Cover Categories Research Articles

Land use and land cover changes and their driving forces in selected forest reserves in Central Tanzania

Miombo woodlands benefit humans in a variety of ways, including by providing food and ecosystem services. However, Tanzania's Miombo woodlands are declining due to ever-increasing unsustainable human activities. This study used landsat images for the years 1996, 2006, and 2021 to quantify the extent of land use and cover change in the Mgori and Minyughe forest reserves of the Singida region in Central Tanzania. We conducted interviews with 154 villagers living near the forest reserves to determine the drivers of land use and cover change. The findings showed that increased cultivation, illegal settlements, forest harvesting for timber, charcoal production, and livestock grazing are the primary drivers of land use and cover changes. Over the study period, cultivated land in the Mgori forest reserve increased by 33.56%, settlements by 32.67%, and bushland by 0.52%, while woodlands and grassland decreased by −0.60% and −0.22%, respectively. In the Minyughe forest reserve, cultivated land has grown by 23.81%, scattered cultivation by 16.79%, settlements by 6.58%, and water by 1.02%. During the same time period, significant loss occurred in woodland with a dynamic index of (−3.50%), bushland (−0.75%), grassland (−0.40%), and seasonally inundated grassland (−1.86%). User accuracy was highly reliable in cultivated land, bushland, woodland, and grassland cover. The overall Kappa coefficient values provided substantial accuracy for a specific land cover category. In order to combat deforestation and forest cover loss, the study recommends that the Tanzanian government strengthen conservation in both forest reserves.

Impacts of New Town developments on carbon sinks: Implications from the Case of Seoul Metropolitan Area, Korea

The Intergovernmental Panel on Climate Change (IPCC) ambitious goal of achieving carbon neutrality by 2050 has been embraced by a variety of countries including South Korea. In South Korea, an increasingly vital neutrality strategy is enhancing carbon absorption through land use planning. A key consideration in these strategies is the comprehensive evaluation of carbon sinks present in various land cover categories. These carbon sinks—most notably forests, grasslands, and wetlands—play a critical role in mitigating the effects of climate change by locking carbon dioxide out of the atmosphere. However, forests and grasslands have long been prime avenues for new exurban development. This study analyzes the effects of large-scale development across several key carbon sinks for the Seoul Metropolitan Area of South Korea. Our findings indicate that the key carbon sinks increased from 2007 to 2021. Regardless, urban development has flourished where greenbelts were released, leading to significant depletion of forests, grasslands, and wetlands. However, converting farmland to New Town developments with massive park systems has contributed to an increase in carbon sinks in some parts of the region. In the perspective of South Korea’s goals for carbon neutrality, absorption is insufficient. Therefore, it is necessary to reflect the loss of sinks as a cost of sprawl and frame policies to expand storage through afforestation or reassessment of idle, undeveloped land.

“Hg distribution and accumulation in soil and vegetation in areas impacted by artisanal gold mining in the Southern Amazonian region of Madre de Dios, Peru.”

Artisanal and small-scale gold mining (ASGM) is the primary global source of anthropogenic mercury (Hg) emissions. It has impacted the Amazon rainforest in the Peruvian region of Madre de Dios. However, few studies have investigated Hg's distribution in terrestrial ecosystems in this region. We studied Hg's distribution and its predictors in soil and native plant species from artisanal mining sites. Total Hg concentrations were determined in soil samples collected at different depths (0–5 cm and 5–30 cm) and plant samples (roots, shoots, leaves) from 19 native plant species collected in different land cover categories: naked soil (L1), gravel piles (L2), natural regeneration (L3), reforestation (L4), and primary forest (L5) in the mining sites. Hg levels in air were also studied using passive air samplers. The highest Hg concentrations in soil (average 0.276 and 0.210 mg kg−1 dw.) were found in the intact primary forest (L5) at 0–5 cm depth and in the plant rooting zones at 5–30 cm depth, respectively.Moreover, the highest Hg levels in plants (average 0.64 mg kg−1 dw) were found in foliage of intact primary forest (L5). The results suggest that the forest in these sites receives Hg from the atmosphere through leaf deposition and that Hg accumulates in the soil surrounding the roots. The Hg levels found in the plant leaves of the primary forest are the highest ever recorded in this region, exceeding values found in forests impacted by Hg pollution worldwide and raising concerns about the extent of the ASGM impact in this ecosystem. Correlations between Hg concentrations in soil, bioaccumulation in plant roots, and soil physical-chemical characteristics were determined. Linear regression models showed that the soil organic matter content (SOM), pH, and electrical conductivity (EC) predict the Hg distribution and accumulation in soil and bioaccumulation in root plants.

