Land Change Research Articles

Environmental modeling of impacts of agricultural land changes using Markov chain and machine learning (case study: Shanghai metropolis, China)

Environmental modeling of impacts of agricultural land changes using Markov chain and machine learning (case study: Shanghai metropolis, China)

Potential ecological risk assessment based on loss of ecosystem services due to land use and land cover change: A case study of Beijing-Tianjin-Hebei region

Ecosystem services (ESs) bridge the gap between human wellbeing and ecosystem processes. Land use and land cover change (LUCC) affect ecosystem services by altering the structure and function of ecosystems. Beijing-Tianjin-Hebei (BTH), one of the most developed urban agglomerations in China, is subject to intense LUCC that affects ESs while increasing the regional ecological risk (ER). Therefore, to achieve the coordinated development of socioeconomic and ecological quality, there is an urgent need to establish a regional ecological risk assessment (ERA). ESs were introduced into the ERA to clarify their ecological significance and to compensate for the deficiencies of the traditional ERA. In this study, the ERA utilizes key ESs as endpoints to assess potential ER through a new framework by multiplying the probability of LUCC by the resultant loss of key ESs. We found that cultivated land was the most dominant outflow type and built-up land was the predominant inflow type. From 2020 to 2030, there is a greater possibility of changes in cultivated land, forest, grassland, and built-up land as well as in relatively stable water bodies and unused land. Overall, ESs show a positive trend. The overall potential ER was relatively minimal, with a significant northwestern spatially concentrated characteristic of high values. This study helps understand the potential development process of ERs and provides a scientific basis for risk management.

Multi-Scenario Ecological Network Conservation Planning Based on Climate and Land Changes: A Multi-Species Study in the Southeast Qinghai–Tibet Plateau

The Qinghai–Tibet Plateau ecosystem is fragile, experiencing rapid changes in land cover driven by both climate change and human activities, leading to habitat fragmentation and loss and resulting in biodiversity decline. Habitat ecological networks (HA-ENs) are considered effective solutions for habitat connectivity and biodiversity conservation in response to these dual drivers. However, HA-EN studies typically rely on current or historical landscape data, which hinders the formulation of future conservation strategies. This study proposes three future scenarios—improvement, deterioration, and baseline scenarios—focused on the southeastern Qinghai–Tibet Plateau (SE-QPT). The habitats of 10 species across three classes are extracted, integrating land use and climate change data into habitat ecological network modeling to assess the long-term dynamics of HA-ENs in the SE-QPT. Finally, conservation management strategies are proposed based on regional heterogeneity. The results show the following: Climate change and human activities are expected to reduce the suitable habitat area for species, intensifying resource competition among multiple species. By 2030, under all scenarios, the forest structure will become more fragmented, and grassland degradation will be primarily concentrated in the southeastern and western parts of the study area. Compared to 1985 (71,891.3 km2), the habitat area by 2030 is projected to decrease by 12.9% (62,629.3 km2). The overlap rate of species habitats increases from 25.4% in 1985 to 30.9% by 2030. Compared to the HA-EN control in 1985, all scenarios show a decrease in connectivity and complexity, with only the improvement scenario showing some signs of recovery towards the control network, albeit limited. Finally, based on regional heterogeneity, a conservation management strategy of “two points, two cores, two corridors, and two regions” is proposed. This strategy aims to provide a framework for future conservation efforts in response to climate change and human activities.

A framework for natural resource management with geospatial machine learning: a case study of the 2021 Almora forest fires

