Land Transformation Research Articles (Page 1)

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

In the continuous process of urban development, brownfield landscapes have emerged as a crucial method of remediation. Beyond ecological restoration, they are increasingly recognized as essential components of landscape composition. Within urban renewal frameworks, brownfield landscapes facilitate the transformation and revitalization of derelict industrial land through innovative planning, design, and ecological restoration. This strategy reestablishes ecological balance, enhances land-use value, and generates economic growth and social vitality through functional integration and spatial reconfiguration. Focusing on the landscape dimension, this study takes Luoyang City's brownfield renewal as a case study to explore strategies for integrating brownfield landscapes with industrial cultural resources in contemporary urban renewal. It illustrates the area's typicality as a representative example of brownfield regeneration. The study identifies a three-level integration mechanism in regeneration: spatially, combining site layering and functional insertion; temporally, constructing narrative continuity between industrial civilization and the city's cultural context; and socially, establishing shared recognition of heritage landscape value through multi-stakeholder participation. The concept of dynamic suturing is also proposed, emphasizing that brownfield renewal must balance physical transformation with preservation of intangible collective memory. The findings provide a new-old symbiosis renewal paradigm for similar cities, offering practical insights into industrial city transformation under current urban development demands and strategies for high-quality growth.

The lower Mahanadi basin in eastern India is experiencing significant land and soil transformations that directly influence agricultural sustainability and ecosystem resilience. In this study, we used geospatial techniques to analyze the spatial-temporal variability of soil quality and land cover between 2011 and 2020 in the lower Mahanadi basin. The results revealed that the cropland decreased from 39,493.2 to 37,495.9 km2, while forest cover increased from 12,401.2 to 13,822.2 km2, enhancing soil organic carbon (>290 g/kg) and improving fertility. Grassland recovered from 4826.3 to 5432.1 km2, wastelands declined from 133.3 to 93.2 km2, and water bodies expanded from 184.3 to 191.4 km2, reflecting positive land–soil interactions. Soil quality was evaluated using the Simple Additive Soil Quality Index (SQI), with core indicators bulk density, organic carbon, and nitrogen, selected to represent physical, chemical, and biological components of soil. These indicators were chosen as they represent the essential physical, chemical, and biological components influencing soil functionality and fertility. The SQI revealed spatial variability in texture, organic carbon, nitrogen, and bulk density at different depths. SQI values indicated high soil quality (SQI > 0.65) in northern and northwestern zones, supported by neutral to slightly alkaline pH (6.2–7.4), nitrogen exceeding 5.29 g/kg, and higher organic carbon stocks (>48.8 t/ha). In contrast, central and southwestern regions recorded low SQI (0.15–0.35) due to compaction (bulk density up to 1.79 g/cm3) and fertility loss. Clay-rich soils (>490 g/kg) enhanced nutrient retention, whereas sandy soils (>320 g/kg) in the south increased leaching risks. Integration of LULC with soil quality confirms forest expansion as a driver of resilience, while agricultural intensification contributed to localized degradation. These findings emphasize the need for depth-specific soil management and integrated land-use planning to ensure food security and ecological sustainability.

This study evaluates soil contamination risks in Tolworth Court Farm, Kingston upon Thames, a semi-urban site undergoing transition for residential redevelopment. Composite random sampling was applied to six sections (A–F), and analyses included soil pH, organic matter (Walkley–Black), and heavy metals (ICP–AES). Results indicated moderately acidic soils (pH 4.56) with medium organic matter (5.02%). Cadmium concentrations averaged 1.13 mg/kg, exceeding the UK Soil Guideline Value (1.0 mg/kg), with Section A reaching 1.52 mg/kg. Cadmium was also found to be the most mobile metal (2.21%), suggesting potential risks of groundwater contamination and plant uptake. Lead, chromium, nickel, and zinc concentrations remained below regulatory thresholds but require continued monitoring. The study highlights the importance of integrating soil assessments into sustainable urban redevelopment frameworks, with targeted remediation measures such as pH adjustment, phytostabilization, and long-term monitoring to reduce exposure risks. The findings provide evidence-based guidance for policymakers and urban planners, ensuring environmental safety in post-industrial land transformation projects.

