AQUATIC MACROPHYTE MODELING USING GIS AND LOGISTIC MULTIPLE-REGRESSION

Citation
S. Narumalani et al., AQUATIC MACROPHYTE MODELING USING GIS AND LOGISTIC MULTIPLE-REGRESSION, Photogrammetric engineering and remote sensing, 63(1), 1997, pp. 41-49
Citations number
50
Categorie Soggetti
Geosciences, Interdisciplinary",Geografhy,"Photographic Tecnology","Remote Sensing
Journal title
Photogrammetric engineering and remote sensing
ISSN journal
00991112 → ACNP
Volume
63
Issue
1
Year of publication
1997
Pages
41 - 49
Database
ISI
SICI code
Abstract
Aquatic macrophytes are non-woody plants, larger than microscopic size , that grow in water. They are an essential component of wetland commu nities because they provide food and habitat for a variety of wildlife , and they regulate the chemistry of the open water. Unfortunately, th ey also hinder human activities by clogging reservoirs and affecting r ecreational activities. Given their impact on environmental processes as well as on human activities, it is important that aquatic macrophyt es be monitored and managed wisely. This research focuses on developin g a predictive model, based on several biophysical variables, to deter mine the future distribution of aquatic macrophytes. Par Pond, a cooli ng reservoir at the Savannah River Site in South Carolina, was selecte d as the study area. Four biophysical variables, including water depth , percent slope, fetch, and soils, were digitized into ct geographic i nformation system (GIS) database. A logistic multiple regression (LMR) model was developed to derive coefficients for each variable. The mod el 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 a quatic macrophyte occurrence at each water level. Application of the L MR model showed that the total area of wetland would decline by nearly 114 ha between the 200- and 181-foot contours. The modeling technique s described here are useful for predicting areas of aquatic macrophyte growth and distribution, and can be used by environmental scientists to develop effective management strategies.