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MODIS Data-based Crop Classification using Selective Hierarchical Classification
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  • Journal title : Korean Journal of Remote Sensing
  • Volume 32, Issue 3,  2016, pp.235-244
  • Publisher : The Korean Society of Remote Sensing
  • DOI : 10.7780/kjrs.2016.32.3.3
 Title & Authors
MODIS Data-based Crop Classification using Selective Hierarchical Classification
Kim, Yeseul; Lee, Kyung-Do; Na, Sang-Il; Hong, Suk-Young; Park, No-Wook; Yoo, Hee Young;
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 Abstract
In large-area crop classification with MODIS data, a mixed pixel problem caused by the low resolution of MODIS data has been one of main issues. To mitigate this problem, this paper proposes a hierarchical classification algorithm that selectively classifies the specific crop class of interest by using their spectral characteristics. This selective classification algorithm can reduce mixed pixel effects between crops and improve classification performance. The methodological developments are illustrated via a case study in Jilin city, China with MODIS Normalized Difference Vegetation Index (NDVI) and Near InfRared (NIR) reflectance datasets. First, paddy fields were extracted from unsupervised classification of NIR reflectance. Non-paddy areas were then classified into corn and bean using time-series NDVI datasets. In the case study result, the proposed classification algorithm showed the best classification performance by selectively classifying crops having similar spectral characteristics, compared with traditional direct supervised classification of time-series NDVI and NIR datasets. Thus, it is expected that the proposed selective hierarchical classification algorithm would be effectively used for producing reliable crop maps.
 Keywords
Crop;MODIS;Hierarchical Classification;NDVI;
 Language
Korean
 Cited by
 References
1.
Antonarakis, A., K.S., Richards, and J. Brasington, 2008. Object-based land cover classification using airborne LiDAR, Remote Sensing of Environment, 112(6): 2988-2998. crossref(new window)

2.
Bruzzone, L., M. Marconcini, U. Wegmuller, and A. Wiesmann, 2004. An advanced system for the automatic classification of multitemporal SAR images, IEEE Transactions on Geoscience and Remote Sensing, 42(6): 1321-1334. crossref(new window)

3.
Chen, J., J. Chen, A. Liao, X. Cao, L. Chen, X. Chen, C. He, G. Han, S. Peng, M. Lu, W. Zhang, X. Tong, and J. Mills, 2015. Global land cover mapping at 30m resolution: A POK-based operational approach, ISPRS Journal of Photogrammetry and Remote Sensing, 103: 7-27. crossref(new window)

4.
Conrad, C., R.R. Colditz, S. Dech, D. Klein, and P.L.G. Vlek, 2011. Temporal segmentation of MODIS time series for improving crop classification in Central Asian irrigation systems, International Journal of Remote Sensing, 32(23): 8763-8778. crossref(new window)

5.
Doraiswamy, P.C., A.J. Stern, and B. Akhmedov, 2007. Crop classification in the US Corn Belt using MODIS imagery, Proc. of 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, July 23-28, pp. 809-812.

6.
Franklin S. and M. Wulder, 2002. Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas, Progress in Physical Geography, 26(2): 173-205. crossref(new window)

7.
Hao, P., Y. Zhan, L. Wang, Z. Niu, and M. Shakir, 2015. Feature selection of time series MODIS data for early crop classification using random forest: A case study in Kansas, USA, Remote Sensing, 7(5): 5347-5369. crossref(new window)

8.
Jain, A.K., 2010. Data clustering: 50 years beyond Kmeans, Pattern Recognition Letters, 31(8): 651-666. crossref(new window)

9.
Kim, Y., N.-W. Park, S. Hong, K. Lee, and H.Y. Yoo, 2014. 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, 30(4): 493-503 (in Korean with English abstract). crossref(new window)

10.
Lee, E., J.H. Kastens, and S.L. Egbert, 2016. Investigating collection 4 versus collection 5 MODIS 250 m NDVI time-series data for crop separability in Kansas, USA, International Journal of Remote Sensing, 37(2): 341-355. crossref(new window)

