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Forest spatial information is regularly established and managed as basic data for national forest planning and forest policy establishment. Among them, the grade of vegetation conservation shall be investigated and evaluated according to the value of vegetation conservation. As the collection of field data over large or remote areas is difficult, unmanned aerial vehicles (UAVs) are increasingly being used for this purpose. Consequently, there is a need for research on UAV-monitoring and three-dimensional (3D) image generation techniques. In this study, a new method that can efficiently collect and analyze UAV spatial data to survey and assess forests was developed. Both UAV-based and LiDAR imaging methods were evaluated in conjunction with the ground control point measurement method for forest surveys. In addition, by fusing the field survey database of each target site and the UAV optical and LiDAR images, the Gongju, Samcheok, and Seogwipo regions were analyzed based on deep learning. The kappa value showed 0.59, 0.47, and 0.78 accuracy for each of the sites in terms of vegetation type (artificial or natural), and 0.68, 0.53, and 0.62 accuracy in terms of vegetation layer structure. The results of comparative analysis with ecological natural maps by establishing vegetation conservation levels show that about 83.9% of the areas are consistent. The findings verified the applicability of this UAV-based approach for the construction of geospatial information on forests. The proposed method can be useful for improving the efficiency of the Vegetation Conservation Classification system and for conducting high-resolution monitoring in forests worldwide.
Yongyan Zhu; Seongwoo Jeon; Hyunchan Sung; Yoonji Kim; Chiyoung Park; Sungeun Cha; Hyun-Woo Jo; Woo-Kyun Lee. Developing UAV-Based Forest Spatial Information and Evaluation Technology for Efficient Forest Management. Sustainability 2020, 12, 10150 .
AMA StyleYongyan Zhu, Seongwoo Jeon, Hyunchan Sung, Yoonji Kim, Chiyoung Park, Sungeun Cha, Hyun-Woo Jo, Woo-Kyun Lee. Developing UAV-Based Forest Spatial Information and Evaluation Technology for Efficient Forest Management. Sustainability. 2020; 12 (23):10150.
Chicago/Turabian StyleYongyan Zhu; Seongwoo Jeon; Hyunchan Sung; Yoonji Kim; Chiyoung Park; Sungeun Cha; Hyun-Woo Jo; Woo-Kyun Lee. 2020. "Developing UAV-Based Forest Spatial Information and Evaluation Technology for Efficient Forest Management." Sustainability 12, no. 23: 10150.
The Aral Sea was the one of the largest lakes in the world, but almost 60,000 km2 of the water body has been dried due to the over‐irrigation. It led land degradation, and the detection of areas where vegetation can be established is getting important. In this study, we aimed to find potential vegetation establishment area using remote sensed data in the Aral Sea to support the decision‐making related to afforestation. Various indices such as normalized difference vegetation index (NDVI), topsoil grain size index (TGSI), soil salinity index (SSI), and normalized multi‐band drought index (NMDI) was calculated from satellite imagery. As an indicator of vegetation existence, NDVI was classified into three groups and set as a base for classifying other indices by performing statistical analyses. Based on decision tree method, indices were combined, and the potential vegetation establishment area was detected. As results, NDVI was higher in the southeast than the west of the study area. The results of statistical analyses showed that TGSI had a positive correlation with NDVI, while SSI and NMDI had a negative correlation. Based on this, the potential vegetation area was detected as 7,295.21 km2 (61.34%) of the “unsuitable” area, 2,818.64 km2 (23.7%) of the “intermediate” area, 1,612.15 km2 (13.56%) of the ‘suitable’ area, and finally 166.42 km2 (1.4%) of the “very suitable”. With this map it is possible to identify advantageous area to plant in the Aral Sea, and contribute to the planning for rehabilitation and prevent land degradation. This article is protected by copyright. All rights reserved.
Jiwon Kim; Cholho Song; Sujong Lee; Hyun‐Woo Jo; Eunbeen Park; Hangnan Yu; Sungeun Cha; Jiae An; Yowhan Son; Asia Khamzina; Woo‐Kyun Lee. Identifying potential vegetation establishment areas on the dried Aral Sea floor using satellite images. Land Degradation & Development 2020, 31, 2749 -2762.
AMA StyleJiwon Kim, Cholho Song, Sujong Lee, Hyun‐Woo Jo, Eunbeen Park, Hangnan Yu, Sungeun Cha, Jiae An, Yowhan Son, Asia Khamzina, Woo‐Kyun Lee. Identifying potential vegetation establishment areas on the dried Aral Sea floor using satellite images. Land Degradation & Development. 2020; 31 (18):2749-2762.
