Land Use Cover Change Research Articles

Spatio-temporal pattern change of LULC and its response to climate in the Loess Plateau, China

Exploring land use/cover (LULC) change is essential for the sustainable development of ecologically fragile areas. The main objective of this study was to clarify the characteristics and differences in the spatiotemporal changes of LULC on the Loess Plateau (LP) based on the transfer matrix and land use dynamics and to quantitatively describe the impact of natural factors on LULC using a geodetector. The results indicated that the overall LULC change in the LP was characterized by a decrease in the area of cropland, grassland, and bare land, and an increase in the area of woodland and built-up land. This trend shows a clear phase-change characteristic around 2000. LULC changes were primarily affected by human activities in the southeastern agricultural region. The project of returning farmland to forest and grassland had a great impact on LULC change in the central region. Vegetation was sensitive to temperature and precipitation, and the impact of LULC change was significantly higher than that in the humid region in the northwest arid region. NDVI, PRE, and TEM were determined to be the main contributors to LULC changes in the LP. These results provide a scientific basis for the sustainable development of LP.

Devastation of the cerrado of mato grosso do sul and the advance of arenization in the pardo river watershed

Despite the significant biodiversity in the Cerrado, the process of occupation, subsequent economic cycles, and intensification of agriculture in a predatory manner have caused profound alterations in this biome. This study analyzed changes in land use and land cover in the Cerrado of Mato Grosso do Sul between 1985 and 2022 by mapping and also investigating the formation and dynamics of sand dunes in the Bauru sedimentary basin, focusing on the Pardo River watershed. MapBiomas Network data, collection 8, were used for land use and land cover analysis. Arenization foci cartography was conducted using brightness index 2 based on Sentinel-2 satellite images in Sentinel Application Platform software. The historical analysis of land use and cover in the Cerrado of Mato Grosso do Sul revealed a rapid transformation of the landscape, with a drastic reduction of native vegetation and an intensification of large-scale monocultures. Between 1985 and 2022, the Cerrado of Mato Grosso Sul lost 4.6 million hectares of native vegetation (forest formations, savannahs, grasslands, and wetlands), representing 24.54% of this territory. Recent changes in land use include pasture-soybean and pasture-eucalyptus. In the Pardo River watershed, arenization foci cover an area of 17,834.34 hectares, with varying dimensions ranging from less than 1 hectare to 376.41 hectares. The arenization process occurs in different slope compartments, such as the base, slope, and interfluves. The physical and chemical characteristics of Quartzipsamments, such as low organic carbon content in soil, are associated with high rates of surface exposure and weakened load-dependent structures, leading to degradation. Arenization occurs where natural vegetation has been suppressed and soil has been depleted by poorly managed production processes.

Impacts of LULC changes on runoff from rivers through a coupled SWAT and BiLSTM model: A case study in Zhanghe River Basin, China

Changes in river runoff have a significant impact on the sustainable use of water resources in a watershed, and these changes are closely linked to variations in land use/land cover (LULC). This research explores an innovative approach in the Zhang River Basin (ZRB), China, by coupling a concept-based hydrological model, the Soil and Water Assessment Tool (SWAT), with a deep-learning model, the Bidirectional Long Short-Term Memory Network (Bi-LSTM), to improve the accuracy of river runoff simulations. By analyzing LULC changes in 2002, 2012, and 2022, this study developed three SWAT models and three coupled SWAT-BiLSTM models to quantitatively assess the impacts of these changes on river runoff through eight LULC scenarios. The findings revealed significant LULC changes from 2002 to 2022, with cropland and grassland areas decreasing while forest and urban land areas increased. The total area of grassland, forest, and cropland made up over 93 % of the basin, indicating active land type conversions. Calibration and validation results demonstrated that the SWAT-BiLSTM model outperformed the conventional SWAT model, yielding higher accuracy in runoff simulations. Specifically, the SWAT-BiLSTM model achieved R2 values of 0.89 and 0.90 during calibration and validation, compared to the SWAT model's R2 values of 0.76 and 0.79. Scenario analyses indicated that expansions in farmland, grassland, and urban areas were correlated with increased river runoff, while an expansion in forested areas led to reduced runoff. Notably, urban land changes had the most pronounced impact on runoff, emphasizing the need for careful runoff management and flood risk mitigation in urban planning. By combining SWAT and Bi-LSTM models, this study provides an innovative assessment of the impact of LULC changes on water resources in the ZRB. The results offer valuable insights for water resource management, LULC optimization, and flood risk management, highlighting the potential application of deep learning techniques in hydrological simulation. This research serves as a scientific basis for policy-making and sustainable land use planning in the ZRB and similar regions.

