Automated predictive ecological mapping in a Forest Region of B.C., Canada, 2001–2005

Abstract

Procedures for automated predictive thematic mapping were developed and applied to project areas totaling more than 3 million ha of forested land in British Columbia, Canada. The effective scale of mapping was 1:20,000 using data at a grid resolution of 25 m. The methods can be described as a form of automated feature extraction or object recognition where the objects of interest consist of ecological site types. The methods implement a hybrid of automated, semi-automated and manual procedures that develop and apply heuristic, rule-based conceptual models of ecological-landform relationships. The methods rely heavily upon terrain derivatives extracted from available digital elevation models (DEMs) in addition to satellite imagery and manually digitized maps of ancillary environmental conditions. The primary input has been the BC provincial Terrain Resource Information Management (TRIM) digital elevation model (DEM) surfaced to a regular grid of 25 m. Other input layers include manually interpreted maps of parent material texture, depth and ecological exception classes, manually prepared maps of the spatial distribution of ecological zones of the BC Biogeoclimatic Ecosystem Classification (BEC) system and, to a limited extent, LandSat7 digital satellite imagery. The procedures do not use any field sampling to develop or train classification rules. A knowledge-based approach is used to establish classification rules which are defined and implemented using a Semantic Import (SI) Model implementation of fuzzy logic. All rules are constructed by examining and deconstructing published field guides that define the required ecological output classes and that document the current expert understanding of the conditions and criteria that control the spatial distribution of these desired output classes. An iterative, trial and error, process is used to develop, apply, evaluate and revise object recognition rules that relate ranges of values of key input data layers to an expert-assigned likelihood of occurrence for each ecological class of interest. Local expert knowledge is used at each stage to evaluate each new set of output results and to guide refinement of the fuzzy SI model classification rules. Field sample observations obtained along randomly selected closed traverses were collected following a line intercept approach and used to assess the accuracy of the final predictions of ecological classification. Application of the procedures has progressed from an initial pilot project through projects to evaluate operational scale-up to full-scale commercial application to millions of hectares. Costs have been reduced from a high of $3.50/ha to less than $0.20/ha. Rates of progress increased from 150,000 ha per person year to more than 2.0 million ha per person year. Independent assessments of map accuracy produced results superior to the highest accuracies reported for all alternatives, including traditional manual mapping methods. We conclude that we have formalized and automated many of the concepts and techniques previously used to create thematic maps of ecosystems using manual interpretation of stereo air photos and ancillary data combined with field observations. We have shown that automated feature extraction is able to capture and apply the concepts of landform control referenced by typical landform-based ecological models and classification systems. We have demonstrated that it is possible to produce accurate and cost-effective ecological-landform maps by applying fuzzy and Boolean logic and automated landform analysis procedures to widely available spatial data.