利用云计算技术绘制大开普敦都市区非正规住区的地理范围图

IF 3.8 Q2 ENVIRONMENTAL SCIENCES
Siyamthanda Gxokwe, Timothy Dube
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引用次数: 0

摘要

尽管遥感方法为了解城市土地覆被动态(包括非正规住区的面积)提供了前所未有的机会,但光谱混淆等挑战依然存在,特别是在将非正规住区等土地覆被类型与规划的正规住区区分开来时。地球观测(EO)数据分析工具的改进,如谷歌地球引擎(GEE)云计算平台的引入,通过其先进的数据处理和过滤算法,为提高这些住区与其他城市土地覆被类别的可分离性提供了前景,该算法允许多源和多时态数据的协同使用,从而改进了对这些住区的检测和监测。本研究利用 GEE 云计算平台的先进数据分析能力和分辨率更高的哨兵-2 数据,绘制了开普敦大都市区非正规住区的地理范围图。分类得出了六个土地覆被等级:正规住区、非正规住区、水域、裸露或建筑密集区、植被地和耕地。已建成的正规住区是最主要的类别,占开普敦总面积的 70%,而开放水域是最不主要的类别,占 2%。非正规住区约占所有住区的 7%。虽然总体准确度在可接受范围内(68%),但植被地和正规住区等一些类别由于与其他类别光谱相似,类别准确度较低。研究结果凸显了 GEE 平台的重要性,以及在绘制非正规住区地图时,环境和光谱特征以及各种哨兵-2 导出的水、建筑和植被指数之间的相互作用。这些发现对于促进改善城市规划、提供服务以及协助缓解开普敦大都会地区的社会和环境问题至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using cloud computing techniques to map the geographic extent of informal settlements in the greater Cape Town Metropolitan Area

Although remote sensing approaches offer unprecedented opportunities to understand urban land cover dynamics including informal settlements areal extent, challenges such as spectral confusions still persist, particularly when segregating land cover types like informal settlements from planned formal settlements. The improvements in Earth Observation (EO) data analytic tools such as introduction of Google Earth Engine (GEE) cloud computing platform, provide prospects to improve separability of these settlements from other urban land cover classes, via their advanced data processing and filtering algorithm, which allows for the synergic use of multisource and multi-temporal data, thus improving detection and monitoring of these settlements. This study harnessed the advance data analytic powers of GEE cloud computing platform coupled with higher resolution Sentinel-2 data to map the geographical extent of informal settlement in the Cape Town Metropolitan Area. The classification yielded six land cover classes: formal settlements, informal settlements, water, bare or built-up areas, vegetated lands, and croplands. Built-up formal settlement was the most dominant class, accounting for 70% of the total Cape Town surface area, while open water was the least dominant, accounting for 2%. Informal settlements accounted for approximately 7% of all settlements. Although overall accuracy was within acceptable limits (68%), some classes, such as vegetated lands and formal settlements, reported low class accuracies due to spectral similarities with other classes. The findings highlight the importance of the GEE platform, as well as the interaction of contextual and spectral characteristics, as well as various sentinel-2 derived water, built up, and vegetation indices in mapping informal settlements. These findings are critical for the facilitation of improved urban planning, provision of services and assisting in alleviating social as well as environmental issues within the Cape Town Metropolitan area.

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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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