Land Use And Land Cover Maps Research Articles

Eucalyptus tree expansion and land use and land cover dynamics in Ethiopia- empirical evidence from Gurage Zone, Ethiopia

Gurage Zone is one of the areas of Ethiopia experiencing rapid land use and land cover (LULC) changes over the past few decades. This study assessed the contribution of eucalyptus expansion to LULC changes based on a detailed household level survey, key informant interviews, focus group discussions and a set of Landsat imageries. Both descriptive statistics and econometric model were employed for the analysis of drivers of eucalyptus plantation. LULC change detection technique was used to examine the changes in LULC over time. This study achieved a mean classification accuracy of 89.17% and 0.83 overall Kappa statistics for the tree LULC maps. The result of descriptive analysis revealed that more than 42% of the sampled households have planted eucalyptus trees on crop land. The study indicated that eucalyptus plantation which was fourth in LULC ranking in 2000 has increased by 68.3% in 2021 to become the second largest land cover next to crop land. On the other hand, from 2010 to 2021, the area covered by crops showed a decline. This indicates eucalyptus tree to be the major cause of LULC change in the study area. The econometric estimation identified the micro-level drivers such as land size and access to market to be important factors influencing eucalyptus plantation. This study suggests that the rapid expansion of eucalyptus plantation on fertile soil requires an immediate policy response in order to balance food crop production and eucalyptus plantation in Gurage Zone.

Cultural heritage and urban morphology: land use transformation in ‘Kumbh Mela’ of Prayagraj, India

A Hindu pilgrimage site where millions of people gather to seek solutions to problems related to everyday life, the “Kumbh Mela” is recognised as one of India’s intangible cultural heritages. The site of the observance rotates between four cities, Haridwar, Ujjain, Nashik and Prayagraj. The infrastructure and services in the city that is hosting such a gathering undergo numerous changes as a result of the sizeable gathering. The goal of the study is to monitor the changes in Land Use and Land Cover (LULC) in the city of Prayagraj before and after the “Kumbh Mela” and to evaluate the effect on infrastructure changes in the citry. Analyzing the changes that such pilgrimages and events bring about in cities are fascinating. In 2019, the city of Prayagraj hosted the “Kumbh”, hence, Pre-Kumbh (2016), Kumbh (2019), and Post-Kumbh (2022) phases were taken under consideration for LULC change assessment. Supervised classification was conducted using Maximum Likelihood Classifier to prepare LULC maps. It was observed that urbanisation has significantly increased at the expense of vegetation, fallow land, and agricultural land. The success of the local agroforestry policy and plantation drive is demonstrated by the interesting increase in vegetation that has been observed. The construction of a civil airport, the widening of existing roads, the installation of new railroad tracks, and the adornment of roundabouts on existing roads are the major infrastructure changes. The findings offer fresh insights of increasing urban infrastructure from a cultural perspective. The findings will aid in better planning and policy development for the resilient and sustainable infrastructure, as well as in the development of a long-term strategy for the temporary habitation in the “Kumbh” area of such cities.

Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia

Land use and land cover (LULC) change detection and prediction studies are crucial for supporting sustainable watershed planning and management. Hence, this study aimed to detect historical LULC changes from 1985 to 2019 and predict future changes for 2035 (near future) and 2065 (far future) in the Gumara watershed, Upper Blue Nile (UBN) Basin, Ethiopia. LULC classification for the years 1985, 2000, 2010, and 2019 was performed using Landsat images along with vegetation indices and topographic factors. The random forest (RF) machine learning algorithm built into the cloud-based platform Google Earth Engine (GEE) was used for classification. The results of the classification accuracy assessment indicated perfect agreement between the classified maps and the validation dataset, with kappa coefficients (K) of 0.92, 0.94, 0.90, and 0.88 for the LULC maps of 1985, 2000, 2010, and 2019, respectively. Based on the classified maps, cultivated land and settlement increased from 58.60 to 83.08% and 0.06 to 0.18%, respectively, from 1985 to 2019 at the expense of decreasing forest, shrubland and grassland. Future LULC prediction was performed using the cellular automata–Markov (CA–Markov) model under (1) the business-as-usual (BAU) scenario, which is based on the current trend of socioeconomic development, and (2) the governance (GOV) scenario, which is based on the Green Legacy Initiative (GLI) program of Ethiopia. Under the BAU scenario, significant expansions of cultivated land and settlement were predicted from 83.08 to 89.01% and 0.18 to 0.83%, respectively, from 2019 to 2065. Conversely, under the GOV scenario, the increase in forest area was predicted to increase from 2.59% (2019) to 4.71% (2065). For this reason, this study recommends following the GOV scenario to prevent flooding and soil degradation in the Gumara watershed. Finally, the results of this study provide information for government policymakers, land use planners, and watershed managers to develop sustainable land use management plans and policies.

