Hydrological modeling of flood impacts under land use and land cover change: A systematic review of tools, trends, and challenges.

<p>The land use and land cover (LULC) change induces hydrologic variability in a catchment and studying this variability is central to efficient water management practice in a catchment. The assessment of the alteration in hydrological processes due to LULC change and its influence on overall river ecosystem functioning is even more pertinent to developing nations that face the issue of water scarcity and pollution. In this work, we investigate the influence of the LULC change over a period of ~40 years (1970-2013) on the variability of natural or virgin flow in the Ramganga river, a major tributary of the Ganga river, India. For LULC change data, object-based image classification was performed on high-resolution satellite imageries acquired for the Ramganga river basin – CORONA (1970) and LISS IV (2013) images. The natural or virgin flows (i.e., the flow in the river without regulation practices such as construction of dams or barrages) were estimated by performing hydrological modeling using the Soil and Water Assessment Tool (SWAT). Initially, the SWAT model was set up, calibrated, and validated for the present flow scenario (i.e. with all management practices present) using LULC data of the year 2013. Natural flows were derived by removing all interventions and keeping agricultural practices only rain-fed. Next, keeping all parameters unchanged, the LULC data of the year 2013 was replaced by the LULC data of the year 1970. This enabled us to study the effects of LULC change on river hydrology between the period 1970-2013. The model showed good agreement between the observed and simulated flows with R<sup>2</sup> values of 0.82 for the calibration period (2002-2014) and 0.68 for the validation period (1990-1999). The Nash-Sutcliffe efficiency values were 0.81 and 0.66 for calibration and validation periods respectively. The comparison of LULC data between the study period (1970 and 2013) reveals that land cover classes of agriculture, built-up, mixed forest, barren land, shrubs and bushes, and water areas were altered by nearly 6%, 102%, -7%, -59%, -75%, and -2% respectively (‘-’ sign indicates decrement in the land cover area). The influence of this LULC change was evident in the results from the hydrological model. For the years 2002-2013 (calibration period), the natural flows estimated using the LULC map of 2013 at the basin outlet were observed to be higher by 3-12% compared to flows estimated using the LULC input of 1970. The estimates of mean monthly flows for the years 2002-2013 at the basin outlet reveal that while the natural flows estimated using the LULC map of 2013 were higher compared to flow estimates using the LULC map of 1970 for most of the months, the flows during the dry months (May-July) were observed to be lower for the former compared to the latter. Our work provides valuable insights into hydrological variability in a major sub-basin of the Ganga river induced due to LULC changes and we advocate that alterations associated with LULC must be incorporated into water management strategies.</p>

Change analyses and prediction of land use and land cover changes in Bernam River Basin, Malaysia

Novel approach for the LULC change detection using GIS & Google Earth Engine through spatiotemporal analysis to evaluate the urbanization growth of Ahmedabad city

Geospatial analysis of land use and land cover (LULC) changes is crucial for understanding the dynamics of urban development and its impacts on the environment. This study presents an integrated approach utilizing Re-mote Sensing (RS) and Geographic Information Systems (GIS) to detect and analyze LULC changes over the past 40 years in the Amman Zarqa Basin in Jordan. The study employed satellite imagery from multiple sensors, including Landsat and Sentinel, covering a span of four decades (1980-2020). LULC classifications were performed using supervised and unsupervised classification methods, considering a range of LULC categories relevant to the study area. The results revealed significant LULC changes in the Basin over the study period. Urban expansion was found to be the dominant driver of land transformation, leading to the conversion of agricultural land, soil, and open spaces into built-up areas. The urban growth rate exhibited an accelerating trend, particularly during the past two decades, reflecting rapid population growth and urbanization in the region. Ground truthing technique was used to validate the results using 200 points distributed over the basin, the confusion matrix ranges from 79 to 85%, which reveals high accuracy. This research serves as a foundation for future studies on urban growth, land management, and environmental impact assessments, supporting sustainable development in the Amman Zarqa Basin and similar regions facing rapid urbanization.

