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Land Cover Classification: Concepts and Definitions

This article explores the classification concepts and definitions of land cover, including the differences between classification and legend. It also discusses the flexibility and standardization of classification systems. The article provides an example of a land cover class and the stepwise selection of classifiers for creating unique codes and names. Additionally, it introduces modules such as legend creation, field data collection, and translation of external classifications into the Land Cover Classification System (LCCS).

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Land Cover Classification: Concepts and Definitions

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  1. Classification Concepts Definition Land cover is the observed (bio)physical cover on the earth’s surface. It includes vegetation and man-made features as well as bare rock, bare soil and water surfaces.

  2. Classification Concepts Reference Classification System >50m >5m 30m 1 2 3 4 ... 100% DEFINED GEOGRAPHIC 33 47 AREA 50% 78 given scale and data type 83 10% 97 9899100 derived mapping legend units Difference between Classification and Legend Legend is defined as the “application of a classification in a particular area ”(Di Gregorio and Jansen, 1998).

  3. Conceptual Basis high level of low standardization flexibility understandable list of limited number of limited number of class names generic classes generic classes increasing number of classes enormous list of adequate number class names with of detailed classes unclear or too narrow class boundary low level of high standardization flexibility Current a-priori classifications in relation to flexibility Class boundary definitions will be clear and classes will contain aggregated land cover types. By increasing the number of classes in an a-priori system, the problem arises of how a user will find its way. Flexibility that will allow the accommodation of any occurring land cover. Class boundary definitions are based on very slight differences

  4. Conceptual Basis New approach to classification -1- Increasingflexibility while maintaining mapability The classification should be flexible in the sense that it should address the potential for the classification system to describe enough classes to cope with the real world. At the same time flexibility should adhere to strict class boundary definitions that are unambiguous and clear. Basic principle A land cover class is defined by the combination of a set of independent diagnostic attributes, the so-calledclassifiers that are arranged to assure a high degree of mapability, i.e. geographical accuracy.

  5. Conceptual Basis The initial Dichotomous Phase of LCCS Below the Dichotomous Phase is shown consisting of pairs of buttons of which the user can select one at the time. Classifier used: Presence of Vegetation Classifier used: Edaphic Condition Classifier used: Artificiality of Cover

  6. Conceptual Basis The subsequent Modular-Hierarchical Phase of LCCS The sets of classifiers tailored to the major land cover and hierarchically arranged according to mapability (classifiers presented in dark blue).

  7. Conceptual Basis Example “Natural and Semi-Natural Terrestrial Vegetation (A12)” : Example “Natural and Semi-Natural Terrestrial Vegetation (A12)” : Code: Classifiers Used: Boolean Formula: Standard Class Name: Code: Life Form & Cover A3A10 Closed Forest 20005 Height A3A10B2 High Closed Forest 20006 Spatial Distribution A3A10B2C1 Continuous Closed Forest 20007 Leaf Type A3A10B2C1D1 Broadleaved Closed Forest 20095 Leaf Phenology A3A10B2C1D1E2 Broadleaved Deciduous Forest 20097 2nd Layer: LF, C, H A3A10B2C1D1E2F2F5F7G2 Multi-Layered Broadleaved Deciduous Forest 20628 3rd Layer: LF, C, H A3A10B2C1D1E2F2F5F7G2 Multi-Layered Broadleaved Deciduous F2F5F10G2 Forest With Emergents 20630 • Concept for creation of a land cover class • Stepwise selection of classifiers that will generate: • a string of codes, the so-called Boolean Formula; • a Standard Class Name; and • a unique Numerical Code. With each classifier option selected, the string of codes grows, the class name changes and so does the code.

  8. Classification Module LEGEND MODULE FIELD DATA MODULE CLASSIFICATION MODULE build up legend edit classes create user-defined land cover classes display legend save and retrieve print export standardized general field data collection specific field data collection automatic extraction of land cover class from field data saving of field data in synthetic form print and export all classifiers and attributes glossary conditions to create land cover classes database of all possible classes including name, code and description images and interpretation database TRANSLATOR MODULE translation of external classifications into LCCS similarity of external single classes through LCCS comparison of two external classifications through LCCS comparison of two LCCS classes Overview of the software application

  9. Legend Module How to add User-defined Attributes? Land cover classes can be “cloned” in order to add specific user-defined attributes to the standard class. A user may want to further define a classifier and/or attribute already used, or add a new attribute. A standard set of clone options is provided. The user-defined code is specified in this window as well as its meaning. The Type of Clone to be made is selected in this window. The result is a code added to the Boolean code (e.g. (1) in the example above)

  10. Classification Concepts A PRIORI CLASSIFICATION CONCRETE FIELD SITUATION ADVANTAGES: - HIERARCHICAL ORDER - STANDARDIZATION DISADVANTAGES: - IMPLICIT RIGIDITY OF THE SYSTEM ADVANTAGE: - HIGH DEGREE OF FLEXIBILITY DISADVANTAGES: - DEPENDING ON AREA - NO STANDARDIZATION OR HIERARCHICAL ORDER A POSTERIORI CLASSIFICATION A priori versus a posteriori classification Example of a very general a priori classificationbased on four classes (triangle in black and white and circle in black and white) representing the field situation below. Due to the generalization of the classes, the user is obliged to make the best fit of one of the hundred possibilities in the field into one of the four classes, which may result in selecting a class that does not represent well the actual situation. Example of a posteriori classification. The classes fit better the actual situation in the field situation but the area described is only a portion of the total.

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