Land Cover Variables Research Articles

Spatiotemporal Variability in Snow and Land Cover in Sefid-Rud Basin, Iran

Snow cover has a key role in balancing the Earth’s surface temperature and can help in filling rivers and reservoirs. In this study, 8-day MOD10A2 images are employed to monitor the spatiotemporal changes in snow cover in the Sefid-Rud basin and its eleven sub-basins during 2000–2019. The non-parametric Mann–Kendall (MK) test and its associated Sen’s slope estimator are utilized to estimate the trends in annual, seasonal, and monthly snow cover changes. The Sen’s slope results show a decrease in the snow cover for the basin, statistically significant toward the central and southern parts of the basin. In the winter season, a decreasing trend is observed, where its decreasing rate is higher than the annual rate. The trends in the calendar months are like the seasons, i.e., December, January, and February exhibit a decreasing trend, like the winter season. The Goltapeh-Zarinabad and Ghorveh-Dehgolan sub-basins show decreasing snow cover rates of −0.51 and −0.68 (%/year) during 2000–2019, respectively, the only two sub-basins whose gradients are statistically significant at the 95% confidence level. The Pearson correlation analysis between elevation and snow cover for each year shows that the highest and lowest correlations are 0.81 for 2007 and 0.59 for 2017. Finally, analysis of the MCD12Q1 land cover data shows that a significant portion of non-vegetated lands have turned into grasslands, mainly in the central part of the basin, where the significant gradual snow cover decline is observed. The results can guide stakeholders and policymakers in the development of a sustainable environment in the face of climate change.

Identifying Factors Mediating the Carnivore Richness–Productivity Relationship Across a Precipitation‐Driven Ecotone

ABSTRACTAimPrimary productivity is an important mechanism influencing the species richness of animal taxa such as mammals. However, the factors that mediate the relationship between carnivore species richness, for example, and productivity are not well understood. We examined whether the relationship between carnivore species richness and primary productivity followed a hump‐shaped relationship and explored potential pathways by which productivity influences carnivore communities.LocationWestern Great Basin, USA.MethodsWe deployed a camera‐trap grid across a strong precipitation—and the corresponding productivity—gradient and estimated carnivore species richness using a novel application of a multisession community occupancy model. To quantify the relationship between carnivore species richness and productivity, we included the linear and quadratic effects of average annual precipitation as covariates on carnivore species richness. We fit additional community models linking carnivore species richness to vegetation and land cover variables to identify factors that mediate the relationship between carnivore richness and productivity.ResultsThe linear effect of precipitation on carnivore richness was positive and the quadratic effect was negative, supporting the hump‐shaped relationship where richness declines at the highest levels of productivity. Similarly, carnivore species richness followed a quadratic relationship with vegetation height along the productivity gradient. The relationship between carnivore species richness and proportion tree cover was positive, with limited evidence that open vegetation types in the surrounding landscape increased site‐level richness.Main ConclusionsIn addition to supporting the notion that primary productivity has a strong and nonlinear influence on local‐scale carnivore species richness, our results suggest that vegetation structure may be an important mediator of this relationship. Although future changes in precipitation and productivity due to climate change are uncertain, our results indicate that targeting areas of intermediate productivity and vegetation height, such as woodlands, may be key for conserving species‐rich carnivore communities along productivity gradients.

Metabarcoding reveals ecologically distinct fungal assemblages in river and groundwater along an Austrian alpine to lowland gradient.

Biodiversity, the source of origin, and ecological roles of fungi in groundwater are to this day a largely neglected field in fungal and freshwater ecology. We used DNA-based Illumina high-throughput sequence analysis of both fungal gene markers 5.8S and internal transcribed spacers region 2 (ITS2), improving taxonomic classification. This study focused on the groundwater and river mycobiome along an altitudinal and longitudinal transect of a pre-alpine valley in Austria in two seasons. Using Bayesian network modeling approaches, we identified patterns in fungal community assemblages that were mostly shaped by differences in landscape (climatic, topological, geological), and environmental conditions. While river fungi were comparatively more diverse, unique fungal assemblages could be recovered from groundwater, including typical aquatic lineages such as Rozellomycota and Olpidiomycota. The most specious assemblages in groundwater were not linked to the input of organic material from the surface, and as such, seems to be sustained by characteristic groundwater conditions. Based on what is known from closely related fungi, our results suggest that the present fungal communities potentially contribute to mineral weathering, carbon cycling and denitrification in groundwater. Furthermore, we were able to observe the effects of varying land cover due to agricultural practices on fungal biodiversity in groundwater ecosystems. This study contributes to improving our understanding of fungi in the subsurface aquatic biogeosphere.