A GIRS-based analysis of urban green space losses with land-use changes and its relationship with surface urban heat island in the city of Tabriz

This research investigates the relationship between urbanization, land-use land-cover (LULC) changes, and surface urban heat islands (SUHIs) in Tabriz, Iran. Using Landsat satellite data from 2000 to 2021, the study analyzed changes in urban green space (UGS) and their impact on SUHIs. The maximum likelihood classification algorithm was employed to assess LULC changes, while the perceptron neural network model and cellular automaton Markov chain model were used to simulate and predict future LULC dynamics. Various indices, including the UGS Change Intensity Index (CII), Land Use Dynamic Degree Index (LUDD), and UGS Land Index, were used to quantify urban expansion, green space changes, and per capita green space (PCG) availability. The findings revealed a significant increase in built-up area and a subsequent decline in green space. While green space initially increased, it later decreased, leading to a reduction in both UGS area and PCG. Surprisingly, the analysis of heat islands associated with green space and built-up areas showed that built-up areas had a more pronounced mitigating effect on SUHIs compared to green space. Consequently, the green space heat island effect intensified, while the built-up area heat island effect decreased over time.

Analysis of land use land cover change dynamics in Habru District, Amhara Region, Ethiopia

This study examines spatiotemporal changes in Land Use Land Cover (LULC) patterns within Habru District, Amhara Region, Ethiopia, between 1985 and 2021. Employing Landsat imagery and a supervised classification approach, we mapped six LULC classes: water body, settlement area, cultivated land, bare land, shrub land, and forest land. Ground control points (GCP) were obtained through field observations to ensured image accuracy. Complementing the remote sensing analysis, focus group discussions (FGDs) and key informant interviews were conducted to understand local perspectives on LULC changes. The analysis revealed significant LULC transformations over the study period. Between 1985 and 2021, there was a significant increase in cultivated land and settlement areas, which expanded from 12.27 % to 0.23 %–42.12 % and 2.58 % of the total area, respectively. Conversely, shrub and forest lands saw a marked reduction during the same period, declining from 71.52 % to 12.88 %–45.35 % and 7.06 % of the total area, respectively. Water body area showed a minor increase, while bare land exhibited negligible change. Agricultural land and settlement area expansion, population growth, land tenure insecurity, economic challenge, and climatic change were identified as key drivers of observed LULC changes. Our findings underscore the urgency of implementing multifaceted interventions to address these drivers and promote sustainable resource use practices. Such actions are critical for safeguarding Habru District's natural resources and ensuring the long-term viability of ecosystem services.

Exploring the dynamics and future projections of land use land cover changes by exploiting geospatial techniques; A case study of the Kabul River Basin