This study presents a spatially explicit, multidecadal analysis of how land use and land cover (LULC) change and socio-demographic dynamics have influenced land surface temperature (LST) patterns in the Phoenix metropolitan area between 2001 and 2021. Using Landsat-derived summer LST, socio-demographic indicators, and land cover data, we quantify urban land transformation and socio-demographic changes over two decades. To account for spatial heterogeneity, we apply Multiscale Geographically Weighted Regression (MGWR), which improves upon conventional regression models by allowing for variable-specific spatial scales. Results show that the 2001–2011 period was characterized by rapid suburban expansion and widespread conversion of croplands and open space to higher-intensity development, while 2011–2021 experienced more limited infill development. Correlation analysis reveals that agricultural and open space conversions were linked to population and housing growth, whereas redevelopment of existing urban areas was often associated with socio-demographic decline. MGWR results highlight that agricultural land conversion drives localized warming, while shrub/scrub-to-developed transitions are linked to broader-scale cooling. By combining spatial sampling, area-weighted interpolation, and MGWR, this study offers a fi-ne-grained assessment of urban thermal dynamics in a fast-growing desert region. The findings provide actionable insights for planners and policymakers working toward sustainable and climate-resilient urban development in arid environments.

This study explores the application of GIS and Remote Sensing in land use/land use change mapping of Akure, a humid rainforest zone of Nigeria using Landsat imagery from the years 2015, 2020, and 2025. Four land use classes: Vegetation, Developed Areas, Barren Land, and Outcrops were mapped using a supervised classification technique (Support Vector Machine). The results showed a noticeable increase in developed areas and a decline in forest areas (vegetation), pointing to urban expansion and increasing human influence on the environment. The use of GIS for spatial analysis and map production, alongside remote sensing for periodic observation, enabled a clear visualization of land cover changes across the years. Marked reduction in vegetation was noted during the study, with only 117.91 km² remaining unchanged, while losses to developed land were 14.57 km², and 42.15 km² land area was lost to barren land, while 21.93 km² was lost to outcrops. Barren land was either a source or a recipient of land transformation. Stable barren surfaces amounted to 14.68 km², but large inflows came in from vegetation (42.15 km²) and outcrops (6.18 km²). Land use/Land cover change from vegetation to other purposes poses severe ecological risks, including loss of biodiversity, reduced agricultural productivity, and diminished carbon sequestration capacity, which ultimately could result to increased food poverty and climate depletion. The accuracy for each classified map exceeded 85%, with Kappa coefficients greater than 0.80, which is more than the threshold for land cover change studies.

Abstract Amid a global trend of intensifying climate extremes, 2023 marked Earth’s hottest year on record that triggered widespread disruptions. As global warming continues, understanding the compound events (CEs) characterized by dry hazards is crucial especially in Asia, where dense populations heighten vulnerability. This study examines the intricate characteristics of 2023 compound heatwave, drought, and fire (CHDF) events in Asia by analyzing spatial and temporal patterns from 1960 to 2023 during the extended warm season (April–October). Results reveal that the 2023 CHDF notably diverged from historical trends, with heatwave-driven CEs exhibiting a four- to eightfold increase in spatial extent since 1980. The severity and duration of 2023 HDF reached return periods of approximately 23 and 40 years, respectively. In particular, urban cores in East and Southeast Asia emerged as hotspots for CHDF events, pointing to the influence of land transformation. Attribution analysis further highlights the dominant role of human-induced climate change in amplifying the likelihood of 2023-like CEs, with DF responding more modestly than heatwave-driven CEs. Projections under SSP245 suggest that 2023-level CHDF events may stabilize by the 2060s with mitigation, while SSP585 indicates continued escalation and earlier emergence (≈1–5 years). Findings from this study emphasize the growing risk of compound dry hazards and the pressing need for targeted mitigation efforts and adaptive strategies to reduce Asia’s vulnerability to climate extremes.

