S. Narumalani et al., AQUATIC MACROPHYTE MODELING USING GIS AND LOGISTIC MULTIPLE-REGRESSION, Photogrammetric engineering and remote sensing, 63(1), 1997, pp. 41-49
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.