Detecting small-scale landslides along electrical lines using robust satellite-based techniques

Robust Satellite Technique (RST) was applied to detect small-scale landslides along electrical lines in Sicily, Italy. To this end, electrical poles were selected as targets within the study area. The methodology, implemented in Google Earth Engine (GEE) environment, exploits the Copernicus Sentinel-2 platform to identify anomalous land cover variation, in terms of Normalized Difference Vegetation Index (NDVI), possibly related to small displacements affecting electric poles. Since the applied methodology is based on land cover change, dense vegetation plays an important role in detecting small-scale landslides. Therefore, we targeted months with the highest vegetation density, such as February, March, and April from 2016–2023. The results obtained reveal that out of the five targeted electrical poles, four of them exhibited anomalies > 2-sigma indicating significant changes in land cover possibly related to local ground movement as confirmed by aerial photos collected in the period 2015–2023. Our findings reveal anomalies of −2.17 and −2.36 on 7/17/2017 and 9/05/2017 for pole 1. For pole 2, the results show an anomaly of −2.02 on 8/11/2018. The results also indicate anomalies of −4.40 and −2.99 on 7/09/2021 and 9/27/2022 for pole 3. For pole 4, the findings show an anomaly of −3.10 on 1/18/2019.

Machine learning algorithms for the evaluation of risk by tick-borne pathogens in Europe

Background Tick-borne pathogens pose a major threat to human health worldwide. Understanding the epidemiology of tick-borne diseases to reduce their impact on human health requires models covering large geographic areas and considering both the abiotic traits that affect tick presence, as well as the vertebrates used as hosts, vegetation, and land use. Herein, we integrated the public information available for Europe regarding the variables that may affect habitat suitability for ticks and hosts and tested five machine learning algorithms (MLA) for predicting the distribution of four prominent tick species across Europe. Materials and methods A grid of cells 20 km in diameter was prepared to cover the entire territory, containing data on vegetation, points of water, habitat fragmentation, forest density, grass extension, or imperviousness, with information on temperature and water deficit. The distribution of the hosts (162 species) was modelled and included in the dataset. We used five MLA, namely, Random Forest, Neural Networks, Naive Bayes, Gradient Boosting, and AdaBoost, trained with reliable coordinates for Ixodes ricinus, Dermacentor reticulatus, Dermacentor marginatus, and Hyalomma marginatum in Europe. Results Both Random Forest and Gradient Boosting best predicted ticks and host environmental niches. Our results demonstrate that MLA can identify trait-matching combinations of environmental niches. The inclusion of land cover and land use variables has a superior capacity for predicting areas suitable for ticks, compared to classic methods based on the use of climate data alone. Conclusions Flexible MLA-driven models may offer several advantages over traditional models. We anticipate that these results may be extrapolated to other regions and combinations of tick-vertebrates. These results highlight the potential of MLA for inference in ecology and provide a background for the evolution of a completely automatized tool to calculate the seasonality of ticks for early warning systems aimed at preventing tick-borne diseases.

Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba

The semantic segmentation of satellite and UAV remote sensing imagery is pivotal for address exploration, change detection, quantitative analysis and urban planning. Recent advancements have seen an influx of segmentation networks utilizing convolutional neural networks and transformers. However, the intricate geographical features and varied land cover boundary interferences in remote sensing imagery still challenge conventional segmentation networks’ spatial representation and long-range dependency capabilities. This paper introduces a novel U-Net-like network for UAV image segmentation. We developed a link aggregation Mamba at the critical skip connection stage of UNetFormer. This approach maps and aggregates multi-scale features from different stages into a unified linear dimension through four Mamba branches containing state-space models (SSMs), ultimately decoupling and fusing these features to restore the contextual relationships in the mask. Moreover, the Mix-Mamba module is incorporated, leveraging a parallel self-attention mechanism with SSMs to merge the advantages of a global receptive field and reduce modeling complexity. This module facilitates nonlinear modeling across different channels and spaces through multipath activation, catering to international and local long-range dependencies. Evaluations on public remote sensing datasets like LovaDA, UAVid and Vaihingen underscore the state-of-the-art performance of our approach.

