Land use and land cover (LULC) classification is becoming faster and more accurate thanks to new deep learning algorithms. Moreover, new high spectral- and spatial-resolution datasets offer opportunities to classify land cover with greater accuracy and class specificity. However, deploying deep learning algorithms to characterize present-day, modern land cover based on state-of-the-art data is insufficient for understanding trends in land cover change and identifying changes in and drivers of ecological and social variables of interest. These identifications require characterizing past land cover, for which imagery is often lower-quality. We applied a deep learning pipeline to classify land cover from historical, low-quality RGB aerial imagery, using a case study of Vancouver, Canada. We deployed an atrous convolutional neural network from DeepLabv3+ (which has previously shown to outperform other networks) and trained it on modern Maxar satellite imagery using a modern land cover classification. We fine-tuned the resultant model using a small dataset of manually annotated and augmented historical imagery. This final model accurately predicted historical land cover classification at rates similar to other studies that used high-quality imagery. These predictions indicate that Vancouver has lost vegetative cover from 1995-2021, including a decrease in conifer cover, an increase in pavement cover, and an overall decrease in tree and grass cover. Our workflow may be harnessed to understand historical land cover and identify land cover change in other regions and at other times.

The water availability concerns have been increasing due to significant impacts of land use land cover change, and climate variability. In terms of developing countries, it is one of the biggest challenges to overcome and manage sustainability in the present and future. This study aims to evaluate the change in hydrological components and simulation of sediment yield and water yield on the large-scale basin of Kotri barrage with a change in runoff due to a change in land use land cover. This study has been done on the watershed as well as the sub-watershed level to have an accurate estimation and simulation by finding the response of hydrological components toward its natural and human-induced factors using the Soil and Water Assessment tool with high-resolution geospatial-temporal inputs over the Kotri catchment. The sediment and water yield were quantified using 42 years of simulation (1981-2022) on the sub-basin level, projected to land use land cover 1990, 2000, 2010, and 2022. The increase in deforestation, agriculture, and settlement areas resulted increase in sediment load in the catchment. The sub-basins 14, 11, 12, and 13, with a high elevation and slope and with less vegetation showed higher sediment load and water yield than the sub-basins with gentle slope and with high natural vegetation cover. The sub-basins 10, 4, and 1 showed high water yield availability compared to basins 2, 3, 5, 6, 7, 8, 9. This may be the result of vegetation differences. However, contained less sediment load than basins 14, 11, 12, and 13. The main objective was to quantify the significant changes affecting catchment and sub-catchment areas, to have a better understanding of the management plan regarding land use land cover. The simulated data was further projected to prediction using machine algorithms (autoregressive integrated moving average) model for precipitation prediction, and (seasonal autoregressive integrated moving average with exogenous factors) model to predict the sediment yield and water yield in the catchment to 2060.

This study investigated the impacts of LULC changes on selected ecosystem services in Maze National Park (MzNP) and its environs in southwestern Ethiopia. Landsat images from 1985, 2005, and 2020 were used to examine land use land cover (LULC) changes. Images were classified using the Random Forest (RF) classifier, and their accuracy was computed in QGIS. Ecosystem service values (ESVs) were then estimated using the benefit transfer method employing Ecosystem Service Valuation Database (ESVD) coefficients. Additionally, socioeconomic survey was conducted to understand the local community\'s perceptions regarding the dynamics of ecosystem services. The findings revealed a significant increase in croplands (103.7 %) and built-up areas (31.32 %), while riverine forests, water bodies, and wooded grasslands declined. The overall ESVs decreased by 20 %, from 2038.42 million USD in 1985 to 1628.72 million USD in 2020, mainly driven by reductions in riverine forests and wooded grasslands. As for the individual ESVs for the period 1985 to 2020, only food production increased by 0.7 million USD, while water supply, climate regulation, raw materials, and recreation and tourism declined by 180.35, 2.67, 45.72, and 481.62 million USD, respectively. The coefficient of sensitivity ranged from 0.01 to 0.94, <1, revealed that our estimates are relatively robust. Ecosystem services such as grazing, recreation, wild food, and firewood are highly valued by local residents, but they are declining over time due to environmental degradation and restrictions on access to the park. Thus, understanding LULC changes and their impacts on ESVs can help decision-makers design effective protected area management plans and reduce potential conflicts over resource uses. Further investigations are suggested to more accurately quantify ESVs using high resolution satellite imageries and different valuation methods.

