Extraction of urban land-use information is base step of urban change detection. However, challenges remain in
automatic delineation of urban areas and differentiation of finer inner-city land cover types. The extraction accuracy of
built-up area is still unsatisfactory. This is mainly due to the heterogeneity nature of urban areas, where continuous and
discrete elements occur side by side. Another reason is the mixed pixel problem, which is particularly serious in an urban
environment. The built-up areas in arid areas may confuse with nearby bare soil and stony desert, which present very
similar spectral characteristics as construction materials such as concrete, while they are often surrounded by farmland.
This study focuses on improving urban land use and land cover classification approach in typical city of China's west
arid areas using multi-sensor data. Pixel-based classification of the NDBI and Maximum Likelihood Classification
(MLC) and object-oriented image classification were used in the study and the classification dataset including Landsat
ETM (1999), CBERS (2005), and Beijing-1 (2006). The accuracy is assessed using high-resolution images, aerial
photograph and field investigation data. The traditional pixel-based classification approach typically yield large
uncertainty in the classification results. Object-oriented processing techniques are becoming more popular compared to
traditional pixel-based image analysis.
This study attempts to analyse the spatial pattern of land cover change trajectories derived through multi-temporal
remote sensing image processing. The study is based on a previous study which utilise the landscape metrics to analyse
the spatio-temporal pattern of farmland change trajectories in an arid environment of western China. The focus of this
paper is on the ephemeral farmlands that were cultivated and abandoned in succession during the study period. The
multi-temporal images were firstly classified independently and farmland change trajectories were established using GIS.
Then the "abandoned farmland" and "ephemeral farmland" trajectories were identified and further classified according to
the change scenarios. The spatial pattern of these ephemeral farmlands were analysed to explore the nature and causes of
the change, particularly the likelihood of farmland abandonment which has been recognised as a major reason for land
degradation of China's aridzone.
In this research, we focus on the spatial pattern of the urban expansion. The spatial pattern of the urban area can be
quantitatively delineated by many spatial variables. Numerous spatial variables have been examined to evaluate their
applicability to the urban change. These metrics include road network accessibility, built-up density and some landscape
metrics. Remote sensing technology was used for monitoring dynamic urban change. Multi-temporal Landsat TM
images (1988, 1991, 1994, 1997, 2000, and 2002) were used for the change detection using post-classification
comparison method. The road network and its change were extracted from multitemporal images using the GDPA
algorithm. Contagion, one of the landscape metrics, was selected, because it it can describe the heterogeneity of the
suburban area, where the landuse change is most likely to happen. Analysis has also been conducted to identify the
relationship between urban change and these spatial variables.
Multi-temporal imagery has been used for landuse and land cover change detection since the very early stage of remote
sensing technology. As large amount of remotely sensed data have been collected, historical land cover changes and
change patterns can be reconstructed by a time series recorded by images. This paper reports a study on the methodology
for quantifying spatial pattern of land cover changes in an arid zone during a 13-year period and the attempts to identify
the key factors for these changes. The approach is based on the post-classification method. Multi-temporal images were
independently classified to establish change trajectories for the farmland land cover type. A set of class-level metrics is
then calculated on the trajectory classes, including Percentage of Landscape (PLAND), Normalized Landscape Shape
Index (NLSI), Interspersion and Juxtaposition Index (IJI) and Area Weighted Fractal Dimension Index (FRAC_AM).
These metrics and their relationship were shown as good indicators on the environmental impact in the fragile ecosystem
due to the rapid expansion of farmland accompanied with the limited water resources. The results show that spatial
pattern metrics of land cover change trajectories provide an effective measurement on landscape changes, which can
further be interpreted for agriculture planning and management.
The diversity of the spatial scale of landscape raises the requirement of multiscale analysis of remote sensing (RS) images. Usually the first step to analyze remote sensing images is image segmentation, in which the muitiscale effect should be taken into account to achieve satisfactory segmentation results. This paper describes an effective approach to segment remote sensing images in multiscale. Based on the fact that in a specific scale of a remote sensing image the same objects are similar, the image is first segmented in a small scale by uniting the most similar objects. After that, a set of multiscale objects with full topological relationship can be obtained. Based on the set of multiscale objects, the authors explore the application of this approach in object-oriented information extraction from remote sensing images.
The unsatisfactory result of traditional pixel-based classification methods in classifying high resolution remotely sensed imagery may be improved by employing image segmentation. Based on a brief review of image segmentation, this paper introduces an image segmentation method--FNEA--which is used in eCognition, the first commercial object-oriented image processing software in the world, for automatic object extraction from high-resolution satellite images and automatic updating of GIS databases. From the point of information extraction, the author analyzes the advantages and disadvantages of the algorithm by using several examples and put forward possible improvements.
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