Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms.

The consequences of droughts are far-reaching, impacting the natural environment, water quality, public health, and accelerating economic losses. Applications of remote sensing techniques using satellite imageries can play an influential role in identifying drought severity (DS) and impacts for a broader range of areas. The Barind Tract (BT) is a region of Bangladesh located northwest of the country and considered one of the hottest, semi-arid, and drought-prone regions. This study aims to assess and predict the drought vulnerability over BT using Landsat satellite images from 1996 to 2031. Several indices, including Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Soil Moisture Content (SMC), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). VHI has been used to identify and predict DS based on VCI and TCI characteristics for 2026 and 2031 using Cellular Automata (CA)-Artificial Neural Network (ANN) algorithms. Results suggest an increasing patterns of DS accelerated by the reduction of healthy vegetation (19 %) and surface water bodies (26 %) and increased higher temperature (>5 °C) from 1996 to 2021. In addition, the VHI result signifies a massive increase in extreme drought conditions from 1996 (2 %) to 2021 (7 %). The DS prediction witnessed a possible expansion in extreme and severe drought conditions in 2026 (15 % and 13 %) and 2031 (18 % and 24 %). Understanding the possible impacts of drought will allow planners and decision-makers to initiate mitigating measures for enhancing the communities preparedness to cope with drought vulnerability.

Change detection in a rural landscape: A case study of processes and main driving factors along with its response to thermal environment in Farim, Iran.

This study aims to investigate the alteration of Land Use/Land Cover (LULC) change and its response to changes in land surface temperature (LST) and heat island phenomena of a rural district known as Farim in the north of Iran from 1990 to 2020 using multi-date Landsat data. The random forest-based algorithm, supported by Google Earth Engine, is used to execute the LULC classification with an overall accuracy of more than 92%. Based on the LULC results, in terms of area changes, the classes of bare land, rice fields, and water bodies encountered an increase, but woods and dry farms decreased. The present study also incorporates the trends of land cover change that are analyzed using regression based on the temporal datasets of the three leading driving factors: temperature, precipitation, and population. The result demonstrates that the main changing factors of the mostly changed class (bare land) are population/precipitation and temperature/population. Additionally, the effect of LULC change on seasonal LST and urban heat island (UHI) is also analyzed in this study. The result witnessed a significant LST rise in the summer and winter seasons of about 12.87 °C and 14.2 °C, respectively over the study period. The Urban Thermal Field Variance Index (UTFVI), characterizing the heat island phenomenon, shows that the strongest UTFVI zone is in the central area and the none UTFVI zone is in the surrounding region. Moreover, both seasons have seen a significant rise in none UTFVI zones compared to decreasing strongest UTFVI zone. The result of the present study will be helpful for urban planners and climate researchers who study future land cover change and its associated driving factors.

Effect of outdoor thermal comfort condition on visit of tourists in historical urban plazas of Sevilla and Madrid.

The tourism plays a significant role in economics and development of any country through revenue generation from multiple sources. Spain has been tourist place and host million foreign tourist. The tourism is highly depended on climate and thermal comfortness of the visiting place. This present research aimed to analyze the outdoor thermal comfort conditions in microclimates of the urban ancient plazas of the two important cities of Spain, namely Sevilla and Madrid on a hot humid stress day of the year. The microclimatic measurement, questionnaire survey, and simulation results were examined to evaluate the thermal comfort condition of six different urban plazas in Sevilla and Madrid to distinguish the supreme time to visit each ancient site. The results have suggested that the outdoor thermal comfort range for the tourist in the historical plazas of Sevilla and Madrid varies from 28.42 to 30.87 °C and 24.5 to 29.82 °C in the hot summer. Despite the high heat stress condition, the result of questionnaires survey shows that about 38.11% and 28.09% of tourists in Sevilla and Madrid, respectively, were satisfied with the thermal conditions. As witnessed from the result of the Envi-met simulation, Plaza de Santa Ana of Madrid and Plaza Nueva of Sevilla is the best place for visitors in the early morning hours. Additionally, during the peak hours, the thermal comfort of Alameda de Hercules of Sevilla and Plaza de Santa Ana of Madrid is the most suitable historical places for visitors, whereas in the evening hours, Plaza Nueva of Sevilla and Plaza de Mayor of Madrid with wider semi-open spaces and relatively suitable vegetation bring more favorable conditions for visitors. The comparison of the simulation result with the questionnaire reveals that the urban plazas with relatively high thermal stresses have a higher rate of thermal dissatisfaction.

The Impact of the Land Cover Dynamics on Surface Urban Heat Island Variations in Semi-Arid Cities: A Case Study in Ahmedabad City, India, Using Multi-Sensor/Source Data.

This study examines the behavior of land surface temperature (LST) and surface urban heat island (SUHI) from MODIS data over Ahmedabad city, Gujarat state (India), from 2003 to 2018. Summer and winter LST patterns were analyzed, both daytime and nighttime. Ahmedabad, one of the fastest growing metropolitan cities in India, is characterized by a semi-arid climate. The investigation focuses on the SUHI variations due to warming or cooling trends of both urban and rural areas, providing quantitative interpretations by means of multi-sensor/source data. Land cover maps, normalized differential vegetation index, surface albedo, evapotranspiration, urban population, and groundwater level were analyzed across the years to assess their impact on SUHI variations. Moreover, a field campaign was carried out in summer 2018 to measure LST in several rural and urban sites. During summer daytime, the rural zone exhibits a higher average LST than the urban area, resulting in a mean negative SUHI, typical of arid cities, while a slight positive SUHI (mean intensity of 0.4 °C) during winter daytime is present. An evident positive SUHI is found only during summer (1.8 °C) and winter nighttime (3.2 °C). The negative SUHI intensity is due to the low vegetation presence in the rural area, dominated by croplands turning into bare land surfaces during the pre-monsoon summer season. Higher LST values in the rural area than in the urban area are also confirmed by the field campaign, with an average difference of about 5 °C. Therefore, the impact of the rural LST in biasing the SUHI is evident, and a careful biophysical interpretation is needed. For instance, within the urban area, the yearly intensity of the summer daytime SUHI is not correlated with the evapotranspiration, while the correspondent summer daytime LST exhibits a significant negative correlation (-0.73) with evapotranspiration. Furthermore, despite the city growth across the years, the urban area does not generally reveal a temporal increase of the magnitude of the heat island but an enlargement of its spatial footprint.

Isolation and lipoxygenase-inhibition studies of phenolic constituents from Ehretia obtusifolia.

The phenolic compounds methyl 2-O-feruloyl-1a-O-vanillactate (1), caffeic anhydride (2), and trans 4-hydroxycyclohexyl-2-O-p-coumaroyl beta-D-glucopyranoside (3) have been isolated from the AcOEt-soluble fraction of Ehretia obtusifolia, along with methyl rosmarinate (4) and rosmarinic acid (5), which are reported for the first time from this species. Their structures were determined by means of 1D- and 2D-NMR techniques. Compounds 1-5 inhibited lipoxygenase in a concentration-dependent manner, with Ki values ranging from 0.85-57.6 microM. Compounds 1, 2, 4, and 5 showed noncompetitive inhibition, whereas 3 was found to be an uncompetitive inhibitor of lipoxygenase.