Land Use Cover Research Articles

Assessment of land use/ land cover change derived catchment hydrologic response: An integrated parsimonious hydrological modeling and alteration analysis based approach

Land use/land cover (LULC) change, often a consequence of natural or anthropogenic drivers, plays a decisive role in governing global catchment dynamics, and subsequent impact on regional hydrology. Insight into the complex relationship between the drivers of LULC change and catchment hydrology is of utmost importance to decision makers. Contemplating the dynamic rainfall-runoff response of the Indian catchments, this study proposes an integrated modeling-based approach to identify the drivers and relative contribution to catchment hydrology. The proposed approach was evaluated in the tropical climate Nagavali River Basin (NRB) (9512 km2) of India. The Soil and Water Assessment Tool (SWAT) hydrological model, which uses daily-scale rainfall, temperature, wind speed, relative humidity, solar radiation, and streamflow information was integrated with the Indicators of Hydrologic Alteration (IHA) technique to characterize the plausible changes in the flow regime of the NRB. Subsequently, the Partial Least Squares Regression (PLSR) based modeling analysis was performed to quantify the relative contribution of individual LULC components on the catchment water balance. The outcomes of the study revealed that forest land has been significantly converted to agricultural land (45–59%) across the NRB resulting in mean annual streamflow increase of 3.57 m3/s during the monsoon season. The affinity between land use class and streamflow revealed that barren land (CN = 83–87) exhibits the maximum positive response to streamflow followed by the built-up land (CN = 89–91) and fallow land (CN = 88–93). The period 1985–1995 experienced an increased ET scenario (911–1050 mm), while the recent period (2005–2020) experienced reduced ET scenario owing to conversion of forest to agricultural land. Certainly, the study endorses adopting the developed methodology for understanding the complex land use and catchment-scale hydrologic interactions across global-scales for early watershed management planning.

Evaluating the Climate Change Trends and Spatiotemporal Variations of Evapotranspiration: A Case Study in El-Beheira Governorate, Egypt

Aims: Evapotranspiration (ET) is crucial for determining crop water requirements, while climate change and global warming are major concerns. Therefore, in this study, we aimed at evaluating the climate change trends and the spatiotemporal variations in ET in a selected area located in El-Beheira Governorate, Egypt, while accounting for potential land use/land cover (LULC) changes. Methodology: We analyzed three Landsat images acquired in 1984, 2001, and 2020 to assess LULC changes in the study area. Climate change was studied from 1984 to 2021 using data from NASA's Prediction of Worldwide Energy Resource (POWER) project. We also used the Terra MODIS MOD16A3GF Version 6.1 ET product from 2000 to 2021 to evaluate the spatiotemporal variations of ET. Results: LULC change detection showed that the unchanged agricultural area from 1984 to 2020 accounted for 29.3% of the study area and was used to evaluate the changes of ET. Evaluating climate change revealed that there was an increased trend in temperature, relative humidity (RH), and precipitation, while no change was observed in wind speed. Similarly, the anomalies changes of temperature, RH, and precipitation had an increasing trend while wind speed was constant. On the other hand, the yearly ET values calculated from 2000 to 2021 had an increasing trend. There was a moderate correlation between RH and precipitation, as well as between ET and precipitation. Conclusion: The increased trends in climatic parameters such as temperature will eventually require changes in crop patterns to adjust to these changes and maintain a profitable yield. Furthermore, the increase in ET will result in an increase in crop water requirements, which is problematic, especially considering the limited water resources in Egypt.

