Adaptive Classification of Landscape Process and Function: An Integration of Geoinformatics and Self-Organizing Maps

Conference · Fri Jul 17 00:00:00 EDT 2009

OSTI ID:990134

Coleman, Andre M

The advanced geospatial information extraction and analysis capabilities of a Geographic Information System (GISs) and Artificial Neural Networks (ANNs), particularly Self-Organizing Maps (SOMs), provide a topology-preserving means for reducing and understanding complex data relationships in the landscape. The Adaptive Landscape Classification Procedure (ALCP) is presented as an adaptive and evolutionary capability where varying types of data can be assimilated to address different management needs such as hydrologic response, erosion potential, habitat structure, instrumentation placement, and various forecast or what-if scenarios. This paper defines how the evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight into complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Establishing relationships among high-dimensional datasets through neurocomputing based pattern recognition methods can help 1) resolve large volumes of data into a structured and meaningful form; 2) provide an approach for inferring landscape processes in areas that have limited data available but exhibit similar landscape characteristics; and 3) discover the value of individual variables or groups of variables that contribute to specific processes in the landscape. Classification of hydrologic patterns in the landscape is demonstrated.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 990134
• Report Number(s): PNNL-SA-65453; TRN: US201020%%140
• Resource Relation: Conference: Geospatial Crossroads at Geoinformatics Forum 2009, 11-22
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
CLASSIFICATION
COMPUTER NETWORKS
EVALUATION
GEOGRAPHIC INFORMATION SYSTEMS
HABITAT
MANAGEMENT
NEURAL NETWORKS
PATTERN RECOGNITION
GIS
geoinformatics
classification
self-organizing maps
landscape
artificial neural network
evolutionary computing