• Title/Summary/Keyword: Land cover typology

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A Study on the Forest Classification for Ecosystem Services Valuation - Focused on Forest Type Map and Landcover Map - (생태계 서비스 가치평가를 위한 산림 유형 분류 방안 - 임상도와 토지피복지도 활용을 중심으로 -)

  • Jeon, Seong Woo;Kim, Jaeuk;Jung, Huicheul
    • Journal of the Korean Society of Environmental Restoration Technology
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    • v.16 no.3
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    • pp.31-39
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    • 2013
  • Some researcher started studies of natural capital from 1980's. But many researches are going along with the theme lately. Most assessment of ecosystem services are approaching a general assessment using a land-cover map. Therefore they have some problems such as overestimate, underestimate, and double counting, and so on. This study suggested a detailed typology for quantitative assessment about ecosystem services. It compared land-cover map and forest type map to select a based map and made criteria with reference to the literature and field survey. It subdivided a forest typology using ecological feature (natural forest, artifical forest), forest type (coniferous forest, mixed forest, hardwood forest) and age of stand in forest type map. Each forest type is widely distributed 21~40 ages of forests and biggest area is 21~40 ages of mixed forest in all forest typology. Further researches have to progress consistently assessment using detailed typology and function of forest ecosystem services.

A Study on the Application of IUCN Global Ecosystem Typology Using Land Cover Map in Korea (토지피복지도를 활용한 IUCN 생태계유형분류 국내 적용)

  • Hee-Jung Sohn;Su-Yeon Won;Jeong-Eun Jeon;Eun-Hee Park;Do-Hee Kim;Sang-Hak Han;Young-Keun Song
    • Korean Journal of Environment and Ecology
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    • v.37 no.3
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    • pp.209-220
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    • 2023
  • Over the past few centuries, widespread changes to natural ecosystems caused by human activities have severely threatened biodiversity worldwide. Understanding changes in ecosystems is essential to identifying and managing threats to biodiversity. In line with this need, the IUCN Council formed the IUCN Global Ecosystem Typology (GET) in 2019, taking into account the functions and types of ecosystems. The IUCN provides maps of 10 ecosystem groups and 108 ecological functional groups (EFGs) on a global scale. According to the type classification of IUCN GET ecosystems, Korea's ecosystem is classified into 8 types of Realm (level 1), 18 types of Biome (level 2), and 41 types of Group (level 3). GETs provided by IUCN have low resolution and often do not match the actual land status because it was produced globally. This study aimed to increase the accuracy of Korean IUCN GET type classification by using land cover maps and producing maps that reflected the actual situation. To this end, we ① reviewed the Korean GET data system provided by IUCN GET and ② compared and analyzed it with the current situation in Korea. We evaluated the limitations and usability of the GET through the process and then ③ classified Korea's new Get type reflecting the current situation in Korea by using the national data as much as possible. This study classified Korean GETs into 25 types by using land cover maps and existing national data (Territorial realm: 9, Freshwater: 9, Marine-territorial: 5, Terrestrial-freshwater: 1, and Marine-freshwater-territorial: 1). Compared to the existing map, "F3.2 Constructed lacustrine wetlands", "F3.3 Rice paddies", "F3.4 Freshwater aquafarms", and "T7.3 Plantations" showed the largest area reduction in the modified Korean GET. The area of "T2.2 Temperate Forests" showed the largest area increase, and the "MFT1.3 Coastal saltmarshes and reedbeds" and "F2.2 Small permanent freshwater lakes" types also showed an increase in GET area after modification. Through this process, the existing map, in which the sum of all EFGs in the existing GET accounted for 8.33 times the national area, was modified so that the total sum becomes 1.22 times the national area using the land cover map. This study confirmed that the existing EFG, which had small differences by type and low accuracy, was improved and corrected. This study is significant in that it produced a GET map of Korea that met the GET standard using data reflecting the field conditions.