Informatics Publishing Limited

Xuewei Wu ¹, Wenfeng Gong ², Yaming Xu ¹

Affiliations
1 College of Geodesy and Geomatics, Wuhan University, Hubei, CN
2 College of Hydraulic and Electrical Engineering, Heilongjiang University, CN

Abstract

Spatial distribution characteristics and dynamic changes over 21 years in Longfeng wetland in Daqing city, China, were analysed using remote sensing and geographic information systems. Landsat TM images were used as the main data source. Based on geo-information image analysis theory, the spatiotemporal change of land use cover was obtained, and the spatial and temporal evolution mechanisms were revealed. This study provides a method to study the driving forces of urban wetland degeneration, and it could aid in developing protective measures for wetland in rapidly urbanizing regions. The results indicated that the swamp land composed the landscape matrix and exhibited a trend of decreasing area over time, whereas the built-up land exhibited an increasing trend. Cropland and wetland were the main sources of land-use change. The stable model accounted for the largest area, swamp-swamp-swamp was its largest area change mode. The repeated model accounted for the least area, as the cropland-unused land-cropland represented the largest area change in this model. Both natural factors and human activities have made an impact on wetland degeneration, but human activities were the main driving force for the spatiotemporal evolution of land use cover pattern.

Keywords

Urban Wetland, Geo-Information Image, Analysis Theory (GIAT), Urbanization Longfeng Wetland Natural Reserve.

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Wenfeng Gong ¹, Li Yuan ², Wenyi Fan ¹, Xiaofeng Wang ¹, Philip Stott ³

Affiliations
1 College of Hydraulic and Electrical Engineering, Heilongjiang University, Harbin 150086, CN
2 College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, CN
3 School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy 5371, AU

Abstract

Land use cover (LUC) classification is one of the most important applications of optical remotely sensed data, while LUC mapping outcomes are used for global, local mapping, ecosystem assessment and environmental process monitoring. Hence, in this study, in order to evaluate the advantages and drawbacks of supervised classification schemes, the paper chose the optical image data of Landsat 8 OLI in Miyun county to test supervised classification and introduced Parallelepiped Method (PM), Minimum Distance (MD), Maximum Likelihood Classifier (MLC) and Support Vector Machines (SVMs) to improve classification accuracy of LUC mapping and to obtain the reliable LUC distribution. The four classified images reveal that the study area is dominated by considerable areas of forest land, with the overall accuracy found to be 87.89% (kappa = 0.8524) using SVMs, 85.26% (kappa = 0.8205) using MLC, 82.11% (kappa = 0.7813) using MD, and 74.74% (kappa = 0.6920) using PM. Based on the overall accuracy and kappa statistics, SVMs might be the first option in terms of classification accuracy without taking into account of the time costly and standard PC and laptops. MLC was the second accurate model classifiers from the classified image, which was always used to obtain LUC map information for economic potential in time and cost; and PM has shown the lowest overall classification accuracy with greater omission errors and commission errors.

Keywords

Land Use Cover, Image Classification, Support Vector Machines, Maximum Likelihood Classifier.

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Yujuan Zhang ¹, Jianhua Wang ², Wenfeng Gong ³, Weichao Shang ¹

Affiliations
1 Department of Survey Engineering, Heilongjiang Institute of Technology College, Harbin, CN
2 College of Information and Computer Engineering, Harbin Normal University, Harbin, CN
3 College of Hydraulic and Electrical Engineering, Heilongjiang University, Harbin 150086, CN

Abstract

Analyses of land use change (LUC) is fundamental to the understanding of economical development, social contradiction and environmental problems, therefore, in this paper, the Songhua River watershed was selected as a case study area for quantitative evaluation of eco-environment quality. Technologies of RS (remote sensing), GIS (geographic information system), the analytic hierarchy process (AHP) methods, and other statistical methods were employed to implement. The results shown that during the recent decade, more than 87% of the total land area in the study area was dominated by agriculture, forestry, animal husbandry production; the areas of foreland and water body were increased, while others decreased .The eco-environment response to LUC displayed spatial distribution, it can be concluded that the excellent eco-environment quality, with the stable ecosystem, was mainly located in mountain region covering a larger vegetation, the poor and bad level of eco-environment quality associated with plain region that is dominated by the greater extent of human activities. The change of eco-environment quality grades in the study area was mainly associated with LUC. The social economic indicators were not integrated into that ecological model, thereby, in the course of economic development, more attention should be paid to this aspect in future studies in order to better coordinate the relationship between economical development and ecological processes.

Keywords

Eco-Environmental Quality, Dynamic Ecological Pattern, Spatial Distribution, Songhua River.

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