BackgroundWildfires have a substantial impact on air quality and ecosystems by releasing greenhouse gases (GHGs), trace gases, and aerosols into the atmosphere. These wildfires produce both light-absorbing and merely scattering aerosols that can act as cloud condensation nuclei, altering cloud reflectivity, cloud lifetime, and precipitation frequency. Uttarakhand province in India experiences frequent wildfires that affect its protected ecosystems. Thus, a natural resource management system is needed in this region to assess the impact of wildfire hazards on land and atmosphere. We conducted an analysis of a severe fire event that occurred between January and April 2021 in the Kumaun region of Uttarakhand, by utilizing open-source geospatial data. Near-real-time satellite observations of pre- and post-fire conditions within the study area were used to detect changes in land and atmosphere. Supervised machine learning algorithm was also implemented to estimate burned above ground biomass (AGB) to monitor biomass stock.ResultsThe study found that 21.75% of the total burned area burned with moderate to high severity, resulting in a decreased Soil Adjusted Vegetation Index value (> 0.3), a reduced Normalized Differential Moisture Index value (> 0.4), and a lowered Normalized Differential Vegetation Index (> 0.5). The AGB estimate demonstrated a significant simple determination (r2 = 0.001702) and probability (P < 2.2 10−16), along with a positive correlation (r ≤ 0.24) with vegetation and soil indices. The algorithm predicted that 17.56 tonnes of biomass per hectare burned in the Kumaun forests. This fire incident resulted in increased emissions of carbon dioxide (CO2; ~ 0.8 10−4 kg carbon h−1), methane (CH4; ~ 200 10−9 mol fraction in dry air), carbon monoxide (CO; 2000 1015 molecules cm−2 total column), and formaldehyde (HCHO; 3500 1013 molecules cm−2 total column), along with increased aerosol optical thickness (varying from 0.2 to 0.5).ConclusionsWe believe that our proposed operational framework for managing natural resources and assessing the impact of natural hazards can be used to efficiently monitor near-real-time forest-fire-caused changes in land and atmosphere. This method makes use of openly accessible geospatial data that can be employed for several objectives, including monitoring carbon stocks, greenhouse gas emissions, criterion air pollution, and radiative forcing of the climate, among many others. Our proposed framework will assist policymakers and the scientific community in mitigating climate change problems and in developing adaptation policies.

Land use land cover change in the African Great Lakes Region: a spatial–temporal analysis and future predictions

The African Great Lakes Region has experienced substantial land use land cover change (LULCC) over the last decades, driven by a complex interplay of various factors. However, a comprehensive analysis exploring the relationships between LULCC, and its explanatory variables remains unexplored. This study focused on the Lake Kivu catchment in Rwanda, analysing LULCC from 1990 to 2020, identifying major variables, and predicting future LULC scenarios under different development trajectories. Image classification was conducted in Google Earth Engine using random forest classifier, by incorporating seasonal composites Landsat images, spectral indices, and topographic features, to enhance discrimination and capture seasonal variations. The results demonstrated an overall accuracy exceeding 83%. Historical analysis revealed significant changes, including forest loss (26.6 to 18.7%) and agricultural land expansion (27.7 to 43%) in the 1990–2000 decade, attributed to political conflicts and population movements. Forest recovery (24.8% by 2020) was observed in subsequent decades, driven by Rwanda’s sustainable development initiatives. A Multi-Layer Perceptron neural network from Land Change Modeler predicted distinct 2030 and 2050 LULC scenarios based on natural, socio-economic variables, and historical transitions. Analysis of explanatory variables highlighted the significant role of proximity to urban centers, population density, and terrain in LULCC. Predictions indicate distinct trajectories influenced by demographic and socio-economic trends. The study recommends adopting the Green Growth Economy scenario aligned with ongoing conservation measures. The findings contribute to identifying opportunities for land restoration and conservation efforts, promoting the preservation of Lake Kivu catchment’s ecological integrity, in alignment with national and global goals.Graphical

Late-glacial and Holocene shifts in the mountain landscapes of the Cantabrian range (northern Spain) in response to changing climate, fire occurrence and land use

