Characterising land cover and detecting land-cover changes using spatial methods is an area of research that has been attracting increasing attention recently. We compare performances of selected pattern recognition methods for characterising different land covers using unclassified Landsat Thematic Mapper (TM) data for a lowland site in north-eastern Costa Rica. Two spatial statistics (fractal dimension, using the isarithm and triangular prism surface area (TPSA) methods, and Moran's I index of spatial autocorrelation) and selected landscape indices (Shannon's diversity index, contagion, and fractal dimension from perimeter/area) were investigated. Mean values of each metric for each cover type were calculated for subset areas representing forest, agriculture, pasture, and scrub, for all seven Landsat-TM bands and the Normalized Difference Vegetation Index (NDVI). Fractal dimension ( D TPSA ) and Moran's I were found to be useful for characterising spatial complexity of Landsat-TM data, whereas the stand...

Spatial methods for characterising land cover and detecting land-cover changes for the tropics

Traditional image processing techniques have proven inadequate for urban mapping using high spatial resolution remotesensing images. This study examined and evaluated wavelet transforms for urban texture analysis and image classification using high spatial resolution ATLAS imagery. For the purpose of comparison and to evaluate the effectiveness of the wavelet approaches, two different fractal approaches (isarithm and triangular prism), spatial autocorrelation (Moran’s I and Geary’s C), and spatial co-occurrence matrix of the selected urban classes were examined using 65 � 65, 33 � 33, and 17 � 17 samples with a pixel size of 2.5 m. Results from this study suggest that a multi-band and multi-level wavelet approach can be used to drastically increase the classification accuracy. The fractal techniques did not provide satisfactory classification accuracy. Spatial autocorrelation and spatial co-occurrence techniques were found to be relatively effective when compared to the fractal approaches. It can be concluded that the wavelet transform approach is the most accurate of all four approaches.

/pdf/wavelets-for-urban-spatial-feature-discrimination-3jp93xen4x.pdf

Wavelets for Urban Spatial Feature Discrimination: Comparisons with Fractal, Spatial Autocorrelation, and Spatial Co-Occurrence Approaches

/pdf/a-study-of-lacunarity-based-texture-analysis-approaches-to-33i4hbdr9u.pdf

A study of lacunarity-based texture analysis approaches to improve urban image classification

There has been growing interest in the application of fractal geometry to observe spatial complexity of natural features at different scales. This study utilized three different fractal approaches--isarithm, triangular prism, and variogram--to characterize texture features of urban land-cover classes in high-resolution image data. For comparison purpose and to better evaluate the efficiency of fractal approaches in image classification, spatial autocorrelation techniques (Moran's I and Geary's C ), simple standard deviation, and mean of the selected features were also examined in this study. The discriminant analysis was carried out to discriminate between classes of urban land cover on the basis of texture measures (variables). This study demonstrated that the spatial autocorrelation approach was superior to the fractal approaches. In some cases, simple standard deviation and mean value of the samples gave better accuracy than all or some of the fractal approaches. The results obtained from this analysis...

Fractal approaches in texture analysis and classification of remotely sensed data: Comparisons with spatial autocorrelation techniques and simple descriptive statistics

A comparison of fractal dimension estimators based on multiple surface generation algorithms

With the rapid increase in spatial data, especially in the NASA-EOS (Earth Observing System) era, it is necessary to develop efficient and innovative tools to handle and analyze these data so that environmental conditions can be assessed and monitored. A main difficulty facing geographers and environmental scientists in environmental assessment and measurement is that spatial analytical tools are not easily accessible. We have recently developed a remote sensing/GIS software module called Image Characterization and Modeling System (ICAMS) to provide specialized spatial analytical tools for the measurement and characterization of satellite and other forms of spatial data. ICAMS runs on both the Intergraph-MGE and Arc/info UNIX and Windows-NT platforms. The main techniques in ICAMS include fractal measurement methods, variogram analysis, spatial autocorrelation statistics, textural measures, aggregation techniques, normalized difference vegetation index (NDVI), and delineation of land/water and vegetated/non-vegetated boundaries. In this paper, we demonstrate the main applications of ICAMS on the Intergraph-MGE platform using Landsat Thematic Mapper images from the city of Lake Charles, Louisiana. While the utilities of ICAMS' spatial measurement methods (e.g., fractal indices) in assessing environmental conditions remain to be researched, making the software available to a wider scientific community can permit the techniques in ICAMS to be evaluated and used for a diversity of applications. The findings from these various studies should lead to improved algorithms and more reliable models for environmental assessment and monitoring.

Environmental Assessment and Monitoring with ICAMS (Image Characterization and Modeling System) Using Multiscale Remote-Sensing Data

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Environmental Assessment and Monitoring with ICAMS (Image Characterization and Modeling System) Using Multiscale Remote-Sensing Data