Analysis of land use/ land cover changes and landscape fragmentation in the Baro-Akobo Basin, Southwestern Ethiopia

This study investigated the relationship between land use/land cover (LULC) changes and forested landscape fragmentation in the southwestern region of Ethiopia. Satellite images from 1986, 2002 and 2019 were collected and analyzed using standard procedures in ERDAS 2015 software. Fragstat 4.2.1 software was utilized to assess landscape fragmentation by examining a raster datasets derived from the classified LULC map over the research period. The study identified seven LULC classes in the study area. Findings revealed a substantial reduction in shrubland by 46.3%, dense forest by 23.75%, open forest by 17.3%, and wetland by 32.63%, while cropland increased by 38.06%, agroforestry by 20.29%, and settlements by 163.8% during the study period. These changes varied across different agroecological zones and slope gradients. Landscape metrics results indicated an increase in the number of patches and patch density for all LULC classes, demonstrating significant fragmentation of the landscape. The largest patch index, mean patch areas, and the percentage of landscape occupied by open forest, dense forest, shrubland, and wetland declined as a result of conversion to cropland, agroforestry, and settlement. Conversely, the largest patch index, the mean patch area and the percentage of the landscape occupied by agroforestry, cropland and settlement increased, indicating their increasing dominance in the landscape over the study periods. The findings highlighted the potential deleterious impacts of ongoing land use change and fragmentation on the environment, ecosystem function and local livelihoods. Therefore, it is crucial to implement appropriate conservation efforts and sustainable land management practices to mitigate the rapid change and fragmentation of land use and its negative impacts on sub-watershed ecosystems.

QGIS-based modeling and analysis of urban dynamics affecting land surface temperature towards climate hazards in coastal zones of Portugal

Climate risk creates considerable concern due to the density of natural and socio-economic assets in coastal areas. Monitoring land use/cover changes, detecting population growth, and analyzing their impact on land surface temperature (LST) are necessary for effective urban management. In this study, land use/land cover (LULC), population, and LST changes in coastal regions of Portugal. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery was examined using World Bank population and CORINE data. Changes in land use types and LST values from 1990 to 2018 were analyzed. At the same time, LULC predictions were made using the Modules for Land Use Change Simulation (MOLUSCE) plug-in included in the QGIS software, and population projections were analyzed with LULC predictions in 2046. The results show the significant impact of land use on temperatures. It has been demonstrated that green and water areas can effectively cool cities. In the LULC changes between 1990 and 2018, the Leiria region stands out, with an annual increase of 4.04% in built areas from 121.58 to 259.06 km2. According to the simulations between 2018 and 2046, it was predicted that 18.74% of agricultural areas and 14.43% of forest areas would be transformed into built environments. The study is also essential as it confirms that the MOLUSCE plug-in can be effectively applied to land cover simulation on a large regional scale.

Dynamics and Predictions of Urban Expansion in Java, Indonesia: Continuity and Change in Mega-Urbanization

This paper is situated within the discussion of mega-urbanization, a particular urbanization process that entails a large-scale agglomeration. In this paper, our focus is on urbanization in Java, Indonesia’s most dynamic region. We add to the literature by investigating the change and prediction of the land use/land cover (LULC) of mega-urbanization in Java. This research uses a vector machine approach to support the classification of land cover change dynamics, cellular automata-Markov (CA Markov), and the Klassen typology technique. This paper indicates that major metropolitan areas are still expanding in terms of built-up areas, generating a larger urban agglomeration. However, attention should be also given to the urbanization process outside existing metropolis’ boundaries given that more than half of the built-up land coverage in Java is located in non-metropolitan areas. In terms of future direction, the projection results for 2032 show that the Conservative scenario can reduce and slow down the increase in built-up land on the island of Java. On the other hand, the Spatial Plan (RTRW) scenario facilitates a rapid increase in the LULC of built-up land from 2019. The urban spatial dynamics in Java raises challenges for urban and regional planning as the process is taking place across multiple administrative authorities.

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.

A comprehensive research on open surface drinking water resources in Istanbul using remote sensing technologies.

