The major objective of
this research wasto model the current spatial distribution of: i) Net Primary
Productivity (NPP); ii) Erosion; and iii) Forest fire risk to assess and
understand the environmental risk pattern. These outputs were incorporated and
evaluated within a GIS environment using multi-criteria analysis (MCA) to derive
environmental risk pattern of Seyhan Watershed.
One of the spatial models used in this study is Carnegie Ames Stanford Approach (CASA), developed by NASA and Stanford University. This model is a biogeochemical approach which designed to model annual NPP amounts to predict carbon budgets at regional scale using satellite images and climate data including, air temperature, precipitation and solar radiation. Also, aFire Risk Model is used to derive regional forest fire risk in GIS environment. This model utilises topography-related information and climate data in MCA process. And last, the Revised Universal Soil Loss Equation (RUSLE) is used to predict soil loss potential on a statistical basis.
The current spatial distribution of climate data were generated from 48 climate stations in and around the study area using co-kriging. Environmental risk map was produced using the outputs of NPP, erosion and forest fire risk modelling with MCA and land cover data.
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