Advanced CMD predictor screening approach coupled with cellular automata-artificial neural network algorithm for efficient land use-land cover change prediction

The prediction of future land use and land cover (LULC) change is essential for natural resource management and the development of environmental monitoring strategies, especially for a non-resilient system. Thus, a novel LULC prediction model was proposed by coupling a hybrid predictor screening approach with the Cellular Automata coupled Artificial Neural Network (CA-ANN) approach. The hybrid predictor screener was formed by integrating the outcomes of Correlation Coefficient (CC), Mutual Information (MI), and Decision Tree (DT) methods and termed as the CMD technique. The CA-ANN model was trained by LULC maps of 1995 and 2005 and tested by the map of 2020 for these four sets of variables (i.e., CC, MI, DT, and CMD). The results revealed that the CMD predictors were the most influential variables for LULC prediction, followed by the DT-based variables. In contrast, the CC-based predictors were found to be the least effective, followed by the MI variables. Thus, the CMD-CA-ANN model was established as the benchmark model in this study. The study showed that elevation and distance from roads were more critical variables for LULC prediction compared to slope and aspect parameters. The proposed model demonstrated that by 2080, there would be an increase in the area of built-up land (5.79%), cropland (4.35%), shrubland (1.62%), and water bodies (0.3%) and a decrease in the area of forested land (1.34%) and barren land (10%) in the study area. The research has the potential to significantly contribute to the identification and management of significant environmental changes.

An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems

Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%–91.7%) across all the zones and 89.1% (50%–100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%–97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%–98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales.

Land use and land cover change in East Java from 2015 to 2021: Use optical imagery and Google Earth engine

Abstract This study analysed the changes in land use and land cover (LULC) in East Java Province by comparing two LULC maps interpreted from optical imagery. The images captured from 2015 to 2017 were selected to represent the initial LULC maps. Then, the images collected from 2020 to 2021 were considered the recent LULC maps. The input imagery was prepared using the Google Earth engine (GEE). The Random Forest algorithm was used for classification. In this study, eight significant LULC classes were categorised, i.e., built-up area (BU), heterogeneous-agricultural land (HAL), bare soil (BS), paddy field (PF), open water (OW), vegetation (VG), shrubland (SH), and wetland (WL). Next, the training samples were interpreted from Google Earth Pro. Then, the GEE satellite base map and the ground control points (GCPs) were collected. The collected GCPs were split into 70% training and 30% validation data. The results showed that significant LULC Change was more marked in the most urbanised areas (in and around the big cities), followed by LULC change in and around medium towns and rural areas. Four classes experienced an area increase, i.e., BU (+30.23%), HAL (+30.77%), BS (+24.52%), and PF (+14.36%). As a consequence, the other four classes compensated for the increase, i.e., OW (−32.79%), VG (−25.72%), SH (−6.59%), and WL (−25.53%). Regional development from 2015 to 2021 has increased built-up areas. Conversely, the development has reduced OW, VG, SH, and WL. The LULC changes have significantly changed the natural landscape to a human-dominated one.