AUD-Net: A Unified Deep Detector for Multiple Hyperspectral Image Anomaly Detection via Relation and Few-Shot Learning.

This article addresses the problem of the building an out-of-the-box deep detector, motivated by the need to perform anomaly detection across multiple hyperspectral images (HSIs) without repeated training. To solve this challenging task, we propose a unified detector [anomaly detection network (AUD-Net)] inspired by few-shot learning. The crucial issues solved by AUD-Net include: how to improve the generalization of the model on various HSIs that contain different categories of land cover; and how to unify the different spectral sizes between HSIs. To achieve this, we first build a series of subtasks to classify the relations between the center and its surroundings in the dual window. Through relation learning, AUD-Net can be more easily generalized to unseen HSIs, as the relations of the pixel pairs are shared among different HSIs. Secondly, to handle different HSIs with various spectral sizes, we propose a pooling layer based on the vector of local aggregated descriptors, which maps the variable-sized features to the same space and acquires the fixed-sized relation embeddings. To determine whether the center of the dual window is an anomaly, we build a memory model by the transformer, which integrates the contextual relation embeddings in the dual window and estimates the relation embeddings of the center. By computing the feature difference between the estimated relation embeddings of the centers and the corresponding real ones, the centers with large differences will be detected as anomalies, as they are more difficult to be estimated by the corresponding surroundings. Extensive experiments on both the simulation dataset and 13 real HSIs demonstrate that this proposed AUD-Net has strong generalization for various HSIs and achieves significant advantages over the specific-trained detectors for each HSI.

Machine Learning for Urban Heat Island (UHI) Analysis: Predicting Land Surface Temperature (LST) in Urban Environments

This study investigates how urban configuration influences the distribution of heat, known as the Urban Heat Island (UHI) effect, in Shiraz, Iran. Several Machine Learning algorithms are employed to analyze Land Surface Temperature (LST) data across various land cover types, including built-up, soil, and vegetation. The analysis reveals that Deep Neural Networks (DNNs) and Extreme Gradient Boosting (XGB) models excel at predicting LST, outperforming other methods. These results highlight the significant impact of land use on LST patterns within the metropolitan regions. Furthermore, the study assesses the influence of specific configuration metrics within each land cover category. This allows researchers to pinpoint which urban morphology features most significantly affect LST. These insights can inform targeted interventions and management strategies implemented to mitigate heat and improve thermal comfort in specific areas of Shiraz.

Implementation of geographic information system in relation to land cover changes in the Leuser National Park area from 1990 to 2020

In recent years, there have been pressing issues that need immediate attention regarding land cover change in Leuser National Park, particularly in BPTN Region III Stabat, Indonesia. The objective of this study is to analyze the land cover changes that have occurred between the years 1990 and 2020 in BPTN Region III Stabat, Leuser National Park, Indonesia. The methodology employed includes temporal and spatial analysis, with data processing carried out using ArcMap 10.4 software. The land cover categories considered in the analysis include primary dryland forest, secondary dryland forest, shrubland, plantation, open land, water body, dryland agriculture, and mixed dryland agriculture. The findings of this study indicate that the largest land cover area was recorded in 1990, with primary dryland forest covering approximately 192,188.92 hectares, while the smallest land cover area was open land, covering around 3.31 hectares. Over the entire period from 1990 to 2020, the largest decrease in land cover area was observed in secondary dryland forest, amounting to approximately 5,546.23 hectares, whereas the largest increase in land cover area was observed in dryland agriculture, totaling around 4,201.75 hectares.

