• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.611-619
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• 2013

Although the need of crop production has increased in Mongolia, crop cultivation is very limited because of the harsh climatic and topographic conditions. Crop lands are sparsely distributed with relatively small sizes and, therefore, it is difficult to survey the exact area of crop lands. The study aimed to find an easy and effective way of accurate classification to map crop lands in Mongolia using satellite images. To classify the crop lands over the study area in northern Mongolia, four classifications were carried out by using 1) Thematic Mapper (TM) image August 23, 2) TM image of July 6, 3) combined 12 bands of TM images of July and August, and 4) both TM images of July and August by layered classification. Wheat and potato are the major crop types and they show relatively high variation in crop conditions between July and August. On the other hands, other land cover types (forest, riparian vegetation, grassland, water and bare soil) do not show such difference between July and August. The results of four classifications clearly show that the use of multi-temporal images is essential to accurately classify the crop lands. The layered classification method, in which each class is separated by a subset of TM images, shows the highest classification accuracy (93.7%) of the crop lands. The classification accuracies are lower when we use only a single TM image of either July or August. Because of the different planting practice of potato and the growth condition of wheat, the spectral characteristics of potato and wheat cannot be fully separated from other cover types with TM image of either July or August. Further refinements on the spatial characteristics of existing crop lands may enhance the crop mapping method in Mongolia.

• Journal of Environmental Science International
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• v.23 no.12
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• pp.2057-2069
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• 2014

This study was carried out to estimate the BVOCs emissions with the emission factors which reflected the native conditions of forests in Jeju Island. This study made effective use of the previous data for the weather data and the emission rate of each organic volatile component measured at 10 species of conifers and broad leaved trees. The CORINAIR method and the grid system of $1km{\times}1km$ for whole area of Jeju Island were adopted in calculating the BVOCs emission emitted from forest. The vegetation information for Jeju Island was referred to GIS and a government report. By the results of BVOCs emission for Jeju Island, the 85% of monoterpene emission was emitted from conifers and the others was from broad leaved trees. Most of monoterpene emission was attributed to Pinus thunbergii and Cryptomeria japonica. The broad leaved trees greatly contributed to the isoprene emission and Quercus serrata played a dominant role in emission of isoprene. The total amount of BVOCs emission was estimated as $3612ton\;yr^{-1}$ in Jeju Island. The 51.1% of total emission was contributed to conifers, the 44.9% to broad leaved trees, and the 4.0% to grassland. Of total emission of BVOCs, monoterpene accounted for 32.3%, isoprene for 28.0%, and OVOCs for 39.7%. The BVOCs emission estimated by this study was less than that estimated by other previous study. This means that it is important to survey the emission rate at native conditions and gather the detailed information for various species of vegetation on target region.

• Journal of Cadastre & Land InformatiX
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• v.44 no.2
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• pp.103-112
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• 2014

The objective of this study is to suggest the possibility on land cover classification using hyperspectal imagery on area which includes lands and waters. After atmospheric correction as a preprocessing work was conducted on hyperspectral imagery acquired by airborne hyperspectral sensor CASI-1500, the effect of atmospheric correction to a few land cover class in before and after atmospheric correction was compared and analyzed. As the result of accuracy of land cover classification by highspectral imagery using reference data as airphoto and digital topographic map, maximum likelihood method represented overall accuracy as 67.0% and minimum distance method showed overall accuracy as 52.4%. Also product accuracy of land cover classification on road, dry field and green house, but that on river, forest, grassland showed low because the area of those was composed of complex object. Therefore, the study needs to select optimal band to classify specific object and to construct spectral library considering spectral characteristics of specific object.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.1
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• pp.57-78
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• 2017

There are several statistical classification algorithms available for land use/land cover classification. However, each has a certain bias or compromise. Some methods like the parallel piped approach in supervised classification, cannot classify continuous regions within a feature. On the other hand, while unsupervised classification method takes maximum advantage of spectral variability in an image, the maximally separable clusters in spectral space may not do much for our perception of important classes in a given study area. In this research, the output of an ANN algorithm was compared with the Possibilistic c-Means an improvement of the fuzzy c-Means on both moderate resolutions Landsat8 and a high resolution Formosat 2 images. The Formosat 2 image comes with an 8m spectral resolution on the multispectral data. This multispectral image data was resampled to 10m in order to maintain a uniform ratio of 1:3 against Landsat 8 image. Six classes were chosen for analysis including: Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC), the six features reflected differently in the infrared region with wheat producing the brightest pixel values. Signature collection per class was therefore easily obtained for all classifications. The output of both ANN and FCM, were analyzed separately for accuracy and an error matrix generated to assess the quality and accuracy of the classification algorithms. When you compare the results of the two methods on a per-class-basis, ANN had a crisper output compared to PCM which yielded clusters with pixels especially on the moderate resolution Landsat 8 imagery.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.7 no.4
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• pp.32-43
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• 2004