Potential environmental implications of sandbar afforestation: Insights from ecosystem restoration initiatives in a sandbar of Brahmaputra River Assam, India

Plantation in riverine sandbars offers an excellent opportunity for restoration of ecosystem with a vast potential for enhancing carbon stock. Afforestation on barren islands is challenging task; however, Padmashree Jadav Payeng has single-handedly transformed a sandbar of the river Brahmaputra into a forested landscape in India’s northeastern state of Assam. Such inspiring initiative needs more attention in terms of scientific assessment to quantify ecosystem value and services that can be enriched through such activities. This study uses remote sensing data viz., Landsat4–5TM and Sentinel 2 A data to provide a detailed information on spatio-temporal variability of land use land cover of study region from 1990 to 2021. It aims to conduct a scientific and systematic assessment of biophysical changes that have occurred in the sandbars, understand the status of afforestation, and evaluate the current levels of aboveground biomass (AGB), belowground biomass (BGB), and carbon stock in the selected sandbars. The spatial distribution of AGB, BGB and carbon stock is derived using linear regression model between SAR backscatter and field-based AGB. The results demonstrate that forest cover increased by 31.92 % and sandbar area is reduced by 26.87 % from 1990 to 2021. Spatially derived AGB ranges from 9 to 1295.89 Mg per ha, BGB from 2.35 to 290.50 Mg per ha and carbon stock from 6.18 to 763.34 Mg per ha. Ficus religiosa exhibits a high biomass, ranging from 564.7 Mg per ha to 994.7 Mg per ha, and a high carbon stock, ranging from 8 to 557.30 Mg per ha, attributed to its larger diameter at breast height (DBH). Moreover, phytosociological assessment was conducted for the studied forest, which reveal a total tree species richness of 79. The forest exhibits a total tree density of 395 individuals per hectare. Different biodiversity indices provide a comprehensive understanding of species composition; where results show a Shannon diversity index of 2.81, a Simpson's index of 0.08, a Menhinick's richness index of 2.7, and a Margalef's richness index of 5.26. The article provides detailed information on change in forest cover and present status of AGB, BGB and Carbon stock, providing evidence-based narratives on ecosystem restoration. The initiative by a single person has created a forested landscape and habitat for a large number of wild animals, contributing to better carbon stock and a healthier ecosystem. With regard to the current debate of carbon market, the study suggests the need of instrumentalization of carbon credits for such restoration activities to encourage increase in carbon stock. It also concludes that in-depth research should be taken up on long-term effectiveness of such plantation activities and the potential for scaling up these initiatives for ecosystem restoration in view of climate change mitigation and sustainable forest development.

Effect of Land Cover Variations on the Fertility Status of Soils of Umuhu in Ngor-Okpala Area of Imo State

The study was conducted to ascertain the effect of four different land covers (cassava cultivated field (CCF), oil palm plantation (OPP), plantain plantation (PP) and forest land (FL)) on the fertility status of soils of Umuhu in Ngor-Okpala area of Imo State, Nigeria. Random sampling technique was used in the collection of soil samples from the four different land covers. The sampling was done at the beginning, middle and end of each land cover. Twelve (12) composite samples were collected from all the land cover types with 3 per land cover. The composite samples were prepared for routine laboratory analyses. Data generated were analyzed statistically using analysis of variance. The textural classes of all the sampled soils were of loamy sand. The soil which had plantain plantation as its cover had the highest pH (6.4), Total N (0.229 %), Soil organic matter 4.70%, Available P (27.05 mg kg-1), Exch. Ca (6.492 cmolc kg-1), Exch. Mg (3.942 cmolc kg-1), Exch. K (0.953 cmolc kg-1), Exch. Na (0.06 cmolc kg-1), Exch. H (1.2 cmolc kg-1) and CEC (28.4 cmolc kg-1) with its Exch. Al in trace amount (<0.001 cmolc kg-1). The soil under CCF, despite being under low cover, did not show much degeneration due to the shifting cultivation being practiced in the study location. However, the result obtained from the study indicated that the different land covers especially that under plantain plantation (PP) have affected the soils fertility at different rates. This observed factor is strongly controlled by high content of organic materials, a dense vegetative cover which mitigates erosion effects and the addition of ash as a means of fertilizing the soils under plantain plantation being practiced in the study area.