A new well-dated (18 14C dates on short-lived terrestrial plant macrofossils, 210Pb and 137Cs dating) fine-resolution palaeoecological sequence (pollen, coprophilous fungi, conifer stomata, macrofossils, charcoal) from Lago de Isoba (1400 m a.s.l.) spanning the past c. 17,000 years provides novel insights into the long-term dynamics of the high-elevation Cantabrian landscape in response to climate variability and human activities. Vegetation changes were mainly climate-driven during the Late-glacial and the Early Holocene. Thus, steppe-like environments prevailed during the Greenland Stadial 2.1a (GS-2.1a ≈ ‘Oldest Dryas’). Warmer and moister conditions during the Greenland Interstadial 1 (GI-1 ≈ ’Bølling-Allerød’) enabled successive expansions of Juniperus, Betula and Pinus. Steppe-like vegetation re-expanded during the cooler and drier Greenland Stadial 1 (GS-1 ≈ ‘Younger Dryas’). Afterwards, the Early Holocene warming triggered the rapid spread of mixed pine-broad-leaved deciduous mountain forests. Pollen, stomata and macrofossils indicate continuous, long-lasting (c. 10,850–1250 cal yr BP) local occurrence of Pinus sylvestris stands. The records of Cerealia and Plantago lanceolata pollen types (first Holocene occurrences dated at c. 5100 cal yr BP) evidence increased land use during the Late Holocene, which triggered substantial changes in forest ecosystems. Following a phase of intensified arable and pastoral farming, which involved the use of fire to create and maintain forest clearings, pinewoods expanded and reached their maximum development between c. 3600 and 3300 cal yr BP. Pinus probably outcompeted temporarily other woody taxa thanks to its resilience to low-intensity disturbances. Later, pinewoods underwent a stepwise, human-induced decline ending in their final massive collapse after c. 1150 cal yr BP. The highest intensity of agricultural and pastoral farming took place during the Middle Ages, following maximum fire occurrence. We conclude that the present-day open landscape has been shaped by millennia of land use that partly blurred the impact of climate variability.

How Plans Prepare for Future Uncertainty: Integrating Land Change Modeling and the Plan Integration for Resilience Scorecard™

How Plans Prepare for Future Uncertainty: Integrating Land Change Modeling and the Plan Integration for Resilience Scorecard™

Riverscape genomics of the endangered freshwater mussel Lampsilis rafinesqueana

Abstract Historical periods of rapid climate change and associated direct and indirect impacts such as glaciation and restructuring of drainage systems are important in shaping biodiversity, lineage divergence, and contemporary genetic variation amongst many aquatic taxa. Through an understanding of the impacts of these ancient changes in land‐ and waterscapes on current diversity, practitioners can account for a species' unique evolutionary history when developing conservation strategies. We investigated intraspecific genetic diversity of the critically endangered freshwater mussel, Lampsilis rafinesqueana, endemic to the interior region of the U.S.A. Our study (1) characterises genetic variation within and amongst populations to infer biogeographic history and (2) applies genetic information to potential conservation strategies. We applied restriction‐site associated DNA sequencing and mitochondrial DNA sequencing to investigate genetic diversity within and amongst populations of L. rafinesqueana from across the species' distribution. We then considered this information in the context of historical riverscape processes and applicability to conservation. Variation within rivers was similar, but we found different genetic populations associated with two major ecoregions, the Central Interior Highlands and Osage Plains. The plains population displayed an Ne value approximately 10‐fold lower than the highlands population. Historical demographic modelling suggests the two populations diverged approximately 1.2 million years ago and then both populations underwent a substantial decrease in population size around 19,700 years ago. These two major events are each correlated with known periods of historic rapid climate change. We identified two distinct populations of a federally endangered mussel, L. rafinesqueana. Contemporary genetic diversity of this species was strongly influenced by direct and indirect impacts of rapid historic shifts in climate. The highlands population has undergone a recent expansion, typical of post‐Pleistocene climate change, while also having a much larger Ne value than the plains population. Our results suggest low levels of gene flow amongst the two distinct populations across generations. We recommend that conservation managers for this species take into account relative difference in diversity between populations when developing management strategies. In many species, assisted gene flow amongst genetically unique populations should only be considered in extreme circumstances; however, in L. rafinesqueana, low levels of assisted migration amongst populations may be a valuable tactic to maintain natural levels of genetic variation. Our study shows that biogeographical frameworks can be useful for developing conservation strategies that serve to retain important genomic variation that represents the evolutionary potential of target freshwater species.

