Ecotope features must be mapped before they are classified.

Ecotope features are first mapped and then classified, because both feature recognition and mapping are conducted by applying standardized, scale-explicit rules, providing a basis for standardized feature classification.  Features are mapped and classified by image interpretation first, and then by groundtruthing, in feature interpretation & groundtruthing cycles.  Each cycle is just one iteration of the , which must be repeated at least two times and then validated to complete an ecotope map of a sample cell.

1. Image Interpretation

Viewing imagery in a GIS, a trained mapper identifies and classifies features across each sample cell.
1A. Feature mapping
Features are identified in imagery and drawn as polygons into a GIS data layer by a trained mapper using a scale-explicit, rule-based, feature identification strategy.
1B. Feature Classification
Once mapped, each feature is classified by interpretation of imagery and context.

2. Groundtruthing

By visiting all features and confusing areas in the field, a trained mapper checks, corrects, and improves feature mapping and classification across each sample cell aided by imagery and interpreted ecotope feature maps.
2A. Feature mapping
Feature identification and edge mapping is checked and corrected in concordance with scale rules.
2B. Feature classification
Feature classification in the field is generally more reliable than that from interpretation, and many classes can only be identified by groundtruthing.

3. Repeat & Validate

This cycle of image interpretation and groundtruthing is repeated at least two times, with at least one validation check by a different trained mapper to check for agreement with standard mapping and classification rules.
The Feature Interpretation & Groundtruthing CYCLE

Citation for AEM: Ellis E. C., H. Wang, H. Xiao, K. Peng, X. P. Liu, S. C. Li, H. Ouyang, X. Cheng, and L. Z. Yang. 2006. Measuring long-term ecological changes in densely populated landscapes using current and historical high resolution imagery. Remote Sensing of Environment 100(4):457-473.