Vegetation Structure of Pedicularis ishidoyana Koidz. & Ohwi in South Korea Natural Habitats

Biodiversity loss is progressing despite biodiversity being essential for human survival, prosperity, and well-being. Conservation, restoration, and sustainable use of the habitat, given that its change is the most prominent factor causing the deterioration of biodiversity, represents a highly effective way of securing biodiversity. Therefore, we assessed and monitored habitat quality as a proxy for biodiversity with habitat quantity in Jeju Island, South Korea. We used an InVEST model with data on the habitat type, suitability, sensitivity, accessibility, and threat factors. Natural habitats throughout Jeju had rapidly decreased in area by 24.9% from 1989 to 2019, and this change contributed to the degradation of habitat quality by 15.8%. We provided significant evidence on the critical degradation of habitat for a long period of over 30 years and highlighted the urgent need for policies and behaviors that enhance biodiversity. We proposed appropriate strategies to prompt people to conserve better, restore effectively, and use biodiversity sustainably. We expect that our findings will provide scientific and evidence-based guidance for policy-making on biodiversity enhancement and will further support achievement of the Sustainable Development Goals and Aichi Biodiversity Targets, in addition to compliance with the New Deal for Nature and People.

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Analysis for the relationship of environmental factors and vegetation structure at natural streamside valley and riparian forest in South Korea

Journal of Ecology and Environment ◽

10.5141/ecoenv.2015.043 ◽

2015 ◽

Vol 38 (4) ◽

pp. 405-413 ◽

Cited By ~ 2

Author(s):

Kyu-Tae Cho ◽

Rae-Ha Jang ◽

Young-Han You

Keyword(s):

Environmental Factors ◽

South Korea ◽

Vegetation Structure ◽

Riparian Forest ◽

Relationship Of ◽

The Relationship

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Bird species richness, composition and abundance in pastures are affected by vegetation structure and distance from natural habitats: a single tree in pastures matters

Emu - Austral Ornithology ◽

10.1080/01584197.2017.1398591 ◽

2017 ◽

Vol 118 (2) ◽

pp. 201-211 ◽

Cited By ~ 2

Author(s):

Mauricio Neves Godoi ◽

Rudi Ricardo Laps ◽

Danilo Bandini Ribeiro ◽

Camila Aoki ◽

Franco Leandro de Souza

Keyword(s):

Species Richness ◽

Vegetation Structure ◽

Bird Species ◽

Natural Habitats ◽

Bird Species Richness ◽

Single Tree

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Vegetation Structure of Hovenia dulcis Community in South Korea

The Korean Journal of Ecology ◽

10.5141/jefb.2002.25.1.033 ◽

2002 ◽

Vol 25 (1) ◽

pp. 33-39

Author(s):

Chung-Weon Yun ◽

Byung-Chun Lee

Keyword(s):

South Korea ◽

Vegetation Structure ◽

Hovenia Dulcis

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Classification of Expansive Grassland Species in Different Growth Stages Based on Hyperspectral and LiDAR Data

Remote Sensing ◽

10.3390/rs10122019 ◽

2018 ◽

Vol 10 (12) ◽

pp. 2019 ◽

Cited By ~ 11

Author(s):

Adriana Marcinkowska-Ochtyra ◽

Anna Jarocińska ◽

Katarzyna Bzdęga ◽

Barbara Tokarska-Guzik

Keyword(s):

Remote Sensing ◽

Vegetation Structure ◽

Natura 2000 ◽

Grass Species ◽

Lidar Data ◽

Growth Stages ◽

Natural Habitats ◽

Waveform Data ◽

Grassland Species ◽

Full Waveform

Expansive species classification with remote sensing techniques offers great support for botanical field works aimed at detection of their distribution within areas of conservation value and assessment of the threat caused to natural habitats. Large number of spectral bands and high spatial resolution allows for identification of particular species. LiDAR (Light Detection and Ranging) data provide information about areas such as vegetation structure. Because the species differ in terms of features during the growing season, it is important to know when their spectral responses are unique in the background of the surrounding vegetation. The aim of the study was to identify two expansive grass species: Molinia caerulea and Calamagrostis epigejos in the Natura 2000 area in Poland depending on the period and dataset used. Field work was carried out during late spring, summer and early autumn, in parallel with remote sensing data acquisition. Airborne 1-m resolution HySpex images and LiDAR data were used. HySpex images were corrected geometrically and atmospherically before Minimum Noise Fraction (MNF) transformation and vegetation indices calculation. Based on a LiDAR point cloud generated Canopy Height Model, vegetation structure from discrete and full-waveform data and topographic indexes were generated. Classifications were performed using a Random Forest algorithm. The results show post-classification maps and their accuracies: Kappa value and F1 score being the harmonic mean of producer (PA) and user (UA) accuracy, calculated iteratively. Based on these accuracies and botanical knowledge, it was possible to assess the best identification date and dataset used for analysing both species. For M. caerulea the highest median Kappa was 0.85 (F1 = 0.89) in August and for C. epigejos 0.65 (F1 = 0.73) in September. For both species, adding discrete or full-waveform LiDAR data improved the results. We conclude that hyperspectral (HS) and LiDAR airborne data could be useful to identify grassland species encroaching into Natura 2000 habitats and for supporting their monitoring.

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