Entomophilous Pollen and Bees as Biomonitors and Bioindicators of the Anthropogenic Triad

The progressive increase in the concentration of harmful pollutants in the environment threatens human health. To record them, researchers use biomonitors; plant pollen and honeybees (Apis mellifera) have proven themselves well in this capacity. The peculiarity of bees is that while searching for food, they collect pollutants from the environment: air, soil and water within a radius of no more than 3 km. Not only plant pollen, but also honeybees can act as bioindicators of the anthropogenic triad: climate change and traditional land use systems and environmental pollution. In various search engines, Internet users are interested in these issues, because their point of refraction is beekeeping products, including honey. It includes almost the entire spectrum of allergenic pollen, starting from wormwood pollen — the regional allergen of the republic and ending with pollen of trees and shrubs. The problem of allergies to ornamental plants is still poorly understood. Reports of occupational flower allergies are few and usually involve gardeners, greenhouse workers, landscapers, and florists. Handling, smelling and caring for flowers can cause rhino conjunctivitis, asthma, urticaria, and contact dermatitis. When studying the pollen spectrum in the air of cities, entomophilous pollen grains of the families Malvaceae, Fabaceae, Asteraceae, and Rosaceae were identified. In the modern world, the concept of “One Health” has gained popularity, which recognizes the interconnectedness of the health of people, animals and ecosystems, therefore it is accepted both at the national and global levels. Within its framework, we are forming a new concept: “Entomophilous pollen and bees as biomonitors and bioindicators of the anthropogenic triad: climate change and land use systems; environmental pollution”. The purpose of the presented article is to systematize the results obtained and literature sources within the framework of the formulated concept.

Gender vulnerability and coping strategies to changing provisioning ecosystem services in Bongo and Kassena-Nankana West districts in the Upper East Region of Ghana

ABSTRACT Ecosystems provide a wide variety of life-sustaining services but are currently facing severe degradation due to changing land use and cover. Women are presumed to be more affected by declines in provisioning ecosystem services (PES). These claims have not been empirically proven across all contexts and have been contested by recent studies. Few studies in Ghana have attempted to examine this gendered environmental change phenomenon. Underpinned by the vulnerability theory, this paper employed an eclectic methodological approach to examine gender differences in vulnerability and coping with changing PES in Ghana. The findings reveal that both males and females depend highly on agricultural and woodland ecosystems for their livelihood needs. The deterioration in the total areas of the major ecosystems has resulted in significant declines in vital PES such as wild edible fruits and vegetables, medicinal plants and building materials for both men and women over the past decade. The study further reveals that the decreases in fuelwood supply affect women more than men because the former group mostly cooks food in their households, using fuelwood. As adaptation mechanisms, male farmers mostly planted trees on their farms to reclaim unfertile land and conserve forests, while female farmers engage in fruits and vegetable trade in the markets, as well as used non-indigenous spices to enhance the taste of food. Both males and females have also resorted to relying on orthodox medicines contrary to the past. To increase ecosystem resilience, the mainstreaming of ecosystem integrity adaptation practices into national adaptation plans is paramount.

Ecohydrological and hydrogeological dynamics of groundwater springs in Eastern Himalaya, India

Groundwater springs are critical to achieving Sustainable Development Goals (SDG 6, access to clean water) in the Himalaya and remain highly vulnerable to climate change and land-use and land cover change. In a first from Eastern Himalaya, we analysed the relative controls of land-use, precipitation, soil properties, and hydrogeology on the diel and seasonal variability in three representative springs using high-frequency discharge monitoring. Kamrang spring is a high-discharge depression spring fed by a homogenous aquifer, whereas Mamley and Gaddi show dual-flow characteristics attributed to primary matrix-based flows and secondary conduit (karst) or unconsolidated storage-based flows, respectively. The first reports of strong diel fluctuations in springflows show significantly higher amplitude in the depression spring (22 ± 41 l min−1) than the fracture (15 ± 26 l min−1) and karst springs (12 ± 24 l min−1), attributed to evapotranspiration and hydrogeology, respectively. The forest spring (Gaddi, low soil hydraulic conductivity, Ksat) showed a faster response at intense precipitation (>30 mm h−1), whereas the agriculture springs (Kamrang and Mamley, high Ksat) showed the lowest lags at low-moderate intensities (<20 mm h−1). The depression spring showed high recharge potential, whereas the karst and fracture springs were constrained by their relatively smaller recharge area and low Ksat, respectively. The per capita daily water availability was barely sufficient to support the minimum (20 l) and mandated (55 l) requirements for 30–70% and 2–47% of days a year, respectively. Thus, future precipitation intensification and land-use change will disproportionately impact the >5th-order karst and fracture springs. The study provides an integrated analytical framework for understanding Himalayan springs, which are critical for achieving SDG 6 (access to clean water) and a baseline for developing appropriate springshed models for effective management of freshwater ecosystems (SDG 15) against future climate change impacts (SDG 13), as well as informing the water security assessment in the Himalaya.

