JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Estimation of Paddy Field Area in North Korea Using RapidEye Images
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Estimation of Paddy Field Area in North Korea Using RapidEye Images
Hong, Suk Young; Min, Byoung-Keol; Lee, Jee-Min; Kim, Yihyun; Lee, Kyungdo;
  PDF(new window)
 Abstract
Remotely sensed satellite images can be applied to monitor and obtain land surface information on inaccessible areas. We classified paddy field area in North Korea based on on-screen digitization with visual interpretation using 291 RapidEye satellite images covering the whole country. Criteria for paddy field classification based on RapidEye imagery acquired at different time of rice growth period was defined. Darker colored fields with regular shape in the images with false color composite from early May to late June were detected as rice fields. From early July to late September, it was hard to discriminate rice canopy from other type of vegetation including upland crops, grass, and forest in the image. Regular form of readjusted rice field in the plains and uniform texture when compared with surrounding vegetation. Paddy fields classified from RapidEye imagery were mapped and the areas were calculated by administrative district, province or city. Sixty six percent of paddy fields () were distributed in the west coastal regions including Pyeongannam-do, Pyeonganbuk-do, and Hwanghaenam-do. The paddy field areas classified from RapidEye images showed less than 1% of difference from the paddy field areas of North Korea reported by FAO/WFP (Food and Agriculture Organization/World Food Programme).
 Keywords
Paddy field;Classification;RapidEye;North Korea;
 Language
Korean
 Cited by
1.
MODIS 영상을 이용한 논벼 생산량 추정모형의 적합도 개선을 위한 연구,김배성;김재환;고성보;

한국산학기술학회논문지, 2013. vol.14. 11, pp.5417-5422 crossref(new window)
2.
Classification of Soil Desalination Areas Using High Resolution Satellite Imagery in Saemangeum Reclaimed Land,;;;;;;

한국토양비료학회지, 2013. vol.46. 6, pp.426-433 crossref(new window)
3.
RapidEye 위성영상의 시계열 NDVI 및 객체기반 분류를 이용한 북한 재령군의 논벼 재배지역 추출 기법 연구,이상현;오윤경;박나영;이성학;최진용;

한국농공학회논문집, 2014. vol.56. 3, pp.55-64 crossref(new window)
4.
시계열 식생지수와 과거 작물 재배 패턴을 이용한 대규모 작물 분류도의 조기 제작 - 미국 아이오와 주 사례연구 -,김예슬;박노욱;홍석영;이경도;유희영;

대한원격탐사학회지, 2014. vol.30. 4, pp.493-503 crossref(new window)
5.
초분광 센서 영상의 연구동향과 활용전망,하림;이혁;강태구;

한국농공학회지, 2014. vol.56. 2, pp.33-37
6.
RADARSAT-2 SAR를 이용한 서산 및 평양 지역의 벼 생육 모니터링 적용성 평가 -RapidEye와의 비교를 통해-,나상일;홍석영;김이현;이경도;

한국농공학회논문집, 2014. vol.56. 5, pp.55-65 crossref(new window)
7.
MODIS NDVI와 강수량 자료를 이용한 북한의 벼 수량 추정 연구,홍석영;나상일;이경도;김용석;백신철;

대한원격탐사학회지, 2015. vol.31. 5, pp.441-448 crossref(new window)
8.
Status of Rice Paddy Field and Weather Anomaly in the Spring of 2015 in DPRK,;;;;;;;

한국토양비료학회지, 2015. vol.48. 5, pp.361-371 crossref(new window)
9.
Landsat 영상을 활용한 북한 주요도시의 도시화 지수 분석,김준현;