Evaluation of hydraulic adequacy of runoff disposal in urban watersheds of rapidly expanding towns

ABSTRACT The growth of cities significantly alters natural catchments by increasing impervious surfaces and necessitating the installation of an appropriate drainage system. Arba Minch is rapidly expanding and facing street flooding. The objective of this study is to assess the hydraulic adequacy of runoff disposal in urban watersheds located in rapidly expanding towns. Stormwater Management Model (SWMM) was used to perform rainfall-runoff simulation. Personal Computer SWMM (PCSWMM) was used to carry out calibration and validation in the watershed. The primary and secondary data were used. Five Land Use/Land Cover (LULC) categories identified were asphalt, cobble, vegetation, bare soil, and roof. The result shows 33.49% area is covered with highly impervious land cover. The overall calibration and validation are a very good fit with the observed flow. The total runoff volume of 9494.15 km3 was generated from the town area which is 13.7 km2. The peak runoff determined from 373 sub-catchments varied from 0.01 to 4.48 m3s−1. According to the simulated result, 7.46% of existing drainage channels were flooded. Anticipating future runoff generation, this study evaluates the inadequacy of existing drainage channels in urban areas. Hydraulic analysis is recommended before constructing drainage structures to protect from flooding.

A deep inverse convolutional neural network-based semantic classification method for land cover remote sensing images

The imbalance of land cover categories is a common problem. Some categories appear less frequently in the image, while others may occupy the vast majority of the proportion. This imbalance can lead the classifier to tend to predict categories with higher frequency of occurrence, while the recognition effect on minority categories is poor. In view of the difficulty of land cover remote sensing image multi-target semantic classification, a semantic classification method of land cover remote sensing image based on depth deconvolution neural network is proposed. In this method, the land cover remote sensing image semantic segmentation algorithm based on depth deconvolution neural network is used to segment the land cover remote sensing image with multi-target semantic segmentation; Four semantic features of color, texture, shape and size in land cover remote sensing image are extracted by using the semantic feature extraction method of remote sensing image based on improved sequential clustering algorithm; The classification and recognition method of remote sensing image semantic features based on random forest algorithm is adopted to classify and identify four semantic feature types of land cover remote sensing image, and realize the semantic classification of land cover remote sensing image. The experimental results show that after this method classifies the multi-target semantic types of land cover remote sensing images, the average values of Dice similarity coefficient and Hausdorff distance are 0.9877 and 0.9911 respectively, which can accurately classify the multi-target semantic types of land cover remote sensing images.

Assess the level of damage to the coral reef ecosystem based on substrate coverage on Bira Island, Kepulauan Seribu, Indonesia

Abstract. Komala R, Noer MI, Miarsyah M, Widyartini DS, Handayani. 2024. Assess the level of damage to the coral reef ecosystem based on substrate coverage on Bira Island, Kepulauan Seribu, Indonesia. Biodiversitas 25: 971-977. Coral reefs are coastal ecosystems that have an important ecological and economic role. Environmental pressures cause damage to ecosystems and decrease coral biodiversity. This study aims to analyze the level of damage to coral reef ecosystems based on substrate cover and obtain the latest information on coral reef ecosystem resources. The study employs a descriptive method with a survey design, utilizing Underwater Photo Transect (UPT) sampling at various depths along the coastline. The data is analyzed using Coral Point Count with Excel extension (CPCe) for substrate cover percentage and the Shannon-Wienner index for coral diversity. The findings reveal nine categories of basic substrate cover, with living hard corals constituting 32.51%, indicating a fairly damaged state. Coral resources are identified across 53 genera and 15 families, exhibiting medium diversity across stations and depths. The study highlights the need to monitor environmental threats that could further damage coral reefs and decrease diversity. The coral reef ecosystem in Bira Island is in a relatively damaged condition, with a decline in coral biodiversity, indicating an imbalance in the ecosystem. Several recommended steps include implementing coral restoration programs, monitoring coral growth, establishing critical coral habitat protection zones, and conducting conservation efforts.

