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The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for geospatial applications. Crucial to the advancements highlighted here is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. This editorial first introduces the background and motivation of this special issue followed by an overview of the ten included articles. Conclusion and future research directions are provided in the last section.
Zhenlong Li; Wenwu Tang; Qunying Huang; Eric Shook; Qingfeng Guan. Introduction to Big Data Computing for Geospatial Applications. ISPRS International Journal of Geo-Information 2020, 9, 487 .
AMA StyleZhenlong Li, Wenwu Tang, Qunying Huang, Eric Shook, Qingfeng Guan. Introduction to Big Data Computing for Geospatial Applications. ISPRS International Journal of Geo-Information. 2020; 9 (8):487.
Chicago/Turabian StyleZhenlong Li; Wenwu Tang; Qunying Huang; Eric Shook; Qingfeng Guan. 2020. "Introduction to Big Data Computing for Geospatial Applications." ISPRS International Journal of Geo-Information 9, no. 8: 487.
High performance computing, as an important component of state-of-the-art cyberinfrastructure, has been extensively applied to support geospatial studies facing computational challenges. Investigations on the utility of high performance computing in geospatially related problem-solving and decision-making are timely and important. This chapter is an introduction to geospatial applications of high performance computing reported in this book. Summaries of all other chapters of this book, adapted from contribution from their authors, are highlighted to demonstrate the power of high performance computing in enabling, enhancing, and even transforming geospatial analytics and modeling.
Wenwu Tang; Shaowen Wang. Navigating High Performance Computing for Geospatial Applications. GIS and Environmental Monitoring 2020, 1 -5.
AMA StyleWenwu Tang, Shaowen Wang. Navigating High Performance Computing for Geospatial Applications. GIS and Environmental Monitoring. 2020; ():1-5.
Chicago/Turabian StyleWenwu Tang; Shaowen Wang. 2020. "Navigating High Performance Computing for Geospatial Applications." GIS and Environmental Monitoring , no. : 1-5.
Cartography and geovisualization allow for the abstraction and transformation of spatial information into visual presentations that facilitate our understanding of geographic phenomena of interest. However, cartographic mapping, which are central in cartography and geovisualization, often faces a computational challenge when spatial data become massive or the algorithms that process these data are complicated. In this chapter, I conduct a review to investigate the use of high-performance computing in the domain of cartography and geovisualization. The review focuses on major cartographic mapping steps, including map projection, cartographic generalization, mapping methods, and map rendering. Further, specific challenges facing cartography and geovisualization are discussed by focusing on big data handling and spatiotemporal mapping.
Wenwu Tang. Cartographic Mapping Driven by High-Performance Computing: A Review. GIS and Environmental Monitoring 2020, 159 -172.
AMA StyleWenwu Tang. Cartographic Mapping Driven by High-Performance Computing: A Review. GIS and Environmental Monitoring. 2020; ():159-172.
Chicago/Turabian StyleWenwu Tang. 2020. "Cartographic Mapping Driven by High-Performance Computing: A Review." GIS and Environmental Monitoring , no. : 159-172.
Agent-based models have been increasingly applied to the study of space-time dynamics in real-world systems driven by biophysical and social processes. For the sharing and communication of these models, code reusability and transparency play a pivotal role. In this chapter, we focus on code reusability and transparency of agent-based models from a cyberinfrastructure perspective. We identify challenges of code reusability and transparency in agent-based modeling and suggest how to overcome these challenges. As our findings reveal, while the understanding of and demands for code reuse and transparency are different in various domains, they are inherently related, and they contribute to each step of the agent-based modeling process. While the challenges to code development are daunting, continually evolving cyberinfrastructure-enabled computing technologies such as cloud computing, high-performance computing, and parallel computing tend to lower the computing-level learning curve and, more importantly, facilitate code reuse and transparency of agent-based models.
Wenwu Tang; Volker Grimm; Leigh Tesfatsion; Eric Shook; David Bennett; Li An; Zhaoya Gong; Xinyue Ye. Code Reusability and Transparency of Agent-Based Modeling: A Review from a Cyberinfrastructure Perspective. GIS and Environmental Monitoring 2020, 115 -134.
