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Mapping environmental carrying capacity using an artificial neural network: A first experiment
Author(s) -
Lein J. K.
Publication year - 1995
Publication title -
land degradation and development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 81
eISSN - 1099-145X
pISSN - 1085-3278
DOI - 10.1002/ldr.3400060103
Subject(s) - pace , computer science , artificial neural network , carrying capacity , remote sensing , population , environmental resource management , earth observation , satellite , land use , environmental science , artificial intelligence , geography , engineering , civil engineering , ecology , demography , geodesy , aerospace engineering , sociology , biology
The economic development activities of an increasing world population threaten the assimilative capacity of our environment and have stimulated interest in the concept of environmental carrying capacity. While the pace of land transformations has encouraged the refinement of information technologies such as satellite remote sensing to provide a synoptic view of earth‐system processes, the volume of information these systems generate and the high level of expertise required to translate these data retard effective and timely land‐management decision making. This paper introduces a methodology that employs an artificial neural network trained to recognize categories of population support capacity from satellite data acquired from the NOAA‐AVHRR. The network, functioning as an ‘intelligent’ mapping tool, achieved a classification accuracy of 77.5 per cent for the study site and points to the potential role a model of this type may play in land degradation monitoring.