Environmental assessment and monitoring with image characterization and modeling system using multiscale remote sensing data

Abstract

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 ICAMS (Image Characterization And Modeling System) 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 the 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 article, we demonstrate the main applications of ICAMS on the Intergraph–MGE platform using Landsat–Thematic Mapper images from the city of Lake Charles, Louisiana. Through the availability of ICAMS to a wider scientific community, we hope to generate various studies so that improved algorithms and more reliable models for environmental assessment and monitoring can be developed. © 1998 John Wiley & Sons, Inc.

Effects of Spatial and Spectral Resolutions on Fractal Dimensions in Forested Landscapes

“…In interpreting these results, it is convenient to make use of previous research on fractal analysis from remote sensing data. It has been demonstrated by numerous investigators [1,6,7,9] that fractal dimensions derived from remotely sensed data fall within a practical range of 2.5 to 3.0, with a value of 2.5 effectively representing a water body and 3.0 corresponding to a three dimensional image. Thus, for the FD to effectively capture the variation in surface complexity of a remotely sensed image, one would like for the computed sample values to freely range within those bounds.…”

“…The visible bands 1, 3, and 3 of MODIS, Landsat TM, and IKONOS respectively have the most similar wavelengths and were used in the analysis of this study. The GIS module ICAMS [9,37] was used to perform the fractal analysis of the remotely sensed data. ICAMS provides the ability to calculate fractal dimensions of remotely sensed images using the isarithm, variogram [3] and triangular prism methods [42].…”

“…Many studies reported in the literature have used multiple resolution remote sensing data to study changes in vegetation indices [30,36], surface complexity and variation [1,[7][8][9]37], classification accuracy and errors [34,35,38], image representation and storage [31], and ecosystem and landscape analysis in general [39,40]. However, much of the previous research has been devoted to exploring the magnitude and impact of scale or resolution effects by aggregation of a single data set (i.e., re-sampling the data) [32].…”

“…Previous work employing spatial statistical techniques, particularly fractal analysis of remote sensing data, have found that the influence of atmospheric effects on the overall statistical analysis results is very little [1,6,9], thus atmospheric correction of the satellite data for the analysis of this study was not deemed necessary.…”

“…One area where fractal analysis has recently been found useful is in characterization of surfaces using remotely sensed data [1,[6][7][8][9]. However, problems arise because the computed fractal dimensions will be affected by the spatial scale of the remotely sensed data as well as the spectral characteristics of the instrument and the bands in which the data are recorded.…”