• Title/Summary/Keyword: vegetated area

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Classification of Urban Green Space Using Airborne LiDAR and RGB Ortho Imagery Based on Deep Learning (항공 LiDAR 및 RGB 정사 영상을 이용한 딥러닝 기반의 도시녹지 분류)

  • SON, Bokyung;LEE, Yeonsu;IM, Jungho
    • Journal of the Korean Association of Geographic Information Studies
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    • v.24 no.3
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    • pp.83-98
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    • 2021
  • Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

Estimation of Soil Moisture Using Sentinel-1 SAR Images and Multiple Linear Regression Model Considering Antecedent Precipitations (선행 강우를 고려한 Sentinel-1 SAR 위성영상과 다중선형회귀모형을 활용한 토양수분 산정)

  • Chung, Jeehun;Son, Moobeen;Lee, Yonggwan;Kim, Seongjoon
    • Korean Journal of Remote Sensing
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    • v.37 no.3
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    • pp.515-530
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    • 2021
  • This study is to estimate soil moisture (SM) using Sentinel-1A/B C-band SAR (synthetic aperture radar) images and Multiple Linear Regression Model(MLRM) in the Yongdam-Dam watershed of South Korea. Both the Sentinel-1A and -1B images (6 days interval and 10 m resolution) were collected for 5 years from 2015 to 2019. The geometric, radiometric, and noise corrections were performed using the SNAP (SentiNel Application Platform) software and converted to backscattering coefficient of VV and VH polarization. The in-situ SM data measured at 6 locations using TDR were used to validate the estimated SM results. The 5 days antecedent precipitation data were also collected to overcome the estimation difficulty for the vegetated area not reaching the ground. The MLRM modeling was performed using yearly data and seasonal data set, and correlation analysis was performed according to the number of the independent variable. The estimated SM was verified with observed SM using the coefficient of determination (R2) and the root mean square error (RMSE). As a result of SM modeling using only BSC in the grass area, R2 was 0.13 and RMSE was 4.83%. When 5 days of antecedent precipitation data was used, R2 was 0.37 and RMSE was 4.11%. With the use of dry days and seasonal regression equation to reflect the decrease pattern and seasonal variability of SM, the correlation increased significantly with R2 of 0.69 and RMSE of 2.88%.

Future Changes in Global Terrestrial Carbon Cycle under RCP Scenarios (RCP 시나리오에 따른 미래 전지구 육상탄소순환 변화 전망)

  • Lee, Cheol;Boo, Kyung-On;Hong, Jinkyu;Seong, Hyunmin;Heo, Tae-kyung;Seol, Kyung-Hee;Lee, Johan;Cho, ChunHo
    • Atmosphere
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    • v.24 no.3
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    • pp.303-315
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    • 2014
  • Terrestrial ecosystem plays the important role as carbon sink in the global carbon cycle. Understanding of interactions of terrestrial carbon cycle with climate is important for better prediction of future climate change. In this paper, terrestrial carbon cycle is investigated by Hadley Centre Global Environmental Model, version 2, Carbon Cycle (HadGEM2-CC) that considers vegetation dynamics and an interactive carbon cycle with climate. The simulation for future projection is based on the three (8.5/4.5/2.6) representative concentration pathways (RCPs) from 2006 to 2100 and compared with historical land carbon uptake from 1979 to 2005. Projected changes in ecological features such as production, respiration, net ecosystem exchange and climate condition show similar pattern in three RCPs, while the response amplitude in each RCPs are different. For all RCP scenarios, temperature and precipitation increase with rising of the atmospheric $CO_2$. Such climate conditions are favorable for vegetation growth and extension, causing future increase of terrestrial carbon uptakes in all RCPs. At the end of 21st century, the global average of gross and net primary productions and respiration increase in all RCPs and terrestrial ecosystem remains as carbon sink. This enhancement of land $CO_2$ uptake is attributed by the vegetated area expansion, increasing LAI, and early onset of growing season. After mid-21st century, temperature rising leads to excessive increase of soil respiration than net primary production and thus the terrestrial carbon uptake begins to fall since that time. Regionally the NEE average value of East-Asia ($90^{\circ}E-140^{\circ}E$, $20^{\circ}N{\sim}60^{\circ}N$) area is bigger than that of the same latitude band. In the end-$21^{st}$ the NEE mean values in East-Asia area are $-2.09PgC\;yr^{-1}$, $-1.12PgC\;yr^{-1}$, $-0.47PgC\;yr^{-1}$ and zonal mean NEEs of the same latitude region are $-1.12PgC\;yr^{-1}$, $-0.55PgC\;yr^{-1}$, $-0.17PgC\;yr^{-1}$ for RCP 8.5, 4.5, 2.6.

Dataset of Long-term Investigation on Change in Hydrology, Channel Morphology, Landscape and Vegetation Along the Naeseong Stream (II) (내성천의 수문, 하도 형태, 경관 및 식생 특성에 관한 장기모니터링 자료 (II))

  • Lee, Chanjoo;Kim, Dong Gu;Hwang, Seung-Yong;Kim, Yongjeon;Jeong, Sangjun;Kim, Sinae;Cho, Hyeongjin
    • Ecology and Resilient Infrastructure
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    • v.6 no.1
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    • pp.34-48
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    • 2019
  • Naeseong Stream is a natural sand-bed river that flows through mountainous and cultivated area in northern part of Gyeongbuk province. It had maintained its inherent landscape characterized by white sandbars before 2010s. However, since then changes occurred, which include construction of Yeongju Dam and the extensive vegetation development around 2015. In this study, long-term monitoring was carried out on Naeseong Stream to analyze these changes objectively. This paper aims to provide a dataset of the investigation on channel morphology and vegetation for the period 2012-2018. Methods of investigation include drone/terrestrial photography, LiDAR aerial survey and on-site fieldwork. The main findings are as follows. Vegetation development in the channel of Naeseong Stream began around 1987. Before 2013 it occurred along the downstream reach and since then in the entire reach. Some of the sites where riverbed is covered with vegetation during 2014~2015 were rejuvenated to bare bars due to the floods afterwards, but woody vegetation was established in many sites. Bed changes occurred due to deposition of sediment on the vegetated surfaces. Though Naeseong Stream has maintained its substantial sand-bed characteristics, there has been a slight tendency in bed material coarsening. Riverbed degradation at the thalweg was observed in the surveyed cross sections. Considering all the results together with the hydrological characteristics mentioned in the precedent paper (I), it is thought that the change in vegetation and landscape along Naeseong Stream was mainly due to decrease of flow. The effect of Yeongju Dam on the change of the riverbed degradation was briefly discussed as well.