AMA StyleWenwu Tang, Volker Grimm, Leigh Tesfatsion, Eric Shook, David Bennett, Li An, Zhaoya Gong, Xinyue Ye. Code Reusability and Transparency of Agent-Based Modeling: A Review from a Cyberinfrastructure Perspective. GIS and Environmental Monitoring. 2020; ():115-134.
Chicago/Turabian StyleWenwu Tang; Volker Grimm; Leigh Tesfatsion; Eric Shook; David Bennett; Li An; Zhaoya Gong; Xinyue Ye. 2020. "Code Reusability and Transparency of Agent-Based Modeling: A Review from a Cyberinfrastructure Perspective." GIS and Environmental Monitoring , no. : 115-134.
Settlement models help to understand the social–ecological functioning of landscape and associated land use and land cover change. One of the issues of settlement modeling is that models are typically used to explore the relationship between settlement locations and associated influential factors (e.g., slope and aspect). However, few studies in settlement modeling adopted landscape visibility analysis. Landscape visibility provides useful information for understanding human decision-making associated with the establishment of settlements. In the past years, machine learning algorithms have demonstrated their capabilities in improving the performance of the settlement modeling and particularly capturing the nonlinear relationship between settlement locations and their drivers. However, simulation models using machine learning algorithms in settlement modeling are still not well studied. Moreover, overfitting issues and optimization of model parameters are major challenges for most machine learning algorithms. Therefore, in this study, we sought to pursue two research objectives. First, we aimed to evaluate the contribution of viewsheds and landscape visibility to the simulation modeling of - settlement locations. The second objective is to examine the performance of the machine learning algorithm-based simulation models for settlement location studies. Our study region is located in the metropolitan area of Oyo Empire, Nigeria, West Africa, ca. AD 1570–1830, and its pre-Imperial antecedents, ca. AD 1360–1570. We developed an event-driven spatial simulation model enabled by random forest algorithm to represent dynamics in settlement systems in our study region. Experimental results demonstrate that viewsheds and landscape visibility may offer more insights into unveiling the underlying mechanism that drives settlement locations. Random forest algorithm, as a machine learning algorithm, provide solid support for establishing the relationship between settlement occurrences and their drivers.
Minrui Zheng; Wenwu Tang; Akinwumi Ogundiran; Jianxin Yang. Spatial Simulation Modeling of Settlement Distribution Driven by Random Forest: Consideration of Landscape Visibility. Sustainability 2020, 12, 1 .
AMA StyleMinrui Zheng, Wenwu Tang, Akinwumi Ogundiran, Jianxin Yang. Spatial Simulation Modeling of Settlement Distribution Driven by Random Forest: Consideration of Landscape Visibility. Sustainability. 2020; 12 (11):1.
Chicago/Turabian StyleMinrui Zheng; Wenwu Tang; Akinwumi Ogundiran; Jianxin Yang. 2020. "Spatial Simulation Modeling of Settlement Distribution Driven by Random Forest: Consideration of Landscape Visibility." Sustainability 12, no. 11: 1.
Wenwu Tang; Jianxin Yang. Agent-Based Land Change Modeling of a Large Watershed: Space-Time Locations of Critical Threshold. Journal of Artificial Societies and Social Simulation 2020, 23, 1 .
AMA StyleWenwu Tang, Jianxin Yang. Agent-Based Land Change Modeling of a Large Watershed: Space-Time Locations of Critical Threshold. Journal of Artificial Societies and Social Simulation. 2020; 23 (1):1.
Chicago/Turabian StyleWenwu Tang; Jianxin Yang. 2020. "Agent-Based Land Change Modeling of a Large Watershed: Space-Time Locations of Critical Threshold." Journal of Artificial Societies and Social Simulation 23, no. 1: 1.
The urban growth boundary (UGB) plays an important role in the regulation of urban sprawl and the conservation of natural ecosystems. The delineation of UGBs is a common strategy in urban planning, especially in metropolitan areas undergoing fast expansion. However, reliable tools for the delineation of informed UGBs are still not widely available for planners. In this study, a patch-based cellular automaton (CA) model was applied to build UGBs, in which urban expansions were represented as organic and spontaneous patch growing processes. The proposed CA model enables the modeler to build various spatial and socio-economic scenarios for UGB delineation. Parameters that control the patch size and shape, along with the spatial compactness of an urban growth pattern, were optimized using a genetic algorithm. A random forest model was employed to estimate the probability of urban development. Six scenarios in terms of the demand and the spatial pattern of urban land allocation were constructed to generate UGB alternatives based on the simulated urban land maps from the CA model. Application of the proposed model in Ezhou, China from 2004 to 2030 reveals that the model proposed in this study can help urban planners make informed decisions on the delineation of UGBs under different scenarios.
