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Impervious surfaces are essential elements for the urban ecological environment. Machine-learning-based approaches have achieved successful breakthroughs in impervious surface extraction. These methods require large sets of labeled impervious surface data to train a model. However, it is a challenge to acquire massive impervious surface sample data because of the complexity, time consumption, and high cost. To address this issue, we explore a method to generate massive impervious surface training samples using points of interest (POIs) data and vehicle trajectory global positioning system (GPS) data. Furthermore, a neural-network-based method was proposed for impervious surface extraction based on the generated training samples. One Landsat-8 image of Shenzhen City, China, was selected to test our approach. The extraction accuracy of the impervious surface was 90.88\%, and the overall accuracy based on this method was improved by 8.57% and 8.45% compared with the support vector data description (SVDD) and weighted one-class support vector machine (WOC-SVM) methods, respectively. The results show that the method integrating POI, trajectory data, and satellite imagery can be a viable candidate for impervious surface extraction.
Yiliang Wan; Yuwen Fei; Tao Wu; Rui Jin; Tong Xiao. A Novel Impervious Surface Extraction Method Integrating POI, Vehicle Trajectories and Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.
AMA StyleYiliang Wan, Yuwen Fei, Tao Wu, Rui Jin, Tong Xiao. A Novel Impervious Surface Extraction Method Integrating POI, Vehicle Trajectories and Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.
Chicago/Turabian StyleYiliang Wan; Yuwen Fei; Tao Wu; Rui Jin; Tong Xiao. 2021. "A Novel Impervious Surface Extraction Method Integrating POI, Vehicle Trajectories and Satellite Imagery." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.
The exploitation and utilization of agricultural water-land resources are important sources of greenhouse gas (GHG) emissions in agricultural production. The study of water-land-energy-emission systems can provide an important basis for reducing agricultural GHG emissions. Based on calculations of agricultural CO2e emissions in the 30 provinces of China from 2006 to 2017, this study explores the contribution of the integrated patterns of water and land resources (IPWL), agricultural carbon emission intensity, and economic output per unit water resources to agricultural CO2e emissions per unit sown area using a spatiotemporal index decomposition approach. The conclusions are as follows. (1) In 2006–2017, the IPWL in China fluctuates, increasing from 1.19 × 106 m3/km2 to 1.35 × 106 m3/km2 and is generally distributed as “high in the south and low in the north.” (2) In the temporal domain, a comparison between the periods 2006–2011 and 2006–2017 shows that, with the increase in IPWL, fewer provinces inhibit the IPWL to agricultural CO2e emissions per unit sown area; furthermore, this effect has been weakened in seven provinces. (3) In the spatial domain, the higher the IPWL is in most areas in China, the higher the contribution rate of IPWL is to agricultural CO2e emissions per unit sown area. Finally, several suggestions are offered for mitigating agricultural GHG emissions in China.
Chuxiong Deng; Rongrong Li; Binggeng Xie; Yiliang Wan; Zhongwu Li; Changchang Liu. Impacts of the integrated pattern of water and land resources use on agricultural greenhouse gas emissions in China during 2006-2017: a water-land-energy-emissions nexus analysis. Journal of Cleaner Production 2021, 308, 127221 .
AMA StyleChuxiong Deng, Rongrong Li, Binggeng Xie, Yiliang Wan, Zhongwu Li, Changchang Liu. Impacts of the integrated pattern of water and land resources use on agricultural greenhouse gas emissions in China during 2006-2017: a water-land-energy-emissions nexus analysis. Journal of Cleaner Production. 2021; 308 ():127221.
Chicago/Turabian StyleChuxiong Deng; Rongrong Li; Binggeng Xie; Yiliang Wan; Zhongwu Li; Changchang Liu. 2021. "Impacts of the integrated pattern of water and land resources use on agricultural greenhouse gas emissions in China during 2006-2017: a water-land-energy-emissions nexus analysis." Journal of Cleaner Production 308, no. : 127221.
