EXPERIMENTAL RESEARCH ON RICE PLANTING AREA OF HILLY REGION IN SOUTHERN CHINA USING REMOTELY SENSED DATA

Lai Geying; Yang Xingwei

Vol.11, NO.1, 2000

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2000 > 11(1): 47-54

Lai Geying, Yang Xingwei. Experimental research on rice planting area of hilly region in southern China using remotely sensed data. J Appl Meteor Sci, 2000, 11(1): 47-54.

Citation:

Lai Geying, Yang Xingwei. Experimental research on rice planting area of hilly region in southern China using remotely sensed data. J Appl Meteor Sci, 2000, 11(1): 47-54.

Lai Geying, Yang Xingwei. Experimental research on rice planting area of hilly region in southern China using remotely sensed data. J Appl Meteor Sci, 2000, 11(1): 47-54.

Citation:

Lai Geying, Yang Xingwei. Experimental research on rice planting area of hilly region in southern China using remotely sensed data. J Appl Meteor Sci, 2000, 11(1): 47-54.

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EXPERIMENTAL RESEARCH ON RICE PLANTING AREA OF HILLY REGION IN SOUTHERN CHINA USING REMOTELY SENSED DATA

Shanghai Research Institute of Meteorological Science, Shanghai 200030

Received Date: 1998-06-02
Rev Recd Date: 1998-09-14
Publish Date: 2000-02-29

Abstract

Abstract

Taking Zhejiang Province as the experimental area, the experimental research on rice planting area of hilly region in southern China is carried out by using NOAA/AVHRR data. The main contents concern the contrast tests on the practical approaches. Both digital elevation model (DEM) and digital slope model (DSM) derived from the digital relief map are used for the purpose of improving the classification accuracy of AVHRR data in large-area hilly region. The results show that the accuracies of maximum-likelihood (MLH) classification could satisfy the professional requirements of estimating rice planting area, and fuzzy supervised classification (FSC) has better classification accuracy and stability than that of MLH. In addition, DSM may improve the results of extracting paddy field signatures from AVHRR, particularly may improve the spatial precision.
- Remotely sensed data;
- Hilly region;
- Rice planting area;
- Digital topographic model

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References(23)

Figures and Tables

图 1 浙江省数字高程模型 (DEM) 影像图

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图 2 浙江省数字坡度模型 (DSM) 影像图

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表 1 分类识别方法的试验结果

表 2 地貌因子参与分类的结果 (最大似然法)

表 3 稻区分类的空间位置精度分析表

References

[1]	江南, 何隆华, 王延颐.江苏省水稻遥感估产研究.长江流域资源与环境, 1996. 2(5):160~165. http://www.cnki.com.cn/Article/CJFDTOTAL-CJLY602.011.htm
[2]	吴炳方, 刘海燕.水稻种植面积估计的运行化遥感方法.遥感学报, 1997, 1(2):1~8. http://www.cnki.com.cn/Article/CJFDTOTAL-YGXB199701007.htm
[3]	吴健平, 杨星卫.用NOAA/AVHRR数据估算上海地区水稻种植面积.应用气象学报, 1996, 7(2):190~194. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19960229&flag=1
[4]	陈军. DTM在遥感影像分类中的应用.武汉测绘学院学报, 1984, 5(1):36~42. http://cdmd.cnki.com.cn/Article/CDMD-10424-2004133547.htm
[5]	Jones A R, Settle J, Wyatt B K. Use of digital terrain data in the interpretation of Spot-1 HRv multispectral imagery. Int. J. Remote Sens., 1988, 9(4):669~682. doi: 10.1080/01431168808954885
[6]	Schut C. Review of Interpolation Methods for Digital Terrain Models. 13th Congress of the Intersociety for Photogrammetry, Helsinki, 1976, 346~363.
[7]	Frelerick, Doyle J. Digital terrain models:An overview. Photogram. Eng. Remote Sens., 1978, 44(12):235~252.
[8]	傅乐元.数字地形模型及其地形分析.遥感信息, 1986, 5(2):34~40. http://www.cnki.com.cn/Article/CJFDTOTAL-YGXX198602012.htm
[9]	柯正谊, 何建邦, 池天河.数字地面模型.北京:中国科学技术出版社, 1993. 1~11.
[10]	Shimabukuro Y E, et al. The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. IEEE Trans. Geosci. Remote Sens., 1991, 29(1):16~19. doi: 10.1109/36.103288
[11]	Smith M O, et al. Vegetation in deserts:A regional measure of abundance from multispectral images. Remote Sens. Environ., 1990, 31(2):1~26. doi: 10.1016-0034-4257(90)90074-V/
[12]	Cross S M, et al. Subpixel measurement of tropical cover using AVHRR data. Int. J. Remote Sens., 1991, 12(5):1119~1129. doi: 10.1080/01431169108929715
[13]	Hlavka C A, et al. Unmaxing AVHRR imagery to assess clearcuts and forest regrowth in Oregon. IEEE Trans. Geosci. Remote Sens., 1995, 33(3):788~795. doi: 10.1109/36.387594
[14]	Quarmby N A, et al. Linear mixture modeling applied to AVHRR data for crop area estimation. Int. J. Remote Sens., 1992, 13(3):15~25. doi: 10.1080/01431169208904046
[15]	Fangju Wang. Fuzzy supervised classification of remote sensing images. IEEE Trans. Geosci. Remote Sens., 1990, 28(2):194~201. doi: 10.1109/36.46698
[16]	李四海.提高遥感数据分类应用性的有效途径.国土资源遥感, 1995, 8(12):1~7. http://www.cnki.com.cn/Article/CJFDTOTAL-GTYG504.000.htm
[17]	Jayantha E, Siamak K. Hierarchical maximum-likelihood classification for improved accuracies. IEEE Trans. Geosci. Remote Sens., 1997, 35(4):810~816. doi: 10.1109/36.602523
[18]	Bolstad P V, Lillesand T M. Rapid maximum-likelihood classification. Photogram. Eng. Remote Sens., 1991, 57 (3):64~74. http://cat.inist.fr/?aModele=afficheN&cpsidt=19507068
[19]	Maselli F, Conese C, Petkov L, Resti R. Inclusion of prior probabilities derived from a nonparametric process into the maximum-likelihood classifier. Photogram. Eng. Remote Sens., 1992, 58(4):201~207. http://cat.inist.fr/?aModele=afficheN&cpsidt=5541730
[20]	Mather P M. A computationally-efficient maximum-likelihood classifier employing prior probabilities for remotely sensed data. Int. J. Remote Sens., 1993, 14(5):1223~1342. doi: 10.1080/01431168508948456
[21]	杨凯, 陈军.辅助数据在遥感影像计算机分类中的应用.环境遥感, 1986, 1(3):56~65. http://www.cnki.com.cn/Article/CJFDTOTAL-YGXB198603006.htm
[22]	Ray S S, Pokharna S S, Ajai, et al.. Cotton production estimation using IRS-1B and meteorological data. Int. J. Remote Sens., 1994, 15(5):1085~1090. doi: 10.1080/01431169408954141
[23]	吴键平.遥感数据分类结果的精度分析.遥感技术与应用, 1995, 6(2):45~56. http://www.cnki.com.cn/Article/CJFDTOTAL-YGJS501.002.htm