Assessing Changes in the Distribution Patterns of the European Wildcat in Hungary.

The European wildcat (Felis silvestris Schreber, 1777) is an endangered and elusive carnivore that is slowly recovering in Central Europe after persecution and a decline in its distribution over the past two centuries, and specific conservation plans are needed in most of its range. Knowledge of the continent-wide distribution and status of this species is still poor. Using an online questionnaire, we evaluated the nationwide distribution of wildcats across three time periods (2004, 2014, and 2022) in Hungary. The species' reported occurrence was analyzed according to binominal logistic regression using the percent cover of land cover categories as explanatory variables. We found that the spatial cover of broad-leaved forest was positively associated with the occurrence of wildcats, and the analysis revealed a positive trend in the larger 2004-2022 time frame. We also recorded that although wildcats have disappeared from areas of the central, southern, and western parts of Hungary, regions in the eastern, northern, and south-western areas appear to retain stable populations.

Vegetation Type Mapping in Southern Patagonia and Its Relationship with Ecosystem Services, Soil Carbon Stock, and Biodiversity

Vegetation Type (VT) mapping using Optical Earth observation data is essential for the management and conservation of natural resources, as well as for the evaluation of the supply of provisioning ecosystem services (ESs), the maintenance of ecosystem functions, and the conservation of biodiversity in anthropized environments. The main objective of the present work was to determine the spatial patterns of VTs related to climatic, topographic, and spectral variables across Santa Cruz province (Southern Patagonia, Argentina) in order to improve our understanding of land use cover at the regional scale. Also, we examined the spatial relationship between VTs and potential biodiversity (PB), ESs, and soil organic content (SOC) across our study region. We sampled 59,285 sites sorted into 19 major categories of land cover with a reliable discrimination level from field measurements. We selected 31 potential predictive environmental dataset covariates, which represent key factors for the spatial distribution of land cover such as climate (four), topography (three), and spectral (24) factors. All covariate maps were generated or uploaded to the Google Earth Engine cloud-based computing platform for subsequent modeling. A total of 270,292 sampling points were used for validation of the obtained classification map. The main land cover area estimates extracted from the map at the regional level identified about 142,085 km2 of grasslands (representing 58.1% of the total area), 38,355 km2 of Mata Negra Matorral thicket (15.7%), and about 25,189 km2 of bare soil (10.3%). From validation, the Overall Accuracy and the Kappa coefficient values for the classification map were 90.40% and 0.87, respectively. Pure and mixed forests presented the maximum SOC (11.3–11.8 kg m−2), followed by peatlands (10.6 kg m−2) and deciduous Nothofagus forests (10.5 kg m−2). The potential biodiversity was higher in some shrublands (64.1% in Mata Verde shrublands and 63.7% in mixed shrublands) and was comparable to those values found for open deciduous forests (Nothofagus antarctica forest with 60.4%). The provision of ESs presented maximum values at pure evergreen forests (56.7%) and minimum values at some shrubland types (Mata Negra Matorral thicket and mixed shrubland) and steppe grasslands (29.7–30.9%). This study has provided an accurate land cover and VT map that provides crucial information for ecological studies, biodiversity conservation, vegetation management and restoration, and regional strategic decision-making.