Global land cover change has caused significant environmental degradation and biodiversity loss. It affects ecosystem functions, livelihoods, and climate variation and has drawn substantial attention in recent decades. In the Kabul River Basin (KRB), there are limited studies on the historical Land Use/Land Cover (LULC) pattern, transition, intensity and future perspective. Therefore, this study aims to investigate long-term LULC changes and major drivers of LULC in the KRB over the past thirty years (1990–2020) and then to project the future LULC pattern for the years 2030, 2040 and 2050. Landsat Imageries of (1990–2020) were used as input data by utilizing the Random Forest Classifier algorithm (RF) in the Google Earth Engine (GEE) to classify the LULC. The LULC was then projected for the future, using the Cellular Automata Markov Chain Model (CA-MCM). The results demonstrated drastic LULC changes, controlled primarily by urbanization and agriculture expansion, which expanded from 467 Km2 (0.7 %) to 2312 km2 (3.4 %) and 6528 km2 (9.6 %) to 10812 (15.9 %), between 1990 and 2020. In contrast, bare land decreased from 70606 km2 (82.1 %) to 48212 km2 (70.9 %) between 1990 and 2020. In addition, the study depicts that the expansion in built-up and vegetation areas in the KRB during the study period were at the utilization of bare land. Future LULC predictions indicated that between 2020 and 2050, bare land would trend downward from 48212 km2 (70.9 %) to 46172 km2 (67.9 %), while vegetation and built-up areas would trend upward from 2312 km2 (3.4 %) to 3640 km2 (5.3 %), 10812 km2 (15.9 %) to 11622 km2 (17.1 %), and water bodies and snowcover would slightly vary from 1.2 % to 0.9 % and 7.9 %–9.0 %. In addition, the results of LULC dynamics reveal a significant strong positive correlation between population and built, as well as population and vegetation. Conversely, there is a strong negative correlation between population and bare land. Our results provide precise insights on LULC patterns and trends in the KRB, which could be employed to design a sustainable framework for land use and ecosystem protection.

Human-induced landscape modification in the in the last two centuries in the Po delta plain (northern Italy)

Historical maps with high degree of accuracy permit quantitative reconstructions of past land use and land cover (LULC), crucial to assess the impact of human activities on landscape evolution. After georeferencing in a modern reference system, the Carta del Ferrarese commissioned by Napoleon Bonaparte and completed in 1814, has been used to quantify the changes in LULC, occurred in the last two centuries in the Po delta plain. The map depicts a palaeo-landscape dominated by wetlands (49% of the study area) and agricultural areas (41%), whereas forests appear already largely depleted (5%). The Piantata Padana, a traditional agroforestry system with live trees used to support grapevines, is dominant (62% of the agricultural areas). The comparison with the 2014 LULC map highlights a dramatic reduction (85%) of wetland areas and the replacement of the Piantata Padana with bare arable lands, with the consequent removal of 4-40 million trees. Soils of areas formerly occupied by wetlands show high organic-carbon content, highlighting the potential of humid areas in carbon sequestration. Land reclaiming, prompted by the introduction of steam pumps, favoured the economic development of the area, but concurred to CO2 emissions through the oxidation of soil organic substances, energy consumption from pumping stations, and the extensive use of hydrocarbon fuels in agriculture. Although urbanisation is limited in the Po delta plain, this area appears nowadays largely shaped by human activities, with the dominance of lands devoted to agriculture, dissected by a dense network of draining channels. The landscape changes recorded in the last two centuries in the Po coastal plain have been uniquely driven by human activities, like in several coastal plains worldwide.

Morphometric analysis and LULC change dynamics of Nayar watershed for the sustainable watershed management

The morphometric analysis of the watersheds is essential for the conservation of natural resources, including soil, water, and vegetation. The Morphometric analysis defines the linear, areal and relief aspects of the watershed. It involves the comprehensive analysis of various factors such as drainage network, surface water flow, and other topographical features. The aim of this study is to develop a sustainable watershed management strategy for the Nayar watershed based on morphometry and Land Use Land Cover (LULC) change dynamics. The Nayar watershed has a total area of 1956.33 km2. The multispectral satellite imagery, Digital Elevation Model (DEM) data, and Survey of India (SOI) Toposheets were used for understanding the topographical and morphological characteristics along with the LULC change dynamics. The findings of this research conclude that the Nayar watershed has parallel and dendritic drainage patterns with high relief. According to the LULC change dynamics, the Nayar watershed's area change comprising 57.60 km2 and 57.15 km2, are from Agricultural Land to Wasteland and from Forest Cover to Wasteland, respectively. This shift in the hilly region will increase the risks of landslides and erosion. Furthermore, the change of 110.03 km2 area of Forest Cover to Agricultural Land raises further challenges due to loss of natural vegetation in long run. In summary the change in LULC of Nayar watershed is vulnerable to risk of natural calamities like landslide, erosion. This work would be helpful to many experts and decision-makers for sustainable watershed management and natural resource management.

