Peer Reviewed Articles
41 Aquatic Macrophyte Modeling Using GIS and Logistic Multiple
Regression
Sunil Narumalani, John R. Jensen, Shan Burkhalter, John D. Althausen, and
Halkard E. Mackey, Jr.
Abstract
Download
Full Article
Focuses on developing a predictive model, based on several biophysical
variables, to determine the future distribution of aquatic macrophytes. Par
Pond, a cooling reservoir at the Savannah River Site in South Carolina, was
selected as the study area. Four biophysical variables, including water depth,
percent slope, fetch, and soils, were digitized into a geographic information
system (GIS) database. A logistic multiple regression (LMR) model was developed
to derive coefficients for each variable. The model was applied to seven water
depths ranging from the 181-foot contour to the 200-foot contour at Par Pond
to determine the probability of aquatic macrophyte occurrence at each water
level. Application of the LMR model showed that the total area of wetland would
decline by nearly 114 ha between the 200- and 181-foot contours. The modeling
techniques described here are useful for predicting areas of acquatic macrophyte
growth and distribution, and can be used by environmental scientists to develop
effective management strategies.
51 Forest Ecosystem Modeling in the Russian Far East Using
Vegetation and Land-Cover Regions Identified by Classification of GVI
Greg G. Gaston, Peggy M. Bradley, Ted S. Vinson, and Tatayana P. Kilchugina
Abstract
Download
Full Article
Vegetation and landcover regions identified through unsupervised classification
of Global Vegetation Index (GVI) data provide an appropriate ecosystem and
species description for model input parameters. The timing and magnitude of
photosynthesis as indicated by NDVI observed from four year average monthly
GVI composites were used to identify 42 distinct regions of the former Soviet
Union (FSU). The image classes provide a consistent framework of vegetation
and land- cover information across the FSU. Qualitative comparison on a pixel-by-pixel
basis with detailed topographic maps and other data showed that, in general,
despite the widely acknowledged problems with GVI, surface conditions were
well identified by the GVI classification. The image class descriptions for
the continental scale analysis required a supplemental description of the species
specific to regional ecosystems before they could be used as a forest ecosystem
model input parameter. Model predictions for carbon pools in test sites located
in the Amur region of Russia compared well with carbon estimates made using
other techniques.
59 Landscape Cover-Type Modeling Using a
Multi-Scene Thematic Mapper Mosaic
Collin G. Homer, R. Douglas Ramsey, Thomas C. Edwards, Jr., and Allan
Falconer
Abstract
Download
Full Article
Landscape ecological applications of remotely sensed data are needed
over increasingly larger areas and at finer spatial scales. Within the framework
of the National Biological Service Gap Analysis program, 36 Utah cover types
were modeled from a state- wide Landsat TM mosaic created from 24 scenes at
30-metre resolution (219 883 sq km). The state was subset into three ecoregions
for classification, with cover-type association to spectral classes defined
using a two-step modeling approach. Steps included post-classification correlation
of 1758 state-wide field training sites to spectral classes, and post-classification
ancillary GIS modeling using ecological parameters of elevation, slope, aspect,
and location to further refine spectral classes representing multiple cover
types. Thirty-four of 36 cover- type classes were totally or partially identified
using digital modeling, with five of 36 classes requiring both digital and
analog methods. This methodology provides a framework to optimize landscape
remote sensing cover-type modeling using a multiple scene mosaic.
69 Obtaining spatial and temporal vegetation data
from Landsat MSS and AVHRR/NOAA satellite images for a hydrologic model
Yin Zhangshi, T. H. L. Williams
Abstract
Download
Full Article
Spatial vegetation data were obtained by classifying Landsat Multispectral
Scanner (MS) images into vegetation types. Temporal vegetation data were obtained
by a series of Normalized Difference Vegetation Index (NDVI) images from AVHRR/NOAA
satellite images. An empirical vegetation model was developed to relate vegetation
parameter Leaf Area Index (LAI) to the NDVI data. The obtained spatial and
temporal vegetation data were used in a hydrologic model to to model hydrologic
processes of the Mud Creek watershed in south-central Oklahoma. The research
results show that the vegetation data obtained from the satellite imagery are
more realistic than those obtained from a crop growth model. The accuracy of
modeled monthly and annual runoff using vegetation data from the satellite
images is improved by about 13 and 5%, respectively, compared with the hydrology
using the crop growth model.
79 Supervised Classification of Landsat Thematic Mapper Imagery
in a Semi-Arid Rangeland by Nonparametric Discriminant Analysis
Steven T. Knick, John T. Rotenberry, and Thomas J. Zarriello
Abstract
Download
Full Article
Uses a nonparametric discriminant function in a supervised classification
of Landsat Thematic Mapper satellite imagery of a similar equals 240 000-ha
semi-arid region in the Snake River Plains, southwestern Idaho. First, agriculture
pixels were classified by distance from the soil baseline and water pixels
by the thermal band value. Next, successive nonparametric discriminant functions
were used to separate grassland and shrubland categories with subsequent classifications
of vegetation within major classes. Accuracy in separating grasslands and shrublands
was 80 percent and remained consistent relative to different thresholds in
minimum percent ground cover defining shrublands. Within major grassland and
shrubland groups, the authors achieved 64 percent accuracy in separating dominant
vegetation classes. Distinction between density categories of vegetation based
on percent ground cover was not possible in the study.
87 Using Thematic Mapper Data to Identify Contrasting Soil
Plains and Tillage Practices
A.P. van Deventer, A.D. Ward, P.H. Gowda, and J.
G. Lyon
Abstract
Download
Full Article
Landsat-5 TM data from 11 May 1990 for Seneca County, Ohio were used
to develop TM-based probability models for classifying agricultural management
practices and soil properties. Both soil plain and tillage logistic regression
models classified 89% of the fields correctly. Simple ratio and normalized
differences of TM bands 5 and 7 proved most useful for classifying tillage
practices. TM bands 1, 2, 3, and 4 were found useful for identifying soil plain.
Spectral differences were attributed to soil color differences between lake
and till plain soils and surface residue differences between lake and till
plain soils and surface residue differences between conservation and conventional
tillage. The developed models were tested with independent data from 15 additional
fields and classified 88% of the soil plain and 93% of the tillage attributes
correctly. Using TM data to identify drainage practices, organic mater content,
and soil texture was generally inadequate for scientific purposes.
| Top | Home |