Jianxin Yang; Jian Gong; Wenwu Tang; Yang Shen; Chunyan Liu; Jing Gao. Delineation of Urban Growth Boundaries Using a Patch-Based Cellular Automata Model under Multiple Spatial and Socio-Economic Scenarios. Sustainability 2019, 11, 6159 .
AMA StyleJianxin Yang, Jian Gong, Wenwu Tang, Yang Shen, Chunyan Liu, Jing Gao. Delineation of Urban Growth Boundaries Using a Patch-Based Cellular Automata Model under Multiple Spatial and Socio-Economic Scenarios. Sustainability. 2019; 11 (21):6159.
Chicago/Turabian StyleJianxin Yang; Jian Gong; Wenwu Tang; Yang Shen; Chunyan Liu; Jing Gao. 2019. "Delineation of Urban Growth Boundaries Using a Patch-Based Cellular Automata Model under Multiple Spatial and Socio-Economic Scenarios." Sustainability 11, no. 21: 6159.
Urban land use change modeling can enhance our understanding of processes and patterns of urban growth that emerge from human-environment interactions. Cellular automata (CA) is a common approach for urban land use change modeling that allows for discovering and analyzing potential urban growth pathways through scenario building. Fundamental components of CA such as neighborhood configuration, transition rules, and representation of geographic entities have been examined in depth in the literature. However, trade-offs in the quantitative composition that urban gains from different non-urban land types and their dynamic feedback with the spatial configuration of urban growth are often ignored. The urban CA model proposed in this study links the quantitative composition with the spatial configuration of urban growth by incorporating a trade-off mechanism that adaptively adjusts the combined suitability of occurrence for non-urban land types based on analysis of transition intensity. Besides, a patch growing module based on seeding and scanning mechanisms is used to simulate the occurrence and spreading of spontaneous urban growth, and a time Monte Carlo (TMC) simulation method is employed to represent uncertainties in the decision-making process of urban development. Application of the model in an ecologically representative city, Ezhou, China, reveals improvement on model performance when feedback between the quantitative composition and spatial configuration of urban growth is incorporated. The averaged figure of merit and K-fuzzy indices are 0.5354 and 0.1954 with respect to cell-level agreement and pattern similarity, indicating the utility and reliability of the proposed model for the simulation of realistic urban growth.
Jianxin Yang; Jian Gong; Wenwu Tang; Cheng Liu. Patch-based cellular automata model of urban growth simulation: Integrating feedback between quantitative composition and spatial configuration. Computers, Environment and Urban Systems 2019, 79, 101402 .
AMA StyleJianxin Yang, Jian Gong, Wenwu Tang, Cheng Liu. Patch-based cellular automata model of urban growth simulation: Integrating feedback between quantitative composition and spatial configuration. Computers, Environment and Urban Systems. 2019; 79 ():101402.
Chicago/Turabian StyleJianxin Yang; Jian Gong; Wenwu Tang; Cheng Liu. 2019. "Patch-based cellular automata model of urban growth simulation: Integrating feedback between quantitative composition and spatial configuration." Computers, Environment and Urban Systems 79, no. : 101402.
Anthropogenic activities often lead to the degradation of valuable natural habitats. Many efforts have been taken to counteract this degradation process, including the mitigation of human-induced stressors. However, knowing-doing gaps exist in stakeholder’s decision-making of prioritizing sites to allocate limited resources in these mitigation activities in both spatially aggregated and cost-effective manner. In this study, we present a spatially explicit prioritization framework that integrates basic cost effectiveness analysis (CEA) and spatial clustering statistics. The advantages of the proposed framework lie in its straightforward logic and ease of implementation to assist stakeholders in the identification of threat mitigation actions that are both spatially clumped and cost-effective using innovative prioritization indicators. We compared the utility of three local autocorrelation-based clustering statistics, including local Moran’s I, Getis-Ord Gi*, and AMOEBA, in quantifying the spatial aggregation of identified sites under given budgets. It is our finding that the CEA method produced threat mitigation sites that are more cost-effective but are dispersed in space. Spatial clustering statistics could help identify spatially aggregated management sites with only minor loss in cost effectiveness. We concluded that integrating basic CEA with spatial clustering statistics provides stakeholders with straightforward and reliable information in prioritizing spatially clustered cost-effective actions for habitat threat mitigation.