Harnessing Machine Learning Algorithms to Model the Association between Land Use/Land Cover Change and Heatwave Dynamics for Enhanced Environmental Management

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km2. The increase in PM2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning.

Variability in land surface temperature concerning escalating urban development using thermal data of Landsat sensor: A case study of Lower Kharun Catchment, Chhattisgarh, India

Over the past few years, there has been a revitalized emphasis on comprehending the shifts in land cover and their implications for a range of environmental factors. This investigation seeks to analyze how changes in land surface temperatures (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and alterations in land cover intersect within the lower Kharun catchment area. The primary dataset utilized in this study is for 2001 and 2021 Landsat 7 and 8, part of the Landsat program managed by the United States Geological Survey (USGS), offer essential Earth observation data using their multispectral and thermal sensors which are designed to detect thermal radiation emitted from the Earth's surface. When these bands are properly processed, they enable accurate temperature measurements. Visual interpretation was conducted on these images, categorizing them into five specific classes of land cover these were vegetation, open land, settlement, waterbodies, and cultivation. Following this, spectral indices like NDVI and NDBI were calculated, and LST was derived using a single-channel algorithm. Subsequently, correlation analysis was utilized to explore the interconnectedness or mutual relationship among the spatial distribution of these parameters. Over the period from 2001 to 2021, the most significant changes in land use were observed in the settlement area and cultivation, which increased by 6.92 and 6.23 sq. km, respectively. Conversely, open land, vegetation, and waterbodies experienced decreases of 7.13, 5.56, and 0.46 sq. km, respectively. The patterns in which LST, NDBI, and NDVI are distributed, exhibited corresponding variations following changes in land cover. The observed alterations in LST, NDBI, and NDVI are believed to be primarily influenced by the expansion of built-up areas. A noticeable association suggests that as built-up areas increase, both NDBI and LST values typically rise.Furthermore, a correlation observed between LST with NDVI was negative, suggesting an inverse relationship between these parameters. On the other hand, the correlation of LST with NDBI observed was positive, indicating that these parameters exhibit a direct relationship. Overall, these findings seem to be complex and highlight the interactions between changing land cover and environmental parameters, underscoring the importance of understanding these relationships for effective land management and environmental monitoring.

Impact of land use/land cover change on surface water hydrology in Akaki river catchment, Awash basin, Ethiopia

Watershed reactions to environmental changes is critical for water resource use and management planning. The change in land use/land cover (LUCC) alters surface water hydrology and water balance, subsequently it affects the availability of water resources. This study examined Akaki River catchment surface hydrological responses to future land use scenarios. The future LULC simulated using Cellular Automata-Markov Chain (CA-Markov) model in IDRISI software for 2031, 2041, and 2051 under Business as Usual (BAU) and Forest Patch Protection (FPP) scenarios. Water yield (WY), sediment export (SE), and soil loss (SL) were simulated using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The findings indicate that under BAU, forest and settlement areas increased 42.64% and 31.47%, whereas water body, grass land, and agriculture dropped 31.99%, 25.72%, and 10.4%, respectively, from 2021 to 2051. The FPP scenario shows a 66.42% increase in forest coverage and a 31.97% decrease in cultivated land between 2021 and 2051. Overall settlement expansion will expand by 31.44%, while water body and grassland will decline by 11.12% and 24.9%. As a result of LULC changes in BAU scenario, the proportions of WY (4.36%), SE (26%), and SL (22.45%) increased. The hydrological components exhibited more gradual variations of 0.55%, 18.8%, and 14.24% for the WY, SE, and SL, respectively, under the FPP scenario. Therefore, the alteration has resulted in a concerning state within the area, necessitating the implementation of comprehensive management techniques to restore the hydrology of the catchment.

