Special Issue "Advances and Innovations in Land Use/Cover Mapping"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 October 2016).

Special Issue Editors

Guest Editor
Prof. Qiming Zhou Website E-Mail
Department of Geography, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
Interests: change detection and land cover modelling with remote sensing; digital terrain analysis and hydrological modeling; climate change and its impacts on water resources and ecosystems; aridzone studies
Guest Editor
Prof. Zhilin Li E-Mail
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

Special Issue Information

Dear Colleagues,

With the advancement of geo-spatial information technology, it is now widely recognized that more advanced and innovative approaches and methodologies are urgently needed to take advantages of new technology and data sources to better serve the public and society. Land use/cover has been a focus of geo-spatial information applications, and an enormous amount of work has been reported in the literature. However, the rapid development of new technology, such as big data, and increasing sources of spatial data, such as various types of remotely-sensed data, present a new challenge to the existing practice and methodology for land use/cover mapping.

This Special Issue aims to establish a scholarly exchange platform to publish the newest results in the advancement and innovation in land use/cover mapping. We would like to focus on new ideas, approaches, principles, methodologies, and technologies for a better and innovative representation, analysis, and mapping of the changing land use/cover.

Themes of the Special Issue include, but are not limited to, the following topics:

New approaches in geo-spatial representation;
Innovations in land use/cover mapping;
Big data for land use/cover mapping;
Impact and efficiency analysis of land use/cover maps;
New methods/techniques to represent and map land use/cover change;
High-resolution land use/cover representation and mapping;
Three-dimensional land use/cover representation and mapping;
Urban land use/cover representation and mapping;
Issues and solutions of global land use/cover mapping;
Multi-scale land use/cover representation and mapping;
Real-time land use/cover mapping and updating

Prof. Qiming Zhou
Prof. Zhilin Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (11 papers)