Istanbul is a megacity with a population of 15.5 million and is one of the fastest-growing cities in Europe. Due to the rapidly increasing population and urbanization, Istanbul's daily water needs are constantly increasing. In this study, eight drinking water basins that supply water to Istanbul were comprehensively examined using remote sensing observations and techniques. Water surface area changes were determined monthly, and their relationships with meteorological parameters and climate change were investigated. Monthly water surface areas of natural lakes and dams were determined with the Normalized Difference Water Index (NDWI) applied to Sentinel-2 satellite images. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in months when optical images were unavailable. The study was carried out using 3705 optical and 1167 SAR images on the Google Earth Engine (GEE) platform. Additionally, to determine which areas of water resources are shrinking, water frequency maps of the major drinking water resources were produced. Land use/land cover (LULC) changes that occurred over time were determined, and the effects of the increase in urbanization, especially on drinking water surface areas, were investigated. ESRI LULC data was used to determine LULC changes in watersheds, and the increase in urbanization areas from 2017 to 2022 ranged from 1 to 91.43%. While the basin with the least change was in Istranca, the highest increase in the artificial surface was determined to be in the Büyükçekmece basin with 1833.03ha (2.89%). While there was a 1-12.35% decrease in the surface areas of seven water resources from 2016 to 2022, an increase of 2.65-93% was observed in three water resources (Büyükçekmece, Sazlıdere, and Elmalı), each in different categories depending on their size. In the overall analysis, total WSA decreased by 62.33ha from 2016 to 2022, a percentage change of 0.70%. Besides the areal change analysis, the algae contents of the drinking water resources over the years were examined for the major water basins using the Normalized Difference Chlorophyll Index (NDCI) and revealed their relationship with meteorological factors and urbanization.

A landscape in turmoil: characterizing the multi-decadal growth trajectory of brackishwater aquaculture in Medinipur Coastal Plain, India

The spatio-temporal growth trajectory of coastal brackishwater aquaculture (CBA) in the coastal tracts of Purba Medinipur, India and its impending repercussions on the immediate environs, was primarily assessed in this study through application of geostatistics, landscape metrics, and geospatial technologies. Three Community Development (CD) Blocks, namely, Contai-I, Deshapran, and Ramnagar-II were considered to analyze the growth pattern of the area under CBA. Landsat datasets of the assessment years (1991, 2001, 2011, 2021, and 2023) were used to prepare the land use/ land cover (LULC) maps and to derive pertinent landscape metrics of patch and class levels for this region. This brought forward a highly fragmented and dispersive spatial concentration of the CBA farms in the entire study area. Additionally, Census Village-wise growth pattern of CBA was analyzed by conducting a spatial autocorrelation analysis which depicted prominent clustering of villages with a higher concentration of CBA. Results showed that there has been an incessant growth of CBA in the last three decades, however, a sharp drop has been recorded owing to recurrent bouts of diseases, swelling production costs, and a sharp drop-in market rate. The nature of growth and/ or decay of the CBA was predicted for the year 2025 using Cellular Automata and Artificial Neural Network (CA-ANN) model. After careful calibration and validation, the model projected further lessening of the CBA area along with a continued expansion of abandoned aquaculture. Accordingly, ecologically viable livelihood alternatives and environmentally sustainable management measures were suggested for the efficient monitoring of these highly fragile tropical ecosystems.

Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network

The integration of multi-spectral remote sensing and GIS-based analysis is significant for studying land cover changes, providing valuable insights for informed land management and sustainable development. The present study aims to examine land use land cover (LULC) changes of three decades from 1991 to 2021 and predict the future LULC change in Binh Duong province, Vietnam to explore a future research direction on land use change and associated challenges in the study region. Multi-spectral remote sensing data and random forest tree (RFT) were utilized to generate LULC maps. Areal statistics and annual change rate were considered to analyze the categorical land use change detection. Statistical measures such as user's accuracy, producer’s accuracy, kappa coefficient, and confusion matrix were employed to assess the accuracy of LULC classification. To predict future LULC and simulate the spatio-temporal change, we considered previous year LULC maps, independent spatial variables and a combined artificial neural network (ANN) multi-layer perceptron approach. The analysis revealed that there was a huge transition from agricultural lands to residential land with industry and commerce which resulting an expansion of impervious lands and a rapid decline of agricultural land as well as of scrub land and barren lands, and a changeability of forest and plantation, croplands, and waterbodies. Our study revealed that the impervious land has expanded 10 times within 30 years and will increase in the future.

