• Korean Journal of Ornithology
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• v.25 no.2
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• pp.94-100
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• 2018

Bird electrocution on contact with electricity lines is well reported and is quite common in Mongolian open areas. We visited 15-kV electricity distribution pole lines in a Southern Mongolian semi-desert region three times in 2017, in April, July, and September, to assess their risks to birds. The carcasses of 45 electrocuted birds representing 12 species were identified from 250 poles (overall mortality rate of 1.12% every 10 km). The majority of these electrocuted birds were endangered Saker Falcon (n=11) and common Black Kite (n=11). The lacks of roosting or perching sites in our study sites (indeed other open areas in Mongolia) may resulted for such high rate bird electrocution. A 751-base pair (bp) cytochrome c oxidase I (COI) gene was amplified for DNA sequence-based identification of carcasses that were difficult to identify directly. Our findings revealed the high electrocution risk for birds in Mongolian open areas, especially for the birds of prey, with relatively low-efficiency electrocution mitigation approaches. The findings also indicate that there is a need for better understanding of the risk of bird electrocution, particularly in the open areas. This will contribute to the conservation of endangered species.

• The Korean Journal of Quaternary Research
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• v.19 no.2
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• pp.13-17
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• 2005

The Khogno Khan mountain special protected area belongs to Khogno Khan subdistrict of Gurvanbulag district of Bulgan Province and is 46.9 sq.km. with its main feature being mountain forest zone. In this paper we present some results of research of the Anthropological and archaeological team of Mongolian Korean Joint research expeditions carryied out in Khogno Khan mountain special protected area from 27 July to 1 August, 2000. During archaeological reconnaissance we discovered around 27 localities of archaeological monuments belonging to different historical periods (from the Neolithic up to the Mongolian period, $13^{th}-17^{th}$ Century) in the territory of the Khogno Khan special protected area. Based on the results, we especially want to point out 1). The archaeological and historic monuments (from the Neolithic up to modern era) found in the Khogno Khan mountain and its surrounding area show that since the Neolithic period (around 8000 years ago) this area was occupied by the ancestors of Mongolians and it was used during subsequent historic periods on the one hand. 2). On the other hand the Khogno Khan mountain region was one area where there occurred intensive admixture between populations of Kurgan culture, Deerstone culture from the West Mongolia and the population of slab graves culture from Central and East Mongolia during Bronze and Early Iron Age. 3). Today the mountain is one of the area with a unique assemblage of archaeological monuments from different historic periods, what naturally seems like an open-air natural museum of Mongolian Prehistory.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.426-429
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• 2007

One of the widely used applications of remote sensing studies is environmental change detection and biodiversity conservation. The study area Hustai Mountain is situated in the transition zone between the Siberian taiga forest and Central Mongolian arid steppe. Hustai National Park carries out one of several reintroduction programs of takhi (wild horse or Equus ferus przewalskii) from various zoos in the world and it represents one of a few textbook examples of successful reintroduction of an animal extinct in the wild. In this paper we describe the results of an analysis on the change of remaining forest area over the 7-year period since Hustai Mountain was designated as a protected area for reintroduction to wild horses. Today the forested area covers approximately 5% of the Hustai National Park, mostly the north-facing slopes above 1400 m altitude. Birch (Betula platyphylla) and aspen (Populus tremula) trees are predominant in the forest. We used Landsat ETM+ images from two different years and multi temporal MODIS NDVI data. Land types were determined by supervised classification methods (Maximum Likelihood algorithm) verified with ground-truthing data and the Land Change Modeler (LCM) which was developed by Clark Labs. Forested area was classified into three different land types, namely the forest land, mountain meadow and mountain steppe. The study results illustrate that the remaining birch forest has rapidly changed to fragmented forest land and to open areas. Underlying causes for such a rapid change during the 15-year period may be manifold. However, the responsible factors appear to be the drying off and outbreak of forest pest species (such as gypsy moth or Lymantria dispar) in the area.

• 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.