School of Environment and Sustainable Development, Central University of Gujarat, Gandhinagar, 382030, India.
Environ Monit Assess. 2024 Aug 31;196(9):866. doi: 10.1007/s10661-024-13038-7.
In developing countries, examining land use land cover (LULC) change pattern is crucial to understanding the land surface temperature (LST) effect as urban development lacks coherent policy planning. The variability in LST is often determined by continuously changing LULC patterns. In this study, LULC change effect analysis on LST has been carried out using geometric and radiometric corrected thermal bands of multi-spectral Landsat 7 ETM + and 8 TIRS/OLI satellite imagery over Gandhinagar, Gujarat, in the years 2001 and 2022, respectively. Maximum likelihood classification (MLC) was applied to assess LULC change while an NDVI-based single-channel algorithm was used to retrieve LST using Google Earth Engine (GEE). Results showed a substantial change in built-up (+ 347.08%), barren land (- 50.74%), and vegetation (- 31.66%). With the change in LULC and impervious surfaces, the mean LST has increased by 5.47 ℃. The impact of sparse built-up was seen on vegetation and agriculture as a maximum temperature of > 47 ℃ was noticed in all LULC classes except agriculture, where the temperature reached as high as > 49 ℃ in 2022. Since Gandhinagar is developing a twin-city plan with Ahmedabad, this study could be used as a scientific basis for sustainable urban planning to overcome dynamic LULC change and LST impacts.
在发展中国家，研究土地利用/土地覆盖（LULC）变化模式对于理解城市发展缺乏连贯政策规划的地表温度（LST）效应至关重要。LST 的可变性通常由不断变化的 LULC 模式决定。在这项研究中，分别使用多光谱 Landsat 7 ETM+和 8 TIRS/OLI 卫星影像的几何和辐射校正热波段，对古吉拉特邦甘地讷格尔 2001 年和 2022 年的土地利用/土地覆盖变化对地表温度的影响进行了分析。最大似然分类（MLC）被应用于评估土地利用/土地覆盖变化，而基于 NDVI 的单通道算法被用于使用谷歌地球引擎（GEE）检索地表温度。结果显示，建成区（+347.08%）、荒地（-50.74%）和植被（-31.66%）发生了显著变化。随着土地利用/土地覆盖和不透水面的变化，平均地表温度上升了 5.47℃。稀疏建成区对植被和农业产生了影响，因为除农业外，所有土地利用/土地覆盖类型的最高温度都超过了 47℃，而在 2022 年，农业的最高温度达到了 49℃以上。由于甘地讷格尔正在与艾哈迈达巴德共同制定双城计划，因此本研究可以作为可持续城市规划的科学依据，以克服动态土地利用/土地覆盖变化和地表温度的影响。
