Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables

Autor:	Jock A. Blackard, Denis J. Dean
Rok vydání:	1999
Předmět:	Geographic information system Artificial neural network Computer science business.industry Elevation Forestry Statistical model Horticulture Linear discriminant analysis Computer Science Applications Plant cover Cover (algebra) business Agronomy and Crop Science Cartography Spatial analysis
Zdroj:	Computers and Electronics in Agriculture. 24:131-151
ISSN:	0168-1699
DOI:	10.1016/s0168-1699(99)00046-0
Popis:	This study compared two alternative techniques for predicting forest cover types from cartographic variables. The study evaluated four wilderness areas in the Roosevelt National Forest, located in the Front Range of northern Colorado. Cover type data came from US Forest Service inventory information, while the cartographic variables used to predict cover type consisted of elevation, aspect, and other information derived from standard digital spatial data processed in a geographic information system (GIS). The results of the comparison indicated that a feedforward artificial neural network model more accurately predicted forest cover type than did a traditional statistical model based on Gaussian discriminant analysis.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f7bb2fa0bfb8021194f8d2b05b555919 https://doi.org/10.1016/s0168-1699(99)00046-0 Zobrazit plný text záznamu Full Text from ScienceDirect