Targeting a rooftop biotope created in urban area, this study aims at verifying a creation technique reflecting an urban biosphere reserve concept as well as its function as the habitats of various wild animals. To this end, a set of processes of a biosphere reserve-based basic conception and master plan, sectoral plans, construction and monitoring were applied to the rooftop of 12-story UNESCO Building in Seoul. In particular, the rooftop habitats were divided into core area, buffer zone and transition area, and habitats and facilities suitable to the characteristics of each space were planned. By aligning a plantation planning map with environmental conditions such as topography and water, creation of diverse habitats was enabled. As a result, a set of various habitats including wetlands, wild grassland, shrubs, forest trees and vegetable fields was created at the site. Species living in these habitats included 148 plant species, 62 insect species, 2 amphibian species, 3 fishery species and 3 bird species. The rooftop eco-park of UNESCO Building, which was created one year ago, is assessed as an important space for conservation of biodiversity as well as a place where a biosphere reserve concept was well applied. Meanwhile, for this rooftop biotope to be a pioneer of urban biosphere reserve-based types, a number of principles & methodologies suggested in this study need to be applied, In a perspective of landscape ecology, maintenance efforts should be linked with green areas in neighboring areas, which are the sources of species, In addition, considering that the rooftop biotope is a restored ecosystem, theories and approaches from restoration ecology should be applied. On-going monitoring on environmental changes is also required as the site is located in the urban center, Ultimately, rooftop biotopes including the case study area should contribute in promoting the socio-economic, cultural, and spiritual sustainability as well as environmental sustainability of a city.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.153-153
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• 2019

Evapotranspiration (ET) is an important component of hydrological processes. Accurate estimates of ET variation are of vital importance for natural hazard adaptation and water resource management. This study first developed a soil water index (SWI)-based Priestley-Taylor algorithm (SWI-PT) based on the enhanced vegetation index (EVI), SWI, net radiation, and temperature. The algorithm was then compared with a modified satellite-based Priestley-Taylor ET model (MS-PT). After examining the performance of the two models at 10 flux tower sites in different land cover types over East Asia and Australia, the daily estimates from the SWI-PT model were closer to observations than those of the MS-PT model in each land cover type. The average correlation coefficient of the SWI-PT model was 0.81, compared with 0.66 in the original MS-PT model. The average value of the root mean square error decreased from $36.46W/m^2$ to $23.37W/m^2$ in the SWI-PT model, which used different variables of soil moisture and vegetation indices to capture soil evaporation and vegetative transpiration, respectively. By using the EVI and SWI, uncertainties involved in optimizing vegetation and water constraints were reduced. The estimated ET from the MS-PT model was most sensitive (to the normalized difference vegetation index (NDVI) in forests) to net radiation ($R_n$) in grassland and cropland. The estimated ET from the SWI-PT model was most sensitive to $R_n$, followed by SWI, air temperature ($T_a$), and the EVI in each land cover type. Overall, the results showed that the MS-PT model estimates of ET in forest and cropland were weak. By replacing the fraction of soil moisture ($f_{sm}$) with the SWI and the NDVI with the EVI, the newly developed SWI-PT model captured soil evaporation and vegetation transpiration more accurately than the MS-PT model.