Land‐Use and Land‐Cover (LULC) Change Detection in Bilari, Moradabad, U.P., India

AbstractThe post‐globalization of the Indian economy marks the beginning of a new era for various industries, including manufacturing and the service sector. As a result, people begin to migrate from sub‐urban/rural area to nearby major cities in search of employment and access to modern conveniences. Land Use Land Cover (LULC) pattern change is caused by human and natural factors which include geology, elevation, and slope. LULC change assessment is best ways to manage and understand landscape transformation. Bilari block in Moradabad is India's brass city and one of Uttar Pradesh's major cities, its population growth has pushed the rapid urbanization of city in last two decades. In the present study Landsat 7 Enhanced Thematic Mapper (ETM) and Landsat 8 Operational Land Manager (OLI) data for years 2000 and 2023 have been employed to assess. ArcGIS Pro 2.9.0 software is used to perform layer stacking and on the raw Landsat data for pre‐processing. To find LULC changes in the study area between 2000 and 2023, supervised classification is done by adopting Maximum Likelihood Classification (MLC). From the results, it is observed that the area from pervious to impervious changes very quickly in Bilari. This research will aid in regional and urban planning, as well as agricultural management, in the years ahead.

Assessing the impact of Land Use/Land Cover Change on Land Surface Temperatures and SUHI: Case of Pune, India

The emergence of urban heat islands and rising land surface temperatures in developing countries like India is becoming a crucial concern for urban planners and policymakers. This study attempts to assess the impact of Land use and land cover (LULC) change on the Land Surface Temperature (LST) and Surface Urban Heat Islands (SUHI) in Pune, India, using remote sensing and Geographic information systems (GIS). A spatiotemporal analysis of the Landsat satellite imagery from 2000 – 2023 has been used to trace the LULC trends and compute LST variations across the years in land cover types. The thermal band, 10 of the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), is used for computing LST. Spatial indicators such as normalized difference vegetation index (NDVI) and normalized differentiated built index (NDBI) are also calculated. Results indicate that there has been a 30% decline in vegetation across the city over the years, alongside an increase of 16.64°C in LST due to urbanization-induced LULC changes. Furthermore, this study attempts to identify the thermal hotspots in the city. The results of this study can enable the assessment of urbanization and the formulation of informed climate-sensitive urban planning strategies. Keywords: Land Surface Temperature, Pune, Remote Sensing, GIS, Local Climate Zone, SUHI

Time-series studies of land surface temperature in Damascus, Syria through MODIS by Google Earth Engine

Urbanisation and changes in land use and land cover (LULC) significantly impact Urban Heat Islands (UHI). Despite extensive research on UHI, regional variations demand localised studies. This research assesses the UHI effect in Damascus, Syria, where rapid urbanisation threatens environmental balance. Leveraging Google Earth Engine, Landsat 8, and MODIS data, LULC changes, temperature patterns, NDVI, and NDBI, have been analysed with a focus on the summers of 2013, 2017, and 2022. Results revealed a substantial 12 % increase in urban areas between 2013–2022, accompanied by an 11 % decrease in green spaces. Land surface temperatures (LST) rose from 47.1 °C in 2013 to 48 °C in 2022. NDBI analysis confirmed built-up area expansion, while NDVI indicated dwindling vegetation cover. The pronounced UHI effect (48.7 °C) in industrial and densely populated areas during summer were observed. A strong positive correlation existed between LST and NDBI 90 %, and a moderately negative correlation between LST and NDVI. These findings highlight the adverse impact of urbanisation on Damascus’ climate. They underscore the need for urban planning strategies prioritising green space preservation and sustainable development to mitigate the UHI effect and ensure a healthier urban environment.