Perubahan Lahan Desa Bukian Payangan 6 Tahun Terakhir dengan Metode NDVI

Land changes in an area can be known by analyzing the density of vegetation, the density of vegetation in an area can affect human life and the environment. The purpose of this study is to find out the land changes in Bukian Village in the last ten years using NDVI. The method used in this study with the interpretation of Landsat 8-9 OLI images with NDVI was classified and adjusted through vegetation density. The results of this study where in the last 6 years there has been a reduction in rice fields, plantations and forests of 33.5 Ha and the addition of residential land of 28.2 Ha is due to the increase in population and activities of Bukian Village and the construction of tourism facilities.

Time-series China urban land use mapping (2016–2022): An approach for achieving spatial-consistency and semantic-transition rationality in temporal domain

The global urbanization trend is geographically manifested through city expansion and the renewal of internal urban structures and functions. Time-series urban land use (ULU) maps are vital for capturing dynamic land changes in the urbanization process, giving valuable insights into urban development and its environmental consequences. Recent studies have mapped ULU in some cities with a unified model, but ignored the regional differences among cities; and they generated ULU maps year by year, but ignored temporal correlations between years; thus, they could be weak in large-scale and long time-series ULU monitoring. Accordingly, we introduce an temporal-spatial-semantic collaborative (TSS) mapping framework to generating accurate ULU maps with considering regional differences and temporal correlations. Firstly, to support model training, a large-scale ULU sample dataset based on OpenStreetMap (OSM) and Sentinel-2 imagery is automatically constructed, providing a total number of 56,412 samples with a size of 512 × 512 which are divided into six sub-regions in China and used for training different classification models. Then, an urban land use mapping network (ULUNet) is proposed to recognize ULU. This model utilizes a primary and an auxiliary encoder to process noisy OSM samples and can enhance the model's robustness under noisy labels. Finally, taking the temporal correlations of ULU into consideration, the recognized ULU are optimized, whose boundaries are unified by a time-series co-segmentation, and whose categories are modified by a knowledge-data driven method. To verify the effectiveness of the proposed method, we consider all urban areas in China (254,566 km2), and produce a time-series China urban land use dataset (CULU) at a 10-m resolution, spanning from 2016 to 2022, with an overall accuracy of CULU is 82.42%. Through comparison, it can be found that CULU outperforms existing datasets such as EULUC-China and UFZ-31cities in data accuracies, spatial boundaries consistencies and land use transitions logicality. The results indicate that the proposed method and generated dataset can play important roles in land use change monitoring, ecological-environmental evolution analysis, and also sustainable city development.

Fire is associated with forest degradation and economic land concessions, but not land conversion in the rapidly transforming Cambodian landscape

Abstract Cambodia is beset by high deforestation rates and fire frequencies. From 2001–2020, forest cover declined by 34%, and cropland and rubber expanded by 31,401 km2 and 4,530 km2, respectively; meanwhile nearly 8.7 million fires burned 41.6% of the land. Fires may be associated with deliberate land change, stable land management systems, and unplanned forest degradation. We integrated annual land cover and monthly burned area data from 2001–2020 to test three hypotheses. First, if fire is used during land conversion, there would be a detectable pulse in fire activity in the year immediately prior to, and the year of land cover change. Our temporal analysis found statistically elevated burn rates in five of 16 transition tests, however, they were typically &lt;1% outside the predicted range, indicating no practical significance in the association of fire with land cover change. Second, fire prevalence would be lower in land managed for conservation or agricultural production than unclassified or unmanaged land. persistent forest and shrubland burned at significantly higher rates than expected, cropland burned significantly less, burning was almost absent from rubber plantations, and forests in protected areas (PAs) exhibited lower total burn percentages than unclassified land or industrial economic land concessions (ELCs), thus supporting the fire-land management hypothesis. However, total burned area in ELCs was greater than unclassified land for several persistent land cover classes, suggesting that local landowners may prevent fire in commercially valuable private agricultural land, but there may be no such prevention measures in industrial ELCs. Anti-fire policy enforcement in ELCs should be enhanced. Third, forests that converted into (degraded) shrublands would experience increasing fire frequency in the years leading up to transition, a hypothesis we found strong support for through our temporal analysis. Cambodia’s 2013 Law on Fire Prevention and Fire Fighting deputizes the public to enforce its provision, however given that the vast majority of fires occur in persistent forest in state-owned land, collective action problems are likely to hinder its effective implementation.