A picit jeu: Agent-based modelling with serious gaming for a fire-resilient landscape

Wildfire governance requires addressing driving physical, biological and socio-economic processes, by promoting the development of fire-resistant and resilient landscapes. These landscapes can best be achieved by strategies that integrate fuel management for direct prevention with allied socio-economic activities, through the collaboration of stakeholders with different and sometimes conflicting interests. This work aims to address the need for new approaches supporting the participatory process of collective decision-making, helping stakeholders explore land management strategies for landscape fire resilience. We present and discuss a methodology combining agent-based modelling with a role-playing game. It was tested in a valley of the Italian Alps, involving 23 local stakeholders in forest and pasture management in three game sessions. Evaluation was based on observation of game sessions, collection of feedback via immediate post-session debriefing and questionnaires, and long-term (multi-year) assessment carried out through semi-structured interviews. We found the methodology valuable for facilitating discussion among different stakeholders, who were able to identify context-related challenges (land fragmentation and land abandonment, stakeholders' limited collaboration, controversial drives of European funding) and possible strategies for producing a fire-resilient landscape (community management forms of pastoralists activities for maintaining land cover diversity). The approach also triggered a positive process for longer-term change. By analysing the outcomes, we are able to identify four key recommendations for future work using serious gaming for sustainable landscapes: 1) aim for an even composition of session groups, 2) consider the multiple levels of organisation in the area, 3) use the allocation of game roles to disrupt power dynamics, and 4) seek to involve the broadest stakeholder spectrum in developing the game itself.

Earth observation data uncover green spaces’ role in mental health

The prevalence of mental health disorders, a key disability cause, is linked to demographic and socioeconomic factors. However, limited data exists on mental health and the urban environment. Urbanization exposes populations to environmental stressors, particularly affecting low-middle-income countries with complex urban arrangements. We used remote sensing and census data to investigate potential connections between environmental factors and mental health disorders. Land cover variables were assessed using the European Space Agency (ESA) global WorldCover product at 10 m resolution together with the database of mental health diagnosed cases (n = 5769) from the Brazilian Unified Health System’s Department of Informatics (DATASUS) from every health facility of the city of Porto Alegre. The association of mental health data with land cover was established with machine learning algorithms and polynomial regression models. The results suggest that higher trees cover at neighborhood level was associated with better mental health index. A lower mental health index was also found to be associated with an higher Human Development Index. Our results highlight the potential of greenness in the city environment to achieve substantially better mental health outcomes.

Evaluating habitat use and relative abundance of Iowa's river otter with harvest data

AbstractThe North American river otter (Lontra canadensis) was extirpated from much of the United States in the early 20th century due to habitat loss, pollution of waterways, and overharvesting. The Iowa Department of Natural Resources began a river otter reintroduction effort in 1985, which placed otters in 14 sites across the state. Otters have since been known to occur in every county in Iowa and appear to have successfully repopulated their former range throughout the state. Our objective was to relate land cover characteristics and otter abundance using harvest data. We used data collected by agency staff to map the locations of otter harvest in Iowa from 2006 to 2016. We mapped otter harvest locations at the subwatershed level (also called 12‐digit Hydrologic Unit Code or HUC‐12). We related otter harvest to land cover variables and predicted otter abundance by land cover type. We found that roads, forests, larger waterways, and Ictaluridae (catfish) presence were negatively correlated with otter harvest. Variables positively correlated with otter harvest were areas with greater land cover diversity, wetland patch density, average stream density, and waterway and wetland areas. The land cover model predicted otters in equal or greater numbers than the harvest data in 62.8% of HUC‐12s. The areas of greatest otter abundance estimates were located near recreation areas and urban areas, indicating the underutilization of these heavy‐trafficked areas by trappers. Areas of fewer predicted otters were not concentrated in a single area of the state but occurred along the Interstate 80 corridor.