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Research

Open AccessArticle
Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment
ISPRS Int. J. Geo-Inf. 2016, 5(12), 243; https://doi.org/10.3390/ijgi5120243 - 16 Dec 2016
Cited by 6
Abstract
We developed a geographic cellular automata (CA) model based on partial least squares (PLS) regression (termed PLS-CA) to simulate dynamic urban growth in a geographical information systems (GIS) environment. The PLS method extends multiple linear regression models that are used to define the [...] Read more.
We developed a geographic cellular automata (CA) model based on partial least squares (PLS) regression (termed PLS-CA) to simulate dynamic urban growth in a geographical information systems (GIS) environment. The PLS method extends multiple linear regression models that are used to define the unique factors driving urban growth by eliminating multicollinearity among the candidate drivers. The key factors (the spatial variables) extracted are uncorrelated, resulting in effective transition rules for urban growth modeling. The PLS-CA model was applied to simulate the rapid urban growth of Songjiang District, an outer suburb in the Shanghai Municipality of China from 1992 to 2008. Among the three components acquired by PLS, the first two explained more than 95% of the total variance. The results showed that the PLS-CA simulated pattern of urban growth matched the observed pattern with an overall accuracy of 85.8%, as compared with 83.5% of a logistic-regression-based CA model for the same area. The PLS-CA model is readily applicable to simulations of urban growth in other rapidly urbanizing areas to generate realistic land use patterns and project future scenarios. Full article
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
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Open AccessArticle
Spatiotemporal Analysis of Urban Growth Using GIS and Remote Sensing: A Case Study of the Colombo Metropolitan Area, Sri Lanka
ISPRS Int. J. Geo-Inf. 2016, 5(11), 197; https://doi.org/10.3390/ijgi5110197 - 29 Oct 2016
Cited by 17
Abstract
Understanding urban growth spatiotemporally is important for landscape and urban development planning. In this study, we examined the spatiotemporal pattern of urban growth of the Colombo Metropolitan Area (CMA)—Sri Lanka’s only metropolitan area—from 1992 to 2014 using remote sensing data and GIS techniques. [...] Read more.
Understanding urban growth spatiotemporally is important for landscape and urban development planning. In this study, we examined the spatiotemporal pattern of urban growth of the Colombo Metropolitan Area (CMA)—Sri Lanka’s only metropolitan area—from 1992 to 2014 using remote sensing data and GIS techniques. First, we classified three land-use/cover maps of the CMA (i.e., for 1992, 2001, and 2014) using Landsat data. Second, we examined the temporal pattern of urban land changes (ULCs; i.e., land changes from non-built-up to built-up) across two time intervals (1992–2001 and 2001–2014). Third, we examined the spatial pattern of ULCs along the gradients of various driver variables (e.g., distance to roads) and by using spatial metrics. Finally, we predicted the future urban growth of the CMA (2014–2050). Our results revealed that the CMA’s built-up land has increased by 24,711 ha (221%) over the past 22 years (11,165 ha in 1992 to 35,876 ha in 2014), at a rate of 1123 ha per year. The analysis revealed that ULC was more intense or faster during the 2000s (1268 ha per year) than in the 1990s (914 ha per year), coinciding with the trends of population and economic growth. The results also revealed that most of the ULCs in both time intervals occurred in close proximity to roads and schools, while also showing some indications of landscape fragmentation and infill urban development patterns. The ULC modeling revealed that by 2030 and 2050, the CMA’s built-up land will increase to 42,500 ha and 56,000 ha, respectively. Most of these projected gains of built-up land will be along the transport corridors and in proximity to the growth nodes. These findings are important in the context of landscape and urban development planning for the CMA. Overall, this study provides valuable information on the landscape transformation of the CMA, also highlighting some important challenges facing its future sustainable urban development. Full article
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
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Open AccessArticle
Improving Seasonal Land Cover Maps of Poyang Lake Area in China by Taking into Account Logical Transitions
ISPRS Int. J. Geo-Inf. 2016, 5(9), 165; https://doi.org/10.3390/ijgi5090165 - 12 Sep 2016
Cited by 1
Abstract
Land cover maps are fundamental materials for resource management and change detection. Remote sensing technology is crucial for fast mapping with low cost. However, besides the inherent classification errors in the land cover products, numerous illogical transitions exist between the neighboring time points. [...] Read more.
Land cover maps are fundamental materials for resource management and change detection. Remote sensing technology is crucial for fast mapping with low cost. However, besides the inherent classification errors in the land cover products, numerous illogical transitions exist between the neighboring time points. In this study, we introduce a series of logical codes for all the land cover types according to the ecological rules in the study area. The codes represent the transformational logicality of species between different seasons. The classification performance and the codes for all the seasons are imposed on the initial land cover maps which have been produced independently by the conventional hierarchical strategy. We exploit the proposed modified hierarchical mapping strategy to map the land cover of Poyang Lake Basin area, Middle China. The illogical transitions between neighboring seasons and the accuracies based on the labeled samples are calculated for both the initial and modified strategies. The number of illogical pixels have been reduced by 13%–35% for different seasons and the average accuracy has been improved by 9.7% for the specific land cover maps. The accuracy of land cover changes has also presented great improvement of the proposed strategy. The experimental results have suggested the scheme is effective. Full article