Jianxin Yang; Jian Gong; Wenwu Tang. Prioritizing Spatially Aggregated Cost-Effective Sites in Natural Reserves to Mitigate Human-Induced Threats: A Case Study of the Qinghai Plateau, China. Sustainability 2019, 11, 1346 .
AMA StyleJianxin Yang, Jian Gong, Wenwu Tang. Prioritizing Spatially Aggregated Cost-Effective Sites in Natural Reserves to Mitigate Human-Induced Threats: A Case Study of the Qinghai Plateau, China. Sustainability. 2019; 11 (5):1346.
Chicago/Turabian StyleJianxin Yang; Jian Gong; Wenwu Tang. 2019. "Prioritizing Spatially Aggregated Cost-Effective Sites in Natural Reserves to Mitigate Human-Induced Threats: A Case Study of the Qinghai Plateau, China." Sustainability 11, no. 5: 1346.
The study of industrial spatial linkages of urban agglomerations is crucial to recognizing spatial structure and optimizing regional division and cooperation. The existing studies often focus on external spatial interaction at the inter-city level, but few have considered complex internal economic linkages at the inter-sector level. In this study, we established an integrated framework by combining the wave effect gradient field with the gravity model. The wave effect gradient field was used to analyze the inter-sector relation, while the gravity model was adopted to explore the spatial interactions of industry at the inter-city level. The Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR) was taken as a case study, which demonstrates the applicability of the proposed framework. The results indicate that there exists an imbalanced development in the network of industrial linkages in the study region. Each subgroup has presented a self-organized spatial linkage network, but the linkages between subgroups are immature. Compared with other sectors, the high-tech and internet industrial sectors contribute most to economic linkages among cities. Thus, policymakers should take actions to strengthen the inter-subgroup spatial linkages and give priority to the high-tech industries, which is necessary for the integrated and sustainable development of UAMRYR.
Yan Yu; Qianwen Han; Wenwu Tang; Yanbin Yuan; Yan Tong. Exploration of the Industrial Spatial Linkages in Urban Agglomerations: A Case of Urban Agglomeration in the Middle Reaches of the Yangtze River, China. Sustainability 2018, 10, 1469 .
AMA StyleYan Yu, Qianwen Han, Wenwu Tang, Yanbin Yuan, Yan Tong. Exploration of the Industrial Spatial Linkages in Urban Agglomerations: A Case of Urban Agglomeration in the Middle Reaches of the Yangtze River, China. Sustainability. 2018; 10 (5):1469.
Chicago/Turabian StyleYan Yu; Qianwen Han; Wenwu Tang; Yanbin Yuan; Yan Tong. 2018. "Exploration of the Industrial Spatial Linkages in Urban Agglomerations: A Case of Urban Agglomeration in the Middle Reaches of the Yangtze River, China." Sustainability 10, no. 5: 1469.
Urban agglomeration has become the predominant form of urbanization in China. In this process, spatial interaction evidently played a significant role in promoting the collaborative development of these correlated cities. The traditional urban model’s focus on individual cities should be transformed to an urban system model. In this study, a multi-scale simulation model has been proposed to simulate the agglomeration development process of the Wuhan urban agglomeration area by embedding the multi-scale spatial interaction into the transition rule system of cellular automata (CA). A system dynamic model was used to predict the demand for new urban land at an aggregated urban agglomeration area scale. A data field approach was adopted to measuring the interaction of intercity at city scale. Neighborhood interaction was interpreted with a logistic regression method at the land parcel scale. Land use data from 1995, 2005, and 2015 were used to calibrate and evaluate the model. The simulation results show that there has been continuing urban growth in the Wuhan urban agglomeration area from 1995 to 2020. Although extension-sprawl was the predominant pattern of urban spatial expansion, the trend of extensive growth to intensive growth is clear during the entire period. The spatial interaction among these cities has been reinforced, which guided the collaborative development and formed the regional urban system network.