A Texture-Considerate Convolutional Neural Network Approach for Color Consistency in Remote Sensing Imagery

Remote sensing allows us to conduct large-scale scientific studies that require extensive mapping and the amalgamation of numerous images. However, owing to variations in radiation, atmospheric conditions, sensor perspectives, and land cover, significant color discrepancies often arise between different images, necessitating color consistency adjustments for effective image mosaicking and applications. Existing methods for color consistency adjustment in remote sensing images struggle with complex one-to-many nonlinear color-mapping relationships, often resulting in texture distortions. To address these challenges, this study proposes a convolutional neural network-based color consistency method for remote sensing cartography that considers both global and local color mapping and texture mapping constrained by the source domain. This method effectively handles complex color-mapping relationships while minimizing texture distortions in the target image. Comparative experiments on remote sensing images from different times, sensors, and resolutions demonstrated that our method achieved superior color consistency, preserved fine texture details, and provided visually appealing outcomes, assisting in generating large-area data products.

Go west, young bunting: recent climate change drives rapid movement of a Great Plains hybrid zone.

Describing how hybrid zones respond to anthropogenic influence can illuminate how the environment regulates both species distributions and reproductive isolation between species. In this study, we analyzed specimens collected from the Passerina cyanea x P. amoena hybrid zone between 2004 and 2007 and between 2019 and 2021 to explore changes in genetic structure over time. This comparison follows a previous study that identified a significant westward shift of the Passerina hybrid zone during the latter half of the twentieth century. A second temporal comparison of hybrid zone genetic structure presents unique potential to describe finer-scale dynamics and to identify potential mechanisms of observed changes more accurately. After concluding that the westward movement of the Passerina hybrid zone has accelerated in recent decades, we investigated potential drivers of this trend by modeling the influence of bioclimatic and landcover variables on genetic structure. We also incorporated eBird data to determine how the distributions of P. cyanea and P. amoena have responded to recent climate and landcover changes. We found that the distribution of P. cyanea in the northern Great Plains has shifted west to track a moving climatic niche, supporting anthropogenic climate change as a key mediator of introgression in this system.

Extremes of snow and temperature affect patterns of genetic diversity and differentiation in the alpine butterfly Parnassius smintheus.

Weather is an important short-term, local driver of population size and dispersal, which in turn contribute to patterns of genetic diversity and differentiation within species. Climate change is leading to greater weather variability and more frequent extreme weather events. While the effects of long-term and broad-scale mean climate conditions on genetic variation are well studied, our understanding of the effects of weather variability and extreme conditions on genetic variation is less developed. We assessed the influence of temperature and snow depth on genetic diversity and differentiation of populations of the alpine butterfly, Parnassius smintheus. We examined the relationships between a suite of variables, including those representing extreme conditions, and population-level genetic diversity and differentiation across 1453 single nucleotide polymorphisms, using both linear and gravity models. We additionally examined effects of land cover variables known to influence dispersal and gene flow in this species. We found that extreme low temperature events and the lowest recorded mean snow depth were significant predictors of genetic diversity. Extreme low temperature events, mean snow depth and land cover resistance were significant predictors of genetic differentiation. These results are congruent with known effects of early winter weather on population size and habitat connectivity on dispersal in P. smintheus. Our results demonstrate the potential for changes in the frequency or magnitude of extreme weather events to alter patterns of genetic diversity and differentiation.

GeoFRESH – an online platform for freshwater geospatial data processing

ABSTRACT Freshwater ecosystems are characterized by their unique longitudinal and lateral habitat connectivity. As a result, spatial units in freshwater-specific analyses can often not be considered independent of each other. Accounting for this connectivity in modelling analyses requires advanced skills in Geographic Information Systems (GIS) for adequately processing and managing the data. To address this challenge, we developed the GeoFRESH online platform, which is available at https://geofresh.org . The platform provides a graphical, easy-to-use interface to create freshwater-specific analysis-ready data for any given location in the world, based on a high-resolution stream network (https://hydrography.org/hydrography90m/hydrography90m_layers). Users can (i) upload and visualize point coordinates, (ii) automatically assign points to the closest stream network segment, (iii) annotate the point data with a suite of 104 local and/or upstream-aggregated topographic, climatic, land-cover and soil variables, (iv) visualize summary plots, and (v) download the data in csv-format for further analyses. The platform can be expanded given its modular structure and it can serve as a key element to support freshwater science and management relying on high-resolution geospatial analyses. GeoFRESH provides a low-entry interface while being complementary to the hydrographr R-package, and contributes importantly to the re-usability of data as an important aspect of the FAIR principles.

