Nature Environment and Pollution Technology

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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Fengwen Gong ¹, Wenyi Fan ², Li Yuan ³

Affiliations
1 College of Hydraulic and Electrical Engineering, Heilongjiang University, Harbin 150086, CN
2 College of Forestry, Northeast Forestry University, Harbin 150040, CN
3 College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, CN

Abstract

Rapid land use pattern change has taken place in Songhua River basin of old industry base in northeast region of China over the past decades in Harbin region. In this paper, changes in land use pattern in this region were analysed by using Landsat TM data in 1989 and 2007, to quantitatively explore the spatiotemporal LUCC (land use and cover change) characteristics, and based on this information, the regional ecosystem service value was estimated. Cropland and unused land decreased, while built-up land increased greatly. The greatest change rate occurred in water bodies but the least occurred in cropland. The ecosystem service value increased 4.8496×10⁸ yuan, with increasing range of 8.3285%, cropland turned into forestland occurred the greatest positive contribution rate, accounted for 18.9437%, while forestland turned into cropland occurred the greatest negative contribution rate, accounted for 10.2426%. The increase of built-up land impacted the ecosystem service value and ecological environment negatively, and the increase of forestland and water body and the decrease of unused land improved the ecological environment and its ecosystem service values. Those improving the ecological environment were from other types of land use to forestland and water body, however, those worsening the ecological environment were from forest and grassland to cropland and built-up land.

Keywords

Songhua River Basin, Urbanization, Land Use Pattern Change, Ecosystem Service Value, Contribution Rate.

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