Land surface temperature and normalized difference vegetation index relationship: a seasonal study on a tropical city
- PDF / 3,078,925 Bytes
- 14 Pages / 595.276 x 790.866 pts Page_size
- 90 Downloads / 302 Views
Land surface temperature and normalized difference vegetation index relationship: a seasonal study on a tropical city Subhanil Guha1 · Himanshu Govil1 Received: 7 April 2020 / Accepted: 27 August 2020 © Springer Nature Switzerland AG 2020
Abstract Land surface temperature (LST) and its relationship with normalized difference vegetation index (NDVI) are significantly considered in environmental study. The aim of this study was to retrieve the LST of Raipur City of tropical India and to explore its seasonal relationships with NDVI. Landsat images of four specific seasons for three particular years with fourteen years time interval were analyzed. The result showed a gradual rising (3.63 °C during 1991–2004 and 1.54 °C during 2004–2018) of LST during the whole period of study. The mean LST value of three particular years was the lowest (27.21 °C) on green vegetation and the highest (29.81 °C) on bare land and built-up areas. The spatial distribution of NDVI and LST reflects an inverse relationship. The best (− 0.63) and the least (− 0.17) correlation were noticed in the postmonsoon and winter seasons, respectively, whereas a moderate (− 0.45) correlation were found both in the monsoon and pre-monsoon seasons. This LST-NDVI correlation was strong negative (− 0.51) on vegetation surface, moderate positive on water bodies (0.45), and weak positive on the built-up area and bare land (0.14). In summary, the LST is greatly controlled by surface characteristics. This study can be used as a reference for land use and environmental planning in a tropical city. Keywords Landsat · LST · NDVI · Tropical city
1 Introduction Urbanization accelerates the ecological stress by warming the local or global cities for a large extent [1–6]. Presently, many urban areas are suffering with a huge land conversion and resultant new heat zones [7–9]. Remote sensing techniques are significantly effective in detecting the land use/land cover (LULC) change and its consequences [10]. Several satellite sensors are capable to identify these change zones by using their visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands [11]. Apart from the conventional LULC classification algorithms, some spectral indices are used in detecting specific land features. Normalized difference vegetation index (NDVI) can be considered as the most applied spectral index in this scenario [12]. Recently, thermal infrared (TIR) bands are also used by generating some indices for different
types of LULC extraction [13–15]. These remote sensing indices are used significantly in several application fields like rocks and mineral mapping, forest mapping, agricultural monitoring, LULC mapping, hazard mapping, urban heat island mapping and monitoring, among others [14, 16–19]. Land surface temperature (LST) retrieved from several remotely sensed data is widely used in the detection of urban heat island and ecological comfort zone [20–23]. LST can change significantly in a vast homogeneous land surface or even inside a relatively small heterogeneous urban area [
Data Loading...
