjun Qiu; Yehui Wu; Hui Deng. Mapping hydrothermally altered minerals with AST_07XT, AST_05 and Hyperion datasets using a voting-based extreme learning machine algorithm. Ore Geology Reviews 2019, 114, 103116 .
AMA StyleBin Hu, Bo Wan, Yongyang Xu, Liufeng Tao, Xincai Wu, Qinjun Qiu, Yehui Wu, Hui Deng. Mapping hydrothermally altered minerals with AST_07XT, AST_05 and Hyperion datasets using a voting-based extreme learning machine algorithm. Ore Geology Reviews. 2019; 114 ():103116.
Chicago/Turabian StyleBin Hu; Bo Wan; Yongyang Xu; Liufeng Tao; Xincai Wu; Qinjun Qiu; Yehui Wu; Hui Deng. 2019. "Mapping hydrothermally altered minerals with AST_07XT, AST_05 and Hyperion datasets using a voting-based extreme learning machine algorithm." Ore Geology Reviews 114, no. : 103116.
Examining the levels of both the spatial and the temporal clustering of crime events raises the analysis of events as spatial patterns at different time periods to understanding them as spatial processes. There have been, however, only limited efforts in doing so, possibly due to a lack of effective methods for estimating, identifying the difference in the levels of spatial clustering patterns, and visualizing the spatiotemporal trends among the events. We first describe a method for examining the periodicity of space–time crime events. Two indexes are extended from the Moran’s index for spatial autocorrelation and local indicator of spatial association (LISA) to ones for including spatiotemporal event-region data. We also suggest a three-dimensional approach to visualizing spatiotemporal associations among event regions that enables a better understanding of the spatial processes behind the evolution of event regions. An implementation of the suggested methods that use data of three types of crime events in Akron, Ohio, is reported. The suggested approach is capable of summarizing spatiotemporal autocorrelation as well as identifying spatiotemporal clusters of crime events. Compared to layered LISA, this approach works well, and it allows three-dimensional visualizations of spatiotemporal clusters of events that can help to develop effective policing strategies for combating crimes.
Junfang Gong; Shengwen Li; Bo Wan. A Regional Approach to Assessing and Visualizing Spatiotemporal Clustering of Crime Events. Papers in Applied Geography 2019, 5, 26 -44.
AMA StyleJunfang Gong, Shengwen Li, Bo Wan. A Regional Approach to Assessing and Visualizing Spatiotemporal Clustering of Crime Events. Papers in Applied Geography. 2019; 5 (1-2):26-44.
Chicago/Turabian StyleJunfang Gong; Shengwen Li; Bo Wan. 2019. "A Regional Approach to Assessing and Visualizing Spatiotemporal Clustering of Crime Events." Papers in Applied Geography 5, no. 1-2: 26-44.
The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate relationship. However, traditional road-network matching algorithms, which are mainly central processing unit (CPU)-based approaches, may have performance bottleneck problems when facing big data. We developed a particle-swarm optimization (PSO)-based parallel road-network matching method on graphics-processing unit (GPU). Based on the characteristics of the two main stages (similarity computation and matching-relationship identification), data-partition and task-partition strategies were utilized, respectively, to fully use GPU threads. Experiments were conducted on datasets with 14 different scales. Results indicate that the parallel PSO-based matching algorithm (PSOM) could correctly identify most matching relationships with an average accuracy of 84.44%, which was at the same level as the accuracy of a benchmark—the probability-relaxation-matching (PRM) method. The PSOM approach significantly reduced the road-network matching time in dealing with large amounts of data in comparison with the PRM method. This paper provides a common parallel algorithm framework for road-network matching algorithms and contributes to integration and update of large-scale road-networks.
Bo Wan; Lin Yang; Shunping Zhou; Run Wang; Dezhi Wang; Wenjie Zhen. A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture. ISPRS International Journal of Geo-Information 2018, 7, 472 .
AMA StyleBo Wan, Lin Yang, Shunping Zhou, Run Wang, Dezhi Wang, Wenjie Zhen. A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture. ISPRS International Journal of Geo-Information. 2018; 7 (12):472.
