Resolution dependent errors in remote sensing of cultivated areas
Resolution dependent errors in remote sensing of cultivated areas
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DOI：
10.1016/j.rse.2006.04.004
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发表时间：
2006-07-30
影响因子：
13.5
通讯作者：
Woodcock, Curtis E.
中科院分区：
文献类型：
--
作者：
Ozdogan, Mutlu;Woodcock, Curtis E.
遥感已成为估算土地覆盖类面积覆盖的常用和有效的方法。特别令人感兴趣的一类是农业，因为耕地面积估计对于估计产量或灌溉需求等目的很重要。卫星图像的天气覆盖面和自动分析的相对容易性导致了利用遥感进行农业制图的广泛应用。据知，从这些地图得出的面积估计数的准确性与地图的准确性有关。然而，即使在地图非常精确的情况下，也可能出现面积估计的误差。这些误差是由耕地亚像素比例分布的行为以及该行为如何因传感器空间分辨率和类别定义而变化造成的。耕地面积的传感器空间分辨率和阈值的选择用于定义耕地的估计的敏感性进行了探讨，在世界各地的六个农业不同的位置。使用变异函数参数化的子像素比例分布的beta模型，可以对任何空间分辨率的子像素比例分布进行建模。当空间分辨率相对于景观的空间结构（如由变差函数范围测量的）较小时，使用任何类别定义阈值产生非常接近真实面积覆盖的估计。另一方面，随着分辨率相对于变差函数范围变得粗糙，子像素比例不再集中在分布的极值处，并且估计面积和真实面积之间的差异对用于定义类别的所选阈值具有更大的敏感性。因此，对于这里研究的情况，分辨率和类别定义阈值对面积估计有很大的影响。误差较大的空间分辨率取决于景观空间结构，可以使用变异函数进行量化。净效应是，对于相同的空间分辨率，一些地方的面积估计误差将比其他地方大得多。对于中国安徽省的站点，农业领域非常小（平均0.07公顷），即使在45英寸的相对精细的分辨率下，面积估计对类别定义阈值也非常敏感。相反，在加州（美国），可以使用粗至500米的空间分辨率来可靠地估计耕地面积。结果还表明，种植面积占总面积的比例显着影响面积估计的准确性。当面积比例较低时，类别定义阈值也必须较低，以实现准确的面积估计。相反，在农业占主导地位的地区，需要对耕地进行非常严格的分类，以准确估计面积。虽然探讨的背景下，估计耕地面积，这里提出的调查结果是通用的问题，利用遥感面积估计。(c)2006年爱思唯尔公司All rights reserved.
Remote sensing has become a common and effective method for estimating the areal coverage of land cover classes. One class of particular interest is agriculture as area estimates of cultivated lands are important for purposes such as estimating yields or irrigation needs. The synoptic coverage of satellite imagery and the relative ease of automated analysis have led to widespread mapping of agriculture using remote sensing. The accuracy of area estimates derived from these maps is known to be related to the accuracy of the maps. However, even in the situation where the map is very accurate, errors in area estimates may occur. These errors result from the behavior of the distribution of subpixel proportions of cultivated areas, and how that behavior changes as a result of sensor spatial resolution and class definitions. The sensitivity of estimates of cultivated areas to sensor spatial resolution and to the choice of threshold used to define cultivated land is explored in six agriculturally distinct locations around the world. Using a beta model for the distribution of subpixel proportions that is parameterized using variograms, it is possible to model the distribution of subpixel proportions for any spatial resolution. When the spatial resolution is small with respect to the spatial structure of the landscape (as measured by the variogram range) use of any class definition threshold produces an estimate very close to the true area coverage. On the other hand, as the resolution becomes coarse in relation to the variogram range, the subpixel proportions are no longer concentrated at the extremes of the distribution and the difference between the estimated and the true area has greater sensitivity to the selected threshold used to define classes. Thus, for the cases examined here, both the resolution and the class definition threshold have a strong influence on area estimates. The spatial resolutions where errors can be large depend on landscape spatial structure, which can be quantified using variograms. The net effect is that for the same spatial resolution, some places will exhibit much larger errors in area estimates than others. For the site in the Anhui province of China, where agricultural fields are very small (0.07 ha on the average), area estimates are highly sensitive to class definition thresholds even at the relatively fine resolution of 45 in. Conversely, in California (USA) spatial resolutions as coarse as 500 m can be used to reliably estimate cultivated areas. Results also suggest that the proportion of the total area that is cultivated significantly influences the accuracy of area estimates. When the area proportion is low, the class definition threshold must also be low to achieve an accurate area estimate. Conversely, in areas dominated by agriculture, a very stringent class definition of cultivated lands is required for accurate area estimates. While explored in the context of estimation of cultivated areas, the findings presented here are generic to the problem of area estimation using remote sensing. (c) 2006 Elsevier Inc. All rights reserved.