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30 m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of - 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

Lake Hawassa Basin (LHB)-the study area is known for its rich and diverse aquatic and terrestrial natural resource base. However, the prevailing environmental and social problems, such as land degradation, deforestation, pollution, resource exploitation, etc. impacted the existing provisioning services (PS), and the effect becomes remarkable unless sound management is in place. The study aimed at the assessment and mapping of PS to suggest development options for decision-makers. The study employed various methods including primary and secondary data collection, including existing Land Use Land Cover (LULC), desk review, stakeholder consultations, site visits, expert judgment matrix, and ArcGIS v10.1. The study results include 6 PS identified and prioritized from the existing 14 PS, mapping of the spatial pattern of the selected 6 PS at the basin scale, and alternative development options recommended for the decision-making process conducted by decision-makers and development partners to ensure efficient management of ecosystem services in LHB. The importance of this study, as well as the simplicity and user-friendly nature of the methods and approach adopted, enables interested parties to replicate while conducting similar studies in different places within the country or globally. The intervention of adopting this study approach helps also to avoid or minimize the aforesaid biophysical and socioeconomic environmental problems and ensure development activities planned or implemented in the respective study area are environmentally friendly, and socially acceptable, through sustainable management of natural resources. In this regard, decision-makers and development partners shall provide adequate consideration for this study approach and the result of demonstrating basin scale spatial variability of PS. This plays a vital role in the sustainable management of natural resources as well as provisioning services existing in the study area to benefit the community members, ensure human well-being, and secure the livelihood of the people residing within or around the Lake Hawassa Basin.

Understanding the drivers of urban growth and spatiotemporal land use change is important for rational land use and sustainable urban development. Based on the land use data, GIS data of explanatory variables, experts\' knowledge and field observation, the study used a binary logistic regression model (BLRM) to analyze factors that drive rapid urban growth in Bahir Dar city, Ethiopia, using the LOGISTICREG module in IDRISI Selva software. Nine factors were used to reflect the influence of proximity and physical factors on urban growth from 1984 to 2019. This model helped in quantifying and identifying the factors of urban growth, which includes topography (slope, elevation and aspect) and accessibility (Dis. to the main road, Dis. to international airport, Dis. to CBD, Dis. to existing built-up area, Dis. to forest land and Dis. to water body). Furthermore, urban growth probability maps were created based on LRM results, revealing that the biggest urban growth would occur around existing built-up areas along the main roads and near Bahir Dar international airport. The Relative Operating Characteristic (ROC) values of 0.85, 0.90 and 0.93 and PCP values of 96.72 %, 98.46 % and 98.51 % indicate the urban growth probability maps are valid and BLRM had an ideal ability to predict urban growth. So, the study highlighted the relation between urban growth and its drivers in Bahir Dar, giving a decision making framework for better land use management and resource allocation.

Rapid urban developmental growth is a heated debate worldwide due to environmental challenges. This research has examined the spatiotemporal trend of informal built-up growth in Karachi city. Using a geo-information system, the past twenty years (2000-2020) trends of informal built-up growth are examined. For attaining the research objectives, geo-referenced high-resolution maps and satellite images are used for accuracy based spatial data. Karachi is divided into five different land use and land cover (LULC): formal built-up, informal built-up, vacant, water bodies, and green spaces. Spatial data of informal built-up growth change of five different years, 2000, 2005, 2010, 2015, and 2020 are generated through acquired maps digitization using ArcMap. Subsequently, the gains and transfers of Karachi\'s informal built-up growth based on five years 2000-2005, 2005-2010, 2010-2015, and 2015-2020 are analyzed using the Land Change Modeler (LCM) in IDRISI software. Also, land use land cover changes (LULCC) are predicted for the next 40 years (2020-2060) using the integrated Cellular Automata Markov (CA-Markov) simulation model in IDRISI. The results revealed that Karachi\'s built-up is expanding rapidly. Land conversion into the informal built-up area is alarming, as it has changed from 144.31 km2 to 217.19 km2 with 72.88 km2 in the past twenty years (2000-2020) and has occupied green and agricultural land. Most informal built-up areas have transitioned from vacant (71.01 km2) land use land cover (LULC). The informal built-up area could expand from 217.19 km2 to 317.63 km2, with about 100.44 km2 up to 2060. The planned and unplanned development will be towards the city\'s East (E) direction and will convert and ruin agriculture and vacant land. The present study provides suggestions to urban planners, administrative authorities, and policymakers to control informal growth and achieve sustainable development goals in developing countries.