Assessment of land use land cover change and its effects using artificial neural network-based cellular automation

The challenge of urban growth and land use land cover (LULC) change is particularly critical in developing countries. The use of remote sensing and GIS has helped to generate LULC thematic maps, which have proven immensely valuable in resource and land-use management, facilitating sustainable development by balancing developmental interests and conservation measures. The research utilized socio-economic and spatial variables such as slope, elevation, distance from streams, distance from roads, distance from built-up areas, and distance from the center of town to determine their impact on the LULC of 2016 and 2019. The research integrates Artificial Neural Network with Cellular Automta to forecast and establish potential land use changes for the years 2025 and 2040. Comparison between the predicted and actual LULC maps of 2022 indicates high agreement with kappa hat of 0.77 and a percentage of correctness of 86.83%. The study indicates that the built-up area will increase by 8.37 km2 by 2040, resulting in a reduction of 7.08 km2 and 1.16 km2 in protected and agricultural areas, respectively. These findings will assist urban planners and lawmakers to adopt management and conservation strategies that balance urban expansion and conservation of natural resources leading to the sustainable development of the cities.

Time-first approach for land cover mapping using big Earth observation data time-series in a data cube – a case study from the Lake Geneva region (Switzerland)

ABSTRACT Accurate, consistent, and high-resolution Land Use & Cover (LUC) information is fundamental for effectively monitoring landscape dynamics and better apprehending drivers, pressures, state, and impacts on land systems. Nevertheless, the availability of such national products with high thematic accuracy is still limited and consequently researchers and policymakers are constrained to work with data that do not necessarily reflect on-the-ground realities impending to correctly capture details of landscape features as well as limiting the identification and quantification of drivers and rate of change. Hereafter, we took advantage of the Switzerland’s official LUC statistical sampling survey and dense time-series of Sentinel-2 data, combining them with Machine and Deep Learning methods to produce an accurate and high spatial resolution land cover map over the Lake Geneva region. Findings suggest that time-first approach is a valuable alternative to space-first approaches, accounting for the intra-annual variability of classes, hence improving classification performances. Results demonstrate that Deep Learning methods outperform more traditional Machine Learning ones such as Random Forest, providing more accurate predictions with lower uncertainty. The produced land cover map has a high accuracy, an improved spatial resolution, while at the same time preserving the statistical significance (i.e. class proportion) of the official national dataset. This work paves the way towards the objective to produce a yearly high resolution land cover map of Switzerland and potentially implement a continuous land change monitoring capability. However further work is required to properly address challenges such as the need for increased temporal resolution for LUC information or the quality of training samples.

Human intervention caused massive destruction of the second largest mangrove forest, Chakaria Sundarbans, Bangladesh.

