Peer-Reviewed Articles
1345 Choosing an Appropriate Spatial Resolution for Remote Sensing
Investigations
Peter M. Atkinson and Paul J. Curran
Abstract
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Choosing rationally the spatial resolution for remote sensing requires a formal relation between the size of support and some measure of the information content. The local variance in the image has been used to help choose an appropriate spatial resolution. Here we choose spatial resolutions to map continuous variation in properties, such as biomass, using the variogram. The experimental variogram can be separated into components of underlying spatially dependent variation and measurement error. The spatially dependent component can be deregularized to a punctual support, and then regularized to any spatial resolution. The regularized variogram summarizes the information attainable by imaging at that spatial resolution because information exists in the relations between observations only. The investigator con use it to select a combination of spatial resolution and method of analysis for a given investigation. Two examples demonstrate the method.
1353 Spectral Analysis for Articulating Scenic Color Changes in a
Coniferous Landscape
Gary R. Clay and Stuart E. Marsh
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Scenic resources represent a significant economic gain with
regard to regional recreation and tourism. Measuring their
scope and spatial distribution, however, has proved challenging because scenic amenities relate to both the physical environment
and the responses of people interacting with those
settings. The reported research addressed the documentation
of scenic resources, and presented an integrated approach
toward (1) the acquisition and processing of color/change relationships from scanned ground-based photographs. and (2)
the creation of computer simulations using the above photographs
to illustrate the color shifts measured during image
processing. A geographic information system (GIS), the Global
Positioning System (GPS), and image processing technologies
were applied to insure that the simulated environments displayed
high levels of spatial and spectral accuracy. The derived
techniques could ultimately provide managers with a
cost-effective means to assess scenic change, through the use
of indexed color/change data that could be documented, reproduced.
and integrated with other quantitative data.
1363 Detection of Vegetation Changes Associated with Extensive Flooding
in a Forested Ecosystem
William K. Michener and Paula F. Houhoulis
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Monitoring brood-scale ecological responses to disturbance can he facilitated by automated change-detection approaches using remotely sensed data. This study evaluated the effectiveness of five unsupervised change-detection techniques using multispectral, multitemporal SPOT High Resolution
Visible (HRV) data for identifying vegetation responses to extensive flooding of a forested ecosystem associated with Tropical Storm Alberto in July 1994. Standard statistical techniques, logistic multiple regression, and a probability vector model were used to quantitatively and visually assess classification accuracy.
The change-detection techniques were (1) spectral-temporal change classification, (2) temporal change classification based on the Normalized Difference Vegetation index (NDVI), (3) principal components analysis (PCA) of spectral data, (4) PCA of NDVI data, and (5) NDVI image differencing. Spectral-temporal change classification was the least effective of the techniques evaluated. Classification accuracy improved when temporal change classification was based on NDVI data. Both PCA methods were more sensitive to flood-affected Vegetation than the temporal change classifications based on spectral and NDVI data. Vegetation changes were most accurately identified by image differencing of NDVI data. Logistic multiple regression and a probability vector model were especially useful for relating spectral responses to vegetation changes observed during field surveys and identifying areas of agreement and disagreement among the different classification methods.
1375 Emperical Relationships between Structural and Spectral Factors
of Yellowstone Lodgepole Pine Forests
Mark E. Jakubauskas and Kevin P. Price
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Multiple regression analysis was used to examine the relationships between spectral and biotic factors within the lodgepole pine (Pinus contorta var. latifolia) forests of Yellowstone National Park. Field-sampled data on forest overstory and understory conditions were regressed against
Landsat Thematic Mapper (TM] radiance values and transformed TM data for 70 stands. Factors relating to the physical structure of the forest canopy height, basal area, biomass, and leaf area index (LAI)) are best predicted using a combination of visible and middle-infrared Thematic Mapper
bands. Other overstory factors (density, size diversity, mean diameter, and number of overstory species) were not well explained by the TM data or by combinations of TM data with transformed spectral data. Understory factors (number of
seedlings; number of understory species; total cover by forbs, grasses, and shrubs; and total living and nonliving cover)were poorly explained by regression models incorporating spectral and transformed spectral data.
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