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
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Open AccessArticle
Integrating Spatial and Attribute Characteristics of Extended Voronoi Diagrams in Spatial Patterning Research: A Case Study of Wuhan City in China
ISPRS Int. J. Geo-Inf. 2016, 5(7), 120; https://doi.org/10.3390/ijgi5070120 - 15 Jul 2016
Cited by 3
Abstract
Rapid urbanization has caused numerous problems, and the urban spatial structure has been a hot topic in sustainable development management. Urban spatial structure is affected by a series of factors. Thus, the research model should synthetically consider the spatial and non-spatial relationship of [...] Read more.
Rapid urbanization has caused numerous problems, and the urban spatial structure has been a hot topic in sustainable development management. Urban spatial structure is affected by a series of factors. Thus, the research model should synthetically consider the spatial and non-spatial relationship of every element. Here, we propose an extended Voronoi diagram for exploring the urban land spatial pattern. In essence, we first used a principal component analysis method to construct attribute evaluation indicators and obtained the attribute distance for each indicator. Second, we integrated spatial and attribute distances to extend the comparison distance for Voronoi diagrams, and then, we constructed the Voronoi aggregative homogeneous map of the study area. Finally, we make a spatial autocorrelation analysis by using GeoDA and SPSS software. Results show that: (1) the residential land cover aggregation is not significant, but spatial diffusion is obvious; (2) the commercial land cover aggregation is considerable; and (3) the spatial agglomeration degree of the industrial land cover is increased and mainly located in urban fringes. According to the neo-Marxist theory, we briefly analyzed the driving forces for shaping the urban spatial structure. To summarize, our approach yields important insights into the urban spatial structure characterized by attribute similarity with geospatial proximity, which contributes to a better understanding of the urban growth mechanism. In addition, it explicitly identifies ongoing urban transformations, potentially supporting the planning for sustainable urban land use and protection. Full article
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
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Open AccessArticle
Integrating Logistic Regression and Geostatistics for User-Oriented and Uncertainty-Informed Accuracy Characterization in Remotely-Sensed Land Cover Change Information
ISPRS Int. J. Geo-Inf. 2016, 5(7), 113; https://doi.org/10.3390/ijgi5070113 - 14 Jul 2016
Cited by 6
Abstract
Accuracy is increasingly recognized as an important dimension in geospatial information and analyses. A strategy well suited for map users who usually have limited information about map lineages is proposed for location-specific characterization of accuracy in land cover change maps. Logistic regression is [...] Read more.
Accuracy is increasingly recognized as an important dimension in geospatial information and analyses. A strategy well suited for map users who usually have limited information about map lineages is proposed for location-specific characterization of accuracy in land cover change maps. Logistic regression is used to predict the probabilities of correct change categorization based on local patterns of map classes in the focal three by three pixel neighborhood centered at individual pixels being analyzed, while kriging is performed to make corrections to regression predictions based on regression residuals at sample locations. To promote uncertainty-informed accuracy characterization and to facilitate adaptive sampling of validation data, standard errors in both regression predictions and kriging interpolation are quantified to derive error margins in the aforementioned accuracy predictions. It was found that the integration of logistic regression and kriging leads to more accurate predictions of local accuracies through proper handling of spatially-correlated binary data representing pixel-specific (in)correct classifications than kriging or logistic regression alone. Secondly, it was confirmed that pixel-specific class labels, focal dominances and focal class occurrences are significant covariates for regression predictions at individual pixels. Lastly, error measures computed of accuracy predictions can be used for adaptively and progressively locating samples to enhance sampling efficiency and to improve predictions. The proposed methods may be applied for characterizing the local accuracy of categorical maps concerned in spatial applications, either input or output. Full article
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
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Open AccessArticle
A Local Land Use Competition Cellular Automata Model and Its Application
ISPRS Int. J. Geo-Inf. 2016, 5(7), 106; https://doi.org/10.3390/ijgi5070106 - 30 Jun 2016
Cited by 11
Abstract
Cellular automaton (CA) is an important method in land use and cover change studies, however, the majority of research focuses on the discovery of macroscopic factors affecting LUCC, which results in ignoring the local effects within the neighborhoods. This paper introduces a Local [...] Read more.
