...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Arboriculture >Estimating urban leaf area using field measurements and satellite remote sensing data
【24h】

Estimating urban leaf area using field measurements and satellite remote sensing data

机译：使用实地测量和卫星遥感数据估算城市叶片面积

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Accurate estimation of urban leaf area is important in understanding the urban forest's role in heat island mitigation, pollution removal, and carbon sequestration. Remotely sensed satellite data provide an alternative method to inexpensively and nondestructively estimate this important urban biophysical variable. Ceptometer measurements of leaf area index (LAI) at 143 urban sites in Terre Haute, Indiana, U.S., were modeled as a function of reflected radiance flux sensed by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Multiple regression models of LAI were compared to estimates produced by feed-forward back-propagation artificial neural networks. The most accurate estimation was produced by the neural network utilizing the ASTER green band and the ratio of the ASTER red and near-infrared bands. In this case, the simple correlation between the observed and predicted LAI values was moderately high (R = 0.71). The standard error of the LAI estimate was 1.35. In every case, the predictive accuracy of the neural network, models exceeded the multiple regression models. Examination of the parameters in the successful models indicates that the estimation of urban LAI in Terre Haute is physically predicated on the relative proportions of leaf chlorophyll, leaf spongy mesophyll, and indurate matter (e.g., concrete, asphalt, soil) constituting the individual picture elements of the satellite image.
机译：准确估计城市叶片面积对于了解城市森林在减少热岛，清除污染和固碳方面的作用非常重要。遥感卫星数据提供了一种廉价而无损地估算这一重要城市生物物理变量的替代方法。在美国印第安纳州Terre Haute的143个城市站点的叶面积指数（LAI）的测速仪测量结果是由先进星载热发射和反射辐射仪（ASTER）感测到的反射辐射通量的函数模型。将LAI的多元回归模型与前馈反向传播人工神经网络产生的估计值进行比较。由神经网络利用ASTER绿带以及ASTER红与近红外带之比得出的最准确的估算值。在这种情况下，观察到的LAI值与预测的LAI值之间的简单相关性较高（R = 0.71）。 LAI估计的标准误为1.35。在每种情况下，神经网络的预测精度都超过了多元回归模型。对成功模型中参数的检验表明，对Terre Haute的城市LAI的估算是根据构成单个图像元素的叶绿素，叶海绵状叶肉和硬质物质（例如混凝土，沥青，土壤）的相对比例进行物理估算的卫星图像。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号