Context‐dependent effects of geographic, climate and land cover factors on hemipteran assemblages in different ecoregions of China

Abstract Understanding variation in assemblage composition (i.e., beta diversity) along geographic and environmental gradients is a central topic in biogeography and ecology. We compared relationships between hemipteran beta diversity (overall dissimilarity: βtotal, Sorensen dissimilarity; turnover component: βsim, the replacement of species between assemblages; nestedness‐resultant component: βsne, the nested difference in richness between assemblages) and geographic, climatic and land cover distances among four ecoregions (Northeast Ecoregion; Northern Ecoregion; Tibet Ecoregion; Southern Ecoregion) across China. The hemipteran assemblage composition differed markedly among ecoregions. In general, βtotal and βsim both significantly increased with increasing geographic, climatic and land cover distances. Intercepts and slopes of dissimilarity‐distance relationships all varied significantly among different ecoregions, suggesting different levels of within‐ecoregion assemblage heterogeneity and change rate in assemblage composition. Moreover, the slopes of βtotal and βsim were highest in Northeast Ecoregion but lowest in Northern Ecoregion, exhibiting latitudinal and longitudinal dissimilarity pattern. According to variation partitioning analysis, although the overall explained variance was relatively low, spatial variables explained a much greater proportion of variance compared to climate and land cover variables. To conclude, our results showed significant regional differences in assemblage composition and biodiversity among ecoregions. Both dispersal‐related and niche‐related processes affect hemipteran assemblage structure, while the low overall explained variance indicates that high dissimilarity in hemipteran composition is affected by multiple factors and processes that remained undetected in this study. Our results may be relevant for wider insect groups because hemipterans constitute a highly diverse group in terms of phylogenetic origins and functional significance.

Livestock Detection and Counting in Kenyan Rangelands Using Aerial Imagery and Deep Learning Techniques

Accurate livestock counts are essential for effective pastureland management. High spatial resolution remote sensing, coupled with deep learning, has shown promising results in livestock detection. However, challenges persist, particularly when the targets are small and in a heterogeneous environment, such as those in African rangelands. This study evaluated nine state-of-the-art object detection models, four variants each from YOLOv5 and YOLOv8, and Faster R-CNN, for detecting cattle in 10 cm resolution aerial RGB imagery in Kenya. The experiment involved 1039 images with 9641 labels for training from sites with varying land cover characteristics. The trained models were evaluated on 277 images and 2642 labels in the test dataset, and their performance was compared using Precision, Recall, and Average Precision (AP0.5–0.95). The results indicated that reduced spatial resolution, dense shrub cover, and shadows diminish the model’s ability to distinguish cattle from the background. The YOLOv8m architecture achieved the best AP0.5–0.95 accuracy of 39.6% with Precision and Recall of 91.0% and 83.4%, respectively. Despite its superior performance, YOLOv8m had the highest counting error of −8%. By contrast, YOLOv5m with AP0.5–0.95 of 39.3% attained the most accurate cattle count with RMSE of 1.3 and R2 of 0.98 for variable cattle herd densities. These results highlight that a model with high AP0.5–0.95 detection accuracy may struggle with counting cattle accurately. Nevertheless, these findings suggest the potential to upscale aerial-imagery-trained object detection models to satellite imagery for conducting cattle censuses over large areas. In addition, accurate cattle counts will support sustainable pastureland management by ensuring stock numbers do not exceed the forage available for grazing, thereby mitigating overgrazing.

