Validating models of one-way land change: an example case of forest insect disturbance

Context Validation of models of Land Use and Cover Change often involves comparing maps of simulated and reference change. The interpretation of differences between simulated and reference change depends on the characteristics of the process being studied. Our paper focuses on validation of models o...

Full description

Saved in:
Bibliographic Details
Published in:Landscape ecology 2021-10, Vol.36 (10), p.2919-2935
Main Authors: Harati, Saeed, Perez, Liliana, Molowny-Horas, Roberto, Pontius, Robert Gilmore
Format: Article
Language:English
Subjects:
Biomedical and Life Sciences
Councils
Ecology
Environmental Management
False alarms
Insects
Land use
Landscape Ecology
Landscape/Regional and Urban Planning
Life Sciences
Nature Conservation
Remote sensing
Research Article
Simulation
Spatial data
Spatial distribution
Sustainable Development
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Context Validation of models of Land Use and Cover Change often involves comparing maps of simulated and reference change. The interpretation of differences between simulated and reference change depends on the characteristics of the process being studied. Our paper focuses on validation of models of one-way land change processes that spread in space. Objectives Our objective is to develop a method for validation of one-way land change models, such that the method provides objective information about the spatial distribution of errors. Methods Using distance analysis on reference data, we build a baseline model for comparison with simulations. We then simultaneously compare the four maps of reference at initial time, reference at final time, simulation at final time, and baseline at final time. We also use Total Operating Characteristic curves and multiple-resolution map comparison. We illustrate the methods with a simulation of forest insect infestations. Results The methods give insights concerning the reference data and the spatial distribution of misses, hits, and false alarms with respect to initial points of infestations. The new methods reveal that the simulations underestimated change near initial points of spread. Conclusions The spatial distribution of errors is a topic of land change models that deserves attention. For models of one-way, geographically-spreading processes, we recommend that validation should distinguish between near and far allocation errors with respect to initial points of spread.
ISSN:0921-2973
1572-9761
DOI:10.1007/s10980-021-01272-0