• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.107-109
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• 2003

Optical sensor-based land cover categories produce spectral signature confusion along with degraded classification accuracy. In the classification tasks, the goal of fusing data from different sensors is to reduce the classification error rate obtained by single source classification. This paper describes the result of land cover/land use classification derived from solely of Landsat TM (TM) and multisensor image fusion between JERS 1 SAR (JERS) and TM data. The best radar data manipulation is fused with TM through various techniques. Classification results are relatively good. The highest Kappa Coefficient is derived from classification using principal component analysis-high pass filtering (PCA+HPF) technique with the Overall Accuracy significantly high.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.477-487
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• 2010

In North Korea, forests were intensively degraded by forest land reclamation for food production and firewood collection since the mid-1970s. These degraded forests have to be certainly recovered for economic support, environmental protection and disaster prevention. In order to provide detailed land cover information of forest recovery project (A/R CDM), this study was focused to develop an improved classification method for degraded forest using 2.5m SPOT-5 pan-sharpened image. The degraded forest of North Korea shows various different types of texture. This study used GLCM texture bands of segmented image with spectral bands during forest cover classification. When scale factor 40/shape factor 0.3 was used as a parameter set to generate segment image, segment image was generated on suitable segment scale that could classify types of degraded forest. Forest land cover types were classified with an optimum band combination of Band1, Band2, band3, GLCM dissimilarity (band2), GLCM homogeneity (band2) and GLCM standard deviation (band3). Segment-based classification method using spectral bands and texture bands reached an 80.4% overall accuracy, but the method using only spectral bands yielded an 70.3% overall accuracy. As using spectral and texture bands, a classification accuracy of stocked forest and unstocked forest showed an increase of 23~25%. In this research, SPOT-5 pan-sharpened high-resolution satellite image could provide a very useful information for classifying the forest cover of North Korea in which field data collection was not available for ground truth data and verification directly. And segment-based classification method using texture information improved classification accuracy of degraded forest.

• Journal of Forest and Environmental Science
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• v.36 no.3
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• pp.199-206
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• 2020

Satchari National Park is one of the most biodiverse forest in Bangladesh and home of many endangered flora and fauna. 206 tons of CO₂ per hectare is sequestrated in this national park every year which helps to mitigate climate issues. As people living near the area are dependent on this forest, degradation has become a regular phenomenon destroying the forest biodiversity by altering its forest cover. So, it is important to map land cover quickly and accurately for the sustainable management of Satchari National Park. The main objective of this study was to obtain information on land cover change using remote sensing data. Combination of unsupervised NDVI classification and supervised classification using maximum likelihood is followed in this study to find out land cover map. The analysis showed that the land cover is gradually converting from one land use type to another. Dense forest becoming degraded forest or bare land. Although it was slowed down by the establishment of 'National Park' on the study site, forecasting shows that it is not enough to mitigate forest degradation. Legal steps and proper management strategies should be taken to mitigate causes of degradation such as illegal felling.

• Journal of Forest and Environmental Science
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• v.39 no.3
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• pp.167-179
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• 2023

The extent of change in the Land use/Land cover (LULC) of Okomu National Park (ONP) and fringe communities was evaluated. High resolution Landsat imagery was used to identify the major vegetation cover/land use systems and changes around the national park and fringe communities while field visits/ground truthing, involving the collection of coordinates of the locations was carried out to ascertain the various land cover/land use types identified on the images, and the extent of change over three-time series (2000, 2010 and 2020). The change detection was analyzed using area calculation, change detection by nature and normalized difference vegetation index (NDVI). The result of the classification and analysis of the LULC Change of ONP and fringe communities revealed an alarming rate of encroachment into the protected area. All the classification features analyzed had notable changes from 2000-2020. The forest, which was the dominant LULC feature in 2000, covering about 66.19% of the area reduced drastically to 36.12% in 2020. Agricultural land increased from 6.14% in 2000 to 34.06% in 2020 while vegetation (degraded land) increased from 27.18% in 2000 to 38.89% in 2020. The magnitude of the change in ONP and surroundings showed the forest lost -247.136 km² (50.01%) to other land cover classes with annual rate change of 10%, implying that 10% of forest land was lost annually in the area for 20 years. The NDVI classification values of 2020 indicate that the increase in medium (399.62 km² ) and secondary high (210.17 km² ) vegetation classes which drastically reduced the size of the high (38.07 km² ) vegetation class. Consequent disappearance of the high forests of Okomu is inevitable if this trend of exploitation is not checked. It is pertinent to explore other forest management strategies involving community participation.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.354-359
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• 2009