ML‐Based Hybrid SAR and Optical Image LULC Mapping and Change Analysis With Variations in the Air Quality of the Imphal Valley, North‐East India

AbstractImphal Valley, situated in Manipur, India, stands as an intermontane valley of great ecological significance, notably hosting Loktak Lake. This research delves into the Land Use Land Cover Change (LULCC) within the Imphal Valley from 2016 to 2021 and assesses their impact on air quality across distinct land cover types. We used Sentinel‐1 & 2 data, ALOS PALSAR Digital Elevation Model, and applied Random Forest (RF), a machine learning algorithm for effective land use and land cover (LULC) mapping. Additionally, Sentinel‐5P data was utilized to monitor air quality parameters (CO, HCHO, NO2, SO2, Aerosol Index) spanning 2019 to 2021. The overall accuracies for the LULC maps employing a k (k = 3) fold approach for accuracy assessment varied between 88% and 92%, with corresponding Kappa coefficient ranging from 0.85 to 0.90. Noteworthy trends emerged from our analysis, revealing an increase in settlements and horticulture farms and a decline in forested areas and phumdis (floating biomass). Our findings highlight a mean concentration of CO ranging between 0.045 mol/m2 and 0.055 mol/m2 in different land cover types during February and March (2019–2021). Furthermore, we observed maximum mean HCHO, NO2, SO2, and aerosol index concentrations in March. Pollution levels surged during forest fires and shifting agriculture seasons, while aerosol levels declined during the lockdown period. This integrated approach emphasized on the comprehensive analysis of the dynamic interplay between LULCC and air quality in the Imphal Valley. This intricate relationship between LULC changes and air quality dynamics in the Imphal Valley, contribute to our understanding of the environmental dynamics in this ecologically vital region.

Groundwater Recharge and Surface Runoff Modeling Response to Land Use and Land Cover Dynamics in a Mae Wong Watershed of Thailand

This study aims to develop efficient management strategies and assess the spatiotemporal dynamics of land use and land cover (LULC) on surface runoff modeling response dynamics for the long-term sustainability of watersheds. The soil and water assessment tool (SWAT) model was used to evaluate the LULC dynamics on GRSR in the Mae Wong Watershed (MWW) of Thailand. Using Landsat images, three different LULC maps (2011, 2021, and 2031) were created using the cellular automata markov chain (CA-Markov) model, and TerrSet 2020 geospatial monitoring and modeling software. In the overall MWW, the forestland has undergone deforestation and decreased by 2.10% of the total area and 2.72% of the total area has been transformed into agricultural lands due to human activity and population growth. The soil, LULC, weather, and the digital elevation model (DEM) were all used in the SWAT simulation procedure. To understand the groundwater recharge and surface runoff (GRSR) responses of each hydrologic response units (HRUs), the SWAT model was calibrated and verified using streamflow and the sequential uncertainty fitting (SUFI-2) technique from the SWAT calibration and uncertainty program (SWAT-CUP). The results indicate that there is a good agreement for both the calibration and validation phases of all LULC simulations. The study indicated that groundwater recharge has decreased over the last two decades while surface runoff has increased due to the forest area being converted to agricultural land. Thus, the study can support maximizing water management and strategies for systematically attaining sustainability.

Land use and land cover mapping using Landsat-8 and Sentinel-2 data in Al Qunfudhah coast, western Saudi Arabia: A comparative study

Al Qunfudhah Governorate has assumed a tourist status in the strip of Saudi coastal cities on the Red Sea coast. Agriculture, food industries, and engineering industries are among the most important economic activities in the city. As a result, Land use and land cover (LULC) detection is essential for several reasons: urban planning, sustainability, resource management and environmental preservation. This research aims to identify the LULC of the Al Qunfudhah area in western Saudi Arabia by comparing Landsat-8 and Sentinel-2 data outputs and evaluating their accuracy results. Various methods are used to process satellite image data to enhance the contrast between surface features. Some of these techniques are true colour composite (TCC) images, enhanced true colour composite (ETCC) images, false colour composite (FCC) images, principal component analysis (PCA), band ratios and Maximum Likelihood Algorithm (MLA) Supervised Classification. By utilising band combination methods, PCA methods, and band ratio methods, we were able to map a variety of features, including vegetation cover, mangroves, Sabkha and non-Sabkha deposits, wadi basins, shallow and deep shelf areas, islands, sand and mud sediments, rocky reefs and urban areas. As part of the study, ArcGIS 10.8's supervised classification tool was employed to categorise the study region's features and calculate the percentage of each class. In both Landat-8 and Sentinel-2 classification images, the same classes were identified (i.e. sand and mud sediments, sabkhas, wadi basins, aeolian sand and loess sediments, islands, urban areas, vegetation, and mangroves). The accuracy of the classification results was determined using a reliable statistical approach. The accuracy assessments of Landsat-8 and Sentinel-2 revealed accuracy rates of 87% and 89.1% respectively, along with kappa coefficients of 84.9% and 87.3%. For the investigated area, the Landsat-8 and Sentinel-2 satellites were effective in producing LULC maps.