Yan Yu; Jianhua He; Wenwu Tang; Chun Li. Modeling Urban Collaborative Growth Dynamics Using a Multiscale Simulation Model for the Wuhan Urban Agglomeration Area, China. ISPRS International Journal of Geo-Information 2018, 7, 176 .
AMA StyleYan Yu, Jianhua He, Wenwu Tang, Chun Li. Modeling Urban Collaborative Growth Dynamics Using a Multiscale Simulation Model for the Wuhan Urban Agglomeration Area, China. ISPRS International Journal of Geo-Information. 2018; 7 (5):176.
Chicago/Turabian StyleYan Yu; Jianhua He; Wenwu Tang; Chun Li. 2018. "Modeling Urban Collaborative Growth Dynamics Using a Multiscale Simulation Model for the Wuhan Urban Agglomeration Area, China." ISPRS International Journal of Geo-Information 7, no. 5: 176.
Very high resolution digital elevation models (DEM) provide the opportunity to represent the micro-level detail of topographic surfaces, thus increasing the accuracy of the applications that are depending on the topographic data. The analyses of micro-level topographic surfaces are particularly important for a series of geospatially related engineering applications. However, the generation of very high resolution DEM using, for example, LiDAR data is often extremely computationally demanding because of the large volume of data involved. Thus, we use a high-performance and parallel computing approach to resolve this big data-related computational challenge facing the generation of very high resolution DEMs from LiDAR data. This parallel computing approach allows us to generate a fine-resolution DEM from LiDAR data efficiently. We applied this parallel computing approach to derive the DEM in our study area, a bottomland hardwood wetland located in the USDA Forest Service Santee Experimental Forest. Our study demonstrated the feasibility and acceleration performance of the parallel interpolation approach for tackling the big data challenge associated with the generation of very high resolution DEM.
Minrui Zheng; Wenwu Tang; Yu Lan; Xiang Zhao; Meijuan Jia; Craig Allan; Carl Trettin. Parallel Generation of Very High Resolution Digital Elevation Models: High-Performance Computing for Big Spatial Data Analysis. Studies in Big Data 2018, 21 -39.
AMA StyleMinrui Zheng, Wenwu Tang, Yu Lan, Xiang Zhao, Meijuan Jia, Craig Allan, Carl Trettin. Parallel Generation of Very High Resolution Digital Elevation Models: High-Performance Computing for Big Spatial Data Analysis. Studies in Big Data. 2018; ():21-39.
Chicago/Turabian StyleMinrui Zheng; Wenwu Tang; Yu Lan; Xiang Zhao; Meijuan Jia; Craig Allan; Carl Trettin. 2018. "Parallel Generation of Very High Resolution Digital Elevation Models: High-Performance Computing for Big Spatial Data Analysis." Studies in Big Data , no. : 21-39.
The objective of this study is to estimate the biomass and carbon of global-level mangroves as a special type of wetland. Mangrove ecosystems play an important role in regulating carbon cycling, thus having a significant impact on global environmental change. Extensive studies have been conducted for the estimation of mangrove biomass and carbon stock. However, this estimation at a global level has been insufficiently investigated because the spatial scale of interest is large and most existing studies are based on physically challenging fieldwork surveys that are limited to local scales. Over the past few decades, high-resolution geospatial data related to mangroves have been increasingly collected and processed using remote sensing and Geographic Information Systems (GIS) technologies. While these geospatial data create potential for the estimation of mangrove biomass and carbon, the processing and analysis of these data represent a big data-driven challenge. In this study, we present a spatially explicit approach that integrates GIS-based geospatial analysis and high-performance parallel computing for the estimation of mangrove biomass and carbon at the global level. This integrated approach provides support for enabling and accelerating the global-level estimation of mangrove biomass and carbon from existing high-resolution geospatial data. With this integrated approach, the total area, biomass (including above- and below-ground), and associated carbon stock of global mangroves are estimated as 130,420 km2, 1.908 Pg, and 0.725 Pg C for the year of 2000. The averaged aboveground biomass density of global mangroves is estimated as 146.3 Mg ha−1. Our analysis results demonstrate that this integrated geospatial analysis approach is efficacious for the computationally challenging estimation of global mangrove metrics based on high-resolution data. This global-level estimation and associated results are of great assistance for promoting our understanding of complex geospatial dynamics in mangrove forests.
