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This paper couples eco-hydrological modelling with remote sensing, landscape ecological analyses and GIS to develop a series of indicators of water catchment health as part of a geographical audit of environmental health and change at regional scales.
This paper combined land suitability modeling with remote sensing (RS), landscape ecological analysis and geographic information system (GIS) to develop a spatial analyzing system for urban expansion land management.
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Wartenberg et al. (1993) used a GIS to develop an automated method for identifying populations living near high-voltage lines for the purpose of evaluating childhood leukemia and electromagnetic radiation.
The researchers constructed an operable MODFLOW (U.S. Geological Survey, Reston, Virginia, USA) simulation model that accurately reflected hydraulic characteristics of groundwater regime in the study area and used a GIS to develop input variables to the model, including source location of TCE on the RMA site.
Some developers have utilized the graphic user interface (GUI) tool kit of the GIS to develop turnkey applications that launch the model from within the GIS so that it is hidden from the user who simply communicates through a menu.
Technology already exists, such as Geographical Information Systems (GIS), to develop integrated spatial tools to identify and rank PICT regions by likelihood of experiencing ciguatera in the context of climate and environmental change.
We used GIS technology to develop a carbon biomass map of our study area.
A recent report used GIS technology to develop a household-level priority model for childhood lead poisoning in North Carolina (Miranda and Dolinoy 2005).
Thereafter, satellite data and Geographic Information System (GIS) were used to develop a land cover map of the area and to note changes in the landscape over time after implementation of NRMP.
This study aims to develop a GIS-based cellular automata model for exploring the vertical complexities of urban growth.
The main objective of this study is to develop a GIS-based CA model of forest insect infestations that incorporates fuzzy set theory in order to obtain information from high resolution remote sensing (RS) images.
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