Spatial and temporal classification and prediction of LULC in Brahmani and Baitarni basin using integrated cellular automata models.

Monitoring the dynamics of land use and land cover (LULC) is imperative in the changing climate and evolving urbanization patterns worldwide. The shifts in land use have a significant impact on the hydrological response of watersheds across the globe. Several studies have applied machine learning (ML) algorithms using historical LULC maps along with elevation data and slope for predicting future LULC projections. However, the influence of other driving factors such as socio-economic and climatological factors has not been thoroughly explored. In the present study, a sensitivity analysis approach was adopted to understand the effect of both physical (elevation, slope, aspect, etc.) and socio-economic factors such as population density, distance to built-up, and distance to road and rail, as well as climatic factors (mean precipitation) on the accuracy of LULC prediction in the Brahmani and Baitarni (BB) basin of Eastern India. Additionally, in the absence of the recent LULC maps of the basin, three ML algorithms, i.e., random forest (RF), classified and regression trees (CART), and support vector machine (SVM) were utilized for LULC classification for the years 2007, 2014, and 2021 on Google earth engine (GEE) cloud computing platform. Among the three algorithms, RF performed best for classifying built-up areas along with all the other classes as compared to CART and SVM. The prediction results revealed that the proximity to built-up and population growth dominates in modeling LULC over physical factors such as elevation and slope. The analysis of historical data revealed an increase of 351% in built-up areas over the past years (2007-2021), with a corresponding decline in forest and water areas by 12% and 36% respectively. While the future predictions highlighted an increase in built-up class ranging from 11 to 38% during the years 2028-2070, the forested areas are anticipated to decline by 4 to 16%. The overall findings of the present study suggested that the BB basin, despite being primarily agricultural with a significant forest cover, is undergoing rapid expansion of built-up areas through the encroachment of agricultural and forested lands, which could have far-reaching implications for the region's ecosystem services and sustainability.

Sustainable flood hazard mapping with GLOF: A Google Earth Engine approach

This study aims to evaluate the efficacy of Google Earth Engine (GEE) in mapping floods and their aftermath, focusing on the recent event caused by cloud burst rainfall and glacial lake outburst flood (GLOF) of Lhonak glacier lake in the Teesta River basin, North Sikkim. The objective is to utilize GEE, coupled with Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 9 imagery, for precise remote sensing analysis, flood mapping, and Land Use and Land Cover (LULC) classification. The study employs a comprehensive methodology within the GEE platform, involving the acquisition and preprocessing of Sentinel-1 SAR data to create pre- and post-flood images. The difference between these images is calculated to generate flood maps at five-day intervals, providing a temporal evolution of the flood extent. Additionally, LULC mapping is conducted using Landsat 9 data, contributing to an understanding of pre-flood landscape characteristics. The results and discussion reveal significant impacts on various LULC types, with barren rocks, dense and medium forests, settlements, and agricultural lands experiencing notable effects. This research not only enhances our understanding of GLOFs but also serves as a critical tool for informing disaster management strategies, emphasizing the importance of accurate hazard assessment and the need for holistic approaches to mitigate the cascading effects of such events.

Assessment of Land Use changes and its impact on environment in the least populous district of Haryana through Remote Sensing methods (2001-2021)

In today’s world, almost every accessible piece of land today is being utilized for some or the other purpose directly affecting human needs. Not only is the land being used intensively, but is also evolving and changing with time. The rapid and intensive changes in land use and land cover (LULC) have created the need to quantify these changes in numbers and maps, so that we can understand this information to the best use possible. Thus, this paper aims to examine Panchkula district focusing upon the relationship of land use land cover (LULC) and the consequent changes in environment. Three annual LULC maps (2001, 2011 and 2021) were prepared with the help of satellite images. The change detection showed that built-up area has increased by approximately 268% in two decades with a total area of 10261.9 ha area in 2021, thus indicating that water bodies and barren land were converted into settlement for managing the population across the district and introducing infrastructure for the same. However, the forest and river bed area has increased by 19 % and 78.02 % respectively, while agricultural land was noticed to be increased by 55.62 % with a significant increase during 2001-11 and a slight decrease after 2011. The open scrub land has increased by 56 %. Thus, the present study can help with identifying areas experiencing significant changes in ecosystems and natural resources in the district. It can also provide valuable insights into the expansion of urban areas, changes in infrastructure and the encroachment on natural and agricultural lands, thereby contributing to urban planners to understand urban growth patterns, assess the impacts and make informed decisions regarding land zoning, infrastructure development and resource allocations.