ABSTRACTRice field intensification in recent decades has negatively impacted wetland species, partly due to the shortened hydroperiods caused by effective water drainage during mid‐summer, harvesting, and fallow seasons, which increase mortality risks for amphibian larvae. To investigate potentially adaptive responses to these altered conditions, we tested whether Glandirana rugosa, a species with long tadpole stages, accelerates metamorphosis in rice fields to avoid desiccation. We surveyed larval growth and adult breeding phenology across contrasting habitats and conducted a series of rearing experiments to identify factors determining the duration of the larval period. We found G. rugosa larvae often metamorphose before winter in certain rice fields and in a river, whereas larval overwintering commonly occurred in other sites. Frequent larval overwintering was associated with low water temperatures rather than permanent water bodies. In the experiments, larvae metamorphosed earlier at higher temperatures, while population origin, water level, photoperiod, and sex had no clear effects on the larval period. Because G. rugosa larvae possess traits associated with permanent water bodies (e.g., long larval period and no response to reduced water level), the observed developmental plasticity may not be a direct adaptation to temporal pools. Rather, it likely represents a coincidental adaptive expression of thermal reaction norms in rice fields with harvesting drainage. However, their development rate is not fast enough to complete metamorphosis before mid‐summer drainage, possibly precluding their occurrence in many typical rice fields. This study highlights larval life‐history plasticity as an understudied mechanism of population persistence during the nationwide land transformation in Japan.

This article analyzes the model of the production of peri-urban space in Fouchana, located in the south of Greater Tunis, by exploring the complexity of the land production system, which integrates various mechanisms of land access and use. To understand this complexity, a systemic approach was adopted, making it possible to take into account the interdependencies between the physical, political, social and economic dimensions of peri-urban land production. In this context, the method of structural equations, in particular the PLS (Partial Least Squares) method, proves to be an appropriate statistical tool. The aim of this method is to examine the existence of causal links between different variables, and to verify the relevance and coherence of a theoretical model. In our case, the latter examines the relationships between the various factors involved in the transformation of land resources dedicated to housing, in particular with regard to the organization of formal and informal land practices. The results of the study reveal how the processes of land transformation, both formal and informal, influence the social and spatial dynamics of Fouchana. Moreover, they disclose the essential role played by land production in mediating between land mechanisms and urban dynamics, thus revealing the challenges associated with rapid urbanization and growing demographic pressure.

Sustainable urban development requires actionable insights into the thermal consequences of land transformation. This study examines the impact of land use and land cover (LULC) changes on land surface temperature (LST) in Ho Chi Minh city, Vietnam, between 1998 and 2024. Using Google Earth Engine (GEE), three machine learning algorithms-random forest (RF), support vector machine (SVM), and classification and regression tree (CART)-were applied for LULC classification. RF delivered the best performance, achieving an average overall accuracy of 93.09% and a kappa coefficient of 0.916. The findings show that built-up areas expanded by over 12%, primarily at the expense of agricultural lands, while mean LST increased by approximately 1.75°C during the study period. Urban zones exhibited the highest LST values due to the urban heat island (UHI) effect, whereas vegetated and water-covered areas showed strong cooling functions. A positive correlation was identified between LST and built-up areas, while vegetation and water indices were negatively correlated with LST. The study integrates LULC classification, LST retrieval, and biophysical indices to capture urban heat dynamics in a tropical megacity context. The results highlight the importance of green-blue infrastructure in mitigating urban warming and provide valuable guidance for policymakers seeking to enhance urban climate resilience through sustainable land management.