With the rapid development of LBSs (location-based services) in recent years, researchers have increasingly taken an interest in trying to make travel routes more practicable and individualized. Despite the fact that many studies have been conducted on routes using LBS data, the specific routes are deficient in dynamic scalability and the correlations between environmental constraints and personal choices have not been investigated. This paper proposes an improved HMM-based (hidden Markov model) method for planning personalized routes with crowd sourcing spatiotemporal data. It tries to integrate the dynamic public preferences, the individual interests and the physical road network space in the same spatiotemporal framework, ensuring that reasonable routes will be generated. A novel dual-layer mapping structure has been proposed to bridge the gap from brief individual preferences to specific entries of POIs (points-of-interest) inside realistic road networks. A case study on Changsha city has proven that the proposed method can not only flexibly plan people’s travel routes under different spatiotemporal backgrounds but also is close to people’s natural selection by the perception of the group.
Tao Wu; Zhixuan Zeng; Jianxin Qin; Longgang Xiang; Yiliang Wan. An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data. Sensors 2020, 20, 6938 .
AMA StyleTao Wu, Zhixuan Zeng, Jianxin Qin, Longgang Xiang, Yiliang Wan. An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data. Sensors. 2020; 20 (23):6938.
Chicago/Turabian StyleTao Wu; Zhixuan Zeng; Jianxin Qin; Longgang Xiang; Yiliang Wan. 2020. "An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data." Sensors 20, no. 23: 6938.
Understanding the integration process of urban agglomeration is essential for sustainable regional development and urban planning. However, few studies have analyzed the spatial integration patterns of metropolitan regions according to the impacts of landscape ecology along rail transit corridors. This study performed a comprehensive inter-city gradient analysis using landscape metrics and radar charts in order to determine the integration characteristics of an urban agglomeration. Specifically, we analyzed the evolution of spatial heterogeneity and functional landscapes along gradient transects in the Changsha–Zhuzhou–Xiangtan (CZT) metropolitan region during the period of 1995–2015. Four landscape functional zones (urban center, urban area, urban–rural fringe, and green core) were identified based on a cluster analysis of landscape composition, connectivity, and fragmentation. The landscape metric NP/LPI (number of patches/largest patch index) was proposed to identify the urban–rural fringe, which revealed that the CZT region exhibited a more aggregated form, characterized by a single-core, continuous development, and the compression of green space. The integration of cities has resulted in continued compression and fragmentation of ecological space. Therefore, strategies for controlling urban expansion should be adopted for sustainable urban development. The proposed method can be used to quantify the integration characteristics of urban agglomerations, providing scientific support for urban landscape planning.
Yiliang Wan; Chuxiong Deng; Tao Wu; Rui Jin; Pengfei Chen; Rong Kou. Quantifying the Spatial Integration Patterns of Urban Agglomerations along an Inter-City Gradient. Sustainability 2019, 11, 5000 .
AMA StyleYiliang Wan, Chuxiong Deng, Tao Wu, Rui Jin, Pengfei Chen, Rong Kou. Quantifying the Spatial Integration Patterns of Urban Agglomerations along an Inter-City Gradient. Sustainability. 2019; 11 (18):5000.
Chicago/Turabian StyleYiliang Wan; Chuxiong Deng; Tao Wu; Rui Jin; Pengfei Chen; Rong Kou. 2019. "Quantifying the Spatial Integration Patterns of Urban Agglomerations along an Inter-City Gradient." Sustainability 11, no. 18: 5000.
To organize trajectory data is a challenging issue for both studies on spatial databases and spatial data mining in the last decade, especially where there is semantic information involved. The high-level semantic features of trajectory data exploit human movement interrelated with geographic context, which is becoming increasingly important in representing and analyzing actual information contained in movements and further processing. This paper argues for a novel semantic trajectory model named TOST. It considers both semantic and geographic information of trajectory data happens along network infrastructure simultaneously. In TOST, a flexible intersection-based semantic representation is designed to express movement typically constrained by urban road networks by combining sets of local semantic details along the time axis. A relational schema based on this model was instantiated against real datasets, which illustrated the effectivity of our proposed model.