Assessment of LULC change and its impact on Surface Runoff using SCS-CN method for Noakhali Region, Bangladesh

Land use and land cover (LULC) change detection and its subsequent impact on hydrology are of significant importance in the field of urban planning and the sustainable management of water and land resources. The primary objective of this research work is to illustrate LULC map in a specific study area, which encompasses approximately 316.7 km² in the Noakhali region. Additionally, this study tries to examine the enduring impacts of LULC changes on surface runoff within the specified area. In order to achieve this goal, the analysis of LULC change trends in the region was conducted using Landsat OLI/TM data from 2000 to 2020. The analysis focused on five distinct classes: built-up land, vacant land, farmland, vegetation, and waterbodies. The research employs the Soil Conservation Service Curve Number (SCS-CN) model to evaluate the influence of LULC changes on surface runoff. Notably, the findings indicate a substantial increase in built-up areas from 1.05% in 2000 to 11.21% in 2020. This urban expansion is strongly correlated with a notable rise in surface runoff, as evidenced by a high correlation coefficient (R2 = 0.98) between urban expansion and modelled runoff depth. The observed increase in impervious urban areas contributes to a 2.65% rise in average runoff from 2000 to 2020, equivalent to an 8.96 mm increment in average runoff. The study underscores the profound implications for managing urban storm floods and designing urban areas, especially in rapidly expanding cities globally, with a focus on developing nations. The insights gained offer valuable information for informed decision-making in sustainable urban development and water resource management, not only in the Noakhali region but also beyond.

Mapping hydropower expansion and cash crop dynamics in Colombia using Landsat time series

Satellite-based monitoring provides insights on synergies and trade-offs between energy production and land use and land cover (LULC) changes (LULCC) around hydropower dams. Using Landsat data, we mapped LULCC related to hydropower expansion to understand change dynamics of cash crop production in the Magdalena River basin (Colombian Andes). We leveraged secondary map products and an active learning routine to produce thematically detailed LULC maps, for 2009, 2015, and 2020, including key cash crops (rice and oil palm). Our area-adjusted accuracy assessment revealed high overall and class-specific user’s and producer’s accuracies, exceeding 80% for oil palm, rice, non-agricultural vegetation, wetlands, and grasslands, and accuracies of ∼60% for other temporary and permanent crops. We identified substantial losses of rice (10,6%) and grasslands (25,9%) in El Quimbo due to reservoir flooding. For Hidrosogamoso, there was a loss of wetlands downstream. Our products provide the basis for balancing social, economic, and environmental trade-offs related to conflicting land uses.

Real-time flood inundation monitoring in Capital of India using Google Earth Engine and Sentinel database

This study focuses on researching flood inundation and vulnerable areas in Delhi NCT using Remote Sensing (RS) and GIS techniques during flood from July 8 to July 15, 2023, with the entire analysis conducted through satellite and cloud-based processing methods, specifically employing the Google Earth Engine (GEE). Leveraging high-temporal satellites that gather data enables the identification of flooded zones in real-time, during floods, and in the aftermath. Analyzing data collected at different stages of a flood provides valuable insights for pinpointing affected areas. Water naturally flows from high to low elevation areas, and based on elevation data, lowest elevation regions, particularly along the Yamuna riverbank under Delhi NCT, are highly susceptible to flooding, are considered flood-prone areas and flood water inundated. A thorough comprehension and flood-prone area mapping, along with a map illustrating highly inundated zones. After obtaining flood inundation maps and overlaying them with Land Use and Land Cover (LULC) classified maps, the study identified specific areas that experienced flood inundation. The analysis generated a flood zone map, indicating that the flooded area encompasses approximately 110 km², within a total study area of 1488.4 km². The affected areas have elevations ranging from 200 to 210 meters, whereas the maximum elevation in the study area is approximately 326 meters. The GEE platform is employed for processing, utilizing a Supervised classification algorithm for LULC mapping, and an Inverse Distance Weight method for mapping temperature and rainfall. This study utilized the GEE platform to create pre- and post-flood maps based on Sentinel 1 satellite datasets. Generated DEM, and employed it to create various surface estimation maps, including a stream order map. The GIS is employed to enhance the efficiency of monitoring and managing flood disasters, with the high temporal and spatial resolution data playing a pivotal role in flood monitoring.

Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling

This research uses a Classification and Regression Tree (CART) model with Google Earth Engine (GEE) to assess the winter season’s land cover and change detection mapping impact on the evapotranspiration (crop water requirement) parameters. Winter seasons, crucial for agricultural planning, and irrigation water requirement challenges in accurately mapping land cover and detecting changes due to the dynamic nature of farming practices during this period. In this study, Landsat-8 OLI images have been combined to map Land use and Land cover (LULC) and other change detection mapping in Akola Block, Maharashtra, India, during the 2018–2022 winter season. As an discoverer researcher that found detailed information of LULC classes during last 2018 to 2022 winter seasons, the use of the CART model in combination with a cloud-computing GEE demonstrates to be a practical approach for accurate land cover classification and change detection maps to create a pixel-based winter seasons information of study area. The novelty of this study lies in its innovative use of GEE, a powerful platform for remote sensing and geospatial analysis, to create LULC maps with remarkable accuracy. Achieving a 100% training accuracy across the four years under consideration is an exceptional feat, highlighting the reliability and stability of the methodology. Furthermore, the validation accuracy values, ranging from 89 to 94% for the winter seasons of 2018 to 2022, underscore the robustness of this approach. Such consistently high accuracy in mapping LULC over time is a groundbreaking achievement and offers a new dimension to the field of hydrology. For the hydrological community, the implications of this study are profound. Accurate LULC mapping and change detection provide critical data for modeling and analyzing the effects of land use changes on water resources, watershed management, and water quality. The User, Kappa, and Producer accuracy metrics used in this research highlight the model’s performance and its suitability for hydrological applications. These accurate LULC maps can aid in the development of hydrological models, forecasting, and decision-making processes, ultimately contributing to more effective water resource management and environmental conservation. In summary, this study’s innovative use of GEE, its remarkable accuracy in LULC mapping, and its relevance to the hydrological community demonstrate the potential for advanced remote sensing and geospatial tools to significantly improve our understanding of land use changes and their implications for water resources and environmental management.

Land use and land cover change impact on characteristics of surface evapotranspiration in semi-arid environment of Western Rajasthan, India

Abstract The development of the Indira Gandhi Nahar Pariyojana (IGNP) in western Rajasthan has significantly impacted land use and land cover (LULC) changes, which have hugely transformed the hydrologic cycle in recent decades. Hence, we investigate the surface evapotranspiration (ET) response to LULC changes in the IGNP for the period from 1990 to 2020. The Surface Energy Balance algorithm (Surface Energy Balance Algorithm for Land) was applied to the Landsat dataset along with local weather data obtained from the Indian Meteorological Department. A machine learning-based random forest classifier was applied to develop the LULC maps. The higher values of ET are mainly distributed in the northwest. Area covered by croplands has shown an increasing trend over the decades at a rate of 734.76 km2·a−1, urban settlements/ built-up areas increased at a rate of 14.67 km2·a−1, barren area decreased gradually at a rate of 751.67 km2·a−1, and area covered by the few water bodies showed minor fluctuations. Overall, the LULC changes have a smaller influence on ET rates but has a considerable impact on the total ET in the IGNP region due to an increase in the area covered by vegetation as a result of increased water availability.