Abstract Cropland fragmentation (CLF) studies often overlook bidirectional land transformations. This study reassesses county-level CLF in China (1990–2023) using a 30 m-resolution Landsat-based dataset and a novel Modified Landscape Division Index to capture both cropland gains (transfer-in) and losses (transfer-out). We find pronounced CLF increases in urbanized plains, notably the Huang–Huai–Hai Plain (6.13 × 10−2 y), Northeast China Plain (3.69 × 10−2 y), and Sichuan Basin (4.97 × 10−2 y), driven primarily by urbanization (39.5% contribution to CLF from impervious surfaces). Conversely, cropland gains from forests and grasslands mitigate fragmentation, with forest-to-cropland conversion reducing CLF by 40.3%. Counties with CLF increases due to losses outnumber those with decreases by 5.5 times. Regression analysis reveals strong correlations between area changes and CLF (p < 0.01). These insights are relevant for urbanizing regions globally, such as Southeast Asia and Sub-Saharan Africa, highlighting the need for policies like zoning regulations and reforestation incentives to balance urban expansion with cropland restoration for sustainable land use and food security.

The communal land ownership system of the Minangkabau indigenous legal community based on the matrilineal system faces fundamental misalignment with Indonesia's national land law regulations. This research analyzes the characteristics of ulayat land ownership system based on Minangkabau customary law and its misalignment with UUPA. The research method employs a normative juridical approach with legislative analysis and literature study. The results show that Minangkabau ulayat land ownership system has unique characteristics in the form of hereditary communal ownership through maternal lineage with separation of rights and ownership concepts in the philosophy “hak bamiliek harato bapunyo, hak nan banampuharapo nan bamiliek”. The three-tiered hierarchical structure of nagari, tribal, and clan ulayat lands is managed based on the principle of “gangam bauntuak pagang bamasiang” which does not recognize the concept of buying and selling. Misalignment occurs because UUPA adopts an individual ownership paradigm that contradicts the Minangkabau communal system. Regulation through PP 18 of 2021 which enables the transformation of ulayat land into management rights actually creates a legal certainty paradox and potentially destroys the communal ownership system. The fragmentation of ulayat land to only 8.38% remaining intact reflects the failure of legal harmonization. A reconstruction of the national agrarian law paradigm is needed that can accommodate communal ownership characteristics without eliminating the essence of indigenous cultural values.

Achieving water sustainability in many water-scarce regions will require reducing consumptive water use by converting irrigated agricultural land to less water intensive uses. Conventional approaches to this challenge that emphasize water conservation as a singular objective often promote ad hoc practices that temporarily leave land idle while missing an opportunity to enhance landscape resilience and harness synergies of managing water and land together. Multibenefit land repurposing offers an alternative solution to this challenge by strategically transitioning irrigated agricultural land to other beneficial uses that consume less water and provide benefits for multiple constituencies. In practice, multibenefit land repurposing involves the process of collaboration among different groups (e.g., growers and community members) and the outcome of converting irrigated agriculture to other multibenefit uses (e.g., groundwater recharge basins with habitat and water quality benefits). It integrates into a single framework the joint objectives of conserving water, creating benefits for society and the environment, and stimulating the growth of participatory governance networks. But the pathways through which multibenefit land repurposing can achieve these objectives have yet to be demonstrated, either empirically or conceptually. To this end, we illustrate conceptually how multibenefit land repurposing can be designed to enhance water security and enable a transition to more resilient landscapes, building a theory of change around three key elements: (i) creating multibenefit outcomes, (ii) improving strategic regional coordination, and (iii) shifting underlying institutional conditions to promote innovation, adaptation, and cooperation. We draw from experience with the ongoing Multibenefit Land Repurposing Program (MLRP) in California, which has brought together over 100 different organizations in support of eight regional teams to work collectively on coordinated land transformation. We use examples from MLRP to illustrate key components and challenges of the theory of change, including how multibenefit land repurposing may be implemented in practice. Despite being a relatively new approach, we argue that multibenefit land repurposing offers a pathway to building resilient landscapes, including in regions with historically severe and inequitable depletion of water resources.