Tao Wu; Jianxin Qin; Yiliang Wan. TOST: A Topological Semantic Model for GPS Trajectories Inside Road Networks. ISPRS International Journal of Geo-Information 2019, 8, 410 .
AMA StyleTao Wu, Jianxin Qin, Yiliang Wan. TOST: A Topological Semantic Model for GPS Trajectories Inside Road Networks. ISPRS International Journal of Geo-Information. 2019; 8 (9):410.
Chicago/Turabian StyleTao Wu; Jianxin Qin; Yiliang Wan. 2019. "TOST: A Topological Semantic Model for GPS Trajectories Inside Road Networks." ISPRS International Journal of Geo-Information 8, no. 9: 410.
To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distribution histograms. An arithmetic frequency-mean feature is then defined from the red, green and blue band histogram. Secondly, bin-to-bin distance is adapted to measure the change magnitude between the pairwise objects of bi-temporal images. The change magnitude image (CMI) of the bi-temporal images can be generated through object-by-object. Finally, the classical binary method Otsu is used to divide the CMI to a binary change detection map. Experimental results based on two real datasets with different land-cover change scenes demonstrate the effectiveness of the proposed MOHD approach in detecting land-cover change compared with three widely used existing approaches.
Zhiyong Lv; Tongfei Liu; Jón Atli Benediktsson; Tao Lei; Yiliang Wan. Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images. Remote Sensing 2018, 10, 1809 .
AMA StyleZhiyong Lv, Tongfei Liu, Jón Atli Benediktsson, Tao Lei, Yiliang Wan. Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images. Remote Sensing. 2018; 10 (11):1809.
Chicago/Turabian StyleZhiyong Lv; Tongfei Liu; Jón Atli Benediktsson; Tao Lei; Yiliang Wan. 2018. "Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images." Remote Sensing 10, no. 11: 1809.
Rapid urbanization can cause remarkable and high fragmentation of urban landscapes, which will affect the function and services of landscapes and result in a series of ecological and environmental problems. To identify the spatial structure and explore the effects of internal features on this structure, we propose a spatial‐adjacency‐based method for interactions between urban main roads (main road corridors) and the urban landscape. Dongguan was used as the research area, for the sample period 1997–2013. Urban land patches were analyzed comprehensively, based on the spatial relationship with main roads, and according to landscape indices and landscape gradients. Furthermore, to explore the overall spatial attraction and spillover effects of the main road corridors, an extended Voronoi‐based spatial‐adjacency measuring index (EVSAI) was proposed to measure geographic adjacency. Finally, we established one complete set of classification criteria relevant to the changing patterns of the spatial structure. The experimental results indicated a dense network of main road corridors in the regions adjacent to the central area of Dongguan. The findings further revealed that the landscape pattern experienced three stages of spatiotemporal changes spanning the 16 years: central sprawl (1997–2005), peripheral sprawl (2005–2009), and central sprawl (2009–2013).
Rui Jin; Jianya Gong; Min Deng; Yiliang Wan; Wentao Yang. A spatial‐adjacency‐based approach for analyzing urban landscape structure. Transactions in GIS 2018, 22, 1649 -1672.
AMA StyleRui Jin, Jianya Gong, Min Deng, Yiliang Wan, Wentao Yang. A spatial‐adjacency‐based approach for analyzing urban landscape structure. Transactions in GIS. 2018; 22 (6):1649-1672.
Chicago/Turabian StyleRui Jin; Jianya Gong; Min Deng; Yiliang Wan; Wentao Yang. 2018. "A spatial‐adjacency‐based approach for analyzing urban landscape structure." Transactions in GIS 22, no. 6: 1649-1672.
