高分1号冬小麦解译面积核算方法研究
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摘要
[目的]以高分1号(GF-1)融合2m卫星遥感影像为基础数据源,结合土地利用现状数据、高分多源遥感影像和地面样方等数据,对冬小麦分类提取中存在的面积误差问题进行研究和分析。[方法]文章以河南省永城市为研究区,在冬小麦提取结果聚类处理基础上,基于线性地物缓冲区数据,采用GIS空间运算实现线性地物面积扣除,接着分析了样方数据和土地利用现状数据再扣除零星地物面积比例上的差异,并采用样方零星地物平均扣除系数对全市各乡镇耕地与非耕地中冬小麦提取面积进行了相关统计和误差分析。[结果]永城市冬小麦最终解译面积11. 29万hm~2,其中线性地物和零星地物扣除面积分别为6 613. 08hm~2和3 875. 22hm~2,占研究区冬小麦解译面积的5. 86%和3. 32%,与统计上报数据相比,其处理前后误差由14. 12%降低至4. 41%,有效地提高冬小麦提取面积精度。[结论]误差来源分析与修正对冬小麦解译面积核算精度具有重要影响,该研究为县级区域尺度下冬小麦面积提取核算提供了思路和借鉴。
Based on the GF-1 remote sensing image data,integrating the land use data,the field observation data and ZY-3,GF-2 remote sensing images,the wheat area error and reason in classification are researched and analyzed. Taking Yongcheng city,Henan province as a study area. Firstly,based on the winter wheat extraction area and its cluster result,the wheat area was deducted by using GIS spatial operation with the data of buffer of linear features and coefficient of sporadic objects. There were two different ways to deduct the coefficient of sporadic objects between land use data and ground samples,then the difference between them was analyzed. Finally,the statistics and error analysis in cultivated and non-cultivated land were carried out on the extracted area of winter wheat with average deduction coefficient of ground samples. The results showed that the total classification winter wheat area was 1129. 04 km~2 in study area. The deduction linear objects and sporadic objects were 66. 13km~2 and 38.75km~2,accounting for 5. 86% and 3. 32% of the total classification winter wheat area,respectively. In this study,the error decreased from 14. 12% to 4. 41% by reference of official data. It concluded that the error sources,linear and sporadic objects deduction played an important role in classification of winter wheat. This study can provide a way to enhance the accuracy of winter wheat classification on the county scale.
Based on the GF-1 remote sensing image data,integrating the land use data,the field observation data and ZY-3,GF-2 remote sensing images,the wheat area error and reason in classification are researched and analyzed. Taking Yongcheng city,Henan province as a study area. Firstly,based on the winter wheat extraction area and its cluster result,the wheat area was deducted by using GIS spatial operation with the data of buffer of linear features and coefficient of sporadic objects. There were two different ways to deduct the coefficient of sporadic objects between land use data and ground samples,then the difference between them was analyzed. Finally,the statistics and error analysis in cultivated and non-cultivated land were carried out on the extracted area of winter wheat with average deduction coefficient of ground samples. The results showed that the total classification winter wheat area was 1129. 04 km~2 in study area. The deduction linear objects and sporadic objects were 66. 13km~2 and 38.75km~2,accounting for 5. 86% and 3. 32% of the total classification winter wheat area,respectively. In this study,the error decreased from 14. 12% to 4. 41% by reference of official data. It concluded that the error sources,linear and sporadic objects deduction played an important role in classification of winter wheat. This study can provide a way to enhance the accuracy of winter wheat classification on the county scale.
引文
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[19]http://dzb.jryccm.com/shtml/jryc/20170401/109352.shtml.
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[21]权文婷,王钊.冬小麦种植面积遥感提取方法研究.国土资源遥感,2013,25(4):8-15.
[22]刘克宝,刘述彬,陆忠军,等.利用高空间分辨率遥感数据的农作物种植结构提取.中国农业资源与区划,2014,35(1):21-26.
[2]易湘生,马尚杰,游炯,等.遥感调查中耕地解译面积精准核算.农业工程学报,2016,32(S1):169-176.
[3]刘焕军,闫岩,张新乐,等.面向农业区划的作物种植结构遥感提取.中国农业资源与区划,2017,38(8):43-54.
[4]Xiao Zheng,Jiaojun Zhu.A new climatic classification of afforestation in Three-North regions of China with multi-source remote sensing data.Theoretical and Applied Climatology,2015,127:1-16.
[5]Guido Waldhoff,Ulrike Lussem,Georg Bareth.Multi-Data Approach for remote sensing-based regional crop rotation mapping:a case study for the Rur catchment,Germany.International Journal of Applied Earth Observations and Geoinformation,2017,61:55-69.
[6]马尚杰,易湘生,游炯,等.基于GF-1影像的冬小麦种植面积核算及直补政策实施评价.农业工程学报,2016,32(18):169-174.
[7]韩文霆,张立元,张海鑫,等.基于无人机遥感与面向对象法的田间渠系分布信息提取.农业机械学报,2017,48(3):205-214.
[8]徐新刚,李强子,周万村,等.应用高分辨率遥感影像提取作物种植面积.遥感技术与应用,2008,23(1):17-23.
[9]H Ghassemian.A review of remote sensing image fusion methods.Information Fusion,2016,32(PA):75-89.
[10]朱爽,张锦水.面向省级农作物种植面积的遥感估算的分层方法.农业工程学报,2013,29(2):184-191.
[11]邬明权,杨良闯,于博,等.基于遥感与多变量概率抽样调查的作物种植面积测量.农业工程学报,2014,30(2):146-152.
[12]张园园.基于GIS的市级土地利用现状数据集成应用研究.中国科学院研究生院(广州地球化学研究所),2007.
[13]杨宝尧,杜震洪,刘仁义,等.基于第二次全国土地调查数据的土地利用现状制图综合技术研究.国土资源遥感,2012,24(4):107-111.
[14]杨德生,于欢,王学杰,等.土地利用现状数据缩编方法探讨.测绘科学,2012,37(5):159-161.
[15]刘佳,王利民,滕飞.Google Earth影像辅助的农作物面积样方调查.农业工程学报,2015,31(24):149-154.
[16]陈仁喜,李鑫慧,周绍光.面向对象的竞争合作学习遥感影像聚类算法.计算机工程与应用,2016,52(21):8-13.
[17]邓湘金,王彦平,彭海良.高分辨率遥感影像的聚类.电子与信息学报,2003,25(8):1073-1080.
[18]X Long,Q Chen,X Gao,et al.Improvement of SURF Feature Image Registration Algorithm Based on Cluster Analysis.Sensors&Transducers,2014:188-193.
[19]http://dzb.jryccm.com/shtml/jryc/20170401/109352.shtml.
[20]张焕雪,李强子.空间分辨率对作物识别及种植面积估算的影响研究.遥感信息,2014,29(2):36-40.
[21]权文婷,王钊.冬小麦种植面积遥感提取方法研究.国土资源遥感,2013,25(4):8-15.
[22]刘克宝,刘述彬,陆忠军,等.利用高空间分辨率遥感数据的农作物种植结构提取.中国农业资源与区划,2014,35(1):21-26.