Keraniganj Upazila, adjacent to Dhaka city, has experienced significant land use and land cover (LULC) changes over the past three decades due to rapid urbanization and socio-economic transformation. This study aims to investigate the spatial and temporal dynamics of LULC in Keraniganj from 1989 to 2023 and assess the driving factors and environmental implications of these changes. To achieve this, multi-temporal Lands at imagery, remote sensing (RS), and geographic information systems (GIS) techniques were applied, complemented by socio-economic data. Supervised classification algorithms were used to generate accurate LULC maps, and the results were validated using confusion matrices and Kappa statistics. The analysis covered six major LULC categories: bare land/sand fill, urban settlements, cultivated land, rural settlement/homestead vegetation, water bodies, and wetlands/lowlands. The results reveal a substantial increase of 1,738.62 hectares in urban settlements, particularly between 2009 and 2023, primarily at the expense of cultivated land, rural settlements, and natural ecosystems. During the study period, Keraniganj lost 476.46 hectares of cultivated land, 480.51 hectares of rural settlement areas, 1,087.83 hectares of water bodies, and 166.68 hectares of wetlands. These trends indicate extensive land reclamation, unplanned urban growth, and environmental degradation, driven by the area’s proximity to Dhaka, improved transportation infrastructure, and lower living costs. The findings underscore the urgency of implementing sustainable land use policies and integrated urban planning to address the negative impacts of urban expansion, including habitat destruction, reduced biodiversity, heightened flood risks, and declining agricultural productivity. This research contributes valuable insights into LULC dynamics and provides a replicable framework for monitoring and managing land transformation in rapidly urbanizing regions globally. Jagannath University Journal of Life and Earth Sciences, 10(1) 91-122

Land use and land cover (LULC) changes are essential to air pollution dynamics, affecting atmospheric composition and urban microclimates. Previous research explored air pollution trends, but limited studies examined its spatiotemporal relationship with LULC changes in rapidly growing urban regions. This study assessed the impact of LULC changes on air quality in Asansol, Bardhhaman, and Bankura cities of West Bengal, including their buffer zones, from 1990 to 2023. LULC classification was conducted using Landsat 5 and 8 data, processed in Erdas Imagine, identifying six land-use types: water bodies, vegetation, agricultural land, built-up areas, barren land, and sand deposits. Air pollution parameters (CO, NO2, SO2, CH4, and O3) were extracted from Sentinel-5P satellite data and analysed using Google Earth Engine. The findings revealed a decline in agricultural land and vegetation, with urban expansion leading to increased pollutant concentrations. Between 1990 and 2023, agricultural land declined from 66.86 to 56.81% in Asansol and from 77.92 to 68.31% in Bardhhaman, while Bankura showed a marginal increase. Simultaneously, urban areas expanded significantly, contributing to increased CO, NO2, SO2, and O3 levels, especially in densely populated areas. The findings revealed the direct influence of LULC changes on air quality, with urbanisation increasing pollution due to vehicular emissions, industrial activities, and reducing vegetation cover. The study emphasises the urgent need for sustainable land-use planning, effective emission control strategies, and the development of urban green infrastructure to reduce the environmental degradation and health risks caused by rapid and unplanned urban expansion.

