Abstract
Land cover (LC) is an essential variable for environmental monitoring in many application domains. The detection of changes in LC can support the understanding of environmental dynamics. However, LC legends present a high degree of inconsistencies that significantly reduce their usability. This study investigates the effectiveness of ISO standard 19144-2, better known as Land Cover Meta-Language (LCML), to improve the standardization and harmonization of different LC taxonomies and maps. LCML vocabulary and syntactic rules facilitate the integration of natural resources information. LC classes are represented by a sequence of “Basic Elements” and attributes defined as “Properties” and “Characteristics.” Such elements are formalized in a Unified Modeling Language class diagram. This study presents first, a method to evaluate and score the “similarity” of different LCML legends, second, an application of the similarity assessment criteria to an area located in Bangladesh for translating its specific LCML legend into a different taxonomy, i.e., the System of Environmental Economic Accounting, and third, a Python implementation to be incorporated in new or already existing tools. The results obtained show that when class similarity assessment is carried out by Basic Elements only, the process performs well for simple classes. When classes are characterized by similar basic elements (e.g., biotic elements) structure, the introduction of class properties is needed to disambiguate complex situations. The findings indicate that the proposed methodology can exploit LCML land feature semantic representation. Moreover, it can be used for translating LCML classes into different taxonomies, for facilitating class comparison and change detection.
Highlights
E NVIRONMENTAL resources have never been so much degraded, putting at risk billions of people and undermining our efforts to end hunger and shift to greener and moreManuscript received February 7, 2020; revised April 30, 2020 and May 26, 2020; accepted June 8, 2020
Taking into consideration the need to improve consistency and harmonization of LC information for different purposes, this study provides a methodological approach for measuring LC semantic similarity by creating compatible object-oriented land cover databases and applying the Land Cover Meta-Language (LCML) rules and conditions to assess the “Object Based” similarity between LC databases
LC classes are represented in a database by a sequence of basic objects and extra attributes defined as “Properties” and “Characteristics.” The LCML Basic Elements, their relationships, inheritance and properties and characteristics associated with them are formalized in a Unified Modeling Language (UML) class diagram, part of the standard
Summary
E NVIRONMENTAL resources have never been so much degraded, putting at risk billions of people and undermining our efforts to end hunger and shift to greener and moreManuscript received February 7, 2020; revised April 30, 2020 and May 26, 2020; accepted June 8, 2020. There is an increasing and urgent need for monitoring natural resources to support sustainable and informed-based decision-making processes at local, subnational, national, and international levels. LC mapping is a key source of baseline information to support multilateral environmental agreements and the implementation of the United Nations Sustainable Development Goals (UN SDGs indicators) [3]. A methodology that can automatically measure semantic similarity between classification systems is very much needed to move forward the integration of the different LC products and development of consistent approaches. A critical factor in implementing such advanced harmonization activities has been the availability of a common LC classification system structure to be able to accommodate all possible LC categories created by map producers at local, national, regional, or global levels
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