Sylhet, located in the northeastern part of Bangladesh, is characterized by a unique topography and climatic conditions that make it susceptible to flash floods. The interplay of rapid urbanization and climatic variability has exacerbated these flood risks in recent years. Effective monitoring and planning of land use/land cover (LULC) are crucial strategies for mitigating these hazards. While former studies analyzed LULC in parts of Sylhet using traditional GIS approaches, no comprehensive, district-wide assessment has been carried out using long-term satellite data and cloud computing platforms. This study addresses that gap by applying Google Earth Engine (GEE) for an extensive analysis of LULC changes, transitions, and hot/cold spots across the district. Accordingly, this work investigates the LULC changes in Sylhet district over the past twenty-three years (2000–2023). Using satellite imagery from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI), the LULC is classified in six selected years (2000, 2005, 2010, 2015, 2020, and 2023). A supervised machine learning algorithm, the Random Forest Classifier, is employed on the cloud computing platform Google Earth Engine to analyze LULC dynamics and detect changes. The Getis-Ord Gi* statistical model is applied to identify land transformation hot spot and cold spot areas. The results reveal a significant increase in built-up areas and a corresponding reduction in water bodies. Spatial analysis at the upazila level indicates urban expansion in every upazila, with the most substantial increase observed in Beani Bazar upazila, where urban areas expanded by approximately 1500%. Conversely, Bishwanath upazila experienced the greatest reduction in water bodies, with a decrease of about 90%. Sylhet Sadar upazila showed a 240% increase in urban areas and a 72% decrease in water bodies. According to hotspot analysis, Kanaighat upazila has the most amount of unchanging land at 7%, whereas Balaganj upazila has the largest amount of LULC transformation at 5.5%. Overall, the urban area in the Sylhet district has grown by approximately 300%, while water bodies have diminished by about 77%, reflecting trends of urbanization and river-filling. These findings underscore the necessity of ensuring adequate drainage infrastructure to decrease flash flood hazards in the Sylhet district and offer insightful information to relevant authorities, politicians, and water resource engineers.

Amid accelerating climate change, climate-related hazards—such as floods, wildfires, hurricanes, and sea-level rise—increasingly drive land transformations and pose growing risks to housing markets by affecting property valuations, insurance availability, mortgage performance, and broader financial stability. This review synthesizes recent progress in two distinct domains and their linkage: (1) assessing climate-related financial risks in housing markets, and (2) applying AI-driven remote sensing for hazard detection and land transformation monitoring. While both areas have advanced significantly, important limitations remain. Existing housing finance studies often rely on static models and coarse spatial data, lacking integration with real-time environmental information, thereby reducing their predictive power and policy relevance. In parallel, remote sensing studies using AI primarily focus on detecting physical hazards and land surface changes, yet rarely connect these spatial transformations to financial outcomes. To address these gaps, this review proposes an integrative framework that combines AI-enhanced remote sensing technologies with financial econometric modeling to improve the accuracy, timeliness, and policy relevance of climate-related risk assessment in housing markets. By bridging environmental hazard data—including land-based indicators of exposure and damage—with financial indicators, the framework enables more granular, dynamic, and equitable assessments than conventional approaches. Nonetheless, its implementation faces technical and institutional barriers, including spatial and temporal mismatches between datasets, fragmented regulatory and behavioral inputs, and the limitations of current single-task AI models, which often lack transparency. Overcoming these challenges will require innovation in AI modeling, improved data-sharing infrastructures, and stronger cross-disciplinary collaboration.