• Ocean and Polar Research
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• v.41 no.3
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• pp.159-168
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• 2019

Understanding the interaction between climate and the water cycle is critical especially in a drought sensitive region such as California. This study explored hydrologic changes in central and southern California in relation to the glacial-interglacial climate cycles over the last 30 thousand years. To do this, we reconstructed paleovegetation using plant wax carbon isotopic compositions (${\delta}^{13}C$) preserved in marine sediment cores retrieved from the central California continental shelf (ODP Site 1018) and Santa Barbara Basin (ODP Site 893A). The results were then compared to the existing sea surface temperature (SST) and pollen records from the same cores to understand terrestrial hydrology in relation to oceanographic processes. The Last Glacial was generally dry both in central and southern California, indicated by grassland expansion, confirming the previously suggested notion that the westerly storm track that supplies the majority of the precipitation in California may not have moved southward during the glacial period. Southern California was drier than central California during the Last Glacial Maximum (LGM). This drying trend may have been associated with the weakening of the California Current and northerly winds leading to the early increase in SST in southern California and decline in both offshore and coastal upwelling. The climate was wetter during the Holocene in both regions compared to the glacial period and forest coverage increased accordingly. We attribute this wetter condition to the precipitation contribution increase from the tropics. Overall, we found a clear synchronicity between the terrestrial and marine environment which showed that the terrestrial vegetation composition in California is greatly affected by not only the global climate states but also regional oceanographic and atmospheric conditions that regulate the timing and amount of precipitation over California.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1591-1604
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• 2020

This study is a study on classifying land cover by applying high-resolution satellite images to deep learning algorithms and verifying the performance of algorithms for each spatial object. For this, the Fully Convolutional Network-based algorithm was selected, and a dataset was constructed using Kompasat-3 satellite images, land cover maps, and forest maps. By applying the constructed data set to the algorithm, each optimal hyperparameter was calculated. Final classification was performed after hyperparameter optimization, and the overall accuracy of DeeplabV3+ was calculated the highest at 81.7%. However, when looking at the accuracy of each category, SegNet showed the best performance in roads and buildings, and U-Net showed the highest accuracy in hardwood trees and discussion items. In the case of Deeplab V3+, it performed better than the other two models in fields, facility cultivation, and grassland. Through the results, the limitations of applying one algorithm for land cover classification were confirmed, and if an appropriate algorithm for each spatial object is applied in the future, it is expected that high quality land cover classification results can be produced.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.3
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• pp.47-62
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• 2022

The Ministry of Environment has been working to reduce the impact on biodiversity, ecosystems, and social costs caused by soil runoff from highland Agricultural fields by setting up non-point pollution source management districts. To reduce soil loss, runoff path reduction technology has been applied, but it has been less cost effective. In addition, non-point pollution sources cause environmental conflicts in downstream areas, and recently highland Agricultural fields are becoming vulnerable to climate change. The Ministry of Environment is promoting the optimal management plan in earnest to convert arable land into forests and grasslands, but since non-point pollution is not a simple environmental problem, it is necessary to approach it from the aspect of NbS(Nature-Based Solution). In this study, a scenario for applying the nature-based solution was established for three subwatersheds west of Haean-myeon, Yanggu-gun, Gangwon-do. The soil loss distribution was spatialized through GeoWEPP and the amount of soil loss was compared for the non-point pollution reduction effect of mixed forests and grasslands. When cultivated land with a slope of 20% or more and ginseng fields were restored to perennial grasslands and mixed forests, non-point pollution reduction effects of about 32% and 29.000 tons compared to the current land use were shown. Also, it was confirmed that mixed forest rather than perennial grassland is an effective nature-based solution to reduce non-point pollution.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.76-91
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• 2016

The purpose of this study is to analyze the effects of spatial characteristics, such as land cover and topography, on heat wave days from the city of Milyang, which has recently drawn attention for its heat wave problems. The number of heat wave days was calculated utilizing RCP-based South Korea climate data from 2000 to 2010. Land cover types were reclassified into urban area, agricultural area, forest area, water, and grassland using 2000, 2005, and 2010 land cover data constructed by the Ministry of Environment. Topographical features were analyzed by topographic position index (TPI) using a digital elevation model (DEM) with 30 m spatial resolution. The results show that the number of heat wave days was 31.4 days in 2000, which was the highest, followed by 26.9 days in 2008, 24.2 days in 2001, and 24.0 days in 2010. The heat wave distribution was relatively higher in agricultural areas, valleys, and rural areas. The topography of Milyang contains more mountainous slope (51.6%) than flat (19.7%), while large-scale valleys (12.2%) are distributed across some of the western region. Correlation analysis between heat wave and spatial characteristics showed that the correlation between forest area land cover and number of heat wave days was negative (-0.109), indicating that heat wave can be mitigated. Topographically, flat areas and heat wave showed a positive correlation (0.305). These results provide important insights for urban planning and environmental management for understanding the impact of land development and topographic change on heat wave.