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YU Hao, ZHANG Bai, WANG Zongming. Comparative Study on Changes of Croplands Between North Korea and South Korea During 1990-2015[J]. Chinese Geographical Science, 2018, 28(6): 920-934. doi: 10.1007/s11769-018-0985-z
Citation: YU Hao, ZHANG Bai, WANG Zongming. Comparative Study on Changes of Croplands Between North Korea and South Korea During 1990-2015[J]. Chinese Geographical Science, 2018, 28(6): 920-934. doi: 10.1007/s11769-018-0985-z
shu

Comparative Study on Changes of Croplands Between North Korea and South Korea During 1990-2015

doi: 10.1007/s11769-018-0985-z
  • 1.

    Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049, China

Funds:  Under the auspices of Key Deployment Project of Chinese Academy of Sciences (No. KZZD-EW-08-02)
More Information
  • Corresponding author: ZHANG Bai.E-mail:zhangbai@neigae.ac.cn
  • Received Date: 2017-12-19
  • Rev Recd Date: 2018-03-06
  • Publish Date: 2018-12-27
  • Studies on long-term change of cropland is of great significance to the utilization of land resources and the implementation of scientific agricultural policies. The Korean Peninsula, adjacent to China, plays an important role in the international environment of Northeast Asia. The Korean Peninsula includes South Korea and North Korea-two countries that have a great difference in their institutions and economic developments. Therefore, we aim to quantify the spatiotemporal changes of croplands in these two countries using Landsat Thematic Imager (TM) and Operational Land Imager (OLI) imagery, and to compare the differences of cropland changes between the two countries. This paper take full advantage of ODM approach (object-oriented segmentation and decision-tree classification based on multi-season imageries) to obtain the distribution of croplands in 1990 and 2015. Results showed that the overall classification accuracy of cropland data is 91.10% in 1990 and 92.52% in 2015. The croplands were mainly distributed in areas with slopes that were less than 8° and with elevations that were less than 300 m in the Korean Peninsula. However, in other region (slope > 8° or elevation > 300 m), the area and proportion of North Korea's croplands were significantly higher than that of South Korea. Croplands significantly increased by 15.02% in North Korea from 1990 to 2015. In contrast, croplands in South Korea slightly decreased by 1.32%. During the 25 years, policy shift, economic development, population growth, and urban sprawl played primary roles for cropland changes. Additionally, the regional differences of cropland changes were mainly due to different agriculture policies implemented by different countries. The achievements of this study can provide scientific guidance for the protection and sustainability of land resources.
  • [1] ASIASOCIET, 2017. The Geography of the Koreas. http://asiasociety.org/education/geography-koreas
    [2] Bae Y, Sellers J M, 2007. Globalization, the developmental state and the politics of urban growth in Korea:a multilevel analysis. International Journal of Urban and Regional Research, 31(3):543-560. doi:10.1111/j.1468-2427.2007. 00737.x
    [3] Bargiel D, 2017. A new method for crop classification combining time series of radar images and crop phenology information. Remote Sensing of Environment, 198:369-383. doi:10. 1016/j.rse.2017.06.022
    [4] Bartholomé E, Belward A S, 2005. GLC2000:a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26(9):1959-1977. doi: 10.1080/01431160412331291297
    [5] Burney J A, Davisc S J, Lobell D B, 2010. Greenhouse gas mitigation by agricultural intensification. Proceedings of the National Academy of Sciences of the United States of America, 107(26):12052-12057. doi: 10.1073/pnas.0914216107
    [6] Castillejo-González I L, López-Granados F, García-Ferrer A et al., 2009. Object-and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery. Computers and Electronics in Agriculture, 68(2):207-215. doi: 10.1016/j.compag.2009.06.004
    [7] Chen J, Chen J, Liao A P et al., 2015. Global land cover mapping at 30m resolution:a POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103:7-27. doi: 10.1016/j.isprsjprs.2014.09.002
    [8] Chen L, Ren C Y, Zhang B et al., 2017. Spatiotemporal dynamics of coastal wetlands and reclamation in the Yangtze Estuary during past 50 years (the 1960s-2015). Chinese Geographical Science, 28(3):386-399. doi: 10.1007/s11769-017-0925-3
    [9] Cho C J, 2002. The Korean growth-management programs:issues, problems and possible reforms. Land Use Policy, 19(1):13-27. doi: 10.1016/S0264-8377(01)00035-7