Mangroves provide essential ecosystem services including coastal protection by acting as coastal greenbelts; however, human-driven anthropogenic activities altered their existence and ecosystem functions worldwide. In this study, the successive degradation of the second largest mangrove forest, Chakaria Sundarbans situated at the northern Bay of Bengal part of Bangladesh was assessed using remote sensing approaches. A total of five multi-temporal Landsat satellite imageries were collected and used to observe the land use land cover (LULC) changes over the time periods for the years 1972, 1990, 2000, 2010, and 2020. Further, the supervised classification technique with the help of support vector machine (SVM) algorithm in ArcGIS 10.8 was used to process images. Our results revealed a drastic change of Chakaria Sundarbans mangrove forest, that the images of 1972 were comprised of mudflat, waterbody, and mangroves, while the images of 1990, 2000, 2010, and 2020 were classified as waterbody, mangrove, saltpan, and shrimp farm. Most importantly, mangrove forest was the largest covering areaa total of 64.2% in 1972, but gradually decreased to 12.7%, 6.4%, 1.9%, and 4.6% for the years 1990, 2000, 2010, and 2020, respectively. Interestingly, the rate of mangrove forest areadegradation was similar to the net increase of saltpan and shrimp farms. The kappa coefficients of classified images were 0.83, 0.87, 0.80, 0.87, and 0.91 with the overall accuracy of 88.9%, 90%, 85%, 90%, and 93.3% for the years 1972, 1990, 2000, 2010, and 2020, respectively. By analyzing normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and transformed difference vegetation index (TDVI), our results validated that green vegetated area was decreased alarmingly with time in this study area. This destruction was mainly related to active human-driven anthropogenic activities, particularly creating embankments for fish farms or salt productions, and cutting for collection of wood as well. Together all, our results provide clear evidence of active anthropogenic stress on coastal ecosystem health by altering mangrove forest to saltpan and shrimp farm saying goodbye to the second largest mangrove forest in one of the coastal areas of the Bay of Bengal, Bangladesh.

Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data

The rapid increase in population accelerates the rate of change of Land use/Land cover (LULC) in various parts of the world. This phenomenon caused a huge strain for natural resources. Hence, continues monitoring of LULC changes gained a significant importance for management of natural resources and assessing the climate change impacts. Recently, application of machine learning algorithms on RS (remote sensing) data for rapid and accurate mapping of LULC gained significant importance due to growing need of LULC estimation for ecosystem services, natural resource management and environmental management. Hence, it is crucial to access and compare the performance of different machine learning classifiers for accurate mapping of LULC. The primary objective of this study was to compare the performance of CART (Classification and Regression Tree), RF (Random Forest) and SVM (Support Vector Machine) for LULC estimation by processing RS data on Google Earth Engine (GEE). In total four classes of LULC (Water Bodies, Vegetation Cover, Urban Land and Barren Land) for city of Lahore were extracted using satellite images from Landsat-7, Landsat-8 and Landsat-9 for years 2008, 2015 and 2022, respectively. According to results, RF is the best performing classifier with maximum overall accuracy of 95.2% and highest Kappa coefficient value of 0.87, SVM achieved maximum accuracy of 89.8% with highest Kappa of 0.84 and CART showed maximum overall accuracy of 89.7% with Kappa value of 0.79. Results from this study can give assistance for decision makers, planners and RS experts to choose a suitable machine learning algorithm for LULC classification in an unplanned urbanized city like Lahore.

Assessing and predicting changes of ecosystem service values in response to land use/land cover dynamics in Ibb City, Yemen: a three-decade analysis and future outlook

ABSTRACT Assessing ecosystem services values (ESV) within land use/land cover (LULC) changes is crucial for promoting human well-being and sustainable development of regional ecosystems. Yet, the spatial relationship between LULC changes and ecosystem services is still unclear in Yemen. This study aimed to assess the impacts of LULC changes on ESV in Ibb City, over three decades (1990–2020), and to predict the ESV changes in 2050. The hybrid land use classification technique for classifying Landsat images, the CA-Markov model for LULC prediction, and the benefit transfer method (BTM) for assessment ESV were employed. Our findings revealed that there was a continuous increase in built-up areas and barren land, with a decrease in cultivated land and grassland, which are predicted to continue for the next 30 years. Consequently, the total ESV has decreased from US$ 68.5 × 106 in 1990 to US$ 65.2 × 106 in 2020 and is expected to further reduce to US$ 61.2 × 106 by 2050, reflecting the impact of urban expansion and socio-economic activities on ESV. The study provides insights for future ESV monitoring, which will contribute to the formulation of effective land-use strategies for more sustainable ecosystem services, particularly in rapidly urbanizing data-limited regions.