Wenwu Tang; Minrui Zheng; Xiang Zhao; Jiyang Shi; Jianxin Yang; Carl C. Trettin. Big Geospatial Data Analytics for Global Mangrove Biomass and Carbon Estimation. Sustainability 2018, 10, 472 .
AMA StyleWenwu Tang, Minrui Zheng, Xiang Zhao, Jiyang Shi, Jianxin Yang, Carl C. Trettin. Big Geospatial Data Analytics for Global Mangrove Biomass and Carbon Estimation. Sustainability. 2018; 10 (2):472.
Chicago/Turabian StyleWenwu Tang; Minrui Zheng; Xiang Zhao; Jiyang Shi; Jianxin Yang; Carl C. Trettin. 2018. "Big Geospatial Data Analytics for Global Mangrove Biomass and Carbon Estimation." Sustainability 10, no. 2: 472.
Urban agglomeration has become a crucial topic in order for the Chinese government to promote new-type urbanization in China, and its urbanization will greatly affect China’s eco-environment. Existing literature on bidirectional influence between urbanization and eco-environment from the perspective of urban agglomeration is, however, limited. This study establishes a conceptual framework to identify bidirectional relationships between urbanization and eco-environment in urban agglomerations. After evaluating urbanization level and eco-environment quality for each city in an urban agglomeration, this framework determines key interaction factors, and employs a global regression approach to quantify the coercing effects of urbanization on eco-environment and constraining effects of eco-environment on urbanization. Spatial heterogeneity of bidirectional interactions is then examined using local regression, represented by geographically weighted regression. The case study in the urban agglomeration in the middle reaches of the Yangtze River from 2000 to 2015 indicated the existence of bidirectional interactions and coercing threat that was stronger than constraining pressure in this region. The coercion that urbanization posed on the eco-environment began to vary in space significantly from 2010, whereas the constraint of eco-environment on urbanization was spatially stationary. This study will help policy-makers to develop sustainable policies to balance urban development and eco-environment conservation.
Yan Yu; Yan Tong; Wenwu Tang; Yanbin Yuan; Yue Chen. Identifying Spatiotemporal Interactions between Urbanization and Eco-Environment in the Urban Agglomeration in the Middle Reaches of the Yangtze River, China. Sustainability 2018, 10, 290 .
AMA StyleYan Yu, Yan Tong, Wenwu Tang, Yanbin Yuan, Yue Chen. Identifying Spatiotemporal Interactions between Urbanization and Eco-Environment in the Urban Agglomeration in the Middle Reaches of the Yangtze River, China. Sustainability. 2018; 10 (2):290.
Chicago/Turabian StyleYan Yu; Yan Tong; Wenwu Tang; Yanbin Yuan; Yue Chen. 2018. "Identifying Spatiotemporal Interactions between Urbanization and Eco-Environment in the Urban Agglomeration in the Middle Reaches of the Yangtze River, China." Sustainability 10, no. 2: 290.
Fine-resolution remote sensing data have been increasingly available to support the study of land use and land cover change, which couples both human and natural dimensions. However, the classification of fine-resolution remotely sensed data is often highly computationally prohibitive because of, for example, the size of the datasets and the complexity of classification algorithms. To resolve this computational challenge, we present a parallel computing approach for accelerating the land cover classification of fine-resolution remotely sensed data. We developed spatial domain decomposition strategies that divide a computationally demanding task into subtasks that are computationally more efficient. The parallel classification approach allows us to harness cyberinfrastructure-enabled high-performance computing power for the efficient handling of fine-resolution imagery. We applied this parallel computing approach into the object-based land cover classification of 1 m by 1 m aerial imagery in an urban county in North Carolina, the United States. Our experiments indicate that the parallel computing approach substantially accelerates the computationally intensive object-based land cover classification. More importantly, this parallel computing solution provides solid support for facilitating the classification of land cover patterns from remote sensing data at fine spatial resolutions.
W. Tang; W. Feng; M. Zheng; J. Shi. Land Cover Classification of Fine-Resolution Remote Sensing Data. Comprehensive Remote Sensing 2017, 17 -28.