This study describes the results of research work on the long-term analysis of land cover changes caused by mining activities in the Strzelin quarries between 1884 and 2023. The primary aim of this study was to analyse long-term landscape changes resulting from granite and gneiss extraction, using a land cover classification adapted to mining conditions. To achieve this, a methodological framework was developed that combines historical cartographic sources with geospatial data, enabling a reconstruction of post-mining land transformations. The framework incorporated rarely utilized historical maps such as 19th-century Messtischblatt maps, with spatial datasets including orthophotos and geospatial databases. The methodology is based on georeferencing and manual vectorisation of open-access data, followed by classification and topological validation to ensure spatial consistency across the time series. The results of the study indicate significant changes in the landscape, in particular an almost threefold increase in the area of mining sites between 2004 and 2023, as well as the appearance of spontaneous vegetation succession and formation of water bodies in areas where mines have been liquidated. The study also documents parallel urbanisation trends and infrastructure development, highlighting the complex interaction between industrial activity and socio-environmental processes. This work contributes to research on land use and land cover (LULC) by extending the time frame of the analysis beyond the limitations of satellite era data and filling a gap in the literature on land use changes caused by mining activities. The proposed approach is transferable to small and medium-sized areas and supports evidence-based spatial planning, land reclamation, and heritage preservation in post-industrial environments on the basis of actions taken in Strzelin and Mikoszów.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-15881-1.

Soil is a vital non-renewable resource and the main basis of agriculture. However, urbanization and growing non-agricultural demands are steadily reducing agricultural lands. This situation brings with it many environmental problems, especially in cities, and leads to deterioration of air quality at micro and macro scales. One of the important functions of agricultural soils is to store large amounts of carbon through organic matter accumulation. Agricultural areas help improve air quality by limiting wind-borne dust and particulates. The study area was agricultural land prior to 2009. However, half of the study area was opened to construction and currently provides mass housing services. Within the scope of the research, pollutant (PM2.5, PM10, CO, CO2) measurements were made in both study areas, and the obtained data were compared, and the effect of agricultural areas on micro air quality was examined. According to the obtained results, it was observed that carbon monoxide (CO) and carbon dioxide (CO₂) levels in agricultural areas were lower compared to constructed areas. CO levels were measured between 1.09 µg/m³ and 2.87 µg/m³ in agricultural areas and between 2.09 µg/m³ and 2.98 µg/m³ in built-up areas. Similarly, thanks to agricultural vegetation, CO₂ levels remained between 251.47 µg/m³ and 429.97 µg/m³ in agricultural areas and between 411.75 µg/m³ and 520.59 µg/m³ in built-up areas. These findings indicate that agricultural lands enhance carbon absorption, reduce particulate matter, and are vital for sustainable urbanization.

Abstract Purpose Food systems are the largest single contributor to the current biodiversity crisis, and coffee has been recognised as having high biodiversity impacts. Agroforestry is an alternative coffee production system that may have lower impacts on biodiversity than conventional monoculture coffee production. In this study, we compared the biodiversity impacts of agroforestry and conventional coffee production using life cycle assessment (LCA). Methods For this purpose, LCA models for coffee from a conventional production system and agroforestry system in Cauca Valley montane forests ecoregion of Colombia were built based on the literature data. Their biodiversity impacts were calculated as a global potentially disappeared fraction of species (PDF) per year using 1 kg of parchment coffee as the functional unit. We applied the Global Guidance for Life Cycle Assessment (GLAM) method to calculate the land occupation impacts in coffee cultivation stage, and LC-IMPACT was used for all other impacts. Sensitivity analyses were conducted for key uncertainties in the present study. Results and discussion The biodiversity impacts of conventional coffee production were 1.15E-12 PDF•y kg−1 on terrestrial ecosystems (TE), 2.18E-14 PDF•y kg−1 on freshwater ecosystems (FE) and 4.85E-17 PDF•y kg−1 on marine ecosystems (ME). The biodiversity impacts of coffee agroforestry on TE, FE and ME were 6.66E-13 PDF•y kg−1 (42% smaller), 1.42E-14 PDF•y kg−1 (35% smaller) and 3.30E-17 PDF•y kg−1 (32% smaller), respectively. Agroforestry coffee had smaller impacts in every impact category. However, our sensitivity analyses show that different assumptions can affect the size of biodiversity impacts although no changes in conclusion were found. Nonetheless, our results are based on one coffee-producing area, and large fluctuations, e.g. considering yields, could affect the results and conclusions for different regions and agroforestry coffee production overall. Conclusions and recommendations This study shows that existing life cycle impact assessment (LCIA) methods can differentiate between conventional production and agroforestry in terms of land use intensity levels and in impacts related to agricultural inputs, such as fertilisers. However, these comparisons would benefit from more detailed agroforestry-specific land stress characterisation factors. We also recommend the inclusion of land transformation impacts of the coffee cultivation stage in future calculations and the case-specific allocation of agroforestry coffee impacts to multiple products from the agroforestry system to better consider the multifunctionality of agroforestry. Lastly, we recommend future research to compare conventional and agroforestry coffee produced in several other regions to assess whether agroforestry coffee has generally lower biodiversity impacts compared to conventional coffee.