한국측량학회지, 2015. vol.33. 4, pp.277-286 crossref(new window)
1.
Classification of Soil Desalination Areas Using High Resolution Satellite Imagery in Saemangeum Reclaimed Land, Korean Journal of Soil Science and Fertilizer, 2013, 46, 6, 426  crossref(new windwow)
2.
Terrace Fields Classification in North Korea Using MODIS Multi-temporal Image Data, Journal of the Korea Society of Environmental Restoration Technology, 2016, 19, 1, 73  crossref(new windwow)
3.
Northward expansion of paddy rice in northeastern Asia during 2000-2014, Geophysical Research Letters, 2016, 43, 8, 3754  crossref(new windwow)
4.
Evaluation of the Applicability of Rice Growth Monitoring on Seosan and Pyongyang Region using RADARSAT-2 SAR -By Comparing RapidEye-, Journal of The Korean Society of Agricultural Engineers, 2014, 56, 5, 55  crossref(new windwow)
5.
A Study on Estimating Rice Yield in DPRK Using MODIS NDVI and Rainfall Data, Korean Journal of Remote Sensing, 2015, 31, 5, 441  crossref(new windwow)
6.
An Approach for Improvement of Goodness of Fit on the Estimation of Paddy Rice Yield Using Satellite(MODIS) Images, Journal of the Korea Academia-Industrial cooperation Society, 2013, 14, 11, 5417  crossref(new windwow)
7.
Extraction of paddy field in Jaeryeong, North Korea by object-oriented classification with RapidEye NDVI imagery, Journal of The Korean Society of Agricultural Engineers, 2014, 56, 3, 55  crossref(new windwow)
8.
Urbanization Analysis of Major City in North Korea Using Landsat Imagery, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 2015, 33, 4, 277  crossref(new windwow)
9.
Status of Rice Paddy Field and Weather Anomaly in the Spring of 2015 in DPRK, Korean Journal of Soil Science and Fertilizer, 2015, 48, 5, 361  crossref(new windwow)
10.
Early Production of Large-area Crop Classification Map using Time-series Vegetation Index and Past Crop Cultivation Patterns - A Case Study in Iowa State, USA -, Korean Journal of Remote Sensing, 2014, 30, 4, 493  crossref(new windwow)
 References
1.
Boo, K.S., S.P. Kim, U.G. Kim, J.H. Kim, C.S. Kim, I.S. Ryu, G.T. Park, G.Y. Park, S.H. Park, H.R. Son, B.I. Yoo, G.S. Lee, S.G. Lee, S.C. Lim, and J.G. Choi. 2001. Agriculture in North Korea; Status and Perspectives. Seoul National University Press. p.10. (In Korean)

2.
Campbell, J.B. 1996. Introduction to remote sensing, 2nd ed. The Gilford Press, NewYork, NY, USA, p.4-5, 550-551.

3.
Hong, S.Y., S.K. Rim, S.H. Lee, J.C. Lee, and Y.H. Kim. 2008. Spatial analysis of agro-environment of North Korea using remote sensing I. landcover classification from Landsat TM imagery and topography analysis in North Korea. Korean J. Environ. Agric. 27(2):120-132. (In Korean) crossref(new window)

4.
Hong, S.Y., K.H. Jung, C.U. Choi, M.W. Jang, Y.H. Kim, Y.K. Sonn, and S.K. Ha. 2010. Estimation of SCS runoff curve number and hydrograph by using highly detailed soil map (1:5,000) in a small watershed, Sosu-myeon, Goesan-gun. Korean J. Soil Sci. Fert. 43(3):363-373. (In Korean)

5.
Hong, S.Y., Y.H. Kim, E.Y. Choe, Y.S. Zhang, Y.K. Sonn, C.W. Park, K.H. Jung, B.K. Hyun, S.K. Ha and K.C. Song. 2010. Geographical Information system and remote sensing in Soil Science. Korean J. Soil Sci. Fert. 43(5)562-573. (In Korean)

6.
Hong, S.Y., J.N. Hur, J.B. Ahn, J.M. Lee, B.K. Min, C.K. Lee, Y.H. Kim, K.D. Lee, S.H. Kim, G.Y. Kim, and K.M. Shim. 2012. Estimating rice yield using MODIS NDVI and meteorological data in Korea. Korean Journal of Remote Sensing. 28(5):509-520. (In Korean) crossref(new window)

7.
Korea Institute for National Unification. 2009. 2009 North Korea Outline. (In Korean)

8.
Ku, C.Y. and H.Y. Jang. 2006. Landcover classification using high-resolution satellite image with vector polygon data. Geographical J. Korea. 40(3):449-459.

9.
Lee, K.D., S.Y. Hong, and Y.H. Kim. 2012. Farmland use mapping using high resolution images and land use change analysis in Goyang, Namyangju, and Yongin cities. Korean J. of Soil Sci. and Fert. (in printing). (In Korean)

10.
Ministry of Agriculture and Forestry. 2002. A study on the status of agricultural infrastructure and its renovation in North Korea. Final report. (In Korean)

11.
Ministry of Agriculture and Forestry. 2006. Characteristics of agricultural infrastructure of Imjin river basin and inter-Korean cooperation for agricultural development. Final report. (In Korean)

12.
RDA. 1995. Survey study on farming technical level of Korean-China border area compared with Korean for rice, upland crops, horticulture and livestocks. Mid-term Report. (In Korean)

13.
United States Department of Agriculture, Farm Service Agency. 2004. Common Land Unit, FSA handbook. p.112.

14.
Van der Sande, C.J., S.M. Jong and A.P.J. Roo. 2003. A segmentation and classification of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. Int. J. Appl. Earth Obs. Geoinf. 4:217-229. crossref(new window)