Understanding regional economic agglomeration patterns is critical for sustainable economic development, urban planning and proper utilization of regional resources. Taking Guangdong Province of China as the study area, this paper introduces a comprehensive research framework for analyzing regional economic agglomeration patterns and understanding their spatiotemporal characteristics. First, convergence and autocorrelation methods are applied to understand the economic spatial patterns. Then, the intercity spatial interaction model (ISIM) is proposed to measure the strength of interplay among cities, and social network analysis (SNA) based on the ISIM is utilized, which is designed to reveal the network characteristics of economic agglomerations. Finally, we perform a spatial panel data analysis to comprehensively interpret the influences of regional economic agglomerations. The results indicate that from 2001 to 2016, the economy in Guangdong showed a double-core/peripheral pattern of convergence, with strengthened intercity interactions. The strength and external spillover effects of Guangzhou and Shenzhen enhanced, while Foshan and Dongguan had relatively strong absorptive abilities. Moreover, expanding regional communication and cooperation is key to enhancing vigorous economic agglomerations and regional network ties in Guangdong by spatial panel data analysis. Our results show that this is a suitable method of reflecting regional economic agglomeration process and its spatiotemporal pattern.
Rui Jin; Jianya Gong; Min Deng; Yiliang Wan; Xuexi Yang. A Framework for Spatiotemporal Analysis of Regional Economic Agglomeration Patterns. Sustainability 2018, 10, 2800 .
AMA StyleRui Jin, Jianya Gong, Min Deng, Yiliang Wan, Xuexi Yang. A Framework for Spatiotemporal Analysis of Regional Economic Agglomeration Patterns. Sustainability. 2018; 10 (8):2800.
Chicago/Turabian StyleRui Jin; Jianya Gong; Min Deng; Yiliang Wan; Xuexi Yang. 2018. "A Framework for Spatiotemporal Analysis of Regional Economic Agglomeration Patterns." Sustainability 10, no. 8: 2800.
In recent decades, land cover change detection (LCCD) using very high-spatial resolution (VHR) remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM) post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing) techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution) which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution) and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution), respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors’ knowledge, this type of post-processing framework is first proposed here for the field of LCCD using VHR remote sensing images.
Zhiyong Lv; Tongfei Liu; Yiliang Wan; Jón Atli Benediktsson; Xiaokang Zhang. Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images. Remote Sensing 2018, 10, 472 .
AMA StyleZhiyong Lv, Tongfei Liu, Yiliang Wan, Jón Atli Benediktsson, Xiaokang Zhang. Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images. Remote Sensing. 2018; 10 (3):472.
Chicago/Turabian StyleZhiyong Lv; Tongfei Liu; Yiliang Wan; Jón Atli Benediktsson; Xiaokang Zhang. 2018. "Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images." Remote Sensing 10, no. 3: 472.
Accurate mapping of wind ventilation in an urban environment is challenging when large spatial coverage is required. This study has developed a GIS-based model for estimating the frontal area index (FAI) of buildings, infrastructure, and trees using very high resolution airborne light detection and ranging (LiDAR) data, which can also be used to investigate the “wall effect” caused by high-rise buildings at a finer spatial scale along the coasts in the Kowloon Peninsula of Hong Kong. New algorithms were created by improving previous algorithms utilizing airborne LiDAR data in raster unit, as well as considering the backward flow coefficient between windward and leeward buildings. The ventilation corridors estimated by FAI and least cost path (LCP) analysis were analyzed. The optimal ventilation corridors passing through the Kowloon peninsula were observed in the east-west and west-east directions. In addition, these ventilation paths were validated with a computer fluid dynamics (CFD) model i.e. Airflow Analysis in ESRI. The newly developed model calculates finer FAI with greater accuracy when compared with vector-based building polygons. This model further depicts buildings, infrastructure, and trees which are considered as obstacles to wind ventilation. The results can be used by environmental and planning authorities to identify ventilation corridors, and for scenario analysis in urban redevelopment.Department of Land Surveying and Geo-Informatic
Fen Peng; Man Sing Wong; Yiliang Wan; Janet E. Nichol. Modeling of urban wind ventilation using high resolution airborne LiDAR data. Computers, Environment and Urban Systems 2017, 64, 81 -90.