ABSTRACT This study examines the spatio-temporal dynamics of land use and land cover (LULC) changes in the Hunza Valley, Northern Pakistan, from 2000 to 2022, focusing on anthropogenic impacts and climate-induced environmental transformations. Using a time series of Landsat imagery and Random Forest classification, seven LULC categories were analyzed: built-up, bare land, grass land, cultivated land, water body, snow/ice, and forests. The results reveal substantial changes, including a marked expansion in built-up and cultivated land, alongside significant reductions in forest cover, grassl and, and snow/ice extent. Corresponding changes in LULC indices Normal Difference Vegetation Index (NDVI), Normal Difference Built up Index (NDBI), Normal Difference Water Index (NDWI), and Normal Difference Snow Index (NDSI) reflect declining vegetation health, increasing impervious surfaces, and diminishing snow and water presence. Land Surface Temperature (LST) rose from 37.21°C in 2000 to 41.02°C in 2022, with regression analyses showing strong correlations between LST and both natural and human-induced land transformations. Zonal statistics confirm that all land cover types experienced notable warming. The findings underscore the urgent need for integrated land management strategies, including regulating urban expansion through land-use planning, promoting reforestation, and conserving high-altitude snow zones. Integrating LULC monitoring into regional governance is essential for mitigating environmental degradation and enhancing the resilience of mountain ecosystems.

The dragonflies (Insecta: Odonata) of Costa Rica are well-described, providing a solid foundation for studies in community ecology. We described the effects of stream size and habitat matrix (forest vs. pasture/development) on several indices of odonate community structure: abundance, species richness, diversity, and composition. We sampled adults in eight 100 × 5 m plots on six waterways near La Fortuna, Alajuela Province, Costa Rica. Four plots were on waterways in secondary forest: single plots were on large rivers (Río Caño Negro and Río Caliente), and two plots were on a small unnamed stream. Four plots were on smaller waterways in agricultural areas: two on the Río Chachagua and single plots on the Río Chachagüita and a small creek. Plots were surveyed seven times from January–March 2023. The composition of the communities exploiting rivers, the forested stream, and agricultural streams were significantly different using NMDS and PERMANOVA. Plots on the forested stream had significantly fewer individuals, fewer species, and lower diversity than the waterways in agricultural areas, and large rivers were intermediate (GLM and Tukey post-hoc mean comparison tests). Communities exhibited significant nested-subset structure (NODF), with species in forested stream plots nested within river communities that were nested within communities in agricultural areas. These patterns are not explained by differences in mean light levels, which were greatest in rivers. This suggests that evaluating the effect of anthropogenic riparian canopy removal during land transformations may be complicated by initial differences in community composition related to stream/river size.