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作 者:孙华生[1,2] 黄敬峰[1,3] 李波[1,2] 王红说[1,2]
机构地区:[1]浙江大学农业遥感与信息技术应用研究所,杭州310029 [2]浙江省农业遥感与信息技术应用重点研究实验室,杭州310029 [3]环境修复与生态健康教育部重点实验室,杭州310029
出 处:《中国农业科学》2008年第12期4039-4047,共9页Scientia Agricultura Sinica
基 金:国家"863"计划项目(2006AA120101);国家科技支撑课题(2006BAD10A01);国家自然科学基金项目(40571115/D0119)
摘 要:【目的】水稻是我国重要的粮食作物之一。通过遥感技术获取农作物信息是一种有效的手段,然而由于中国的水稻生长地域跨度大,各地的气候、地形等自然条件十分复杂,水稻的生长环境、生长季节和耕作制度在不同的区域差异很大,所以利用遥感方法获取水稻信息在不同区域有较大差别。为提高利用遥感方法获取水稻信息的精度而对全国的水稻种植区域进行合理的区划。【方法】以全国水稻种植区为对象,通过分析并选取对遥感技术信息获取有重要影响的因素,如耕作制度、地形因素、种植结构和大气噪声(云覆盖)等,采用恰当的区划指标,利用定性和定量相结合的分析方法进行区划。【结果】根据耕作制度的差异把全国分为4个水稻遥感信息获取区,然后又根据地形、种植结构和大气噪声等因素对遥感信息获取的影响分成19个亚区。由于研究区的范围较大,各地的自然和社会状况十分复杂,所以研究只考虑了对水稻遥感信息获取时起主要影响作用的因素,而忽略了其它的次要因素。【结论】区划结果对水稻遥感信息获取时选择合适的遥感获取方式、恰当的空间分辨率与时相的遥感图像,以及对遥感信息提取结果准确度的验证等提供参考。【Objective】Paddy rice is one of the important staple crops of China.It’s an efficient way to acquire crops information through remote sensing technology,however,China has diverse climates because of the country’s extensive territory and complex topography,so rice growing environment and planting calendars are very different in different regions,and they may affect rice information acquiring through remote sensing technology.To improve the acquiring precision of rice information,regionalization of the planting area of paddy rice in the whole country was studied.【Method】Major impacting factors,including rice farming systems,topography,planting structure and atmospheric noises(cloud cover),were analyzed and appropriate indices were chosen for the regionalization.The regionalization was executed by qualitative and quantitative analyses.【Result】The rice planting area in the whole country was divided into 4 regions by the difference of rice planting rotation,and subsequently divided 19 sub-regions by the differences of topography,land surface feature structure and atmospheric noise.Major factors were considered while other minor ones were neglected in the study due to the large extent and complexity of the study area.【Conclusion】The study may provide useful information for the selection of remote sensing images with appropriate spatial resolution and date,as well as for the accuracy evaluation of the classification.
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