    [10] Ellis E C, Ramankutty N, 2008. Putting people in the map:anthropogenic biomes of the world. Frontiers in Ecology and the Environment, 6(8):439-447. doi: 10.1890/070062
    [11] Ellis E C, Goldewijk K K, Siebert S et al., 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography, 19(5):589-606. doi:10.1111/j.1466-8238. 2010.00540.x
    [12] Engler R, Teplyakov V, Adams J M, 2014. An assessment of forest cover trends in South and North Korea, from 1980 to 2010. Environmental Management, 53(1):194-201. doi:10. 1007/s00267-013-0201-y
    [13] FAO (Food and Agriculture Organization of the United Nations), 2009. Global agriculture towards 2050. High Level Export Forum. Available at:http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf.
    [14] FAO (Food and Agriculture Organization of the United Nations), 2015. FAO Statistical Database. Available at:htpp://faostat.fao.org/.
    [15] Foley J A, Ramankutty N, Brauman K A et al., 2011. Solutions for a cultivated planet. Nature, 478(7369):337-342. doi: 10.1038/nature10452
    [16] Gilbertson J K, Kemp J, van Niekerk A, 2017. Effect of pan-sharpening multi-temporal Landsat 8 imagery for crop type differentiation using different classification techniques. Computers and Electronics in Agriculture, 134:151-159. doi: 10.1016/j.compag.2016.12.006
    [17] Goldewijk K K, Hall F G, Collatz G J et al., 2007. ISLSCP Ⅱ Historical Land Cover and Land Use, 1700-1990. ORNL DAAC, Oak Ridge, Tennessee, USA. doi: 10.3334/ORNLDAAC/967
    [18] Haggard S, Kang D, Moon C I, 1997. Japanese Colonialism and Korean Development:A Critique. World Development, 25(6):867-881. doi: 10.1016/S0305-750X(97)00012-0
    [19] Hong S K, Koh C H, Harris R R et al., 2010. Land use in Korean tidal wetlands:impacts and management strategies. Environmental Management, 45(5):1014-1026. doi:10. 1007/s00267-006-0164-3
    [20] Huete A R, Liu H Q, Batchily K et al., 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59(3):440-451. doi: 10.1016/S0034-4257(96)00112-5
    [21] Jiang Z Y, Huete A R, Didan K et al., 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10):3833-3845. doi: 10.1016/j.rse.2008.06.006
    [22] Jin C, Xiao X M, Dong J W et al., 2016a. Mapping paddy rice distribution using multi-temporal Landsat imagery in the Sanjiang Plain, northeast China. Frontiers of Earth Science, 10(1):49-62. doi: 10.1007/s11707-015-0518-3
    [23] Jin N, Tao B, Ren W et al., 2016b. Mapping irrigated and rainfed wheat areas using multi-temporal satellite data. Remote Sensing, 8(3):207. doi: 10.3390/rs8030207
    [24] Joo H, Mishra A, 2013. Labor supply and food consumption behavior of farm households:evidence from South Korea. In:2013 Annual Meeting. Washington, D.C.:Agricultural and Applied Economics Association.
    [25] Kastner T, Rivas M J I, Koch W et al., 2012. Global changes in diets and the consequences for land requirements for food. Proceedings of the National Academy of Sciences of the United States of America, 109(18):6868-6872. doi:10. 1073/pnas.1117054109
    [26] Kastner T, Erb K H, Haberl H, 2014. Rapid growth in agricultural trade:effects on global area efficiency and the role of management. Environmental Research Letters, 9(3):034015. doi: 10.1088/1748-9326/9/3/034015
    [27] Lee M B, Kim N S, Kang C et al., 2003. Estimation of soil loss due to cropland increase in Hoeryeung, Northeast Korea. Journal of the Korean Medical Association, 9(3):373-384.
    [28] Lee M B, Jin S J, 2008. A study on characteristics of the spatial distribution of the cropland and forest by the cultivation expansion in North Korea. Journal of the Korean Geomorphological Association, 15(4):29-37
    [29] Lee S D, Miller-Rushing A J, 2014. Degradation, urbanization, and restoration:a review of the challenges and future of conservation on the Korean Peninsula. Biological Conservation, 176:262-276. doi: 10.1016/j.biocon.2014.05.010
    [30] Lei G B, Li A N, Bian J H et al., 2016. Land cover mapping in southwestern China using the HC-MMK approach. Remote Sensing, 8(4):305. doi: 10.3390/rs8040305
    [31] Lim C H, Choi Y, Kim M et al., 2017. Impact of deforestation on agro-environmental variables in Cropland, North Korea. Sustainability, 9(8):1354. doi: 10.3390/su9081354
    [32] Loveland T R, Belward A S, 1997. The IGBP-DIS global 1 km land cover data set, DISCover:first results. International Journal of Remote Sensing, 18(15):3289-3295. doi:10. 1080/014311697217099
    [33] Michalk D L, Mueller H P, 2003. Strategies to improve cropland soils in North Korea using pasture leys. Agriculture, Ecosystems & Environment, 95(1):185-202. doi:10.1016/s 0167-8809(02)00096-8