Chicago/Turabian StyleBo Wan; Lin Yang; Shunping Zhou; Run Wang; Dezhi Wang; Wenjie Zhen. 2018. "A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture." ISPRS International Journal of Geo-Information 7, no. 12: 472.
Accessibility is a major method for evaluating the distribution of service facilities and identifying areas in shortage of service. Traditional accessibility methods, however, are largely model-based and do not consider the actual utilization of services, which may lead to results that are different from those obtained when people’s actual behaviors are taken into account. Based on taxi GPS trajectory data, this paper proposed a novel integrated catchment area (ICA) that integrates actual human travel behavior to evaluate the accessibility to healthcare facilities in Shenzhen, China, using the enhanced two-step floating catchment area (E2SFCA) method. This method is called the E2SFCA-ICA method. First, access probability is proposed to depict the probability of visiting a healthcare facility. Then, integrated access probability (IAP), which integrates model-based access probability (MAP) and data-based access probability (DAP), is presented. Under the constraint of IAP, ICA is generated and divided into distinct subzones. Finally, the ICA and subzones are incorporated into the E2SFCA method to evaluate the accessibility of the top-tier hospitals in Shenzhen, China. The results show that the ICA not only reduces the differences between model-based catchment areas and data-based catchment areas, but also distinguishes the core catchment area, stable catchment area, uncertain catchment area and remote catchment area of healthcare facilities. The study also found that the accessibility of Shenzhen’s top-tier hospitals obtained with traditional catchment areas tends to be overestimated and more unequally distributed in space when compared to the accessibility obtained with integrated catchment areas.
Xiaofang Pan; Mei-Po Kwan; Lin Yang; Shunping Zhou; Zejun Zuo; Bo Wan. Evaluating the Accessibility of Healthcare Facilities Using an Integrated Catchment Area Approach. International Journal of Environmental Research and Public Health 2018, 15, 2051 .
AMA StyleXiaofang Pan, Mei-Po Kwan, Lin Yang, Shunping Zhou, Zejun Zuo, Bo Wan. Evaluating the Accessibility of Healthcare Facilities Using an Integrated Catchment Area Approach. International Journal of Environmental Research and Public Health. 2018; 15 (9):2051.
Chicago/Turabian StyleXiaofang Pan; Mei-Po Kwan; Lin Yang; Shunping Zhou; Zejun Zuo; Bo Wan. 2018. "Evaluating the Accessibility of Healthcare Facilities Using an Integrated Catchment Area Approach." International Journal of Environmental Research and Public Health 15, no. 9: 2051.
Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species.
Dezhi Wang; Bo Wan; Penghua Qiu; Yanjun Su; Qinghua Guo; Run Wang; Fei Sun; Xincai Wu. Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species. Remote Sensing 2018, 10, 1468 .
AMA StyleDezhi Wang, Bo Wan, Penghua Qiu, Yanjun Su, Qinghua Guo, Run Wang, Fei Sun, Xincai Wu. Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species. Remote Sensing. 2018; 10 (9):1468.
Chicago/Turabian StyleDezhi Wang; Bo Wan; Penghua Qiu; Yanjun Su; Qinghua Guo; Run Wang; Fei Sun; Xincai Wu. 2018. "Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species." Remote Sensing 10, no. 9: 1468.