Impact of rainfed agriculture on spatio-temporal patterns of water balance and the interaction between groundwater and surface water in sub-humid plains

The expansion of rainfed agriculture, especially soybean cultivation in sub-humid plains, alters water balance and the exchange between groundwater-surface water (GW-SW). However, to date, there are no studies that analyze how these anthropic disturbances affect hydrological connectivity in these systems, especially the GW-SW interactions. The objective of this study is to analyze how the increase in rainfed agriculture affects the spatio-temporal patterns of the water balance and the GW-SW interaction. For this analysis, a coupled GW-SW flow model was implemented under land use and land cover (LULC) scenarios, to quantify the spatio-temporal dynamics for different components of water balance and GW-SW interactions for the upper creek basin of Del Azul. A simulation was carried out for a period of 13 years (2003–2015) on a daily scale and it was contrasted through three multitemporal LULC maps. The results point that substitution of natural pastures, the reduction of winter crops and the decrease of crop rotation, due to the increase of soybean monoculture in the basin under study, modifies the water balance, especially the annual rates of surface runoff and soil moisture which may increase between 3.5 and 9.4 % and between 1.4 and 4.4 % respectively, thus increasing the annual streamflows between 2.6 and 6.8 % and the groundwater heads between 0.2 and 0.6 m. This leads to changes in the hetereogeneity of the GW-SW interaction, a reduction between 0.3 and 3 % is observed in the discharge from the Pampeano aquifer to the Del Azul stream, while the recharge rates from the Del Azul stream to the Pampeano aquifer increase between 2 and 17.8 %. The application of the SWAT-MODFLOW model under LULC scenarios, improves the prediction of the regional hydrologic connectivity on sub-humid plains, because the hydrological processes occurring in the surface and non-saturated zone are governed by shallow groundwater dynamics.

Spatio-temporal prediction of land use and land cover change in Bahi (Manyoni) Catchment, Tanzania, using multilayer perceptron neural network and cellular automata-Markov chain model.

Evaluation of land use and land cover (LULC) change is among vital tools used for tracking environmental health and proper resource management. Remote sensing data was used to determine LULC change in Bahi (Manyoni) Catchment (BMC) in central Tanzania. Landsat satellite images from Landsat 5 TM and Landsat 8 OLI/TIRS were used, and support vector machine (SVM) algorithm was applied to classify the features of BMC. The obtained kappa values were 0.74, 0.83 and 0.84 for LULC maps of 1985, 2005 and 2021, respectively, which indicates the degree of accuracy from produced being substantial to almost perfect. Classified maps along with geospatial, socio-economic and climatic drivers with sufficient explanatory power were incorporated into MLP-NN to produce transition potential maps. Transition maps were subsequently used in cellular automata (CA)-Markov chain model to predict future LULC for BMC in immediate-future (2035), mid-future (2055) and far-future (2085). The findings indicate BMC is expected to experience significant expansion of agricultural lands and built land from 31.89 to 50.16% and 1.48 to 9.1% from 2021 to 2085 at the expense of open woodland, shrubland and savanna grassland. Low-yield crop production, water scarcity and population growth were major driving forces for rapid expansion of agricultural lands and overall LULC in BMC. The findings are essential for understanding the impact of LULC on hydrological processes and offer insights for the internal drainage basin (IDB) board to make necessary measures to lessen the expected dramatic changes in LULC in the future while sustaining harmonious balance with livelihood activities.

LAND USE AND LAND COVER SIMULATION BASED ON INTEGRATION OF ARTIFICIAL NEURAL NETWORKS WITH CELLULAR AUTOMATA-MARKOV CHAIN MODELS APPLIED TO EL-FAYOUM GOVERNORATE

Abstract. The paper aims to evaluate the effectiveness of the multi-layer perceptron-Markov chain analysis (MLP-MCA) integrated method in predicting future Land Use and Land Cover (LULC) change scenarios in Fayoum due to rapid urbanization. The study employed machine learning algorithms for image classification using Google Earth Engine (GEE) for classification techniques to derive LULC maps from Landsat imagery taken in 2001, 2011, and 2021. The 2001 and 2011 LULC maps were used to predict the LULC scenario for 2021 using MLP-MCA, and the predicted result was validated against the observed 2021 LULC map using Area under the curve (AUC) that was derived from the Receiver Operating Characteristics (ROC). Subsequently, the study predicted future LULC changes for 2031 using two sub-models; sub- Agri and sub-built. The results show that a rapid growth in both built and agricultural area. The findings of this study highlight the potential of the MLP-MCA method in predicting future LULC changes due to urbanization.