Impact of Land use dynamics on the water yields in the Gorgan river basin

This research investigates the future dynamics of water yield services in the Gorgan River Basin in the North of Iran by analyzing land cover changes from 1990 to 2020, using Landsat images and predicting up to 2040 with the Land Change Modeler and InVEST model under three scenarios: continuation, conservation, and mitigation. The results indicate significant shifts in agricultural land impacted water yields, which fluctuated from 324.7 million cubic meters (MCM) in 1990 to 279.7 MCM in 2010, before rising to 320.1 MCM by 2020. The study uniquely assesses the effects of land use changes on water yields, projecting a 13.6 % increase in water yield by 2040 under the continuation scenario, a 3.9 % increase under conservation, and a 1.6 % decrease under mitigation, which limits changes on steep slopes to prevent soil erosion and floods. This underscores the interplay between land use, vegetation cover, and water yield, emphasizing strategic land management for water resource preservation and effective watershed management in the GRB.

Spatiotemporal dynamics of ecosystem supply service intensity in China: Patterns, drivers, and implications for sustainable development

With the steady development of global economy and the rapid increase of population, it is of great significance to quantify the supply capacity of ecosystem services and reveal its driving factors for sustainable development. We quantify the ecosystem supply service intensity (ESSI) using multiple sources of natural and cultural data from 2000 to 2020. We then jointly analyze this data with the information entropy of the land to obtain the temporal and spatial evolution law of ESSI under multiple scales in China. At the same time, according to the spatial distribution of ESSI in China, the concept of China's ecosystem supply service intensity development equilibrium line (ESSIL) is innovatively put forward. The results show that the spatial distribution pattern of China's ESSI is symmetrical with the ESSIL which is nearly orthogonal to Hu Huanyong line. Because of the different regional development policies, different regions with different economic levels have different driving effects on land change. Furthermore, due to the country's large size, the primary ESSI drivers vary greatly throughout its various regions. The assessment of the ESSI changes in China from multi-scale, combined with the effects of land cover change, climate and human activities, and put forward a new pattern distribution mode of ESSI in China, which provides a new perspective for formulating ecologically sustainable development strategies in large-scale areas.

From Expansion to Shrinkage: An Assessment of the Carbon Effect from Spatial Reconfiguration of Rural Human Settlements in the Wuhan Metropolitan Area

Nowadays, the reorganization of rural land-use space exhibits a dynamic process of expansion and shrinkage. Taking the Wuhan Metropolitan Area as an example, this study used the InVEST model to quantitatively assess changes in rural built-up land between 1995 and 2020 and its impact on regional carbon storage. Combined with the PLUS model, further simulations were carried out to predict the heterogeneous mechanisms of shrinkage and expansion of rural habitable space under three scenarios in 2030. The results indicate that the area of rural built-up land in the Wuhan Metropolitan Area showed an overall increasing trend, with shrinkage mainly concentrated in the Wuhan-Ezhou border, Tianmen, and southern Xiantao, while expansion displayed a decentralized point distribution. The PLUS model predicts that, in the scenario of rural built-up land expansion, a significant amount of cropland is encroached upon. This study provides a new perspective for understanding the impact of rural habitat changes on the carbon cycle. Future land management and planning should pay more attention to maintaining ecosystem services and considering the environmental effects of changes in rural built-up land layout.