Impact of Land Use and Land Cover Change on Deforestation in the Central Taraba State: A Geographic Information System and Remote Sensing Analysis

Deforestation, the widespread clearance of forests for various land uses, has become a significant global environmental issue with far-reaching consequences. Deforestation in the study area was identified, categorized, assessed, and interpreted using Landsat 5, 7, and 8 from the years 2008, 2014, and 2020, respectively. A geographic information system (GIS) database of land use and land cover categories and their changes were created. The results showed that several anthropogenic human activities, including agriculture and wood harvesting, were driving a general retreat of the forest area. The study further shows that between 2008 and 2020, forest area decreased by 8.5% with an annual rate of 0.33%, other vegetative areas increased by 2% with an annual rate of 0.077%, and non-vegetative areas increased by 1.5% with an annual rate of 0.58%. The hotspot map shows that the rate at which the reserve is deteriorating and the conversion of the forest area to other vegetation and bare ground are only a matter of time. The study recommended that the government should adopt rigorous policies to protect forest reserves from unauthorized habitation by encouraging the use of alternative firewood fuel sources to reduce the pressure on the forest.

Remote sensing in the analysis between forest cover and COVID-19 cases in Colombia

This article explores the relationship between forest cover and coronavirus disease 2019 (COVID-19) cases in Colombia using remote sensing techniques and data analysis. The study focuses on the CORINE land cover methodology's five main land cover categories: artificial territory, agricultural territories, forests and semi-natural areas, humid areas, and water surfaces. The research methodology involves several phases of the unified method of analytical solutions for data mining (ASUM-DM). Data on COVID-19 cases and forest cover are collected from the Colombian National Institute of Health and Advanced Land Observation Satellite (ALOS PALSAR), respectively. Land cover data is processed using QGIS software. The results indicate an inverse relationship between forest cover and COVID-19 cases, as evidenced by Pearson's index ρ of -0.439 (p-value <0.012). In addition, a negative correlation is observed between case density and forests and semi-natural areas, one of the land cover categories. The findings of this study suggest that higher forest cover is associated with lower numbers of COVID-19 cases in Colombia. The results could potentially inform government organizations and policymakers in implementing strategies and policies for forest conservation and the inclusion of green areas in densely populated urban areas.

THE DIVERSITY AND ABUNDANCE OF CHAETODONTIDAE FAMILY AND ITS RELATIONSHIP WITH LIVE CORAL COVER IN SPERMONDE ISLANDS, MAKASSAR

Coral fish are one of the marine biotas that are highly sensitive to changes in coral reef ecosystems. Therefore, the presence or absence of specific reef fish species can describe the condition of coral reefs. The objective of the current study is to investigate the relationship between the biodiversity and abundance of Chaetodontidae fish and the live coral cover at 13 locations in the Spermonde area, Makassar City. Reef fish were counted using the Underwater Visual Census method. Coral cover percentages were determined using the Underwater Photo transects method. The results found 348 individuals of the Chaetodontidae family, comprising 21 species from four genera. The mean live coral cover categories in Spermonde were in pretty good condition (27.83%). Chaetodontidae in Spermonde Island was not diverse (mean H = 1.74) and not abundant (mean abundant = 721 ind./ha). There was a weak correlation between the diversity of Chaetodontidae and live coral cover, and also weak correlation between the abundance of Chaetodontidae and live coral cover in Spermonde Island, with R2 determination coefficient linear regression were 0.1697 and 0.0471, respectively.