Government Policy Influence on Land Use and Land Cover Changes: A 30-Year Analysis

This study investigated land use and land cover (LULC) patterns and changes in the Bandon Bay area of Thailand from 1991 to 2021 using satellite imagery, the first comprehensive effort to assess historical LULC trends over the past 30 years and forecast future LULC scenarios using the CA-Markov model for 2031, 2041, and 2051. Results showed the predominant LULC during 1991-2001 was the abandoned paddy fields, and during 2006-2021 was the oil palm plantations. During 1991-2001, the abandoned paddy fields changed significantly, with a net gain of 59.28 km2. From 2001-2011 and 2011-2021, the oil palm plantations experienced the most crucial change, with a net gain of 292.94 km2 and 70.06 km2. In 2031, 2041, and 2051, the LULC was predicted to be oil palms, shrimp farms, mangroves, and urban and built-up lands. The LULC changes were consistent with the government policies implemented and indicated government policy as a driving force in LULC dynamics on Bandon Bay area forestry, aquaculture, and agriculture, particularly on oil palm cultivation. Government management and regulation on land use is crucial for reducing the expansion of agricultural areas, especially oil palm plantations and aquaculture areas, to mitigate negative impacts on the Bandon Bay ecosystem. Doi: 10.28991/ESJ-2024-08-05-06 Full Text: PDF

Two decades of land cover change and anthropogenic pressure around Bontioli Nature Reserve in Burkina Faso

Protected areas (PAs) are critical for ecosystem maintenance and providing services that benefit both wildlife and people. Nevertheless, climate change and anthropogenic pressures are posing an increasing challenge. Surrounded by high human population densities, there is still a paucity of information on how the land cover in Burkina Faso's PAs is changing, and what kinds of human activities are the main drivers. In this study, we examined the change in land use and land cover (LULC) in the Bontioli Nature Reserve (NR), one of Burkina Faso's most important protected areas, and assessed anthropogenic pressure within and around. Landsat imagery (ETM+ and OLI-TIRS) is used to categorise and estimate the change in LULC in 2000, 2010, and 2022 with the Random Forest algorithm on the Google Earth Engine platform. Regression analysis was applied to examine the relationship between the LULC categories and population increase. We found significant changes and correlations in LULC trends and population growth over time. From 2000 to 2022, wooded savanna, tree savanna, and shrub savanna decreased by 20.8 %, 6.8 %, and 4.5 %, respectively, while cropland increased by 26.3 %, along with grass savanna by 5 %. Population growth correlated with increased agriculture and decreased vegetative area with R2 of 0.903 and 0.793, respectively. Efforts should be made to create harmony between humans and nature through various approaches such as nature-based solutions to enable efforts for the reserve sustainable management (SDG15).

Simulation and prediction of daytime surface urban heat island intensity under multiple scenarios via fully connected neural network

The intensification of the Surface Urban Heat Island (SUHI), driven by urbanization, land use and land cover (LULC) changes, and population growth, presents significant environmental and public health risks in urban areas. Simulating and predicting SUHI, particularly through the identification of future high SUHI intensity (SUHII) zones, has been recognized as a critical step in mitigating these effects. This study employs a Fully Convolutional Neural Network (FCNN) model, trained on data from four research sites, to simulate the current daytime SUHII across six validation sites in Singapore, utilizing 15 key independent variables identified in previous studies. The model exhibits high validation accuracy, achieving 87.45%. Three projection scenarios, based on projected population growth and LULC changes, predict a decrease in High SUHII across all validation sites, ranging from 98.3% to 9%. This reduction is attributed to the LULC improvements proposed in the 2019 Master Plan. Spatial analysis of the predicted SUHII maps indicates that the majority of High SUHII locations across scenarios remain consistent with the current situation. This research also suggests that the model could be a valuable tool for urban planners, allowing them to assess whether new urban development plans will effectively reduce High SUHII to desired thresholds, thereby mitigating SUHII in urban environments.

