Assessing the Influence of Land Use/Land Cover Alteration on Climate Variability: An Analysis in the Aurangabad District of Maharashtra State, India

Abstract

Examining the influence of land use/land cover transformation on meteorological variables has become imperative for maintaining long-term climate sustainability. Rapid growth and haphazard expansion have caused the conversion of prime agricultural land into a built-up area. This study used multitemporal Landsat data to analyze land use/land cover (LULC) changes, and Terra Climate monthly data to examine the impact of land transformation on precipitation, minimum and maximum temperature, wind speed, and soil moisture in the Aurangabad district of Maharashtra state in India during 1999–2019. Multiple linear regression and correlation analysis were performed to determine the association among LULC classes and climatic variables. This study revealed rapid urbanization in the study area over the years. The built-up area, water bodies, and barren lands have recorded a steep rise, while the agricultural area has decreased in the district. Drastic changes were observed in the climatic variables over the years. The precipitation and wind speed have shown decreasing trends during the study period. A positive relationship between soil moisture and agricultural land was found through a correlation analysis. Conspicuous findings about the positive relationship between the agricultural land and maximum temperature need further investigation. A multiple linear regression analysis demonstrated a negative relationship between the built-up area and precipitation. The intensity of the precipitation has reduced as a consequence of the developmental activities in the study area. Moreover, a positive relationship was observed between the built-up area and maximum temperature. Thus, this study calls for policy implications to formulate a futuristic land-use plan considering climate change projection in the district.

Automatic land cover classification with SAR imagery and Machine learning using Google Earth Engine

“…From results it is observed that RF outperforms SVM in LC applications due to its robustness to outliers and noise also as compared to RF SVM is more sensitive to hyperparameters [38]. Obtained results show that the presented approach achieves improvement of 3% in overall accuracy for land cover classification compared to obtained in [28] since SAR sensors can acquire an image in all weather conditions in addition to these different combinations of polarization provides important LC details of the earth's surface which improves LC classification accuracy.…”

“…Researchers are utilizing GEE in recent years for LC classification. The use of GEE for land cover classification using Landsat8 [24,25,27,28], sentinel2 [29] and combinations of sentinel2 and landsat8 [30] has shown good results. Therefore GEE presents great opportunities in dealing with remote sensing data for LC mapping in Pusad.…”

“…Hence GEE offers an attractive solution to users having low end devices to obtain satisfactory results in less time without the need of high power computers or commercial software. Most researchers previously used Landsat [24,25,27,28], Sentinel-2 [29] and a combination of Landsat and sentinel-2 [30] images for generating LC classification. We chose sentinel-1 images since SAR sensors can acquire an image in all weather conditions in addition to these different combinations of wavelength and polarization provides important information regarding the earth's surface [26].…”

Land Use Change Detection and Evaluation in a Watershed: The Implications for Ofu River Water Quality in Anyigba, Kogi State, Nigeria

“…Generally, LULCC in the tropics increase nitrate, phosphorus (PO 4 -), ammonium (NH 4 + ), electrical conductivity in bodies of water (including Na +, Mg 2+ , Cl Changes in land use/cover also have an impact on water resources through their contribution to processes like the introduction of invasive fauna and flora species into water and siltation [17,18]. Land uses such as agriculture and built up areas have been shown to influence soil moisture and climatic processes such as temperature and precipitation [19]. Water resources are often at the Centre of urban development but, as the city expands, the environmental pressure on its water resources increases [20].…”

Implications of land use/land cover dynamics on urban water quality: Case of Addis Ababa city, Ethiopia

“…Land uses such as agriculture and built up areas have been shown to influence soil moisture and climatic processes such as temperatures and precipitation. The intensity of the precipitation can be reduced as result of the developmental activities whilst the temperatures increase with increased impervious surfaces [ 27 ]. One major problem is that most developing nations lack the resources necessary for efficient urban planning.…”