Spatio-temporal urban land surface temperature variations and heat stress vulnerability index in Thiruvananthapuram city of Kerala, India

ABSTRACT Land use/Land cover (LU/LC) modifications leads to rise in surface temperature is a global concern. This study investigates the effects of LU/LC changes in Land Surface Temperature (LST) variations and effects of Urban Heat Island (UHI) in Thiruvananthapuram city. Furthermore, the study proposed a novel heat stress vulnerability index (HSVI) using Analytical Hierarchy Process (AHP) method. Results shows that transformation of all land uses is to built-up areas, which shows an increase of +118.46% where as others such as vegetation (-24.27%), open area (-64.315) and waterbody (-43.88%) shows drastic decrease over 34 years. The relationship between Normalized Difference Vegetation Index and LST shows a significant change with R2 values between 0.223 in 1988, 0.382 in 2000 and 0.253 in 2022. Moreover, this also contributed to the increase of the mean LST of 26.96 ºC in 1988 to 28.52 ºC in 2022. HSVI of the study indicates that 30.83% of the study area is categorized as very low heat stress vulnerable area followed by 37.22% area in low heat stress vulnerability, 17.88% in moderate heat stress vulnerability, 8.87% in high heat stress vulnerability and 5.19% of the area falling in very high heat stress Vulnerability class.

Losing lemurs: Declining populations and land cover changes over space and time.

Forest loss and degradation due to land cover changes imperil biodiversity worldwide. Subtropical and tropical ecosystems experience high deforestation rates, negatively affecting species like primates. Madagascar's endemic lemurs face exceptionally high risks of population declines and extirpation. We examined how short-term land cover changes within a fragmented landscape in southeastern Madagascar impacted the density of lemur species. Using line transects, we assessed density changes in nine lemur species across five forest fragments. Diurnal surveys were conducted monthly from 2015 to 2019 on 35 transects (total effort = 1268 km). Additionally, 21 transects were surveyed nocturnally in 2015 and 2016 (total effort = 107.5 km). To quantify forest cover changes, we generated land use/land cover (LULC) maps from Sentinel-2 imagery using supervised classification for each year. For the LULC maps, we overlayed species-specific buffers around all transects and calculated the proportion of land cover classes within them. We observed declines in the annual densities of four diurnal and cathemeral lemur species between 2015 and 2019, with species-specific declines of up to 80% (Varecia variegata). While the density of two nocturnal species decreased, one increased fivefold (Cheirogaleus major) between 2015 and 2016. By 2019, Grassland was the dominant land type (50%), while Paddy Fields had the smallest coverage (1.03%). Mature Agricultural Land increased the most (63.37%), while New Agricultural Land decreased the most (-66.36%). Unexpectedly, we did not find evidence that higher forest cover supported a higher lemur population density within sampled areas, but we found support for the negative impact of degraded land cover types on three lemur species. Our study underscores the urgent need to address land-use changes and their repercussions for primate populations in tropical ecosystems. The diverse responses of lemur species to modified habitats highlight the complexity of these impacts and emphasize the importance of targeted conservation efforts.

Land use changes and climate parameters assessments in a tropical highland region of Cameron Highlands, Malaysia

Abstract Tropical highland regions are susceptible to climate change and natural disasters due to their geographical location and hilly terrain. The objectives of this study are to determine land use land cover (LULC) changes in the Cameron Highlands and analyse the climatic parameters of the Cameron Highlands. This study integrates LULC analysis using remote sensing techniques and 10-year climatic parameters data to evaluate the impact of climate variability on the sustainability of the Cameron Highlands. Based on the validation results, the overall accuracy of LULC was 95.42% in 2016, 96.60% in 2018, and 97.40% in 2020. The results show an 18% rise in agriculture, a 16% increase in urban growth, and an 8.14% decline in forest coverage in Tanah Rata and Ringlet, Cameron Highlands, from 2016 to 2020. The Mann -Kendall and Sen slope indicated a statistically significant increasing trend in rainfall (Kendall's Tau, z = 0.102, p < 0.0001 and Sen value= 0.131, p < 0.001, respectively) and temperature (Kendall's Tau, z = 0.151, p < 0.001 and Sen value = 0.294, p < 0.001, respectively) from 2012 to 2021, increasing the area's susceptibility towards climate change impact and natural disasters. This study highlights the vulnerability of the Cameron Highlands to natural disasters, emphasizing the crucial need for efficient land management in slope areas.