AMA StyleW. Tang, W. Feng, M. Zheng, J. Shi. Land Cover Classification of Fine-Resolution Remote Sensing Data. Comprehensive Remote Sensing. 2017; ():17-28.
Chicago/Turabian StyleW. Tang; W. Feng; M. Zheng; J. Shi. 2017. "Land Cover Classification of Fine-Resolution Remote Sensing Data." Comprehensive Remote Sensing , no. : 17-28.
Alexander Hohl; Minrui Zheng; Wenwu Tang; Eric Delmelle; Irene Casas. Spatiotemporal Point Pattern Analysis Using Ripley's K Function. Geospatial Data Science Techniques and Applications 2017, 155 -176.
AMA StyleAlexander Hohl, Minrui Zheng, Wenwu Tang, Eric Delmelle, Irene Casas. Spatiotemporal Point Pattern Analysis Using Ripley's K Function. Geospatial Data Science Techniques and Applications. 2017; ():155-176.
Chicago/Turabian StyleAlexander Hohl; Minrui Zheng; Wenwu Tang; Eric Delmelle; Irene Casas. 2017. "Spatiotemporal Point Pattern Analysis Using Ripley's K Function." Geospatial Data Science Techniques and Applications , no. : 155-176.
Claudio Owusu; Yu Lan; Minrui Zheng; Wenwu Tang; Eric Delmelle. Geocoding Fundamentals and Associated Challenges. Geospatial Data Science Techniques and Applications 2017, 41 -62.
AMA StyleClaudio Owusu, Yu Lan, Minrui Zheng, Wenwu Tang, Eric Delmelle. Geocoding Fundamentals and Associated Challenges. Geospatial Data Science Techniques and Applications. 2017; ():41-62.
Chicago/Turabian StyleClaudio Owusu; Yu Lan; Minrui Zheng; Wenwu Tang; Eric Delmelle. 2017. "Geocoding Fundamentals and Associated Challenges." Geospatial Data Science Techniques and Applications , no. : 41-62.
Carl C. Trettin; Christina E. Stringer; Stanley J. Zarnoch; Wenwu Tang; Zhaohua Dai. Mangrove carbon stocks in Zambezi River Delta, Mozambique. Forest Service Research Data Archive 2017, 1 .
AMA StyleCarl C. Trettin, Christina E. Stringer, Stanley J. Zarnoch, Wenwu Tang, Zhaohua Dai. Mangrove carbon stocks in Zambezi River Delta, Mozambique. Forest Service Research Data Archive. 2017; ():1.
Chicago/Turabian StyleCarl C. Trettin; Christina E. Stringer; Stanley J. Zarnoch; Wenwu Tang; Zhaohua Dai. 2017. "Mangrove carbon stocks in Zambezi River Delta, Mozambique." Forest Service Research Data Archive , no. : 1.
Claudio Owusu; Yu Lan; Minrui Zheng; Wenwu Tang; Eric Delmelle. Geocoding Fundamentals and Associated Challenges. Geospatial Data Science Techniques and Applications 2017, 41 -62.
AMA StyleClaudio Owusu, Yu Lan, Minrui Zheng, Wenwu Tang, Eric Delmelle. Geocoding Fundamentals and Associated Challenges. Geospatial Data Science Techniques and Applications. 2017; ():41-62.
Chicago/Turabian StyleClaudio Owusu; Yu Lan; Minrui Zheng; Wenwu Tang; Eric Delmelle. 2017. "Geocoding Fundamentals and Associated Challenges." Geospatial Data Science Techniques and Applications , no. : 41-62.
Alexander Hohl; Minrui Zheng; Wenwu Tang; Eric Delmelle; Irene Casas. Spatiotemporal Point Pattern Analysis Using Ripley's K Function. Geospatial Data Science Techniques and Applications 2017, 155 -176.
AMA StyleAlexander Hohl, Minrui Zheng, Wenwu Tang, Eric Delmelle, Irene Casas. Spatiotemporal Point Pattern Analysis Using Ripley's K Function. Geospatial Data Science Techniques and Applications. 2017; ():155-176.
Chicago/Turabian StyleAlexander Hohl; Minrui Zheng; Wenwu Tang; Eric Delmelle; Irene Casas. 2017. "Spatiotemporal Point Pattern Analysis Using Ripley's K Function." Geospatial Data Science Techniques and Applications , no. : 155-176.