Land cover types of Hustai National Park (HNP) in Mongolia, a hotspot area with rare species, were classified and their temporal changes were evaluated using Landsat MSS TM/ETM data between 1994 and 2000. Maximum likelihood classification analysis showed an overall accuracy of 88.0% and 85.0% for the 1994 and 2000 images, respectively. Kappa coefficients associated with the classification were resulted to 0.85 for 1994 and 0.82 for 2000 image. Land cover types revealed significant temporal changes in the classification maps between 1994 and 2000. The area has increased considerably by $166.5km^2$ for mountain steppe. By contrast, agricultural areas and degraded areas affected by human being activity were decreased by $46.1km^2$ and $194.8km^2$ over the six year span, respectively. These areas were replaced by mountain steppe area. Specifically, forest area was noticeably fragmented, accompanied by the decrease of $\sim400$ ha. The forest area revealed a pattern with systematic gain and loss associated with the specific phenomenon called as forest free-south slope. We discussed the potential environmental conditions responsible for the systematic pattern and addressed other biological impacts by outbreaks of forest pests and ungulates.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.19 no.1
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• pp.73-83
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• 2016

Forest degradation reduces ecosystem services provided by forest and could lead to change in composition of species. In North Korea, there has been significant forest degradation due to conversion of forest into terrace fields for food production and cut-down of forest for fuel woods. This study analyzed the phenological changes in North Korea, in terms of vegetation and moisture in soil and vegetation, from March to Octorber 2013, using MODIS (MODerate resolution Imaging Spectroradiometer) images and indexes including NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index), and NDWI (Normalized Difference Water Index). In addition, marginal farmland was derived using elevation data. Lastly, degraded terrace fields of 16 degree was analyzed using NDVI, NDSI, and NDWI indexes, and marginal farmland characteristics with slope variable. The accuracy value of land cover classification, which shows the difference between the observation and analyzed value, was 84.9% and Kappa value was 0.82. The highest accuracy value was from agricultural (paddy, field) and forest area. Terrace fields were easily identified using slope data form agricultural field. Use of NDVI, NDSI, and NDWI is more effective in distinguishing deforested terrace field from agricultural area. NDVI only shows vegetation difference whereas NDSI classifies soil moisture values and NDWI classifies abandoned agricultural fields based on moisture values. The method used in this study allowed more effective identification of deforested terrace fields, which visually illustrates forest degradation problem in North Korea.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.2
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• pp.71-84
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• 2021

Restoration of DMZ has come up with the discussion on the peaceful use of the DMZ and the conservation plan of the army. In this study, we aim to identify soil characteristics of 108 sites to figure out environmental conditions around the iron fence of DMZ where vegetation has been removed repeatedly. Based on the soil characteristics and climate variables, hierarchy clustering was performed to categorize sites. As a result, we categorized 108 sites into 4 types: middle elevation region, lowland, East coast lowland, other areas. Group of 'other area' is only high in nutrient and clay proportion. Others are in igneous rock and metamorphic rocks with a high proportion of sand and lower nutrients than the optimum range of growth in Korean forest soil. The middle elevation region has a high altitude, low temperature. The east coast lowland has a high temperature in January and low precipitation. The lowland has a low altitude and high temperature. This category provides the environmental condition around the DMZ fence and can be used to select plants for restoration. The restoration project around the DMZ iron fence should satisfy the security of military plans, which means that functional restoration is prior to ecological restoration such as vegetation management under a power line. Additionally, improvement of soil quality and surface stability through restoration projects is required to enhance the resilience of the ecosystem in DMZ.