Evaluating the relationship between Urban Heat Island and temporal change in land use, NDVI and NDBI: a case study of Bhopal city, India

Singh, P.; Verma, P.; Chaudhuri, A. S.; Singh, V. K.; Rai, P. K.

doi:10.1007/s13762-023-05141-y

Cited by 4 publications

(1 citation statement)

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“…It relies solely on information from optical and thermal sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR), with moderate resolutions and frequent repetition intervals, and Landsat TM/ETM+, which has a moderately high resolution [1,10,18,[23][24][25]. For the thermal sensor, the LST estimation requires specific adjustments for each observation date, as it is not only influenced by soil moisture but also by atmospheric factors such as air temperature, relative humidity and wind speed, as pointed out in a recent study by [26][27][28]. Therefore, TVDI has been applied in various ecosystems around the world, including tropical [18,22] and semi-arid monsoon regions [23,25,29,30].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Evolution of Olive Tree Water Status Using Land Surface Temperature and Vegetation Indices Derived from Landsat 5 and 8 Satellite Imagery in Southern Peru

Quille-Mamani,

Huayna,

Pino-Vargas

et al. 2024

Agriculture

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Land surface temperature (LST) and its relationship with vegetation indices (VIs) have proven to be effective for monitoring water stress in large-scale crops. Therefore, the objective of this study is to find an appropriate VI to analyse the spatio-temporal evolution of olive water stress using LST images and VIs derived from Landsat 5 and 8 satellites in the semi-arid region of southern Peru. For this purpose, VIs (Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2) and Soil Adjusted Vegetation Index (SAVI)) and LST were calculated. The information was processed in Google Earth Engine (GEE) for the period 1985 to 2024, with an interval of every five years for the summer season. The triangle method was applied based on the LST-VIs scatterplot analysis, a tool that establishes wet and dry boundary conditions for the Temperature Vegetation Dryness Index (TVDI). The results indicated a better appreciation of olive orchard water stress over time, with an average of 39% drought (TVDINDVI and TVDISAVI), 24% severe drought (TVDINDVI) and 25% (TVDISAVI) of the total area, compared to TVDIEVI2, which showed 37% drought and 16% severe drought. It is concluded that TVDINDVI and TVDISAVI provide a better visualisation of the water stress map of the olive crop and offer a range of options to address current and future problems in water resource management in the olive sector in semi-arid areas of southern Peru.

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