湖北省油菜种植面积的遥感监测方法探讨
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摘要
遥感技术是农作物种植面积监测的一种快速、经济和准确的新技术。论文研究了适宜于湖北省油菜种植面积的遥感监测的最佳时相、最佳数据源、影像解译方法等。结果表明,湖北省油菜面积遥感监测的最佳识别时相位于3月中旬—4月中旬,即油菜的开花期。对于湖北省省域范围油菜种植面积遥感监测,为了既能兼顾经济上的可行性和技术上的精度,建议对于油菜非成片分布的低山丘陵地区,采用10m-ALOS影像自动分类,对于油菜连片分布的平原地区,采用至少有1景成像于油菜开花期的双时TM影像,同时采用"时序差值+自动分类"方法的技术方案。
Remote sensing technology is a rapid, economic and accurate method for rape planting area monitoring. In this research, we studied the best photographic time, image interpretation method, the best data source for monitoring the rape area based on remote sensing. The results showed that the best recognition time of satellite images for monitoring the rape area generally located at from the mid-March to the mid-April, namely rape anthesis, at Hubei Province. In order to the economic feasibility and the accuracy of monitoring rape area based on remote sensing at Hubei Province, we suggested that the automatic classification method could be used based on 10 m-ALOS image for the low hill region, and more one images at rape anthesis must be used, and the remote sensing method of extracting rape's area was based on NDVI difference between two different images and automatic classification for the plain region planting the rape at Hubei Province.
Remote sensing technology is a rapid, economic and accurate method for rape planting area monitoring. In this research, we studied the best photographic time, image interpretation method, the best data source for monitoring the rape area based on remote sensing. The results showed that the best recognition time of satellite images for monitoring the rape area generally located at from the mid-March to the mid-April, namely rape anthesis, at Hubei Province. In order to the economic feasibility and the accuracy of monitoring rape area based on remote sensing at Hubei Province, we suggested that the automatic classification method could be used based on 10 m-ALOS image for the low hill region, and more one images at rape anthesis must be used, and the remote sensing method of extracting rape's area was based on NDVI difference between two different images and automatic classification for the plain region planting the rape at Hubei Province.
引文
[1] 蒙继华,吴炳方,李强子.全国农作物叶面积指数遥感估算方法[J].农业工程学报,2007,23(2):160-167.
[2] 王秀珍,黄敬峰,李云梅,等.水稻叶面积指数的高光谱遥感估算模型[J].遥感学报,2004,8(1):81-88.
[3] 闫岩,柳钦火,刘强,等.基于遥感数据与作物生长模型同化的冬小麦长势监测与估产方法研究[J].遥感学报,2006,10(5):804-811.
[4] 彭代亮,黄敬峰,王福民,等.基于分区的水稻总产遥感估算研究[J].中国科学:信息科学,2010,40(S1):184-195.
[5] 苗翠翠,江南,彭世揆,等.基于NDVI时序数据的水稻种植面积遥感监测分析:以江苏省为例[J].地球信息科学学报,2011,13(2):273-280.
[6] 魏新彩,王新生,刘海,等.HJ卫星图像水稻种植面积的识别分析[J].地球信息科学学报,2012,14(3):382-388.
[7] 冯伟,朱艳,田永超,等.基于高光谱遥感的小麦籽粒产量预测模型研究[J].麦类作物学报,2007,27(6):1076-1108.
[8] 李卫国,赵春江,王纪华,等.遥感和生长模型相结合的小麦长势监测研究现状与展望[J].国土资源遥感,2007(2):6-9.
[9] BOUVET A,TOAN T L.Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta[J].Remote sensing of environment,2011,115(4):1090-1101.
[10] CONGALTON R G.A review of assessing the accuracy of classifications of remotely sensed data[J].Remote sensing of environment,1991,37(1):35-46.
[11] RICHARDS J A.Classifier performance and map accuracy[J].Remote sensing of environment,1996,57(3):161-166.
[12] STEHMAN S V.Selecting and interpreting measures of thematic classification accuracy[J].Remote sensing of environment,1997,62(1):77-89.