The dynamics of land use and land cover are profoundly affected by the growth, mobility, and demand of people. Thematic maps of land use and land cover (LULC) help planners account for conservation, concurrent uses, and land-use compressions by providing a reference for analysis, resource management, and prediction. The purpose of this research is to identify the transition of land-use changes in the Saroor Nagar Watershed between 2008 and 2014 using the Modules for Land Use Change Evaluation (MOLUSCE) plugin (MLP-ANN) model and to forecast and establish potential land-use changes for the years 2020 and 2026. To predict how these factors affected LULC from 2008 to 2014, MLP-ANN was trained with maps of DEM, slope, distance from the road, and distance to a waterbody. The projected and accurate LULC maps for 2020 have a Kappa value of 0.70 and a correctness percentage of 81.8%, indicating a high degree of accuracy. Changes in LULC are then predicted for the year 2026 using MLP-ANN, which shows a 17.4% increase in built-up area at the expense of vegetation and barren land. The results contribute to the development of sustainable plans for land use and resource management.

Land use land cover (LULC) dynamics is an important aspect of environmental studies. Lahore is one of the wide-ranging urban cities in the world experiencing rapid development in the form of unplanned urban growth and industrialization, which leads to many adverse consequences. This research focuses on the study of spatio-temporal variability of urbanization and its impact on the water quality index (WQI) in Lahore city using remote sensing (RS) and geographical information systems (GIS). Landsat images (Landsat 7 ETM+, Landsat 8 OLI) between 2005 to 2021 were used to observe the changes in urban growth over seventeen years. GIS is used to create the LULC, normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI) maps, to study the urbanization impact on the WQI. The results of this study indicate that the groundwater quality of metropolitan Lahore city has significantly dropped within 17 years. The extent of the built-up area has been expanded from 22.4% to 953.04% with an increase in the poor WQI area from 1.95% to 37.89%, reveals a general decline in groundwater quality with urbanization. Indeed, the trends observed by the linear regression modelling showed a positive and negative correlation (R2 = 0.67 and -0.74) of WQI with % of urban and vegetation areas respectively. GIS and RS tools have been found effective in assessing spatio-temporal phenomena of urbanization and its impact on groundwater quality. Furthermore, this research would be very helpful in making decisions for managing groundwater resources and illegal urban expansion in Lahore city.

Land use/land cover (LULC) changes can occur naturally or due to human activities. In this study, the maximum likelihood algorithm (MLH) and machine learning (random forest algorithm (RF) and support vector machine (SVM)) were investigated for image classification to oversight spatio-temporal land use changes in El-Fayoum governorate, Egypt. The Google Earth Engine has been utilized to pre-process the Landsat imagery, and then upload it for classification. Each classification method was evaluated using field observations and high-resolution Google Earth imagery. LULC changes were assessed, utilizing Geographic Information System (GIS) techniques, over the last 20 years in three different periods: 2000-2012, 2012-2016, and 2016-2020. The results showed that socioeconomic changes occurred during these transitions. The SVM procedure provided the most accurate maps in terms of the kappa coefficient (0.916) compared to MLH (0.878) and RF (0.909) procedures. Therefore, the SVM technique was adopted to classify all available satellite imagery. The results of change detection showed that urban sprawl has occurred and most of the encroachments were on agricultural land. The results showed that agricultural land area decreased from 26.84% in 2000 to 26.61% in 2020 and urban area increased from 3.43% in 2000 to 5.99% in 2020. In addition, urban land expanded rapidly on account of agricultural lands by a total of 4.78% from 2012 to 2016, while it expanded slowly by a total of 3.23% from 2016 to 2020. Overall, this study offers useful insight into LULC changes that might aid shareholders and decision makers in making informed decisions.