AMA StyleFen Peng, Man Sing Wong, Yiliang Wan, Janet E. Nichol. Modeling of urban wind ventilation using high resolution airborne LiDAR data. Computers, Environment and Urban Systems. 2017; 64 ():81-90.
Chicago/Turabian StyleFen Peng; Man Sing Wong; Yiliang Wan; Janet E. Nichol. 2017. "Modeling of urban wind ventilation using high resolution airborne LiDAR data." Computers, Environment and Urban Systems 64, no. : 81-90.
The extraction of road centerline from the classified image is a fundamental image analysis technology. Common problems encountered in road centerline extraction include low ability for coping with the general case, production of undesired objects, and inefficiency. To tackle these limitations, this paper presents a novel accurate centerline extraction method using Gaussian mixture model (GMM) and subspace constraint mean shift (SCMS). The proposed method consists of three main steps. GMM is first used to partition the classified image into several clusters. The major axis of the ellipsoid of each cluster is extracted and deemed to be taken as the initial centerline. Finally, the initial result is adjusted using SCMS to produce precise road centerline. Both simulated and real datasets are used to validate the proposed method. Preliminary results demonstrate that the proposed method provides a comparatively robust solution for accurate centerline extraction from a classified image.
Zelang Miao; Bin Wang; Wenzhong Shi; Hao Wu; Yiliang Wan. Use of GMM and SCMS for Accurate Road Centerline Extraction from the Classified Image. Journal of Sensors 2015, 2015, 1 -13.
AMA StyleZelang Miao, Bin Wang, Wenzhong Shi, Hao Wu, Yiliang Wan. Use of GMM and SCMS for Accurate Road Centerline Extraction from the Classified Image. Journal of Sensors. 2015; 2015 ():1-13.
Chicago/Turabian StyleZelang Miao; Bin Wang; Wenzhong Shi; Hao Wu; Yiliang Wan. 2015. "Use of GMM and SCMS for Accurate Road Centerline Extraction from the Classified Image." Journal of Sensors 2015, no. : 1-13.
The quality aspects of spatial data are very important in the decision-making process. However, the quality inspection of spatial data is still dependent on manual checking, and there is an urgent need to develop an automatic or semi-automatic generic system for spatial data quality inspection. In this paper, we present a general framework that automatically copes with spatial data quality inspection based on various spatial data quality standards and specifications. The framework involves all descriptions of given spatial data, a data quality model characterized by quality elements, scheme batch checking and spatial data quality assessment based on quality control and assessment procedures. It is implemented in Unified Modeling Language with four main sets of classes: data dictionary, quality model, scheme checking and quality assessment. Accordingly, we have designed four structured Extensible Markup Language files for the framework to organize and describe the data dictionary, quality model, scheme check and quality assessment. It is very easy for users to describe the data requirements using the data dictionary file, and to extend the quality elements or check rules using the quality model file. Users can design the specified checks and quality assessment schemes without coding by configuring the scheme check files and quality assessment scheme files. The framework also incorporates a checking tool capable of solving the difficulties inherent in the diversity of spatial data quality standards and specifications. The proposed framework and its implementation, as a quality inspection system, will facilitate automatic multiple spatial data quality inspection and acceptance. As a result, the quality of diversified spatial data can be ensured and improved, which is extremely important in the era of spatial big data.
Yiliang Wan; Wenzhong Shi; Lipeng Gao; Pengfei Chen; Yong Hua. A general framework for spatial data inspection and assessment. Earth Science Informatics 2015, 8, 919 -935.
AMA StyleYiliang Wan, Wenzhong Shi, Lipeng Gao, Pengfei Chen, Yong Hua. A general framework for spatial data inspection and assessment. Earth Science Informatics. 2015; 8 (4):919-935.
Chicago/Turabian StyleYiliang Wan; Wenzhong Shi; Lipeng Gao; Pengfei Chen; Yong Hua. 2015. "A general framework for spatial data inspection and assessment." Earth Science Informatics 8, no. 4: 919-935.