Cellular automaton (CA) is an important method in land use and cover change studies, however, the majority of research focuses on the discovery of macroscopic factors affecting LUCC, which results in ignoring the local effects within the neighborhoods. This paper introduces a Local Land Use Competition Cellular Automata (LLUC-CA) model, based on local land use competition, land suitability evaluation, demand analysis of the different land use types, and multi-target land use competition allocation algorithm to simulate land use change at a micro level. The model is applied to simulate land use changes at Jinshitan National Tourist Holiday Resort from 1988 to 2012. The results show that the simulation accuracies were 64.46%, 77.21%, 85.30% and 99.14% for the agricultural land, construction land, forestland and water, respectively. In addition, comparing the simulation results of the LLUC-CA and CA-Markov model with the real land use data, their overall spatial accuracies were found to be 88.74% and 86.82%, respectively. In conclusion, the results from this study indicated that the model was an acceptable method for the simulation of large-scale land use changes, and the approach used here is applicable to analyzing the land use change driven forces and assist in decision-making. Full article
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
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Open AccessArticle
Analyzing the Impact of Highways Associated with Farmland Loss under Rapid Urbanization
ISPRS Int. J. Geo-Inf. 2016, 5(6), 94; https://doi.org/10.3390/ijgi5060094 - 15 Jun 2016
Cited by 6
Abstract
Highway construction has accelerated urban growth and induced direct and indirect changes to land use. Although many studies have analyzed the relationship between highway construction and local development, relatively less attention has been paid to clarifying the various impacts of highways associated with [...] Read more.
Highway construction has accelerated urban growth and induced direct and indirect changes to land use. Although many studies have analyzed the relationship between highway construction and local development, relatively less attention has been paid to clarifying the various impacts of highways associated with farmland loss. This paper integrates GIS spatial analysis, remote sensing, buffer analysis and landscape metrics to analyze the landscape pattern change induced by direct and indirect highway impacts. This paper explores the interaction between the impact of highways and farmland loss, using the case of the highly urbanized traffic hubs in eastern China, Hang-Jia-Hu Plain. Our results demonstrate that the Hang-Jia-Hu Plain experienced extensive highway construction during 1990–2010, with a clear acceleration of expressway development since 2000. This unprecedented highway construction has directly fragmented the regional landscape and indirectly disturbed the regional landscape by attracting a large amount of built-up land transition from farmland during the last two decades. In the highway-effect zone, serious farmland loss initially occurred in the urban region and then spread to the rural region. Moreover, we found the discontinuous expansion of built-up land scattered the farmland in the rural region and expressway-effect zone. Furthermore, farmland protection policies in the 1990s had the effect of controlling the total area of farmland loss. However, the cohesive farmland structure was still fragmented by the direct and indirect impacts of highway construction. We suggest that an overall farmland protection system should be established to enhance spatial control and mitigate the adverse impacts caused by highway construction. This work improves the understanding of regional sustainable development, and provides a scientific basis for balanced urban development with farmland protection in decision-making processes. Full article
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
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Open AccessArticle
Delineating Urban Fringe Area by Land Cover Information Entropy—An Empirical Study of Guangzhou-Foshan Metropolitan Area, China
ISPRS Int. J. Geo-Inf. 2016, 5(5), 59; https://doi.org/10.3390/ijgi5050059 - 06 May 2016
Cited by 4
Abstract
Rapid urbanization has caused many environmental problems, such as the heat island effect, intensifying air pollution, pollution from runoff, loss of wildlife habitat, etc. Accurate evaluations of these problems demand an accurate delineation of the spatial extent of the urban fringe. Conceptual and [...] Read more.
Rapid urbanization has caused many environmental problems, such as the heat island effect, intensifying air pollution, pollution from runoff, loss of wildlife habitat, etc. Accurate evaluations of these problems demand an accurate delineation of the spatial extent of the urban fringe. Conceptual and analytical ambiguity of the urban fringe and a general lack of consensus among researchers have made its measurement very difficult. This study reports a compound and reliable method to delineate the urban fringe area using a case study. Based on the 'fringe effect' theory in landscape ecology, the existing land cover information entropy model for defining the urban fringe is renewed by incorporating scale theory, cartography and urban geography theory. Results show that the urban fringe area of Guangzhou and Foshan metropolitan area covers an area of 2031 km2, and it occupies over 31% of the total study area. Result evaluation by industry structure data shows satisfactory correspondence with different land cover types. This paper reports the method and outcome of an attempt to provide an objective, repeatable and generally applicable method for mapping its spatial extent from remote sensing imageries, and could be beneficial to relevant urban studies and urban fringe management projects. Full article
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
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Open AccessArticle
Land Surface Water Mapping Using Multi-Scale Level Sets and a Visual Saliency Model from SAR Images
ISPRS Int. J. Geo-Inf. 2016, 5(5), 58; https://doi.org/10.3390/ijgi5050058 - 05 May 2016
Cited by 2
Abstract