Chicago/Turabian StyleJiwon Kim; Cholho Song; Sujong Lee; Hyun‐Woo Jo; Eunbeen Park; Hangnan Yu; Sungeun Cha; Jiae An; Yowhan Son; Asia Khamzina; Woo‐Kyun Lee. 2020. "Identifying potential vegetation establishment areas on the dried Aral Sea floor using satellite images." Land Degradation & Development 31, no. 18: 2749-2762.
The applicability of deep learning to remote sensing is rapidly increasing in accordance with the improvement in spatiotemporal resolution of satellite images. However, unlike satellite images acquired in near-real-time over wide areas, there are limited amount of labeled data used for model training. In this article, three kinds of deep learning applications--data augmentation, semisupervised classification, and domain-adapted architecture--were tested in an effort to overcome the limitation of insufficient labeled data. Among the diverse tasks that can be used for classification, rice paddy detection in South Korea was performed for its ability to fully utilize the advantages of deep learning and high spatiotemporal image resolution. In the process of designing each application, the domain knowledge of remote sensing and rice phenology was integrated. Then, all possible combinations of the three applications were examined and evaluated with pixel-based comparisons in various environments and city-level comparisons using national statistics. The results of this article indicated that all combinations of the applications can contribute to increase classification performance, even though the uncertainty involved in imitating or utilizing unlabeled data remains. As the effectiveness of the proposed applications was experimentally confirmed, enhancement in the applicability of deep learning was expected in various remote sensing areas. In particular, the proposed applications would be significant when they are applied to a wide range of study areas and high-resolution images, as they tend to require a large amount of learning data from diverse environments, owing to high intra-class heterogeneity.
Hyun-Woo Jo; Sujong Lee; Eunbeen Park; Chul-Hee Lim; Cholho Song; Halim Lee; Youngjin Ko; Sungeun Cha; Hoonjoo Yoon; Woo-Kyun Lee. Deep Learning Applications on Multitemporal SAR (Sentinel-1) Image Classification Using Confined Labeled Data: The Case of Detecting Rice Paddy in South Korea. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 7589 -7601.
AMA StyleHyun-Woo Jo, Sujong Lee, Eunbeen Park, Chul-Hee Lim, Cholho Song, Halim Lee, Youngjin Ko, Sungeun Cha, Hoonjoo Yoon, Woo-Kyun Lee. Deep Learning Applications on Multitemporal SAR (Sentinel-1) Image Classification Using Confined Labeled Data: The Case of Detecting Rice Paddy in South Korea. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (11):7589-7601.
Chicago/Turabian StyleHyun-Woo Jo; Sujong Lee; Eunbeen Park; Chul-Hee Lim; Cholho Song; Halim Lee; Youngjin Ko; Sungeun Cha; Hoonjoo Yoon; Woo-Kyun Lee. 2020. "Deep Learning Applications on Multitemporal SAR (Sentinel-1) Image Classification Using Confined Labeled Data: The Case of Detecting Rice Paddy in South Korea." IEEE Transactions on Geoscience and Remote Sensing 58, no. 11: 7589-7601.
The mid-latitude ecotone (MLE)—a transition zone between boreal and temperate forests, which includes the regions of Northeast Asia around 30°–60° N latitudes—delivers different ecosystem functions depending on different management activities. In this study, we assessed forest volume and net primary productivity changes in the MLE of Northeast Asia under different ecological characteristics, as well as various current management activities, using the BioGeoChemistry Management Model (BGC-MAN). We selected five pilot sites for pine (Scots pine and Korean red pine; Pinus sylvestris and P. densiflora), oak (Quercus spp.), and larch forests (Dahurian larch and Siberian larch; Larix gmelinii and L. sibirica), respectively, which covered the transition zone across the MLE from Lake Baikal, Russia to Kyushu, Japan, including Mongolia, Northeast China, and the Korean Peninsula. With site-specific information, soil characteristics, and management descriptions by forest species, we established their management characteristics as natural preserved forests, degraded forests, sandy and cold forest stands, and forests exposed to fires. We simulated forest volume (m3) and net primary productivity (Mg C ha−1) during 1960–2005 and compared the results with published literature. They were in the range of those specified in previous studies, with some site-levels under or over estimation, but unbiased estimates in their mean values for pine, oak, and larch forests. Annual rates of change in volume and net primary productivity differed by latitude, site conditions, and climatic characteristics. For larch forests, we identified a high mountain ecotype which warrants a separate model parameterization. We detected changes in forest ecosystems, explaining ecological transition in the Northeast Asian MLE. Under the transition, we need to resolve expected problems through appropriate forest management and social efforts.
