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首页> 外文会议>International conference on remote sensing for marine and coastal environments >Coastal landcover classification using NASA's Airborne Terrestrial Applications Sensor (ATLAS) data
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Coastal landcover classification using NASA's Airborne Terrestrial Applications Sensor (ATLAS) data

机译：使用NASA的空中陆地应用传感器（ATLAS）数据进行沿海地利计划线分类

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Impervious surface is a key indicator of the extent of urbanization within a given geographic area. Extensive impervious surface area can reduce quality of nearby waterways by increasing runoff volume, increasing peak flow rates, and reducing rainwater infiltration and pollutant filtering by subsurface flow Corbett et al., 1997). Thus, relatively easily attained estimates of impervious surface area would allow both a measure of urbanization and risk to receiving waters. In comparison, vegetated surface area slows runoff and traps pollutants better than open land. Estimates of percent impervious surface, vegetated, and open land, along with morphology of urban land use, where shape and density are key elements, can be measured and analyzed with the use of Remote Sensing and Geographic Information Systems (GIS). NASA's Airborne Terrestrial Applications Sensor (ATLAS) data were used to classify areas of Murrells Inlet, South Carolina into three land-cover classes: impervious surfaces, open land, and vegetation. The spectral range of ATLAS is 0.45-12.2 um and is displayed in 14 channels with a 3 meter (m) Ground Spatial Resolution (GSR). The ATLAS data were rectified, transformed using ENVI's Principal Components Analysis (PCA), classified using a parallelepiped classifier from ERDAS, Inc. Image Analysis extension for Arcview, and converted to vector format for use with the GIS. The accuracy of the classification was estimated using a hybrid approach of ground-truthing and a visual examination of the National Aerial Photography Program's (NAPP) Color Infrared (CIR) photography with a GSR of 1 m. Remotely sensed impervious, vegetated, and open surfaces are being used in empirical relationships to predict risks to and impacts upon the receiving estuary.
机译：不透水表面是给定地理区域内城市化程度的关键指标。广泛的不透水表面积可以通过增加径流量，增加峰值流速，减少雨水渗透和污染物过滤通过地下流量Corbett等，1997）来降低附近水道的质量。因此，相对容易地实现了对不透水表面积的估计将允许城市化的量度和接受水域的风险。相比之下，植被的表面积放缓径流和污染物比开放的土地更好。可以测量和分析使用遥感和地理信息系统（GIS）测量和分析城市土地使用的百分比，植被和开放的土地，以及城市土地使用的形态，其中形状和密度是关键元素。美国宇航局的空中陆地应用传感器（阿特拉斯）数据用于将南卡罗来纳州墨尔克尔斯入口区分为三个陆地覆盖课程：不透水的表面，开放的土地和植被。 Atlas的光谱范围为0.45-12.2μm，并以14个通道显示，具有3米（M）地间空间分辨率（GSR）。使用Envi的主成分分析（PCA）进行纠正，转换使用来自Erdas，Inc。的PARDIPED分类器进行分类，用于ArcView的图像分析扩展，并转换为与GIS一起使用的矢量格式。使用Hybrid方法的地面培养方法和全国航空摄影计划（NAPP）彩色红外线（CIR）摄影的视觉检查估计分类的准确性估计，GSR为1米。远程感知的不透水，植被和开放表面被用于实证关系，以预测对接收河口的风险和影响。

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