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南方丘陵地区水稻种植面积遥感信息提取的试验
EXPERIMENTAL RESEARCH ON RICE PLANTING AREA OF HILLY REGION IN SOUTHERN CHINA USING REMOTELY SENSED DATA
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摘要: 以浙江省为试验区, 针对水稻种植面积遥感信息提取的业务化运行问题, 进行了以下试验:(1) 以传统的单象元统计分类识别方法为分类器, 在地理信息系统支持下, 提取丘陵地区大范围水稻种植面积信息的可行性; (2) 在遥感资料的基础上, 结合地形数据综合提取水稻种植面积专题信息的可行性和有效性; (3) 混合象元分解方法在丘陵地区的有效性和适用性.结果表明, 用最大似然法提取大范围水稻种植面积信息的精度可满足业务化运行的要求; 模糊监督分类有较高的分类精度和较好的稳定性, 具有较强的适应性; 坡度数据作为遥感影像分类的辅助数据层, 可以有效地提高丘陵地区水稻种植面积信息的提取精度, 还可以提高分类的稳定性和空间位置精度.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.
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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
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[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 -
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