Examining the relationship between land-use cover change and sociodemographic characteristics: A case study of Madison County, Tennessee

Examining the relationship between land-use cover change and sociodemographic characteristics: A case study of Madison County, Tennessee

A multi-objective optimization framework for regional land-use allocation: Fully utilizing terrestrial vegetation to mitigate carbon emissions

Human-induced changes in land use and land cover (LULC) considerably impact the global carbon cycle. Rational LULC optimization with full utilization of terrestrial vegetation facilitates reconciling carbon reduction and socio-economic development. To this end, this study develops a multi-objective framework for low-carbon LULC spatial optimization that incorporates carbon suitability, historical suitability, and socio-economic needs. The framework is applied to Hubei Province, China. The key findings include: (1) Considering spatial variations in vegetation carbon sinks could lead to remarkable carbon reductions of 2.75 × 106 t C by 2030 and 7.68 × 106 t C by 2060. It would also lower carbon emissions per unit of gross domestic product (GDP) and enhance carbon sequestration per unit of vegetation by 2030, signifying enhanced carbon reduction efficiency. (2) Integrating carbon sequestration potential and historical suitability can considerably minimize emissions while sustaining socio-economic growth. Our method, compared with strategies based solely on historical development laws, can reduce carbon emissions by approximately 1.3–1.6 × 106 t C·a−1. (3) We also propose targeted low-carbon development suggestions for different LULCs. This study provides optimal spatial LULC allocations and strategic advice for regional LULC planning aimed at low-carbon development, demonstrating the feasibility of reconciling the tension between carbon reduction and socio-economic growth at the regional scale.

Machine learning for urban land use/ cover mapping: Comparison of artificial neural network, random forest and support vector machine, a case study of Dilla town

The ability to accurately classify land use/cover (LULC) is critical for environmental monitoring and land use planning. This study compares three machine learning algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF) for LULC classification using Google Earth images from the years 2006, 2014, and 2022. The objective of this study is to evaluate and identify the best classifier for LULC classification and change detection. Four LULC categories (Built-up, Open area, Farmland, and Agroforestry) were identified. The evaluation criteria included overall accuracy, kappa coefficient, producer's accuracy, user's accuracy, computing time, algorithm stability, and visual quality. The results showed that the RF algorithm outperformed both SVM and ANN algorithms with an average overall accuracy of 0.97, kappa coefficient of 0.98, producer's accuracy of 0.99, and user's accuracy of 0.97, surpassing the accuracies achieved by SVM (0.96, 0.97, 0.98, and 0.97) and ANN (0.89, 0.81, 0.94, and 0.88), with corresponding computing times of 6.33, 15, and 30 s. All classifiers performed stably with different training sizes. Visual quality assessment revealed that RF had the highest precision. Consequently, the built-up change detection result shows, the net change in built-up area between 2006 and 2022 was increased by 0.74 Km2for ANN, 1.74 Km2 for SVM, and 1.66 Km2for RF. The comparison reveals that the RF algorithm showcasing high precision in detecting change, consistent with the data (increased by 1.65 Km2) obtained from Dilla town land administration office. To validate the results, the study considered field surveys, reference images, local experts, and previous studies. Based on the findings, the study concludes that using RF classifier with an object-based approach is an effective way to map LULC and detect changes in the study area over time. Future researchers are recommended to utilize this effective algorithm for addressing LULC related problems in the study area.

A land cover change framework analyzing wildfire-affected areas in bitemporal PRISMA hyperspectral images

Wildfires are becoming increasingly common events, and studying them, monitoring their effects, and assessing the damage they produce, is crucial for planning recovery efforts. The new generation of hyperspectral satellite sensors can provide highly detailed spectral information directly related to materials on the Earth’s surface, allowing the detection of potential changes in monitored areas. These instruments allow the detection of even small land changes, such as those in homogeneous areas of interest. Unlike binary change detection mechanisms that can only produce a map of changes in observed areas, our goal is to provide a mathematical framework to construct semantic maps of land change before and after an impactful event. This feature is particularly useful for monitoring land use and land cover (LULC), agriculture, and damage assessment in fire-affected areas. This paper presents a framework for remote sensing change analysis between bitemporal hyperspectral images, namely SemBLCC, whose core is a hierarchical clustering algorithm based on a rank-two nonnegative matrix factorization. SemBLCC is able to explicitly model the semantic “from-to” transitions between the two involved hyperspectral images, thanks to new spectral libraries specifically designed for the new data acquired by PRISMA (PRecursore IperSpettrale della Missione Applicativa) satellite. SemBLCC has been successfully used to produce LULC change maps of fire-affected areas, allowing accurate assessment of fire damage.