[13] STORY M, CONGALTON R G.Accuracy assessment:A user’s perspective[J].Photogrammetric engineering & remote sensing,1986,52(3):397-399.
[14] 邓帆,王立辉,高贤君,等.基于多时相遥感影像监测江汉平原油菜种植面积[J].江苏农业科学,2018,46(14):200-204.
[15] 李丹丹,刘佳,周清波,等.尺度变化对油菜种植面积遥感监测精度的影响[J].中国农业资源与区划,2014,35(6):85-92.
[16] 苏仁忠.遥感技术在农业信息化中的应用[J].农业工程,2018,8(6):45-47.
[17] 屈泉酉,安萌,梁德印.中国遥感卫星在农业领域的应用现状与未来发展[J].卫星应用,2018(3):28-31.
[18] 杨博,刘义,李鹏伟.基于多源数据融合技术的农业遥感监测在垦区的应用[J].现代化农业,2018(3):7-8.
[19] 唐华俊.农业遥感研究进展与展望[J].农学学报,2018,8(1):167-171.
[20] 丁淘沙.基于遥感数据的农作物经济适宜性评价研究[D].新乡:河南师范大学,2017.
[2] 王秀珍,黄敬峰,李云梅,等.水稻叶面积指数的高光谱遥感估算模型[J].遥感学报,2004,8(1):81-88.
[3] 闫岩,柳钦火,刘强,等.基于遥感数据与作物生长模型同化的冬小麦长势监测与估产方法研究[J].遥感学报,2006,10(5):804-811.
[4] 彭代亮,黄敬峰,王福民,等.基于分区的水稻总产遥感估算研究[J].中国科学:信息科学,2010,40(S1):184-195.
[5] 苗翠翠,江南,彭世揆,等.基于NDVI时序数据的水稻种植面积遥感监测分析:以江苏省为例[J].地球信息科学学报,2011,13(2):273-280.
[6] 魏新彩,王新生,刘海,等.HJ卫星图像水稻种植面积的识别分析[J].地球信息科学学报,2012,14(3):382-388.
[7] 冯伟,朱艳,田永超,等.基于高光谱遥感的小麦籽粒产量预测模型研究[J].麦类作物学报,2007,27(6):1076-1108.
[8] 李卫国,赵春江,王纪华,等.遥感和生长模型相结合的小麦长势监测研究现状与展望[J].国土资源遥感,2007(2):6-9.
[9] BOUVET A,TOAN T L.Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta[J].Remote sensing of environment,2011,115(4):1090-1101.
[10] CONGALTON R G.A review of assessing the accuracy of classifications of remotely sensed data[J].Remote sensing of environment,1991,37(1):35-46.
[11] RICHARDS J A.Classifier performance and map accuracy[J].Remote sensing of environment,1996,57(3):161-166.
[12] STEHMAN S V.Selecting and interpreting measures of thematic classification accuracy[J].Remote sensing of environment,1997,62(1):77-89.
[13] STORY M, CONGALTON R G.Accuracy assessment:A user’s perspective[J].Photogrammetric engineering & remote sensing,1986,52(3):397-399.
[14] 邓帆,王立辉,高贤君,等.基于多时相遥感影像监测江汉平原油菜种植面积[J].江苏农业科学,2018,46(14):200-204.
[15] 李丹丹,刘佳,周清波,等.尺度变化对油菜种植面积遥感监测精度的影响[J].中国农业资源与区划,2014,35(6):85-92.
[16] 苏仁忠.遥感技术在农业信息化中的应用[J].农业工程,2018,8(6):45-47.
[17] 屈泉酉,安萌,梁德印.中国遥感卫星在农业领域的应用现状与未来发展[J].卫星应用,2018(3):28-31.
[18] 杨博,刘义,李鹏伟.基于多源数据融合技术的农业遥感监测在垦区的应用[J].现代化农业,2018(3):7-8.
[19] 唐华俊.农业遥感研究进展与展望[J].农学学报,2018,8(1):167-171.
[20] 丁淘沙.基于遥感数据的农作物经济适宜性评价研究[D].新乡:河南师范大学,2017.