NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisbon, Portugal.
Future Earth, Royal Swedish Academy of Sciences, Stockholm, Sweden.
PLoS One. 2019 Jan 2;14(1):e0208949. doi: 10.1371/journal.pone.0208949. eCollection 2019.
Rapid and extensive urbanization has adversely impacted humans and ecological entities in the recent decades through a decrease in surface permeability and the emergence of Urban Heat Islands (UHI). While detailed and continuous assessments of surface permeability and UHI are crucial for urban planning and management of landuse zones, they mostly involve time consuming and expensive field studies and single sensor derived large scale aerial and satellite imageries. We demonstrated the advantage of fusing imageries from multiple sensors for landuse and landcover (LULC) change assessments as well as for assessing surface permeability and temperature and UHI emergence in a fast growing city, i.e. Tirunelveli, Tamilnadu, India. IRS-LISSIII and Landsat-7 ETM+ imageries were fused for 2007 and 2017, and classified using a Rotation Forest (RF) algorithm. Surface permeability and temperature were then quantified using Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST) index, respectively. Finally, we assessed the relationship between SAVI and LST for entire Tirunelveli as well as for each LULC zone, and also detected UHI emergence hot spots using a SAVI-LST combined metric. Our fused images exhibited higher classification accuracies, i.e. overall kappa coefficient values, than non-fused images. We observed an overall increase in the coverage of urban (dry, real estate plots and built-up) areas, while a decrease for vegetated (cropland and forest) areas in Tirunelveli between 2007 and 2017. The SAVI values indicated an extensive decrease in surface permeability for Tirunelveli overall and also for almost all LULC zones. The LST values showed an overall increase of surface temperature in Tirunelveli with the highest increase for urban built-up areas between 2007 and 2017. LST also exhibited a strong negative association with SAVI. Southeastern built-up areas in Tirunelveli were depicted as a potential UHI hotspot, with a caution for the Western riparian zone for UHI emergence in 2017. Our results provide important metrics for surface permeability, temperature and UHI monitoring, and inform urban and zonal planning authorities about the advantages of satellite image fusion.
快速且大规模的城市化进程在过去几十年中对人类和生态实体造成了负面影响，这主要是因为地表渗透性的降低和城市热岛（UHI）的出现。尽管对地表渗透性和 UHI 进行详细且持续的评估对于城市规划和土地利用区域的管理至关重要，但这些评估通常涉及耗时且昂贵的现场研究以及单一传感器衍生的大规模航空和卫星图像。我们展示了融合来自多个传感器的图像的优势，这些图像可用于评估土地利用和土地覆盖（LULC）变化，以及评估快速增长城市的地表渗透性、温度和 UHI 的出现，例如印度泰米尔纳德邦的蒂鲁内尔维利。我们融合了 2007 年和 2017 年的 IRS-LISSIII 和 Landsat-7 ETM+图像，并使用旋转森林（RF）算法进行分类。然后，我们分别使用土壤调整植被指数（SAVI）和地表温度（LST）指数来量化地表渗透性和温度。最后，我们评估了整个蒂鲁内尔维利以及每个 LULC 区域的 SAVI 和 LST 之间的关系，并使用 SAVI-LST 组合指标检测 UHI 出现的热点。我们融合的图像比非融合图像具有更高的分类精度，即整体kappa 系数值。我们观察到蒂鲁内尔维利的城市（干燥、房地产地块和建成区）区域的覆盖范围总体上增加了，而植被（耕地和森林）区域的覆盖范围则减少了。在 2007 年至 2017 年期间，SAVI 值表明蒂鲁内尔维利整体以及几乎所有 LULC 区域的地表渗透性都广泛降低。LST 值显示了蒂鲁内尔维利地表温度的整体升高，其中 2007 年至 2017 年期间城市建成区的地表温度升幅最大。LST 还与 SAVI 呈强烈的负相关。蒂鲁内尔维利东南部的建成区被描绘为潜在的 UHI 热点，2017 年西部滨水区有 UHI 出现的风险。我们的研究结果为地表渗透性、温度和 UHI 监测提供了重要指标，并告知城市和区域规划当局关于卫星图像融合的优势。
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