Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends
Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends
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DOI：
10.1016/j.rse.2011.02.030
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发表时间：
2012-02-15
影响因子：
13.5
通讯作者：
Weng, Qihao
中科院分区：
文献类型：
--
作者：
Weng, Qihao
不透水表面的知识，特别是不透水表面的大小、位置、几何形状、空间格局和渗透-不透水比，对于全球环境变化和人与环境相互作用的环境科学中的一系列问题和主题具有重要意义。不透水地表数据对城市规划和环境资源管理具有重要意义。因此，城市不透水表面的遥感近来受到了前所未有的关注。在这篇文章中，将研究各种数字遥感方法来提取和估计不透水表面。讨论将集中在城市不透水表面的测绘要求上。特别是，将讨论空间、几何、光谱和时间分辨率对估计和映射的影响，以及基于遥感数据特征选择适当的估计方法。这篇文献综述表明，过去十年的主要方法包括基于像素的方法(图像分类、回归等)、基于亚像素的方法(线性光谱分解、不透气性作为植被覆盖率的补充等)、面向对象的算法和人工神经网络。还探索了数据/图像融合、专家系统和上下文分类方法等技术。大部分研究工作都是为了绘制不同比例尺的城市景观图，以及这类绘制图的空间分辨率要求。相比之下，人们对不透水表面的光谱和几何特性兴趣较少。还需要更多的研究来更好地了解不透水表面的时间分辨率、随时间的变化和演变，以及对城市制图的时间要求。城市遥感的模型、方法和图像分析算法在很大程度上是针对中分辨率(10-100m)的图像而发展的。高空间分辨率卫星图像、星载高光谱图像和激光雷达数据的出现激发了新的研究思路，并以新的模型和算法推动着未来的研究趋势。(C)2011 Elsevier Inc.保留所有权利。
The knowledge of impervious surfaces, especially the magnitude, location, geometry, spatial pattern of impervious surfaces and the perviousness-imperviousness ratio, is significant to a range of issues and themes in environmental science central to global environmental change and human-environment interactions. Impervious surface data is important for urban planning and environmental and resources management. Therefore, remote sensing of impervious surfaces in the urban areas has recently attracted unprecedented attention. In this paper, various digital remote sensing approaches to extract and estimate impervious surfaces will be examined. Discussions will focus on the mapping requirements of urban impervious surfaces. In particular, the impacts of spatial, geometric, spectral, and temporal resolutions on the estimation and mapping will be addressed, so will be the selection of an appropriate estimation method based on remotely sensed data characteristics. This literature review suggests that major approaches over the past decade include pixel-based (image classification, regression, etc.), sub-pixel based (linear spectral unmixing, imperviousness as the complement of vegetation fraction etc.), object-oriented algorithms, and artificial neural networks. Techniques, such as data/image fusion, expert systems, and contextual classification methods, have also been explored. The majority of research efforts have been made for mapping urban landscapes at various scales and on the spatial resolution requirements of such mapping. In contrast, there is less interest in spectral and geometric properties of impervious surfaces. More researches are also needed to better understand temporal resolution, change and evolution of impervious surfaces over time, and temporal requirements for urban mapping. It is suggested that the models, methods, and image analysis algorithms in urban remote sensing have been largely developed for the imagery of medium resolution (10-100 m). The advent of high spatial resolution satellite images, spaceborne hyperspectral images, and LiDAR data is stimulating new research idea, and is driving the future research trends with new models and algorithms. (C) 2011 Elsevier Inc. All rights reserved.