Wintering raptor species distribution in a semiarid Mediterranean region: the relevance of lowlands and open habitats as stopover sites

As land-use cover types affect wintering birds’ strategies, studying their habitat suitability for species conservation is relevant. Predictive spatial models are considered excellent tools for conservation planning and improving our understanding of species distribution. Here we build models to predict the spatial distribution of raptor species that overwinter in SE Spain. We modelled the wintering raptor species distribution based on the presence-only data obtained between 2017 and 2019 and analysed their habitat preferences based on elevation and land-use cover variables. Our results show that altitude and distance to the coastline are the most important environmental factors to affect most species’ habitat suitability. Habitat type-related factors are other important predictors, and raptors subject of this study prefer lowlands and areas close to the coastline for overwintering by selecting open habitats, which include wetlands and irrigated and rainfed crops. This study highlights the importance of the habitat heterogeneity generated by wetlands and herbaceous crops within a low-altitude range as the optimal environment for wintering raptors. Such information should be considered for raptor conservation planning in human-altered landscapes.

Urban engineering insights: Spatiotemporal analysis of land surface temperature and land use in urban landscape

In the field of urban environment engineering, understanding the relationship between land surface temperature (LST) and land use land cover (LULC) is essential in rapidly growing and climatically unstable urban landscapes such as Chengdu. It helps alleviate the magnitude and intensity of Urban Heat Islands (UHIs). Toward this aim, summer and winter Landsat images were acquired in four years from 1992 to 2021 and were used to extract LULC classes, LST and three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) and analyze their spatiotemporal associations. Results showed that built-up areas expanded approximately six times (820.82 Km2, 584.96%) from 1992 to 2021. Meanwhile, their mean LST increased in both seasons, by 9.94 °C in summer and by 0.95 °C in winter. The LST-NDBI correlation was significant and positive in both the summer and winter of the studied years (0.437< r <0.874, p=0.00) while a very high variability was observed in the LST-NDVI (-0.835< r <0.255, p=0.00) and LST-MNDWI (-0.632< r <0.628, p=0.00) correlation coefficients. According to the results, NDBI can be used as a good intra- and inter-annual predictor of LST in Chengdu, especially in the context of its fast-paced physical expansion and increasing intensity of UHI.

The Spatio-Temporal Dynamics of Urban Green Spaces in Gweru City, Zimbabwe

The recognition of urban green spaces (UGS) is critical for sustainable strategies globally. UGS serve a critical function that spans the socioeconomic, environmental, and climatic divides, but their survival is jeopardised by the insatiable resource needs associated with perceived growth in developing nations. We used an explanatory sequential approach to conduct a situational analysis of Gweru City, Zimbabwe, to understand the spatio-temporal dynamics of UGS. Five Landsat pictures from 2000 to 2019 were utilised. At a five-year interval, four land use/land cover (LULC) clusters were identified using maximum likelihood classification: water bodies, green spaces, bare ground, and built-up areas. This was confirmed using real-world data with very high metrics (OA: 94%-97%; Kappa: 0.91-0.96). In addition to the use of satellite images, interviews with residents were used to supplement the data collected through the analysis of satellite images. Accordingly, the findings revealed that UGS in Gweru municipality has decreased by 5% during the last two decades (2000-2019). These findings were supported by locals who acknowledged that UGS were deteriorating in the suburbs, particularly in high-density areas. The terrible circumstances, according to the interviews, were caused by human activities, administrative deficiencies, natural disasters, and geology. As a result, current urban management approaches must support UGS, particularly in cities throughout the globe. Comprehending these variables is crucial for well-informed decision-making and efficient urban design and development