Subpixel land-cover classification for improved urban area estimates using Landsat

Urban areas are Earth’s fastest growing land use that impact hydrological and ecological systems and the surface energy balance. The identification and extraction of accurate spatial information relating to urban areas is essential for future sustainable city planning owing to its importance within global environmental change and human–environment interactions. However, monitoring urban expansion using medium resolution (30–250 m) imagery remains challenging due to the variety of surface materials that contribute to measured reflectance resulting in spectrally mixed pixels. This research integrates high spatial resolution orthophotos and Landsat imagery to identify differences across a range of diverse urban subsets within the rapidly expanding Perth Metropolitan Region (PMR), Western Australia. Results indicate that calibrating Landsat-derived subpixel land-cover estimates with correction values (calculated from spatially explicit comparisons of subpixel Landsat values to classified high-resolution data which accounts for over [under] estimations of Landsat) reduces moderate resolution urban area over (under) estimates by on an average 55.08% for the PMR. This approach can be applied to other urban areas globally through use of frequently available and/or low-cost high spatial resolution imagery (e.g. using Google Earth). This will improve urban growth estimations to help monitor and measure change whilst providing metrics to facilitate sustainable urban development targets within cities around the world.

城市区域是地球上增长速度最快的土地利用类型，会对水文生态系统与地表能量平衡造成影响。鉴于城市区域在全球环境变化及人类-环境交互中的重要地位，精准识别并提取城市相关空间信息，对未来可持续城市规划至关重要。然而，由于地表物质多样会导致反射率测量值产生差异，进而形成光谱混合像元，利用中分辨率（30~250米）影像监测城市扩张仍存在挑战。本研究整合高空间分辨率正射影像与陆地卫星（Landsat）影像，针对澳大利亚西部快速扩张的珀斯都会区（Perth Metropolitan Region, PMR）内各类多样化城市子集的差异进行识别。研究结果显示，通过校正值对Landsat反演得到的亚像素土地覆盖估算结果进行校准（该校正值通过将亚像素Landsat值与分类后的高分辨率数据进行空间显式对比计算得出，可修正Landsat的高估/低估问题），可将珀斯都会区的中分辨率城市面积估算误差（高估/低估）平均降低55.08%。该方法可借助易获取且/或低成本的高空间分辨率影像（例如使用谷歌地球（Google Earth）），推广应用至全球其他城市区域。这将提升城市扩张估算的准确性，助力变化监测与量化工作，同时可为全球各地城市实现可持续城市发展目标提供量化指标支撑。