Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents
Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents
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DOI：
10.3390/rs13183672
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发表时间：
2021-07
期刊：
影响因子：
5
通讯作者：
Johannes H. Uhl;S. Leyk;Zekun Li;Weiwei Duan;Basel Shbita;Yao-Yi Chiang;Craig A. Knoblock
中科院分区：
文献类型：
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作者：
Johannes H. Uhl;S. Leyk;Zekun Li;Weiwei Duan;Basel Shbita;Yao-Yi Chiang;Craig A. Knoblock
在业务遥感时代之前，描述历史城市范围的空间明确、细粒度数据集很少可用。然而，这些数据对于更好地了解长期城市化和土地开发过程以及评估自然-人类耦合系统（例如，荒地-城市界面的动态）是必要的。在此，我们提出了一个框架，该框架联合使用遥感衍生的人类住区数据（即全球人类住区层，GHSL）和扫描的地理参考历史地图来自动生成20世纪初的历史城市范围。通过对历史地图进行无监督的色彩空间分割，在空间上受限于从GHSL中提取的城市范围，我们的方法生成了与多时间GHSL无缝集成的历史定居点范围。我们将我们的方法应用于研究四大洲国家的区域，并根据美国历史聚居数据汇编（HISDAC-US）的历史建筑密度估算和全球环境历史数据库（HYDE）的城市面积估算来评估我们的方法。与HISDAC-US相比，我们的结果达到了曲线下面积值> 0.9，并且与HYDE数据库中基于模型的城市区域基本一致，这表明遥感观测数据和历史制图数据源的整合为有历史地图的国家评估城市化和长期土地覆盖变化开辟了新的、有希望的途径。
Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multitemporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values > 0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.