多星数据协同的地块尺度作物分类与面积估算方法研究
Study on the Crop Classification and Planting Area Estimation at Land Parcel Scale Using Multi-sources Satellite Data
查看参考文献22篇
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文摘
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为了解决多云雨地区遥感数据时空覆盖缺失的问题,以满足对地块尺度作物种植信息日益迫切的应用需求,本文在遥感图谱认知理论框架下发展了一种基于多星数据协同的地块尺度作物识别与面积估算方法。首先,基于米级高分辨率影像提取农田地块对象;其次,通过对多源中分辨率时序影像的有效化处理和指数计算,获取"碎片化"的高时空覆盖有效数据,并以地块对象为单元构建时间序列;然后,在时序分析基础上,建立多维特征空间,结合作物生长物候特征,构建决策树模型进行作物分类识别与面积计算;最后,以湖南省宁远县为研究区开展了水稻种植信息的提取实验。结果表明:本文方法可在农田地块尺度下实现不同水稻类型的准确识别及其种植面积的精细提取,早、中、晚稻的用户精度分别可达94.33%、90.76%和95.95%,总体分类精度为92.51%,Kappa系数为0.90;早、中、晚稻面积提取精度分别为93.37%、91.23%和95.42%。试验结果证明了本文方法的有效性,为其他作物种植信息的精细提取提供了借鉴。 |
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其他语种文摘
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To reduce the missing of remotely sensed data in the spatio-temporal coverage of the cloudy/rainy region and to further meet the urgent need for crop planting information at farmland parcel scale, a method of crop type identification and planting area estimation at parcel scale was developed in this paper by synergistically utilizing the multi-sources satellite imagery,with the support of remote sensing Tupu recognization theory. This method consists of three steps: firstly, based on the high resolution imagery, the objects of farmland parcel with exact boundary were extracted. Secondly, with the effective-data processing technology and the spectral indices calculation based on the multi-temporal medium resolution imagery, the fragmentary effective data was acquired and the time-series data for each object was further obtained. Finally, by constructing a multi-dimensional feature space with the help of time series analysis incorporating the crops'phenological feature, the crop types and their corresponding planting areas were mapped using the Decision Tree classifier. This method had been tested in Ningyuan county, Hunan Province, China. The results showed that, this method can precisely map the different rice types and corresponding planting areas at the farmland parcel scale. The user accuracy of the three rice types, i.e., the early double-season, single-season and late double-season rice,was 94.33%, 90.76 and 95.95%, respectively, and the overall accuracy was 92.51% with a Kappa coefficient of 0.90. The derived area accuracy of these three rice types also reached 93.37%, 91.23% and 95.42%,respectively. This experiment illustrated the effectiveness and usefulness of the proposed method and also provided a salutary lesson for the finely planting information extraction of other crops. |
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来源
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地球信息科学学报
,2016,18(5):708-717 【核心库】
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关键词
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遥感
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图谱认知理论
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作物识别
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地块尺度
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地址
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1.
中国科学院遥感与数字地球研究所, 遥感科学国家重点实验室, 北京, 100101
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广西农业科学院农业科技信息研究所, 南宁, 310001
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1560-8999 |
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学科
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测绘学 |
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基金
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国家863计划
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广西科学研究与技术开发计划
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高分辨率对地观测系统重大专项
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国家自然科学基金
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文献收藏号
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CSCD:5694344
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参考文献 共
22
共2页
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|
1.
Xiao X. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images.
Remote Sensing of Environment,2005,95(4):480-492
|
CSCD被引
131
次
|
|
|
|
|
2.
Xiao X M. Mapping paddy rice agriculture in south and southeast Asia using multi-temporal MODIS images.
Remote Sensing of Environment,2006,100(1):95-113
|
CSCD被引
94
次
|
|
|
|
|
3.
Kuenzer C. Remote sensing of rice crop areas.
International Journal of Remote Sensing,2013,34(6):2101-2139
|
CSCD被引
18
次
|
|
|
|
|
4.
Gumma M K. Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500m data for the year 2010.
ISPRS Journal of Photogrammetry and Remote Sensing,2014,91:98-113
|
CSCD被引
3
次
|
|
|
|
|
5.
Hill M J. Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series.
Remote Sensing of Environment,2003,84(3):367-384
|
CSCD被引
25
次
|
|
|
|
|
6.
Atzberger C. Mapping the spatial distribution of winter crops at sub-pixel level using AVHRR NDVI time series and neural nets.
Remote Sensing,2013,5(3):1335-1354
|
CSCD被引
11
次
|
|
|
|
|
7.
Potgieter A B. Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imager.
Australian Journal of Agricultural Research,2007,58(4):316-325
|
CSCD被引
5
次
|
|
|
|
|
8.
Zheng B J. Remote sensing of crop residue cover using multi-temporal Landsat imagery.
Remote Sensing of Environment,2012,177:99-183
|
CSCD被引
1
次
|
|
|
|
|
9.
Jia K. Crop classification using HJ satellite multispectral data in the north China plain.
Journal of Applied Remote Sensing,2013,7(1):287-297
|
CSCD被引
20
次
|
|
|
|
|
10.
Pan Y Z. Winter wheat area estimation from MODIS-EVI time series data using the crop proportion phenology index.
Remote Sensing of Environment,2012,119:232-242
|
CSCD被引
59
次
|
|
|
|
|
11.
Wardlow B D. Analysis of time-series MODIS 250m vegetation index data for crop classification in the U.S. central great plains.
Remote Sensing of Environment,2007,108(3):290-310
|
CSCD被引
87
次
|
|
|
|
|
12.
Ward B D. Large-area crop mapping using time-series MODIS 250m NDVI data: an assessment for the U.S. Central great plains.
Remote Sensing of Environment,2008,112(3):1096-1116
|
CSCD被引
104
次
|
|
|
|
|
13.
Zhong L H. Efficient corn and soybean mapping with temporal extendibility: a multi-year experiment using Landsat Imagery.
Remote Sensing of Environment,2014,140:1-13
|
CSCD被引
39
次
|
|
|
|
|
14.
Dong J W. Tracking the dynamics of paddy rice planting area in 1986-2010 through time series Landsat images and phenology-based algorithms.
Remote Sensing of Environment,2015,160:99-113
|
CSCD被引
29
次
|
|
|
|
|
15.
骆剑承. 遥感信息图谱计算的理论方法研究.
地球信息科学学报,2009,11(5):664-669
|
CSCD被引
10
次
|
|
|
|
|
16.
周伟. 多光谱遥感影像中云影区域的检测与修复.
遥感学报,2012,16(1):137-142
|
CSCD被引
1
次
|
|
|
|
|
17.
Steven M D. Inter-calibration of vegetation indices from different sensor systems.
Remote Sensing of Environment,2003,88(12):412-422
|
CSCD被引
39
次
|
|
|
|
|
18.
张宏斌. 基于MODIS NDVI和NOAA NDVI数据的空间尺度转换方法研究——以内蒙古草原区为例.
草业科学,2009,26(10):39-45
|
CSCD被引
7
次
|
|
|
|
|
19.
张焕雪. 基于多时相环境星NDVI时间序列的农作物分类研究.
遥感技术与应用,2015,30(2):304-311
|
CSCD被引
29
次
|
|
|
|
|
20.
张峰. 利用时序植被指数监测作物物候的方法研究.
农业工程学报,2004,20(1):155-159
|
CSCD被引
53
次
|
|
|
|
|
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