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INTEGRATING DEMOGRAPHIC AND LANDSAT (TM) DATA AT A WATERSHED SCALE 1
Author(s) -
Kuczenski Tracy K,
Field Donald R.,
Voss Paul R.,
Radeloff Volker C.,
Hagen Alice E.
Publication year - 2000
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.2000.tb04261.x
Subject(s) - thematic mapper , watershed , census , geography , thematic map , land cover , environmental resource management , geographic information system , land use , natural resource management , field (mathematics) , scale (ratio) , landscape epidemiology , ecology , natural resource , remote sensing , data science , cartography , population , landscape assessment , computer science , environmental science , satellite imagery , sociology , landscape design , mathematics , biology , machine learning , demography , pure mathematics
Recurrent calls for integrated resource management urge that an understanding of human activities and populations be incorporated into natural resource research, management, and protection efforts. In this paper, we hypothesize that watersheds can be a valuable geography for organizing an inquiry into the relationship between humans and the environment, and we present a framework for conducting such efforts. The framework is grounded in the emerging field of landscape ecology and incorporates demographic theory and data. Demography has been advanced by technological capabilities associated with the 1990 Census. Employing Geographic Information System (GIS) tools, we couple Landsat Thematic Mapper (TM) land cover data with census‐derived housing density data to demonstrate the operation of our framework and its utility for better understanding human‐landscape interactions. In our investigation of the Kickapoo Watershed and two sub‐ watersheds, located in southwestern Wisconsin, we identify relationships between landscape composition and the distribution and social structure of human populations. Our findings offer insight into the interplay between people and biophysical systems.