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M. Madden

Publications and source records attributed to M. Madden.

4 recordsLinked to original sources

Effects of spatial resolution ratio in image fusion

In image fusion, the spatial resolution ratio can be defined as the ratio between the spatial resolution of the high-resolution panchromatic image and that of the low-resolution multispectral image. This paper attempts to assess the effects of the spatial resolution ratio of the input images on the quality of the fused image. Experimental results indicate that a spatial resolution ratio of 1:10 or higher is desired for optimal multisensor image fusion provided the input panchromatic image is not downsampled to a coarser resolution. Due to the synthetic pixels generated from resampling, the quality of the fused image decreases as the spatial resolution ratio decreases (e.g. from 1:10 to 1:30). However, even with a spatial resolution ratio as small as 1:30, the quality of the fused image is still better than the original multispectral image alone for feature interpretation. In cases where the spatial resolution ratio is too small (e.g. 1:30), to obtain better spectral integrity of the fused image, one may downsample the input high-resolution panchromatic image to a slightly lower resolution before fusing it with the multispectral image.

International Journal of Remote Sensing

Landscape complexity and soil moisture variation in south Georgia, USA, for remote sensing applications

This research addressed the temporal and spatial variation of soil moisture (SM) in a heterogeneous landscape. The research objective was to investigate soil moisture variation in eight homogeneous 30 by 30 m plots, similar to the pixel size of a Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper plus (ETM+) image. The plots were adjacent to eight stations of an in situ soil moisture network operated by the United States Department of Agriculture-Agriculture Research Service USDA-ARS in Tifton, GA. We also studied five adjacent agricultural fields to examine the effect of different landuses/land covers (LULC) (grass, orchard, peanuts, cotton and bare soil) on the temporal and spatial variation of soil moisture. Soil moisture field data were collected on eight occasions throughout 2005 and January 2006 to establish comparisons within and among eight homogeneous plots. Consistently throughout time, analysis of variance (ANOVA) showed high variation in the soil moisture behavior among the plots and high homogeneity in the soil moisture behavior within them. A precipitation analysis for the eight sampling dates throughout the year 2005 showed similar rainfall conditions for the eight study plots. Therefore, soil moisture variation among locations was explained by in situ local conditions. Temporal stability geostatistical analysis showed that soil moisture has high temporal stability within the small plots and that a single point reading can be used to monitor soil moisture status for the plot within a maximum 3% volume/volume (v/v) soil moisture variation. Similarly, t-statistic analysis showed that soil moisture status in the upper soil layer changes within 24 h. We found statistical differences in the soil moisture between the different LULC in the agricultural fields as well as statistical differences between these fields and the adjacent 30 by 30 m plots. From this analysis, it was demonstrated that spatial proximity is not enough to produce similar soil moisture, since t-test's among adjacent plots with different LULCs showed significant differences. These results confirm that a remote sensing approach that considers homogeneous LULC landscape fragments can be used to identify landscape units of similar soil moisture behavior under heterogeneous landscapes. In addition, the in situ USDA-ARS network will serve better in remote sensing studies in which sensors with fine spatial resolution are evaluated. This study is a first step towards identifying landscape units that can be monitored using the single point reading of the USDA-ARS stations network. ?? 2008 Elsevier B.V.

Journal of Hydrology

FFT-enhanced IHS transform method for fusing high-resolution satellite images

Existing image fusion techniques such as the intensity-hue-saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite images such as Ikonos and QuickBird. One problem is color distortion in the fused image, which causes visual changes as well as spectral differences between the original and fused images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite images. This method combines a standard IHS transform with FFT filtering of both the panchromatic image and the intensity component of the original multispectral image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information. ?? 2006 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

ISPRS Journal of Photogrammetry and Remote Sensing

Vegetation classification for south Florida natural areas

Introduction A critical component of any ecological restoration program is documenting the temporal changes in the spatial extent, pattern, and proportion of plant communities within the landscape. The Comprehensive Everglades Restoration Plan (CERP - www.evergladesplan.org), authorized as part of the Water Resources and Development Act (WRDA) of 2000 (U.S. Congress, 2000), is an $8 billion hydrologic restoration project for all of south Florida. CERP includes 68 separate projects to be managed over the next 30 years by the South Florida Water Management District (SFWMD), the U. S. Army Corp of Engineers (USACE), and other State and Federal agencies. Restoration Coordination and Verification (RECOVER) is a system-wide program of the CERP, designed to organize, manage, and provide the highest quality scientific and technical support during implementation of the restoration program (RECOVER, in prep.). It is the role of RECOVER to develop and implement a system-wide Monitoring and Assessment Plan (MAP) (RECOVER, 2004) and to document how well the CERP is meeting its objectives for ecosystem restoration. One critical component of the MAP is vegetation mapping to document changes in the spatial extent, pattern, and proportion of plant communities within the Everglades landscape. A major aspect of the vegetation mapping project was determining a classification system for labeling vegetation categories utilizing a grid method. The grid method was created specifically for use in the CERP RECOVER vegetation monitoring and assessment project (Rutchey and others, in prep). The CERP RECOVER vegetation mapping project utilizes aerial photography and photointerpretation techniques (with ground truthing) to identify and label vegetation classes. A classification system that had sufficient flexibility and detail to enable the designation of vegetation classes using various remote sensing platforms and identification techniques needed to be developed. The classification system had to be hierarchical, represent distinct ecological communities, individual species, and physical characteristics such as density and height. In addition, it was desirable to have a classification system that allowed exotic species and cattail to be identified using density classes. The classification system was developed specifically for peninsular south Florida and the Florida Keys, from Lake Okeechobee in the north to Key West in the south (Figure 1). Specific areas of interest include Everglades National Park, Big Cypress National Preserve, Biscayne National Park, Florida Panther National Wildlife Refuge, Loxahatchee National Wildlife Refuge, the State of Florida Water Conservation Areas, Holeyland Wildlife Management Area, Rotenberger Wildlife Management Area, J.W. Corbett Wildlife Management Area, Pal-Mar Wildlife Management Area, the Lake Okeechobee Littoral Zones, and additional coastal wetlands of south eastern Miami-Dade County. In addition to being used for mapping of CERP affected areas, the National Park Service-South Florida/Caribbean Network is using the classification for mapping the remaining areas of Everglades National Park and Big Cypress National Preserve outside the CERP footprint, Florida Panther National Wildlife Refuge, Biscayne National Park, and Dry Tortugas National Park.

Florida