ABSTRACT Information on urban settlements is crucial for sustainability planning and management. While remote sensing has been used to derive such information, its applicability can be compromised due to the complexity in the urban environment. In this study, we developed a remote sensing method to map land cover types in a large Latin-American city, which is well known for its mushrooming unplanned and informal settlements. After carefully considering the landscape complexity there, we designed a data fusion method combining multispectral imagery and non-spectral data for urban and land mapping. Specifically, we acquired a cloud-free Landsat-8 image and two non-spectral datasets, i.e., digital elevation models and road networks. Then, we implemented a set of experiments with different inputs to evaluate their merits in thematic mapping through a supervised protocol. We found that the map generated with the multispectral data alone had an overall accuracy of 73.3% but combining multispectral imagery and non-spectral data yielded a land cover map with 90.7% overall accuracy. Interestingly, the thermal infrared information helped substantially improve both the overall and categorical accuracies, particularly for the two urban classes. The two types of non-spectral data were critical in resolving several spectrally confused categories, thus considerably increasing the mapping accuracy. However, the panchromatic band with higher spatial resolution and its derived textural measurement only generated a marginal accuracy improvement. The novelties of our work are with the successful separation between the two major types of urban settlements in a complex environment using a carefully designed data fusion approach and the insight into the relative merits of the thermal infrared information and non-spectral data in helping resolve the issue of class ambiguity. These findings should be valuable in deriving accurate urban settlement information which can further advance the research on socio-ecological dynamics and urban sustainability.

中文翻译：

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整合光谱和非光谱数据以改进拉丁美洲大型城市的城市住区测绘

摘要 关于城市住区的信息对于可持续性规划和管理至关重要。虽然遥感已被用于获取此类信息，但由于城市环境的复杂性，其适用性可能会受到影响。在这项研究中，我们开发了一种遥感方法来绘制拉丁美洲一个大型城市的土地覆盖类型图，该城市以其如雨后春笋般的无规划和非正式定居点而闻名。在仔细考虑那里的景观复杂性后，我们设计了一种结合多光谱图像和非光谱数据的数据融合方法，用于城市和土地测绘。具体来说，我们获得了一个无云的 Landsat-8 图像和两个非光谱数据集，即数字高程模型和道路网络。然后，我们实施了一组具有不同输入的实验，以通过监督协议评估它们在主题映射中的优点。我们发现仅使用多光谱数据生成的地图的整体精度为 73.3%，但结合多光谱图像和非光谱数据生成的土地覆盖图的整体精度为 90.7%。有趣的是，热红外信息有助于大大提高整体和分类准确度，特别是对于两个城市类别。这两种类型的非光谱数据对于解决几个光谱混淆类别至关重要，从而大大提高了映射精度。然而，具有更高空间分辨率的全色波段及其派生的纹理测量仅产生边际精度提高。我们工作的新颖之处在于使用精心设计的数据融合方法在复杂环境中成功分离了两种主要类型的城市住区，以及对热红外信息和非光谱数据在帮助解决问题的相对优点的洞察力类歧义问题。这些发现对于获得准确的城市住区信息具有重要价值，可以进一步推进社会生态动态和城市可持续性的研究。