Measuring the Urban Land Surface Temperature Variations in Zhengzhou City Using the Landsat-Like Data

Abstract

Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environments under rapid urban expansion. Current MODIS data is, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data can’t explore temporally the refined analysis due to the low temporal resolution. In order to resolve this situation, we used MODIS and Landsat data to generate “Landsat-like” data by using the flexible spatiotemporal data fusion method (FSDAF), and then studied spatiotemporal variation of land surface temperature (LST) and its driving factors. The results showed that 1) The estimated “Landsat-like” data have high precision; 2) By comparing 2013 and 2016 datasets, LST increases ranging from 1.8°C to 4°C were measurable in areas where the impervious surface area (ISA) increased, while LST decreases ranging from -3.52°C to -0.70°C were detected in areas where ISA decreased; 3) LST has a strongly negative relationship with the Normalized Difference Vegetation Index (NDVI), and a strongly positive relationship with Normalized Difference Built Index (NDBI) in summer; and 4) LST is well correlated with Building density (BD), in a complex conic mode, and LST may increase by 0.460°C to 0.786°C when BD increases by 0.1. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management.

Investigating the Trends and Drivers between Urbanization and the Land Surface Temperature: A Case Study of Zhengzhou, China

“…Low LST was mainly distributed at the edges of the study area and near rivers attached to lakes. This is consistent with the results of previous studies on LST in Zhengzhou city [28,[40][41][42].…”

Study on Spatio-Temporal Changes of Land Use Sustainability in Southwestern Border Mountainous Provinces in Recent 20 Years Based on Remote Sensing Interpretation: A Case Study in Yunnan Province, China

“…Wu Qiuju, Yang Renyi and Yang Zisheng (2022) used RS and GIS to analyze the impact of LUCC on the rationality and SLU in the Dianchi Lake basin [17]. In terms of the index system of LUSE, experts and scholars have developed plenty of stud-ies from different focuses and perspectives [18,19] and roughly constructed four types of indicator systems: "productivity-safety-maintainability-acceptability-viability" [20][21][22][23], "ecology-economy-society" [24][25][26][27] "economy-society-environment-resources" [28][29][30] and PSR or DPSIR [11,[31][32][33][34][35]. From the perspective of evaluation methods, it mainly involves the comprehensive index method, AHP method [1,6,22], superposition analysis method [8], AHP-entropy weight method [35], principal component analysis method [36], improved grey relational analysis (GRA) [28], improved TOPSIS method [29], etc.…”

“…By classifying different elements in complicated issues into interconnected orderly levels, it is a convenient, flexible and applicable method for quantitative analysis of qualitative issues. In terms of research areas, there are national [10], provincial [1,9,12,25,28], municipal [8,23,26,[34][35][36] and county-level regions [6,13,21]. There are both relatively developed ecological economic zones [11] and relatively backward ecological fragile zones [1,13,30,37,38].…”