Skip to Main Content
Integrating ecosystem sampling, gradient modeling, remote sensing, and ecosystem simulation to create spatially explicit landscape inventoriesAuthor(s): Robert E. Keane; Matthew G. Rollins; Cecilia H. McNicoll; Russell A. Parsons
Source: RMRS-GTR-92. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 61 p.
Publication Series: General Technical Report (GTR)
Station: Rocky Mountain Research Station
Download Publication (4.0 MB)
DescriptionPresented is a prototype of the Landscape Ecosystem Inventory System (LEIS), a system for creating maps of important landscape characteristics for natural resource planning. This system uses gradient-based field inventories coupled with gradient modeling remote sensing, ecosystem simulation, and statistical analyses to derive spatial data layers required for ecosystem management. Field data were collected in two large (more than 10,000 km2) study areas along important environmental gradients using modified ECODATA methods. A multilevel database was used to derive response variables for predictive landscape mapping from the ECODATA database. Linkage of gradient models with remote sensing allows a standardized, flexible, detailed, and comprehensive classification of landscape characteristics. Over 40 spatially explicit variables were derived for each study area using existing spatial data, satellite imagery, and ecosystem simulation. This spatial database (the LEIS GIS) described landscape-scale indirect, direct, and resource gradients and provided predictor variables for multivariate predictive landscape models. Statistical programs and GIS were used to spatially model several landscape characteristics as a proof of concept for the LEIS. These proof-of-concept products were: (1) basal area, (2) western redcedar habitat, and (3) fuel models. Output maps were between 65 percent and 90 percent accurate when compared to reference data from each study area. Main strengths of the LEIS approach include: (1) a standardized, repeatable approach to sampling and database development for landscape assessment, (2) combining remote sensing, ecosystem simulation, and gradient modeling to create predictive landscape models, (3) flexibility in terms of potential maps generated from LEIS, and (4) the use of direct, resource, and functional gradient analysis for mapping landscape characteristics.
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationKeane, Robert E.; Rollins, Matthew G.; McNicoll, Cecilia H.; Parsons, Russell A. 2002. Integrating ecosystem sampling, gradient modeling, remote sensing, and ecosystem simulation to create spatially explicit landscape inventories. RMRS-GTR-92. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 61 p.
Keywordsgradient modeling, remote sensing, geographic information systems, ecosystem simulation, predictive landscape mapping, ecosystem management
- Cloud-based computation for accelerating vegetation mapping and change detection at regional to national scales
- Predicting the spread of sudden oak death in California: spatial-temporal modeling of susceptible-infectious transitions
- Development and mapping of fuel characteristics and associated fire potentials for South America
XML: View XML