• Journal of Wetlands Research
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• v.25 no.4
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• pp.408-416
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• 2023

The IUCN Red List of Ecosystems serves as a global standard for assessing and identifying ecosystems at high risk of biodiversity loss, providing scientific evidence necessary for effective ecosystem management and conservation policy formulation. The IUCN Red List of Ecosystems has been designated as a key indicator (A.1) for Goal A of the Kunming-Montreal Global Biodiversity Framework. The assessment of the Red List of Ecosystems discerns signs of ecosystem collapse through specific criteria: reduction in distribution (Criterion A), restricted distribution (Criterion B), environmental degradation (Criterion C), changes in biological interaction (Criterion D), and quantitative estimation of the risk of ecosystem collapse (Criterion E). Since 2014, the IUCN Red List of Ecosystems has been evaluated in over 110 countries, with more than 80% of the assessments conducted in terrestrial and inland water ecosystems, among which tropical and subtropical forests are distributed ecosystems under threat. The assessment criteria are concentrated on spatial signs (Criteria A and B), accounting for 68.8%. There are three main considerations for applying the Red List of Ecosystems assessment domestically: First, it is necessary to compile applicable terrestrial ecosystem types within the country. Second, it must be determined whether the spatial sign assessment among the Red List of Ecosystems categories can be applied to the various small-scale ecosystems found domestically. Lastly, the collection of usable time series data (50 years) for assessment must be considered. Based on these considerations, applying the IUCN Red List of Ecosystems assessment domestically would enable an accurate understanding of the current state of the country's unique ecosystem types, contributing to global efforts in ecosystem conservation and restoration.

• Journal of agriculture & life science
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• v.46 no.4
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• pp.69-84
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• 2012

To provide the basic information on the excavation, conservation, and systematic management plan for preservation of valuable forested wetlands, a field survey was analyzed at public and private forests in Daegu city, 2010. The expected points of FGIS were identified, and buffer zones for the protection of forested wetlands were derived. According to the results from the 11 points of forested wetland, the flora of wetlands in Daegu city were consisted of a total of 169 taxa; 63 families, 131 genera, 148 species, 2 subspecies, 14 varieties, and 5 forms. The species diversity of shrubs in forest wetlands was highest at 1.560, and the evenness was highest in shrub trees at 0.913. Considering the type of wetland, topography, etc., the buffer zone was set at 20~50m from the core area boundary.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.