A Methodology for Automatic Identification of Units with Ecological Significance in Dehesa Ecosystems

The dehesa is an anthropic complex ecosystem typical of some areas of Spain and Portugal, with a key role in soil and biodiversity conservation and in the search for a balance between production, conservation and ecosystem services. For this reason, it is essential to have tools that allow its characterization, as well as to monitor and support decision-making to improve its sustainability. A multipurpose and scalable tool has been developed and validated, which combines several low-cost technologies, computer vision methods and RGB aerial orthophotographs using open data sources and which allows for automated agroforestry inventories, identifying and quantifying units with important ecological significance such as: trees, groups of trees, ecosystem corridors, regenerated areas and sheets of water. The development has been carried out from images of the national aerial photogrammetry plan of Spain belonging to 32 dehesa farms, representative of the existing variability in terms of density of trees, shrub species and the presence of other ecological elements. First, the process of obtaining and identifying areas of interest was automated using WMS services and shapefile metadata. Then, image analysis techniques were used to detect the different ecological units. Finally, a classification was developed according to the OBIA approach, which stores the results in standardized files for Geographic Information Systems. The results show that a stable solution has been achieved for the automatic and accurate identification of ecological units in dehesa territories. The scalability and generalization to all the dehesa territories, as well as the possibility of segmenting the area occupied by trees and other ecological units opens up a great opportunity to improve the construction of models for interpreting satellite images

Abstraction, extension and structural auditing with the UMLS semantic network

The Unified Medical Language System (UMLS) is a two-level biomedical terminological knowledge base, consisting of the Metathesaurus (META) and the Semantic Network (SN), which is an upper-level ontology of broad categories called semantic types (STs). The two levels are related via assignments of one or more STs to each concept of the META. Although the SN provides a high-level abstraction for the META, it is not compact enough. Various metaschemas, which are compact higher-level abstraction networks of the SN, have been derived. A methodology is presented to evaluate and compare two given metaschemas, based on their structural properties. A consolidation algorithm is designed to yield a consolidated metaschema maintaining the best and avoiding the worst of the two given metaschemas. The methodology and consolidation algorithm were applied to the pair of heuristic metaschemas, the top-down metaschema and the bottom-up metaschema, which have been derived from two studies involving two groups of UMLS experts. The results show that the consolidated metaschema has better structural properties than either of the two input metaschemas. Better structural properties are expected to lead to better utilization of a metaschema in orientation and visualization of the SN. Repetitive consolidation, which leads to further structural improvements, is also shown. The META and SN were created in the absence of a comprehensive curated genomics terminology. The internal consistency of the SN\u27s categories which are relevant to genomics is evaluated and changes to improve its ability to express genomic knowledge are proposed. The completeness of the SN with respect to genomic concepts is evaluated and conesponding extensions to the SN to fill identified gaps are proposed. Due to the size and complexity of the UMLS, errors are inevitable. A group auditing methodolgy is presented, where the ST assignments for groups of similar concepts are audited. The extent of an ST, which is the group of all concepts assigned this ST, is divided into groups of concepts that have been assigned exactly the same set of STs. An algorithm finds subgroups of suspicious concepts. The auditor is presented with these subgroups, which purportedly exhibit the same semantics, and thus he will notice different concepts with wrong or missing ST assignments. Another methodology partitions these groups into smaller, singly rooted, hierarchically organized sets used to audit the hierarchical relationships. The algorithmic methodologies are compared with a comprehensive manual audit and show a very high error recall with a much higher precision than the manual exhaustive review