Land use and land cover (LULC) changes are one of the main factors contributing to ecosystem degradation and global climate change. This study used the Gontougo Region as a study area, which is fast changing in land occupation and most vulnerable to climate change. The machine learning (ML) method through Google Earth Engine (GEE) is a widely used technique for the spatiotemporal evaluation of LULC changes and their effects on land surface temperature (LST). Using Landsat 8 OLI and TIRS images from 2015 to 2022, we analyzed vegetation cover using the Normalized Difference Vegetation Index (NDVI) and computed LST. Their correlation was significant, and the Pearson correlation (r) was negative for each correlation over the year. The correspondence of the NDVI and LST reclassifications has also shown that non-vegetation land corresponds to very high temperatures (34.33°C–45.22°C in 2015 and 34.26°C–45.81°C in 2022) and that high vegetation land corresponds to low temperatures (17.33°C–28.77°C in 2015 and 16.53 29.11°C in 2022). Moreover, using a random forest algorithm (RFA) and Sentinel-2 images for 2015 and 2022, we obtained six LULC classes: bareland and settlement, forest, waterbody, savannah, annual crops, and perennial crops. The overall accuracy (OA) of each LULC map was 93.77% and 96.01%, respectively. Similarly, the kappa was 0.87 in 2015 and 0.92 in 2022. The LULC classes forest and annual crops lost 48.13% and 65.14%, respectively, of their areas for the benefit of perennial crops from 2015 to 2022. The correlation between LULC and LST showed that the forest class registered the low mean temperature (28.69°C in 2015 and 28.46°C in 2022), and the bareland/settlement registered the highest mean temperature (35.18°C in 2015 and 35.41°C in 2022). The results show that high-resolution images can be used for monitoring biophysical parameters in vegetation and surface temperature and showed benefits for evaluating food security.

Semiarid Tunisia is characterized by agricultural production that is delimited by water availability and degraded soil. This situation is exacerbated by human pressure and the negative effects of climate change. To improve the knowledge of long-term (1980 to 2020) drivers for Land Use and Land Cover (LULC) changes, we investigated the semiarid Rihana region in central Tunisia. A new approach involving Google Earth Engine (GEE) was used to map LULC using Landsat imagery and vegetative indices (NDVI, MSAVI, and EVI) by applying a Random Forest (RF) classifier. A Rapid Participatory Systemic Diagnosis (RPSD) was used to consider the relation between LULC changes and their key drivers. The methodology relied on interviews with the local population and experts. Focus groups were conducted with practicians of the Regueb Agricultural Extension Services, followed by semi-structured interviews with 52 households. Results showed the following: (1) the RF classifier in Google Earth Engine had strong performance across diverse Landsat image types resulting in overall classification accuracy of ≥0.96 and a kappa coefficient ≥0.93; (2) rainfed olive land increased four times during the study period while irrigated agriculture increased substantially during the last decade; rangeland and rainfed annual crops decreased by 58 and 88%, respectively, between 1980 and 2021; (3) drivers of LULC changes are predominately local in nature, including topography, local climate, hydrology, strategies of household, effects of the 2010 revolution, associated increasing demand for natural resources, agricultural policy, population growth, high cost of agricultural input, and economic opportunities. To summarize, changes in LULC in Rihana are an adaptive response to these various factors. The findings are important to better understand ways towards sustainable management of natural resources in arid and semiarid regions as well as efficient methods to study these processes.

Hydrological response to land use and land cover changes in a sub-watershed of West Liaohe River Basin, China

The impact of land use and land cover change on groundwater recharge in northwestern Bangladesh