Cholho Song; Stephan A. Pietsch; Moonil Kim; Sungeun Cha; Eunbeen Park; Anatoly Shvidenko; Dmitry Schepaschenko; Florian Kraxner; Woo-Kyun Lee. Assessing Forest Ecosystems across the Vertical Edge of the Mid-Latitude Ecotone Using the BioGeoChemistry Management Model (BGC-MAN). Forests 2019, 10, 523 .
AMA StyleCholho Song, Stephan A. Pietsch, Moonil Kim, Sungeun Cha, Eunbeen Park, Anatoly Shvidenko, Dmitry Schepaschenko, Florian Kraxner, Woo-Kyun Lee. Assessing Forest Ecosystems across the Vertical Edge of the Mid-Latitude Ecotone Using the BioGeoChemistry Management Model (BGC-MAN). Forests. 2019; 10 (6):523.
Chicago/Turabian StyleCholho Song; Stephan A. Pietsch; Moonil Kim; Sungeun Cha; Eunbeen Park; Anatoly Shvidenko; Dmitry Schepaschenko; Florian Kraxner; Woo-Kyun Lee. 2019. "Assessing Forest Ecosystems across the Vertical Edge of the Mid-Latitude Ecotone Using the BioGeoChemistry Management Model (BGC-MAN)." Forests 10, no. 6: 523.
The ecosystem across the Democratic People’s Republic of Korea (DPRK) is threatened by deforestation. However, there is very little attention being given to government efforts for afforestation and rehabilitation plan. The most significant barriers to addressing this problem are technique limitations, availability of information, and lack of a stepwise forest management plan. This study identifies spatially suitable tree species, and establishes a stepwise restoration plan to support decision making for restoring degraded forest in the DPRK throughout a suitable restoration map. First off, target species were chosen from reference data, and spatial distribution maps for each tree species were prepared based on social needs as well as natural conditions in the DPRK. The suitable restoration map was calculated by two priorities in a weighting method; suitable priority, and distributional clustering level. Finally, the 23 afforestation species were selected for the suitable restoration map, including 11 coniferous and 12 deciduous tree species. We introduced a stepwise afforestation/restoration plan of degraded forest in the DPRK; general (long-term), detailed (medium-term), implementation (short-term) plans. Maps with different spatial resolutions were prepared for each of the plans. A restoration map with 12.5 km spatial resolution can be used for the general plan at the national level, and maps with 5 km and 1 km spatial resolutions can be used for detailed plan at the local level and implementation plan at the site level, respectively.
Sle-Gee Lee; Hyun-Ah Choi; Hyeji Yoo; Cholho Song; Sungeun Cha; Sang-Won Bae; Yowhan Son; Woo-Kyun Lee. Restoration Plan for Degraded Forest in The Democratic People’s Republic of Korea Considering Suitable Tree Species and Spatial Distribution. Sustainability 2018, 10, 856 .
AMA StyleSle-Gee Lee, Hyun-Ah Choi, Hyeji Yoo, Cholho Song, Sungeun Cha, Sang-Won Bae, Yowhan Son, Woo-Kyun Lee. Restoration Plan for Degraded Forest in The Democratic People’s Republic of Korea Considering Suitable Tree Species and Spatial Distribution. Sustainability. 2018; 10 (3):856.
Chicago/Turabian StyleSle-Gee Lee; Hyun-Ah Choi; Hyeji Yoo; Cholho Song; Sungeun Cha; Sang-Won Bae; Yowhan Son; Woo-Kyun Lee. 2018. "Restoration Plan for Degraded Forest in The Democratic People’s Republic of Korea Considering Suitable Tree Species and Spatial Distribution." Sustainability 10, no. 3: 856.
Sung-Eun Cha. The Relationship Analysis between the Epicenter and Lineaments in the Odaesan Area using Satellite Images and Shaded Relief Maps. Journal of the Korean Association of Geographic Information Studies 2016, 19, 61 -74.
AMA StyleSung-Eun Cha. The Relationship Analysis between the Epicenter and Lineaments in the Odaesan Area using Satellite Images and Shaded Relief Maps. Journal of the Korean Association of Geographic Information Studies. 2016; 19 (3):61-74.
Chicago/Turabian StyleSung-Eun Cha. 2016. "The Relationship Analysis between the Epicenter and Lineaments in the Odaesan Area using Satellite Images and Shaded Relief Maps." Journal of the Korean Association of Geographic Information Studies 19, no. 3: 61-74.