Land surface water mapping is one of the most basic classification tasks to distinguish water bodies from dry land surfaces. In this paper, a water mapping method was proposed based on multi-scale level sets and a visual saliency model (MLSVS), to overcome the [...] Read more.
Land surface water mapping is one of the most basic classification tasks to distinguish water bodies from dry land surfaces. In this paper, a water mapping method was proposed based on multi-scale level sets and a visual saliency model (MLSVS), to overcome the lack of an operational solution for automatically, rapidly and reliably extracting water from large-area and fine spatial resolution Synthetic Aperture Radar (SAR) images. This paper has two main contributions, as follows: (1) The method integrated the advantages of both level sets and the visual saliency model. First, the visual saliency map was applied to detect the suspected water regions (SWR), and then the level set method only needed to be applied to the SWR regions to accurately extract the water bodies, thereby yielding a simultaneous reduction in time cost and increase in accuracy; (2) In order to make the classical Itti model more suitable for extracting water in SAR imagery, an improved texture weighted with the Itti model (TW-Itti) is employed to detect those suspected water regions, which take into account texture features generated by the Gray Level Co-occurrence Matrix (GLCM) algorithm, Furthermore, a novel calculation method for center-surround differences was merged into this model. The proposed method was tested on both Radarsat-2 and TerraSAR-X images, and experiments demonstrated the effectiveness of the proposed method, the overall accuracy of water mapping is 98.48% and the Kappa coefficient is 0.856. Full article
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
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Open AccessArticle
Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran
ISPRS Int. J. Geo-Inf. 2016, 5(5), 57; https://doi.org/10.3390/ijgi5050057 - 28 Apr 2016
Cited by 15
Abstract
The Gorganrood watershed (GW) is experiencing considerable environmental change in the form of natural hazards and erosion, as well as deforestation, cultivation and development activities. As a result of this, different types of Land Cover/Land Use (LCLU) change are taking place on an [...] Read more.
The Gorganrood watershed (GW) is experiencing considerable environmental change in the form of natural hazards and erosion, as well as deforestation, cultivation and development activities. As a result of this, different types of Land Cover/Land Use (LCLU) change are taking place on an intensive level in the area. This research study investigates the LCLU conditions upstream of this watershed for the years 1972, 1986, 2000 and 2014, using Landsat MSS, TM, ETM+ and OLI/TIRS images. LCLU maps for 1972, 1986, and 2000 were produced using pixel-based classification methods. For the 2014 LCLU map, Geographic Object-Based Image Analysis (GEOBIA) in combination with the data-mining capabilities of Gini and J48 machine-learning algorithms were used. The accuracy of the maps was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. The overall accuracy ranged from 89% to 95%, quantity disagreement from 2.1% to 6.6%, and allocation disagreement from 2.1% for 2014 to 2.7% for 2000. The results of this study indicate that a significant amount of change has occurred in the region, and that this has as a consequence affected ecosystem services and human activity. This knowledge of the LCLU status in the area will help managers and decision makers to develop plans and programs aimed at effectively managing the watershed into the future. Full article
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
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Open AccessArticle
Land Cover Extraction from High Resolution ZY-3 Satellite Imagery Using Ontology-Based Method
ISPRS Int. J. Geo-Inf. 2016, 5(3), 31; https://doi.org/10.3390/ijgi5030031 - 08 Mar 2016
Cited by 12
Abstract
The rapid development and increasing availability of high-resolution satellite (HRS) images provides increased opportunities to monitor large scale land cover. However, inefficiency and excessive independence on expert knowledge limits the usage of HRS images on a large scale. As a knowledge organization and [...] Read more.
The rapid development and increasing availability of high-resolution satellite (HRS) images provides increased opportunities to monitor large scale land cover. However, inefficiency and excessive independence on expert knowledge limits the usage of HRS images on a large scale. As a knowledge organization and representation method, ontology can assist in improving the efficiency of automatic or semi-automatic land cover information extraction, especially for HRS images. This paper presents an ontology-based framework that was used to model the land cover extraction knowledge and interpret HRS remote sensing images at the regional level. The land cover ontology structure is explicitly defined, accounting for the spectral, textural, and shape features, and allowing for the automatic interpretation of the extracted results. With the help of regional prototypes for land cover class stored in Web Ontology Language (OWL) file, automated land cover extraction of the study area is then attempted. Experiments are conducted using ZY-3 (Ziyuan-3) imagery, which were acquired for the Jiangxia District, Wuhan, China, in the summers of 2012 and 2013.The experimental method provided good land cover extraction results as the overall accuracy reached 65.07%. Especially for bare surfaces, highways, ponds, and lakes, whose producer and user accuracies were both higher than 75%. The results highlight the capability of the ontology-based method to automatically extract land cover using ZY-3 HRS images. Full article
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
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