Land use and land cover (LULC) change information is essential in urban planning and management. With the rapid urbanization in China, many illegal land developments have emerged in some rapidly developing regions and have caused irreversible environmental problems, posing a threat to sustainable urban development. Short-interval monitoring of LULC change therefore is necessary in these regions to control and prevent illegal land developments at an early stage.
\nConventional optical remote sensing is limited by weather conditions and has difficulties collecting timely data in tropical regions characterized by frequent cloud cover. Radar remote sensing, not affected by clouds, is therefore a potential tool for collecting timely LULC information in these regions. Polarimetric SAR (PolSAR) is more suitable than single-polarization SAR for monitoring LULC change because it can discriminate different types of scattering mechanisms. The overall objective of this study is to conduct short-interval monitoring of LULC change using RADARSAT-2 PolSAR images.
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\nClassification methods that achieve high accuracy for PolSAR images are essential in monitoring LULC change. In this study, a new method, based on the integration of polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms, is proposed for LULC classification using RADARSAT-2 PolSAR data. A comparison between the proposed classification method and Wishart supervised classification which is commonly used for the classification of PolSAR data showed that the proposed method can significantly improve LULC classification accuracy. Polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms have been determined to contribute to the improvement achieved by the proposed classification method.
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\nSelection of appropriate incidence angle is important in LULC classification using PolSAR images because incidence angle influences the intensity and patterns of radar return. Based on the proposed classification method, the present study further investigates the influence of incidence angle on LULC classification using RADARSAT-2 PolSAR images. LULC classifications using incidence angles of 31.50 and 37.56° were conducted separately. The influence of incidence angle on the classification was investigated by comparing the results of the two independent classifications. The comparison showed that large incidence angle performs much better than small incidence angle in the classification of different vegetation types, whereas small incidence angle outperforms large incidence angle in reducing the confusion between urban/built-up areas and vegetation, that between vegetable and barren land, and that among barren land, water, and lawn. Considering that the detection of urban/built-up areas and barren land is important in monitoring illegal land developments, small incidence angle is more suitable than large incidence angle in monitoring illegal land developments.
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\nChange detection methods that achieve high accuracy for PolSAR data are also essential in monitoring LULC change. The current study proposes a new method for LULC change detection using RADARSAT-2 PolSAR images. The proposed change detection method combines change vector analysis (CVA) and post-classification comparison (PCC) to detect LULC changes using object-oriented image analysis. The classification of PolSAR images is based on the proposed classification method. Compared with the PCC based on Wishart supervised classification, the proposed change detection method can achieve much higher accuracy for LULC change detection. Further investigation indicated that CVA, PCC, and object-oriented image analysis all contribute to the higher accuracy achieved by the proposed change detection method.
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\nShort-interval monitoring of LULC change was carried out using a time series of RADARSAT-2 PolSAR images. The monitoring was based on monthly LULC change detection using the proposed change detection method and appropriate incidence angle. The influence of environmental factors on short-interval monitoring of LULC change was investigated by analyzing the monthly change detection results. Paddy harvesting and planting, seasonal crop growth, and change in soil moisture and surface roughness were found to exert significant influence on the short-interval monitoring of LULC change. High accuracy can be achieved for short-interval monitoring of construction sites and bulldozed land using RADARSAT-2 PolSAR images. However, paddy harvesting and growth still cause false alarms on the monitoring of these two LULC classes.
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\nThe study indicated that conducting short-interval monitoring of LULC change using RADARSAT-2 PolSAR images is effective. High accuracy can be achieved for short-interval monitoring of construction sites and bulldozed land using the proposed change detection and classification methods, which can provide important information for the control and prevention of illegal land developments at an early stage.

Ecosystem services are vital services that support life and are the basis for human socio-economic progress. However, changes in land use and land cover (LULC) brought about by urban expansion degrade them. Thus, analysing the impact of land use and land cover (LULC) change on ecosystem service values (ESVs) is crucial for understanding and informing resource policy decisions. This study aims to analyse the impact of land use and land cover changes on ecosystem service values in Mwanza City, Tanzania. To achieve that, the benefits transfer approach was employed to analyse the changes in ESV in response to LULC. We estimated and analysed changes in ESV using satellite image datasets from 1999, 2009, and 2019. The LULC classes that were identified are vegetated land, agricultural land, waterbodies, built-up area, and bareland. The results exhibit that Mwanza City experienced significant LULC changes. While vegetated land, agricultural land, and bareland decreased by 49%, 15%, and 36%, respectively, the built-up area and water bodies increased by 568% and 48%, respectively, during the two decades. The total ESV decreased from 31.35 million US dollars to 26.3 million US dollars between 1999 and 2009 and to 23.96 million US dollars between 2009 and 2019. The waterbodies increased due to the increased volume of water in streams that expanded the floodplains, which resulted from surface runoff attributed to increased paved surfaces as more land was converted into a built-up environment upstream. The built-up area and bareland contributed nothing to ESV. However, the built-up area was the driving force behind the reduction of ESV in other LULC classes, as it was encroaching on them. The study concludes that the decrease in ESV reflects the degradation of ecosystem services due to the change in LULC. Hence, it is recommended that sustainable management of ecosystems be adhered for the proper functioning of the earth’s life-support system.