The Estimation of Cuscus Population and Distribution in Teluk Wondama District, West Papua Province, Indonesia

Cuscus is a marsupial mammal endemic to the eastern part of Indonesia. Populations and their habitats are under pressure due to economic development, population growth and utilization of forest resources. So far, studies on cuscus ecology have mainly focused on protected habitats or conservation areas, this study aims to identify and analyze populations and predict the distribution of cuscus in non-protectes area which is PT Wijaya Sentosa. This research conduct in November 2020 until January 2021 in several land cover categories. The methods used in this study include literature and secondary data studies, field observations using strip-transect and camera trap techniques, interviews using purposive sampling techniques. This study found nine individuals of cuscus from direct observation with strip-transects and two individuals of cuscus from camera trapping. The ground cuscus population (P. gymnotis) had the highest abundance and density, while the common spotted cuscus (S. maculatus) population owned the lowest values. The population structure dominates by male sex and adult age class, and no female sex and young age class were found. Cuscus was more commonly found solitary in the line of observation and not found in every line. The shape of the distribution pattern of the cuscus population at PT Wijaya Sentosa is cluster/aggregated, determined from the value of the dispersion index and the ratio of variance to mean. The most common ground cuscus was found in the 2018 and the 2013 logging area (LOA).

Evaluation of afforestations for avalanche protection with orthoimages using the random forest algorithm

Afforestations provide cost-effective and environmentally friendly protection against natural hazards, compared to technical measures. In Austria, more than 3000 afforestation sites for hazard protection covering 9000 ha have been established between 1906 and 2017, mainly for snow avalanche protection. The actual protective effect depends on avalanche predisposing factors and land cover, i.e. whether forest is present. In this study, predisposing factors and land cover classes were identified and analysed in selected afforestation sites. The protective effect of forest was attributed to the presence of forest cover and tree species. Using RGB images with a ground resolution of 20 × 20 cm, nine land cover categories have been distinguished by means of supervised classification with the random forest algorithm. Those land cover categories were classified with an overall accuracy of 0.87–0.98 and Kappa-values, ranging between 0.81 and 0.93. Images were filtered using a 3 pixel by 3 pixel majority filter, which assigns each cell in the output grid the most commonly occurring value in a moving window centred on each grid cell. This filter further increased the overall accuracy by removing noise pixels while preserving the fine elements of the classified grid. Our results indicate a protective effect for about half of the analysed afforestation sites. The dominance of the land use class “Meadow” at most sites with little avalanche protection effect suggests grazing as a limiting factor. The spatial information provided with the described method allows to identify critical areas in terms of avalanche protection even years after the initial afforestation.

Standing and Probabilistic Injury

Standing to sue often turns on questions of probability. For example, public law plaintiffs must show that they are likely to be affected by allegedly unlawful government surveillance or environmental policies, and consumers may wish to sue private defendants over false credit reporting or data breaches that may or may not cause them financial or reputational harm in the future. This Article offers a framework for resolving a wide range of these “probabilistic standing” issues. Our core claim is that courts and commentators ask too much of standing doctrine in probabilistic cases. First, scholars sometimes seek a unified theory of probabilistic standing to cover categories of cases that ask distinct questions, such as cases involving who is subject to a challenged action, on the one hand, and those involving whether a person subject to such an action is sufficiently likely to be harmed, on the other. Second, courts should not ask how probable elements of a plaintiff’s case must be in order to support standing, but rather who should decide whether a given probability is sufficient. Judges and parties struggle in litigation to assess the actual probability of occurrences, and Article III of the Constitution provides no standard for how probable an injury must be to support a lawsuit. Third, any doctrinal probability threshold for standing would encounter a related problem, which is that the probability of an injury depends significantly on how that injury is framed. Which harms “count” for standing is thus a vital question in assessing the probability of injury, but Article III is an unlikely place to look for answers. Within certain constraints, courts should look instead to the underlying substantive law to define the relevant injuries for standing purposes. Finally, we contend that many of the concerns associated with probabilistic claims are better addressed through the law of remedies and prudential elements of the timing doctrines (mootness and ripeness) than through the constitutional law of standing.