Evaluating water balance components in a tropical ecological zone under land use changes

Water availability for domestic, institutional, and agricultural production is threatened by the continuous change in land use land cover (LULC). These changes in LULC features affect the dynamics of the hydrological processes such as surface runoff, infiltration, and evapotranspiration, resulting in variations in water availability especially, for human consumption and crop yield. Therefore, the Soil and Water Assessment Tool (SWAT) hydrological model was deployed to simulate the Pra River Basin (PRB)’s water balance response to changing LULC from 1986 to 2020. Open and closed forests reduced from an area of 11,071 km2 to 5902 km2 and 5125 km2 to 4990 km2 while settlement and cropland increased from 1100 km2 to 2392 km2 and 5303 km2 to 9320 km2 representing an average annual expansion rate of +24.7 km2/yr and +118 km2/yr respectively over the three decades. The growth in settlement and cropland resulted in an upsurge of more than 33 % and 20 % in surface runoff and water yield respectively, but decreases of over 4 % and 17 % in evapotranspiration and baseflow respectively. These changes have resulted in a seasonal increase in surface runoff, baseflow, and evapotranspiration (ET) during the wet season but a decrease during the dry season. The PRB has experienced a dramatic decline in forest areas, chiefly in areas where settlement and farmlands have been expanded. The areas where surface runoff and water yield showed significant upsurge and reduction in ET are the northwestern and southern enclaves of the basin. These findings can aid in effectively planning and managing land use to control the gradually depleting water resources in the basin.

An application of the remote sensing derived indices for drought monitoring in a dry zone district, in tropical island

Recent research has shown that droughts have intensified in South Asia over the past two decades. As a natural disaster, this has severely impacted people’s livelihoods, especially in the dry zones of Sri Lanka. Thus, to ensure human well-being, security of water resources, and ecosystem health, it is essential to minimize the impacts of droughts using reliable information. Remote sensing (RS) data and techniques help bridge the gap by enabling the analysis of drought phenomena through a diverse array of indices developed in the fields. However, until now, there has been a lack of systematic monitoring and reliable data for accurately characterizing droughts in the study area. As the first comprehensive analysis, we tried to evaluate the spatial–temporal dynamics of drought conditions in two Divisional Secretariat Divisions of the Anuradhapura district, Sri Lanka using eight standardized remote sensing-derived indices over a decade (2013–2023) including the Standardized Precipitation Index (SPI). SPI values indicated that the region has experienced notably below-average precipitation. According to SPI results, a significant portion of the Medawchchiya area experienced arid conditions in 2023. All other indices proved that 2018 was the driest year and 2013 was the wettest year among the three time points, as reflected by their low and high index values. However, according to NVSMI and LST, the wettest year is 2023, with only 1.78 % of areas experiencing severe drought and a maximum LST of 31.4 °C. LULC change detection revealed that 14.3 % of agricultural lands and 5.1 % of forest areas were converted into barren lands over a decade. Overall, this conversion may be another leading factor contributing to increased LST and dryness in the area during the concerned period while increasing the mean LST of barren land by around 4.7 °C per decade. Land surface-related and vegetation-related indices, such as NDWI, NDMI, LST, and NDVI, exhibited a more pronounced impact on short-term drought occurrences. The findings revealed that average precipitation coincides with short-term drought episodes in the area, with 2018 standing out as having the least rainfall and the driest year. The study’s findings may provide additional insights for planning authorities, supporting environmental protection and enhancing agricultural production by mitigating droughts’ impacts through short- and long-term strategies. Although, the study focused on a small area, a similar approach could be extended to other areas by incorporating advanced machine learning techniques and additional drought indices in the future.