11.
Li, J., W.P. Menzel, Z. Yang, R.A. Frey, and S.A. Ackerman, 2003. High-spatial-resolution surface and cloud-type classification from MODIS multispectral band measurements, Journal of Applied Meteorology, 42(2): 204-226. crossref(new window)

12.
Lobell, D. B. and G. P. Asner, 2004. Cropland distributions from temporal unmixing of MODIS data, Remote Sensing of Environment, 93(3): 412-422. crossref(new window)

13.
Melgani, F. and L. Bruzzone, 2004. Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on Geoscience and Remote Sensing, 42(8): 1778-1790. crossref(new window)

14.
Mountrakis, G., J. Im, and C. Ogole, 2011. Support vector machines in remote sensing: A review, ISPRS Journal of Photogrammetry and Remote Sensing, 66(3): 247-259. crossref(new window)

15.
Senthilnath, J., S. Bajpai, S. Omkar, P. Diwakar, and V. Mani, 2012. An approach to multi-temporal MODIS image analysis using image classification and segmentation, Advances in Space Research, 50(9): 1274-1287. crossref(new window)

16.
Simonneaux, V., B. Duchemin, D. Helson, S. Er-Raki, A. Olioso, and A. Chehbouni, 2008. The use of high-resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central morocco, International Journal of Remote Sensing, 29(1): 95-116. crossref(new window)

17.
Small, C., 2001. Estimation of urban vegetation abundance by spectral mixture analysis, International Journal of Remote Sensing, 22(7): 1305-1334. crossref(new window)

18.
Somers, B., G. P. Asner, L. Tits, and P. Coppin, 2011. Endmember variability in spectral mixture analysis: A review, Remote Sensing of Environment, 115(7): 1603-1616. crossref(new window)

19.
Sulla-Menashe, D., M.A. Friedl, O.N. Krankina, A. Baccini, C.E. Woodcock, A. Sibley, G. Sun, V. Kharuk, and V. Elsakov, 2011. Hierarchical mapping of Northern Eurasian land cover using MODIS data, Remote Sensing of Environment, 115(2): 392-403. crossref(new window)

20.
Wardlow, B.D. and S.L. Egbert, 2008. Large-area crop mapping using time-series MODIS 250m NDVI data: An assessment for the U.S. Central Great Plains, Remote Sensing of Environment, 112(3): 1096-1116. crossref(new window)

21.
Wessels, K., R. De Fries, J. Dempewolf, L. Anderson, A. Hansen, S. Powell, and E. Moran, 2004. Mapping regional land cover with MODIS data for biological conservation: Examples from the Greater Yellowstone Ecosystem, USA and Para State, Brazil, Remote Sensing of Environment, 92(1): 67-83. crossref(new window)

22.
Xavier, A.C., B.F. Rudorff, Y.E. Shimabukuro, L.M.S. Berka, and M.A. Moreira, 2006. Multi temporal analysis of MODIS data to classify sugarcane crop, International Journal of Remote Sensing, 27(4): 755-768. crossref(new window)

23.
Xie, Y., Z. Sha, and M. Yu, 2008. Remote sensing imagery in vegetation mapping: A review, Journal of Plant Ecology, 1(1): 9-23. crossref(new window)

24.
Yu, Q., P. Gong, N. Clinton, G. Biging, M. Kelly, and D. Schirokauer, 2006. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery, Photogrammetric Engineering and Remote Sensing, 72(7): 799-811. crossref(new window)

25.
Zhang, J. and G. Foody, 2001. Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: Statistical and artificial neural network approaches, International Journal of Remote Sensing, 22(4): 615-628. crossref(new window)

26.
Zhang, T., J. Qi, Y. Gao, Z. Ouyang, S. Zeng, and B. Zhao, 2015. Detecting soil salinity with MODIS time series VI data, Ecological Indicators, 52: 480-489. crossref(new window)

27.
Zhong, C., C. Wang, and C. Wu, 2015. MODIS-based fractional crop mapping in the US midwest with spatially constrained phenological mixture analysis, Remote Sensing, 7(1): 512-529. crossref(new window)