Land cover changes: Regional verification of global trends with Australia and Argentina as a case study

Land cover change is considered to be one of the main factors of global changes, including climate change and biodiversity loss. This research devoted to an analysis of land cover transformation at regional level, in Australia and Argentina, aiming to verify the global trends of the beginning of XXI century. Basing on the MODIS data, we use rasters for the years 2001, 2012, and 2020, and detect transitions between land cover categories. For the key time period of 2001-2012 we identify land cover transformation processes, compare studied countries, and verify some of the global trends. Then, we discuss some of the natural and anthropogenic factors of land cover changes. One of the most significant process for both countries found out is shrub encroachment, which is a global trend. It is observed on up to hundreds of thousands of square kilometres, and takes 2- 4% of the countries’ areas. Besides this, the widespread processes are the degradation of woody vegetation with an increase in contribution from open spaces in non-forest zones (2-3% of each country area), and overgrowing of croplands (2-3%). In Australia, we detect the increase in contribution from tree vegetation (2% of country area). In Argentina – decline in the contribution from shrublands (2% of country area). The share of total areas of land cover transformation is about the same for the study regions and is estimated about 13-15% of the countries territories.

Evaluation of Coral Reef Health Status in the Fish Sanctuary of San Esteban, Ilocos Sur

Studies on corals are lacking in Ilocos Sur’s marine protected areas (MPAs), hindering management effort development. This study evaluated the health status of coral reefs in the San Esteban Fish Sanctuary. Hard coral cover, hard coral diversity in terms of Taxonomic Amalgamation Units (TAUs), and coral reef health status in terms of live coral coverage, algae coverage, sand coverage, and coral mortality index were measured in two sampling stations inside an over-a-decade-old fish sanctuary that does not have previous data on its coral reef health status. The Underwater Photo Transect (UPT) method was employed to take image samples subjected to the Coral Point Count with Excel extensions (CPCe) software for analysis. The study revealed that the coral reef in San Esteban Fish Sanctuary is in good condition with a hard coral cover of 30.0%, falls under Hard Coral Cover Category C, and a hard coral diversity of 16.8TAUs that falls under Diversity Category D. The fish sanctuary is also in a healthy condition based on excellent live coral coverage, algae coverage, sand coverage, and low coral mortality index. The two stations are more or less the same in terms of the aforementioned parameters. Overall, it is recommended to continuously strengthen the enforcement of the fish sanctuary to protect its coral reefs, measure other parameters that might impact and indicate coral reef health status, employ evaluations inside and outside MPAs in the province, and use high-resolution cameras during image sampling for more efficient analysis.

The importance of urban areas in supporting vulnerable and endangered mammals

The fragmentation of habitats associated with urbanisation poses a significant threat to already vulnerable and endangered mammal species. While the county of Dorset has been identified as one of the UK’s biodiversity hotspots, it is characterised by large-scale urbanisation and intensive agriculture. Focusing on the largest urban area of Dorset—the conurbation of Bournemouth, Christchurch, and Poole—this study aims to investigate the importance of urban areas in supporting vulnerable and endangered mammals by comparing it to four other major land cover categories in the county: arable & horticulture, grassland, woodland, and heathland. The study utilises data collected by the county’s environmental records department through mainly volunteer-contributed ad-hoc sighting data of 10 872 georeferenced mammal records collected between 2000 and 2018. Our findings reveal that, despite having significantly different mammal communities to all other land covers, urban land cover was shown to host four out of five of the vulnerable and endangered species recorded in this study. These included the European rabbit (vulnerable on the IUCN red list), hazel dormouse (vulnerable on the UK red list), West European hedgehog (vulnerable on the UK red list) and European water vole (endangered on the UK red list). Our findings highlight the significance of urban areas as important habitats for mammal biodiversity, presenting an opportunity for the conservation of specific vulnerable and endangered species. Despite limitations in ad-hoc sighting data, our analysis indicates an overrepresentation of certain 'charismatic' species, like foxes and hedgehogs, in urban samples, likely due to their high intrinsic value to the population. While the recognition of urban habitats for 'red list' species is growing, it is also important to acknowledge the relative importance of charismatic species to urban human communities in conservation and management strategies.