The study on land use and land cover (LULC) changes assists in analyzing the change and regulates environment sustainability. Hence, this research analyzes the Northern TN coast, which is under both natural and anthropogenic stress. The analysis of LULC changes and LULC projections for the region between 2009-2019 and 2019-2030 was performed utilizing Google Earth Engine (GEE), TerrSet, and Geographical Information System (GIS) tools. LULC image is generated from Landsat images and classified in GEE using Random Forest (RF). LULC maps were then framed with the CA-Markov model to forecast future LULC change. It was carried out in four steps: (1) change analysis, (2) transition potential, (3) change prediction, and (4) model validation. For analyzing change statistics, the study region is divided into zone 1 and zone 2. In both zones, the water body shows a decreasing trend, and built-up areas are in increasing trend. Barren land and vegetation classes are found to be under stress, developing into built-up. The overall accuracy was above 89%, and the kappa coefficient was above 87% for all 3 years. This study can provide suggestions and a basis for urban development planning as it is highly susceptible to coastal flooding.

This study quantitively examines spatiotemporal Land Use and Land Cover (LULC) changes in Thoothukudi Municipal Corporation from 2010 to 2024, employing advanced remote sensing and Geographic Information System (GIS) techniques integrated with multivariate statistical methods to derive meaningful insights into urban transformation dynamics. For multi-temporal LULC classification, Landsat satellite images were obtained from the USGS Earth Explorer platform, including Landsat-7 ETM + (March 2010) and Landsat-8 OLI/TIRS (January 2015, April 2020, and May 2024). The novelty of this work lies in integrating entropy and regression analyses to link LULC transitions with industrial expansion, revealing accelerated urban sprawl and ecological stress. The analysis reveals significant urban transformation in the study area from 2010 to 2024, primarily driven by large-scale industrial expansion. Built-up areas expanding by 283.5% (15.34–58.81 km2), shrubland declining by 95.7% (29.64–1.27 km2), and water bodies decreased by 21.3% (19.27–15.17 km2). The reliability of the accuracy assessment results was assessed using Kappa statistics. Classification accuracy ranged from 86 to 92%, with Kappa coefficients between 0.82 and 0.90. Shannon’s entropy peaked at 2.46 in 2015, indicating maximum landscape heterogeneity, but declined to 2.11 by 2024, reflecting increasing homogeneity. Linear regression analysis supports this, showing strong positive trends in settlement (slope = + 11.44, R2 = 0.94) and cultivated land (slope = + 3.98, R2 = 0.84), indicating urban and agricultural expansion. These changes highlight rapid urban sprawl, increased ecological pressure, and the need for sustainable urban planning to mitigate future socio-environmental challenges.

Land use and land cover (LULC) changes in Ogbese watershed due to urbanization implies increased areas of low infiltration. This results to higher flow rates downstream the watershed. This study estimates the changes in peak flow rates at the watershed’s outlet for present and future LULC. Rainfall-runoff simulation was achieved with Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS) version 4.2 while future LULC was projected with Markov Chain model. Rainfall inputs to the hydrologic model were obtained from intensity-duration-frequency curves for Ondo state. Landsat 7, Enhanced Thematic mapper plus (ETM+) image and Landsat 8 operational land imager (OLI) with path 190 and row 2 were used to generate LULC images for the years 2002, 2015 and 2019. Six LULC classes were extracted as follows: built up area, bare surface, vegetation, wetland, rock outcrop and waterbody. Future LULC in year 2025 and 2029 were projected with Markov Chain model. The model prediction was verified with Nash Sutcliffe Efficiency index (NSE). NSE value of 0.79 was calculated indicating LULC changes in the watershed was Markovian. Results show that built up area cover in 2019 is projected to increase by 26.1% in 2024 and 39.9% in 2029 and wetland is projected to decreased by 1.2% in 2024 and 2.3% by 2029. Runoff peaks for these LULC projections indicate increase by 0.24% in 2024 and 1.19% in 2029 at the watershed’s outlets for 100-year return period rainfall.