    [34] Ning J, Zhang S W, Cai H Y et al., 2012. A comparative analysis of the MODIS land cover data sets and globcover land cover data sets in Heilongjiang Basin. Journal of GEO-infromtion Sciences, 14(2):240-249
    [35] OECD (Organisation for Economic Co-operation and Development), 2008. Chapter IV. Evaluation and recom-mendations. In:Evaluation of Agricultural Policy Reforms in Korea. OECD, 69-72.
    [36] Olofsson P, Stehman S V, Woodcock C E et al., 2012. A global land-cover validation data set, part I:fundamental design principles. International Journal of Remote Sensing, 33(18):5768-5788. doi: 10.1080/01431161.2012.674230
    [37] Ouyang Zhiyun, Zhang Lu, Wu Bingfang et al., 2015. An ecosystem classification system based on remote sensor information in China. Acta Ecologica Science, 35(2):219-226. (in Chinese)
    [38] Pan Xiaofang, 2008. Rice production and strategy in South Korea. North Rice, 38(4):78-80. (in Chinese)
    [39] Park M S, Lee H, 2014. Forest policy and law for sustainability within the Korean Peninsula. Sustainability, 6(12):5162-5186. doi: 10.3390/su6085162
    [40] Qiang Baifa, 2010. Study on Development of Agricultural Modernization in South Korea. Yangling:Northwest A&F University. (in Chinese)
    [41] Qin Y W, Xiao X M, Dong J W et al., 2015. Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 105:220-233. doi:10.1016/j.isprsjprs.2015. 04.008.
    [42] Song Jizhong, 1998. On the environmental status and environmental policies in the South Korea. Inner Mongolia Environmental Protection, 10:3-6. (in Chinese)
    [43] Song X P, Potapov P V, Krylov A et al., 2017. National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey. Remote Sensing of Environment, 190:383-395. doi:10.1016/j. rse.2017.01.008
    [44] Tang J M, Bu K, Yang J C et al., 2012. Multitemporal analysis of forest fragmentation in the upstream region of the Nenjiang River Basin, Northeast China. Ecological Indicators, 23:597-607. doi: 10.1016/j.ecolind.2012.05.012
    [45] Tao H, Nan Y, Liu Z F, 2017. Spatiotemporal patterns of forest in the transnational area of Changbai Mountain from 1977 to 2015:a comparative analysis of the Chinese and DPRK sub-regions. Sustainability, 9(6):1054. doi: 10.3390/su9061054
    [46] Tilmana D, Balzer C, Hill J et al., 2011. Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences of the United States of America, 108(50):20260-20264. doi: 10.1073/pnas.1116437108
    [47] WORLD GRAIN, 2016. South Korea. Aveilable at:http://www.world-grain.com/Departments/Country-Focus/Country-Focus-Home/South-Korea-2016.aspx
    [48] Wu Wenbin, Yang Peng, Zhang Li et al., 2009. Accuracy assessment of four global land cover datasets in China. Transactions of the CSAE, 25(12):167-173. (in Chinese)
    [49] Xiao X, Boles S, Frolking S et al., 2002. Landscape-scale characterization of cropland in China using Vegetation and landsat TM images. International Journal of Remote Sensing, 23(18):3579-3594. doi: 10.1080/01431160110106069
    [50] Ye Y, Wei X Q, Li F et al., 2015. Reconstruction of cropland cover changes in the Shandong Province over the past 300 years. Scientific Reports, 5(1):13642. doi: 10.1038/srep13642
    [51] Yu Q, Gong P, Clinton N et al., 2006. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72(7):799-811. doi: 10.14358/pers.72.7.799
    [52] Zhang G L, Xiao X M, Biradar C M et al., 2017a. Spatiotemporal patterns of paddy rice croplands in China and India from 2000 to 2015. Science of the Total Environment, 579:82-92. doi: 10.1016/j.scitotenv.2016.10.223
    [53] Zhang Hehan, Guo Qing, 2014. A comparative study of agricultural policies among China, Janpan and South Korea. World Argriculture, (1):55-59. (in Chinese)
    [54] Zhang Lei, Wu Bingfang, Li Xiaosong et al., 2014. Classification system of China land cover for carbon budget. Acta Ecologica Science, 34(24):7158-7166. (in Chinese)
    [55] Zhang X H, Treitz P M, Chen D M et al., 2017b. Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure. International Journal of Applied Earth Observation and Geoinformation, 62:201-214. doi: 10.1016/j.jag.2017.06.010
    [56] Zhou Y T, Xiao X M, Qin Y W et al., 2016. Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images. International Journal of Applied Earth Observation and Geoinformation, 46:1-12. doi: 10.1016/j.jag.2015.11.001
    [57] Zuo L J, Zhang Z X, Zhao X L et al., 2014. Multitemporal analysis of cropland transition in a climate-sensitive area:a case study of the arid and semiarid region of northwest China. Regional Environmental Change, 14(1):75-89. doi:10. 1007/s10113-013-0435-5
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Comparative Study on Changes of Croplands Between North Korea and South Korea During 1990-2015