Not population density, but city size and per capita income influence the urban forest carbon sequestration: A case of growing cities in Arkansas, USA

While urban trees provides significant ecosystem services, the impact of increasing urbanization and population on their carbon sequestration potential remains relatively underexplored in interior sprawling cities, highlighting a critical research gap amid growing urban environmental challenges. Although existing studies suggest a strongly correlation between urban forest cover and carbon sequestration, this study aims to investigate how the urban demography factors influence land cover and carbon sequestration across cities with low, medium, and high population densities. Using the i-Tree Canopy tool, we sampled 18,814 random points to estimate city land cover and carbon sequestration in Arkansas, interior state of the US. Results from the principal component analysis revealed that 74% of the variation in a city's land cover among population density classes is attributed to tree and soil cover percentages. The mean annual (1.40 ± 0.08 T ha-1) and total carbon sequestration rates (35.30 ± 5.57 T ha-1) were significantly higher in the areas with low population density due to a higher average tree cover percentage (46%). Carbon sequestration rate was negatively associated with land cover types such as impervious surfaces like buildings (r = -0.44), roads (r = -0.28), grass cover (r = -0.46), and impervious other (r = -0.53). The log-linear model with scaled variables suggested that factors such as city's size and tree cover has positive influence urban trees' annual and total carbon sequestration, while spatial distribution and per capita income negatively affect these ecosystem befits in growing cities, regardless of population density. Travel time within city may have negative impact, while decadal population change may positively impact on carbon sequestration alongside management effort. Valuing this potential urban forest carbon sequestration for emerging carbon market could provide monetary benefits to the urban communities, city managers, and policymakers, enabling the development of effective urban forest management strategies in the context of urban climate change.

Forest Orchids under Future Climate Scenarios: Habitat Suitability Modelling to Inform Conservation Strategies.

Orchidaceae is one of the largest and most diverse families of flowering plants in the world but also one of the most threatened. Climate change is a global driver of plant distribution and may be the cause of their disappearance in some regions. Forest orchids are associated with specific biotic and abiotic environmental factors, that influence their local presence/absence. Changes in these conditions can lead to significant differences in species distribution. We studied three forest orchids belonging to different genera (Cephalanthera, Epipactis and Limodorum) for their potential current and future distribution in a protected area (PA) of the Northern Apennines. A Habitat Suitability Model was constructed for each species based on presence-only data and the Maximum Entropy algorithm (MaxEnt) was used for the modelling. Climatic, edaphic, topographic, anthropogenic and land cover variables were used as environmental predictors and processed in the model. The aim is to identify the environmental factors that most influence the current species distribution and the areas that are likely to contain habitats suitable for providing refuge for forest orchids and ensuring their survival under future scenarios. This will allow PA authorities to decide whether to invest more resources in conserving areas that are potential refuges for threatened species.

Population Pressure and Changing Land Use Land Cover in Morigaon District, Assam, India - A Geospatial Analysis

Objective: This study intends to prioritize the underlying mechanisms underpinning the spatial-temporal variations in Land Use and Land Cover (LULC) change in the Morigaon district, located in the middle part of Brahmaputra valley of Assam, India covering an area of 1498 km2.At the same time, this study focuses on the impact of population pressure on changing LULC in the study area. Methods: This research adopted geospatial approach for analysing land use land cover change in the study area. Satellite images from the years 1992 to 2022 have been utilized to identify the land transformation. The analysis of the land transformation has taken into account the five main LULC classifications of agriculture, forest, water bodies, built-up lands, and sand deposits. It has been discovered that the LULC classes identified in this study, which comprise both natural and anthropogenic elements, have a very strong link with the population growth in the Morigaon district. To show the relationship between population expansion and LULC indicators Pearson correlation method is used. Findings: Water bodies, forests and agriculture have significantly decreased by 0.13%, 4.66% and 1.64% respectively due to substantial anthropogenic activity during the past 30 years. In contrast, sand deposits and built-up lands increased by 3.13% and 3.30%. To show the relationship between population expansion and LULC indicators Pearson correlation method is used. Very significant relationships have been found between population and built-up lands with rho value 0.95. The relationship between population with agriculture, forest, water bodies and sand deposits have been seen with rho value -0.51, -0.89, -0.64 and 0.97 respectively. The loss of the geographic extent of natural classes like water bodies, forests, signifies that the geometrical progression in the human population has greatly amplified anthropogenic activities. Novelty: The novelty lies in adopting geospatial techniques that would certainly enhance land resource conservation. Environmental concerns have been increasing due to significant land surface change, which is predominantly the result of human-induced activities. In the studied area, socioeconomic, demographic, and proximity factors have a significant impact on the LULC change processes. The findings can be utilized to forecast potential LULC scenarios and offer direction for land management strategy. Keywords: LULC, Satellite Images, Anthropogenic, Pearson Correlation, Proximity Factor