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EVALUATION OF RESOUCESAT-1 AWIFS DATA FOR PRODUCING AN AGRICULTURAL CROP INVENTORY FOR CANADA

机译:评估Resoucesat-1 AWIFS数据为加拿大制作农业作物库存的数据

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Agriculture and Agri-Food Canada (AAFC) is currently developing sensor-independent methods to produce an annual agricultural crop inventory from earth observation data. Classification of croplands requires satellite data that is both spatially extensive, and provides sufficient spatial resolution to discern field-scale information. These two needs are generally in conflict since the choice of input data from commercial satellites represents a trade-off between spatial resolution and swath coverage. The Indian Remote Sensing (IRS) satellite Resourcesat-1 (IRS-P6) collects multi-spectral data from its Advanced Wide Field Sensor (AWiFS) instrument with a nominal spatial resolution of 56 meters at nadir, and a swath size of 740 kilometers. AWiFS data has been utilized successfully over several growing seasons by the United States Department of Agriculture (USDA), but this sensor has not been widely used in a Canadian context. This study will compare the use of multiple multi-spectral optical satellites, including SPOT, Landsat-TM and AWiFS, for crop mapping over three Canadian agricultural sites: an agricultural site in Eastern Ontario, a site in the Red River valley, Manitoba, and a site near Swift Current in southwestern Saskatchewan. These locations represent different crop mixes, as well as differences in the relative size of agricultural fields. The impact of differences in spatial and spectral resolution on classification accuracy will be examined. In addition, the study will consider the use of each sensor type as well as the potential to use these sensors in conjunction with microwave sensors, such as Envisat-ASAR. Landsat-TM has historically provided reliable information at a sufficient scale for most land cover/land use applications. With expected gaps in the Landsat program, it is essential that alternative sensors be explored for this type of application.
机译:农业和农业加拿大加拿大(AAFC)目前正在开发传感器无关的方法,以生产来自地球观测数据的年度农业作物库存。农田分类需要空间广泛的卫星数据,并提供足够的空间分辨率来辨别场比例信息。这两个需求通常在冲突中,因为来自商业卫星的输入数据的选择代表了空间分辨率与SWATH覆盖范围之间的权衡。印度遥感(IRS)卫星资源-1(IRS-P6)从其先进的宽场传感器(AP)仪器中收集多光谱数据,标称空间分辨率为Nadir 56米,速度为740公里。 AWIFS数据已成功地利用美国农业部(USDA)的几个生长季节,但该传感器尚未广泛用于加拿大背景。本研究将对多种多光谱光学卫星进行比较,包括点,土地卫星和APIF,用于三个加拿大农业地点的作物测绘:东安大略省东部的农业部位,红河谷,曼尼托巴及萨斯喀彻温省西南部Swift Current附近的网站。这些位置代表不同的作物混合,以及农业领域的相对大小的差异。将检查空间和光谱分辨率差异对分类准确度的影响。此外,该研究将考虑使用每个传感器类型以及与微波传感器一起使用这些传感器的可能性,例如Envisat-ASAR。 Landsat-TM已经为大多数陆地覆盖/土地使用应用提供了足够的规模提供了可靠的信息。在Landsat计划中具有预期差距,必须为这类应用探索替代传感器。

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