doi: 10.1007/s11769-018-0985-z
  • 1. Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
Funds:  Under the auspices of Key Deployment Project of Chinese Academy of Sciences (No. KZZD-EW-08-02)
    Corresponding author: ZHANG Bai.E-mail:zhangbai@neigae.ac.cn
  • Received Date: 2017-12-19
  • Rev Recd Date: 2018-03-06
  • Publish Date: 2018-12-27

Abstract: Studies on long-term change of cropland is of great significance to the utilization of land resources and the implementation of scientific agricultural policies. The Korean Peninsula, adjacent to China, plays an important role in the international environment of Northeast Asia. The Korean Peninsula includes South Korea and North Korea-two countries that have a great difference in their institutions and economic developments. Therefore, we aim to quantify the spatiotemporal changes of croplands in these two countries using Landsat Thematic Imager (TM) and Operational Land Imager (OLI) imagery, and to compare the differences of cropland changes between the two countries. This paper take full advantage of ODM approach (object-oriented segmentation and decision-tree classification based on multi-season imageries) to obtain the distribution of croplands in 1990 and 2015. Results showed that the overall classification accuracy of cropland data is 91.10% in 1990 and 92.52% in 2015. The croplands were mainly distributed in areas with slopes that were less than 8° and with elevations that were less than 300 m in the Korean Peninsula. However, in other region (slope > 8° or elevation > 300 m), the area and proportion of North Korea's croplands were significantly higher than that of South Korea. Croplands significantly increased by 15.02% in North Korea from 1990 to 2015. In contrast, croplands in South Korea slightly decreased by 1.32%. During the 25 years, policy shift, economic development, population growth, and urban sprawl played primary roles for cropland changes. Additionally, the regional differences of cropland changes were mainly due to different agriculture policies implemented by different countries. The achievements of this study can provide scientific guidance for the protection and sustainability of land resources.

YU Hao, ZHANG Bai, WANG Zongming. Comparative Study on Changes of Croplands Between North Korea and South Korea During 1990-2015[J]. Chinese Geographical Science, 2018, 28(6): 920-934. doi: 10.1007/s11769-018-0985-z
Citation: YU Hao, ZHANG Bai, WANG Zongming. Comparative Study on Changes of Croplands Between North Korea and South Korea During 1990-2015[J]. Chinese Geographical Science, 2018, 28(6): 920-934. doi: 10.1007/s11769-018-0985-z
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