Improving image derived vegetation maps with regression based distribution modeling

S. Z. Dobrowski, J. A. Greenberg, C. M. Ramirez, S. L. Ustin

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Incorporating ecological information into image-based vegetation mapping remains a challenge. Much attention has been placed on the use of ancillary information layers in image classification (e.g. slope, aspect, elevation) in that they provide indirect links to information that is ecologically relevant to species distributions. The objective of this study was to assess the utility of incorporating regression-based distribution model surfaces with image classification results using a consensus theoretic approach. We used spatially explicit non-parametric regression modeling in order to incorporate ancillary information in the production of an existing vegetation map for the Lake Tahoe basin. Probability surfaces for 19 prevalent species or genera were produced using generalized additive modeling (GAM). Models were fit to plot data obtained from multiple resource agencies using land-form based explanatory variables derived from a digital elevation model. Model evaluation was assessed by examining species response curves and through cross-validation, resulting in a range of accuracies for individual species (ROC values from 0.58 to 0.85). Probability surfaces for the study area were subsequently generated within a GIS. These surfaces were spatially re-sampled and used in conjunction with IKONOS imagery for use in vegetation mapping. The GAM surfaces were combined with maximum likelihood image classification results using consensus theory and a simple iterative weighting scheme. Results from the analysis demonstrate that the inclusion of the GAM surfaces improved individual class accuracies and suggests the need for implementing standardized and objective species modeling techniques for improving vegetation maps.

Original languageEnglish
Pages (from-to)126-142
Number of pages17
JournalEcological Modelling
Issue number1-2
StatePublished - Feb 15 2006


We would like to thank the Tahoe Regional Planning Agency and the USDA Forest Service for support of this project. We would particularly like to thank Hugh Safford, Mike Volmer, Shane Romsos, and Marchelle Munnecke for their help and feedback. We also thank our field crew, Jennifer Buck and Upekala Wijayratne. Lastly, we acknowledge the comments provided by Janet Franklin and an anonymous reviewer that greatly improved this manuscript.

FundersFunder number
Tahoe Regional Planning Agency
U.S. Forest Service-Retired


    • Ancillary data
    • Consensus theory
    • Distribution modeling
    • Generalized additive model (GAM)
    • IKONOS
    • Lake Tahoe
    • Multi-source
    • Species mapping
    • Species prediction
    • Vegetation mapping


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