Analysis of the landscape change at River Basin scale based on SPOT and TM fusion remote sensing images: a case study of the Weigou River Basin on the Chinese Loess Plateau

Abstract

Eco-environmental changes which generally manifest as the ecological landscape changes are mainly affected by natural and human factors. Through complex interaction, natural factors and human activities alter the landscape structure and decide the service function of regional ecosystem. With the development of geographical information system (GIS) and satellite remote sensing (RS) techniques, the researches on quantitative analysis of landscape changes have made great strides forward. In this paper, the landscape change dynamics in the Weigou River basin have been investigated by the combined use of highresolution RS images and GIS techniques. The objectives are to determine the landscape transition rates among landscape types and to quantify changes of various landscape indexes using FRAGSTATS, one of the spatial pattern analysis programs for categorical maps. Under man-machine interactive interpretation method, all of the 10-m resolution SPOT + TM images are classified into six primary types (farmland, forest, grassland, water, construction area and desert) and 12 sub-types based on the interpretation key which was established based on 1947 filed pictures in the Weigou River basin. A field check on the data accuracy shows that the total interpretation accuracy approaches 97.53%. Significant land-use change has taken place in the Weigou River basin over the six years from 1998 to 2004 due to rehabilitation measures. The results show that there has been a notable decrease in farmland mainly due to conversion to forest and grassland, the landscape heterogeneity and evenness has increased, and there is a greater connectivity. The dominance of farmland patch has decreased. And hence the ecological environment has started to develop in a reversing direction. The study of the integration of high-resolution RS images and GIS technique is an effective approach to analyze the landscape changes at river basin scale. Keywords Landscape change Á SPOT + TM image Á Remote sensing and GIS Á FRAGSTATS Á Weigou river basin Abbreviations ha Hectares km Kilometer GIS Geographical information system RS Remote sensing HRV High resolution visible TM Thematic mapper FRAGSTATS Spatial pattern analysis program for categorical maps CA Class area PLAND

Impacts on the Urban Environment: Land Cover Change Trajectories and Landscape Fragmentation in Post-War Western Area, Sierra Leone

“…A collection of classes makes up the landscape. Several studies have explored fragmentation at these scales [22,39,[44][45][46]. Exploring the relationship between LULCC, the drivers of change, and the ecological implications of such changes, Kabba and Li [45] concluded that fragmentation at the class and landscape scales in Wuhan, China was greatest in 1987-1994 but weakened in 1994-2005, leading first to species loss in the fragmented areas followed by a return of species as fragmented habitat areas re-aggregated.…”

“…Exploring the relationship between LULCC, the drivers of change, and the ecological implications of such changes, Kabba and Li [45] concluded that fragmentation at the class and landscape scales in Wuhan, China was greatest in 1987-1994 but weakened in 1994-2005, leading first to species loss in the fragmented areas followed by a return of species as fragmented habitat areas re-aggregated. Analysis of fragmentation metrics (class and landscape scale) in the Weigou River Basin on the Chinese Loess Plateau showed increases in landscape heterogeneity and decreases in agricultural patch dominance [46]. Research by Fava et al [47] demonstrated that ecological processes interact with patterns of LCC and fragmentation, and thereby have implications for the success of restoration initiatives in forested areas.…”

“…Second, to reduce the scene-to-scene image variability, radiance values were converted to surface reflectance [54]. Upscaling and downscaling techniques using nearest neighbour resampling were applied to the original images to match the spatial resolution of SPOT and MSS images to the TM and ETM+ images (30 m) since they constituted the majority of the images [46]. [53].…”

“…Second, to reduce the scene-to-scene image variability, radiance values were converted to surface reflectance [54]. Upscaling and downscaling techniques using nearest neighbour resampling were applied to the original images to match the spatial resolution of SPOT and MSS images to the TM and ETM+ images (30 m) since they constituted the majority of the images [46]. Following radiometric correction, all of the images except the 1976 image were classified into seven land cover classes (Table 2) using a pixel-based, supervised maximum likelihood classification Following radiometric correction, all of the images except the 1976 image were classified into seven land cover classes (Table 2) using a pixel-based, supervised maximum likelihood classification (SMLC) algorithm.…”

Analysis of watershed landscape pattern change based on TM images

“…According to Maximum Likelihood Classifier (MLC) , Support Vector Machines (SVM) classifier , the landscape pattern of watershed is divided into: village and town land, farmland, water, bare land, forest land, grass and shrub land and orchard. According to the research area characteristics and other research [1][2][5][6][7][8][9][10] . Adopted in this paper, patch number and area reflect Landscape composition and the entire surface coverage condition, Shannon 's Diversity Index (abbr.…”