Using object oriented technique to extract jujube based on landsat8 OLI image in Jialuhe Basin
DOI: 10.23977/jipta.2016.11004 | Downloads: 51 | Views: 3679
Xinwei Guo 1, Guotao Dong 1, Wenwang Gao 2, Mingyong Cai 3, Dong Fan 1,4, Huijuan Yin 1, Yaokang Lian 5, Suzhen Dang 1
1 Yellow River Institute of Hydraulic Research, YRCC, MWR Key Laboratory of Soil and Water Loss Process and Control in the Loess Plateau, Shunhe Road 45, Zhengzhou, China
2 Xifeng Soil and Water Conservation Experiment Station, South Street 268, Qingyang, China
3 Satellite Environment Center of MEP, Fengde Road, Beijing, China
4 Henan Polytechnic University, Shiji Road 2001, Jiaozuo, China
5 Research Center for Heihe River water resources and Ecological Protection, Qingyang Road 458, Lanzhou, China
Corresponding AuthorGuotao Dong
Vegetation is a crucial factor that affects watershed hydrological processes. To understand the effect of jujube vegetation variation on streamflow in the Jialuhe River Basin, jujube forest was investigated in this study. Object-oriented classification technique was used to extract jujube characteristics. The primary vegetation types were selected to sample the reflectance, and the spectral response curves were obtained. Then, the rule set of decision tree is constructed based on the comprehensive analysis of the image information and the object type. The classification results show that jujube forest is scattered and mainly distributed in the lower reaches. The area of jujube forest is 8.8 km2, which accounts for about 7.8% of the total area.
KEYWORDSJujube, Landsat 8 OLI, object oriented technique, Jialuhe Basin.
CITE THIS PAPER
Guotao, D. , Mingyong, C. , Dong, F. , Wenwang, G. , Yaokang, L. , Huijun, Y. , Suzhen, D. and Xinwei G. (2016) Using object oriented technique to extract jujube based on landsat8 OLI image in Jialuhe Basin. Journal of Image Processing Theory and Applications (2016) 1: 16-20.
 Niemi G.J. and McDonald M.E., Application of Ecological Indicators. Annual Review of Ecology, Evolution, and Systematics, vol 35, 89-111(2004).
 De Kok R, Schneider T, Ammer U, Object based classification and applications in the alpine forest environment. Int Arch Photogramm Remote Sens vol 32, Part 7-4-3 Wg, Valladolid, Spain, 3–4. June (1999).
 Roy D. P, Wulder M A, Loveland T R, et al., Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145:154-172 (2014).
 Fu, K. S., and Mui, J. K. A survey on image segmentation. Pattern Recognition, 13:3–16 (1981).
 De Bruin, S and Molenaar, M, Remote sensing and geographical information systems.In Spatial Statistics for Remote Sensing, edited by A. Stein, F. van der Meer and B. Gorte (Dordrecht: Kluwer Academic), pp. 41–54 (1999).