In the dwindling natural mangrove today, mangrove reforestation projects are conducted worldwide to prevent further losses. Due to monoculture and the low survival rate of artificial mangroves, it is necessary to pay attention to mapping and monitoring them dynamically. Remote sensing techniques have been widely used to map mangrove forests due to their capacity for large-scale, accurate, efficient, and repetitive monitoring. This study evaluated the capability of a 0.5-m Pléiades-1 in classifying artificial mangrove species using both pixel-based and object-based classification schemes. For comparison, three machine learning algorithms—decision tree (DT), support vector machine (SVM), and random forest (RF)—were used as the classifiers in the pixel-based and object-based classification procedure. The results showed that both the pixel-based and object-based approaches could recognize the major discriminations between the four major artificial mangrove species. However, the object-based method had a better overall accuracy than the pixel-based method on average. For pixel-based image analysis, SVM produced the highest overall accuracy (79.63%); for object-based image analysis, RF could achieve the highest overall accuracy (82.40%), and it was also the best machine learning algorithm for classifying artificial mangroves. The patches produced by object-based image analysis approaches presented a more generalized appearance and could contiguously depict mangrove species communities. When the same machine learning algorithms were compared by McNemar’s test, a statistically significant difference in overall classification accuracy between the pixel-based and object-based classifications only existed in the RF algorithm. Regarding species, monoculture and dominant mangrove species Sonneratia apetala group 1 (SA1) as well as partly mixed and regular shape mangrove species Hibiscus tiliaceus (HT) could well be identified. However, for complex and easily-confused mangrove species Sonneratia apetala group 2 (SA2) and other occasionally presented mangroves species (OT), only major distributions could be extracted, with an accuracy of about two-thirds. This study demonstrated that more than 80% of artificial mangroves species distribution could be mapped.
Dezhi Wang; Bo Wan; Penghua Qiu; Yanjun Su; Qinghua Guo; Xincai Wu. Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms. Remote Sensing 2018, 10, 294 .
AMA StyleDezhi Wang, Bo Wan, Penghua Qiu, Yanjun Su, Qinghua Guo, Xincai Wu. Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms. Remote Sensing. 2018; 10 (2):294.
Chicago/Turabian StyleDezhi Wang; Bo Wan; Penghua Qiu; Yanjun Su; Qinghua Guo; Xincai Wu. 2018. "Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms." Remote Sensing 10, no. 2: 294.
The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer products (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm, is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer’s accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user’s accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products; (2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method (Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner.
Run Wang; Bo Wan; Qinghua Guo; Maosheng Hu; Shunping Zhou. Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data. Remote Sensing 2017, 9, 862 .
AMA StyleRun Wang, Bo Wan, Qinghua Guo, Maosheng Hu, Shunping Zhou. Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data. Remote Sensing. 2017; 9 (8):862.
Chicago/Turabian StyleRun Wang; Bo Wan; Qinghua Guo; Maosheng Hu; Shunping Zhou. 2017. "Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data." Remote Sensing 9, no. 8: 862.
Urban areas are one of the most important components of human society. Their extents have been continuously growing during the last few decades. Accurate and timely measurements of the extents of urban areas can help in analyzing population densities and urban sprawls and in studying environmental issues related to urbanization. Urban extents detected from remotely sensed data are usually a by-product of land use classification results, and their interpretation requires a full understanding of land cover types. In this study, for the first time, we mapped urban extents in the continental United States using a novel one-class classification method, i.e., positive and unlabeled learning (PUL), with multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) night stable light data were used to calibrate the urban extents obtained from the one-class classification scheme. Our results demonstrated the effectiveness of the use of the PUL algorithm in mapping large-scale urban areas from coarse remote-sensing images, for the first time. The total accuracy of mapped urban areas was 92.9% and the kappa coefficient was 0.85. The use of DMSP-OLS night stable light data can significantly reduce false detection rates from bare land and cropland far from cities. Compared with traditional supervised classification methods, the one-class classification scheme can greatly reduce the effort involved in collecting training datasets, without losing predictive accuracy.
Bo Wan; Qinghua Guo; Fang Fang; Yanjun Su; Run Wang. Mapping US Urban Extents from MODIS Data Using One-Class Classification Method. Remote Sensing 2015, 7, 10143 -10163.
AMA StyleBo Wan, Qinghua Guo, Fang Fang, Yanjun Su, Run Wang. Mapping US Urban Extents from MODIS Data Using One-Class Classification Method. Remote Sensing. 2015; 7 (8):10143-10163.
Chicago/Turabian StyleBo Wan; Qinghua Guo; Fang Fang; Yanjun Su; Run Wang. 2015. "Mapping US Urban Extents from MODIS Data Using One-Class Classification Method." Remote Sensing 7, no. 8: 10143-10163.