Standard deviation of points is regarded as an effective precision indicator and has been used widely for over 100 years. However, to date, no standard deviation for line objects exists, despite lines being the most fundamental geometric objects in geographic information science. This paper proposes a new theory: the measurement of random line precision using standard deviation. The new theory involves: (1) standard deviation presented graphically, in a band-shape: termed the standard deviation band; (2) the rigorous derivation of analytical equations for the standard deviation band; (3) the probability that a line falls within the standard deviation band. The main contributions of this research include: (1) the derivation of an analytical equation of the standard deviation band; (2) a method to estimate the probability of a line falling within a standard deviation band. These contributions form a foundation for the proposition of further control measures for spatial data quality.
Wenzhong Shi; Yangsheng You; Pan Shao; Yiliang Wan. Standard deviation of line objects in geographic information science. Annals of GIS 2014, 20, 39 -51.
AMA StyleWenzhong Shi, Yangsheng You, Pan Shao, Yiliang Wan. Standard deviation of line objects in geographic information science. Annals of GIS. 2014; 20 (1):39-51.
Chicago/Turabian StyleWenzhong Shi; Yangsheng You; Pan Shao; Yiliang Wan. 2014. "Standard deviation of line objects in geographic information science." Annals of GIS 20, no. 1: 39-51.
This paper presents a geographic information systems (GIS) model to relate biogenic volatile organic compounds (BVOCs) isoprene emissions to ecosystem type, as well as environmental drivers such as light intensity, temperature, landscape factor and foliar density. Data and techniques have recently become available which can permit new improved estimates of isoprene emissions over Hong Kong. The techniques are based on Guenther et al., 1993, Guenther et al., 1999 model. The spatially detailed mapping of isoprene emissions over Hong Kong at a resolution of 100 m and a database has been constructed for retrieval of the isoprene maps from February 2007 to January 2008. This approach assigns emission rates directly to ecosystem types not to individual species, since unlike in temperate regions where one or two single species may dominate over large regions, Hong Kong's vegetation is extremely diverse with up to 300 different species in 1 ha. Field measurements of emissions by canister sampling obtained a range of ambient emissions according to different climatic conditions for Hong Kong's main ecosystem types in both urban and rural areas, and these were used for model validation. Results show the model-derived isoprene flux to have high to moderate correlations with field observations (i.e. r2 = 0.77, r2 = 0.63, r2 = 0.37 for all 24 field measurements, subset for summer, and winter data, respectively) which indicate the robustness of the approach when applied to tropical forests at detailed level, as well as the promising role of remote sensing in isoprene mapping. The GIS model and raster database provide a simple and low cost estimation of the BVOC isoprene in Hong Kong at detailed level. City planners and environmental authorities may use the derived models for estimating isoprene transportation, and its interaction with anthropogenic pollutants in urban areas.
Man Sing Wong; Latifur Rahman Sarker; Janet Nichol; Shun-Cheng Lee; Hongwei Chen; Yiliang Wan; P.W. Chan. Modeling BVOC isoprene emissions based on a GIS and remote sensing database. International Journal of Applied Earth Observation and Geoinformation 2012, 21, 66 -77.
AMA StyleMan Sing Wong, Latifur Rahman Sarker, Janet Nichol, Shun-Cheng Lee, Hongwei Chen, Yiliang Wan, P.W. Chan. Modeling BVOC isoprene emissions based on a GIS and remote sensing database. International Journal of Applied Earth Observation and Geoinformation. 2012; 21 ():66-77.
Chicago/Turabian StyleMan Sing Wong; Latifur Rahman Sarker; Janet Nichol; Shun-Cheng Lee; Hongwei Chen; Yiliang Wan; P.W. Chan. 2012. "Modeling BVOC isoprene emissions based on a GIS and remote sensing database." International Journal of Applied Earth Observation and Geoinformation 21, no. : 66-77.