Fences serve multiple purposes, including livestock management, agriculture, property delineation, and conservation. However, fences often act as ecological barriers, limiting wildlife movement and access to resources, particularly for species like bison (Bison bison) in North America. Despite the substantial impacts of fencing, large-scale datasets on fence densities are lacking. Our goal was to create potential fence density maps for the western and central U.S. and Canada using GIS modelling and freely accessible data. Specifically, we aimed to: (1) map potential fence density and identify high density of fence, (2) contrast the potential fence density map with the patterns of high human influences, and (3) identify areas with high bison habitat suitability and low density of potential fences. Using GIS modelling, we generated potential fence density maps by integrating data on land parcels, croplands, roads, and railroads. Subsequently, we identified regions with high and low potential fence density and compared them with patterns of human influence and bison habitat suitability. We found high total potential fence density in central regions of Canada and the U.S., mainly due to agriculture and transportation corridors. Interestingly, areas with high potential density of fence in the western U.S., often had low other human influence, suggesting that human influence maps may underestimate impacts if they miss fences. We also identified large areas with high bison habitat suitability and low fence density, which are promising for bison restoration. Our findings highlight the importance of assessing fences for wildlife conservation and supporting bison restoration in the Great Plains.
File: silveira_FenceDensity_BioCons_2025.pdf
Long-term monitoring of grasslands is pivotal for ensuring continuity of many environmental services and for supporting food security and environmental modeling. Remote sensing provides an irreplaceable source of information for studying changes in grasslands. Specifically, Spectral Mixture Analysis (SMA) allows for quantification of physically meaningful ground cover fractions of grassland ecosystems (i.e., green vegetation, nonphotosynthetic vegetation, and soil), which is crucial for our understanding of change processes and their drivers. However, although popular due to straightforward implementation and low computational cost, ‘classical’ SMA relies on a single endmember definition for each targeted ground cover component, thus offering limited suitability and generalization capability for heterogeneous landscapes. Furthermore, the impact of irregular data density on SMA-based long-term trends in grassland ground cover has also not yet been critically addressed. We conducted a systematic assessment of i) the impact of data density on long-term trends in ground cover fractions in grasslands; and ii) the effect of endmember definition used in ‘classical’ SMA on pixel- and map-level trends of grassland ground cover fractions. We performed our study for 13 sites across European grasslands and derived the trends based on the Cumulative Endmember Fractions calculated from monthly composites. We compared three different data density scenarios, i.e., 1984–2021 Landsat data record as is, 1984–2021 Landsat data record with the monthly probability of data after 2014 adjusted to the pre-2014 levels, and the combined 1984–2021 Landsat and 2015–2021 Sentinel-2 datasets. For each site we ran SMA using a selection of sitespecific and generalized endmembers, and compared the pixel- and map-level trends. Our results indicated no significant impact of varying data density on the long-term trends from Cumulative Endmember Fractions in European grasslands. Conversely, the use of different endmember definitions led in some regions to significantly different pixel- and map-level long-term trends raising questions about the suitability of the ‘classical’ SMA for complex landscapes and large territories. Therefore, we caution against using the ‘classical’ SMA for remotesensing- based applications across broader scales or in heterogenous landscapes, particularly for trend analyses, as the results may lead to erroneous conclusions.
File: Lewinska-et-al.-2025-Impact-of-data-density-and-endmember-definitions.pdf
Traditional approaches of forest classifications were based on tree species composition, but recently combine phenology
and climate to characterise functional (cyclic and seasonal greenness) rather than structural or compositional
components (phenoclusters). The objective was to compare the conservation value (capacity to support more native
biodiversity) and provision of ecosystem services (ES) in different phenocluster categories of Nothofagus antarctica
forests in Tierra del Fuego (Argentina). We used available models (ES, potential biodiversity) and ground-truth data of
145 stands, comparing phenocluster values using uni- and multivariate analyses. Conservation value and capacity to
supply ES significantly varied among phenocluster categories: (i) cultural, regulating, and provisioning ES and potential
biodiversity at landscape level, (ii) soil carbon and nitrogen, (iii) dominant height, crown cover, basal area, total volume,
and domestic animal stock, and (iv) understory plant richness and cover at stand level. These differences are linked to
the forest capacity to support more native biodiversity and ES. Besides, multivariate analyses supporting the split of
this forest type into four phenocluster subtypes (coast, highland, ecotone with other types, and degraded or secondary
forests). Our findings suggest the needs of specific management and conservation proposals, based on phenoclusters
rather than forest types defined by tree canopy-cover composition.
File: Martinez-Pastur-et-al.-2025_Conservation-value_Nothofagus-antarctica_based-on-phenoclusters.pdf
Land cover change substantially affects ecosystems and leaves long-lasting legacies. Unfortunately, land cover analyses typically begin in the mid-1980s, when 30-m Landsat data became available, missing major global changes that occurred in the 1960s and 1970s. We aimed to quantify long-term land cover changes in the Caucasus (240,000 km2) comparing the magnitude of Soviet-era (1965–1987) versus post-Soviet changes (1987–2015). We (a) mapped land cover based on 1965 Corona spy satellite imagery and (b) quantified long-term changes by comparing 1965 Corona with 1987 and 2015 Landsatbased classifications while accounting for the differences in sensors’ spatial and spectral resolutions. Our Corona-derived map accuracy was 74.4 ± 3.7%, and change accuracies were 66.0 ± 4.2% for 1965–1987 and 61.6 ± 2.8% for 1965–2015. Overall, 30% of the land changed during the Soviet era compared to 20% during the post-Soviet era, highlighting the importance of mapping those early changes. Change trajectories differed considerably during the Soviet era and thereafter. For example, forests were lost during the Soviet era (− 6%) but gained area post-1987 (+ 5%). Croplands were often lost (− 18%) due to grassland gains (+ 11%), which were continuous, but at different rates (4% versus 7%), whereas croplands were lost in both eras, especially post-1987 (3% versus 16%). There were stark differences among countries: Azerbaijan underwent post-Soviet cropland gains, while the Russian Caucasus and Georgia experienced forest gains. Our results highlight the feasibility and value of early spy satellite data for long-term land cover change analyses, particularly in regions with substantial land cover changes then.
File: s10113-025-02360-6.pdf
Knowing where and when croplands have been abandoned or otherwise removed from cultivation is fundamental to evaluating future uses of these areas, e.g. as sites for ecological restoration, recultivation, bioenergy production, or other uses. However, large uncertainties remain about the location and time of cropland abandonment and how this process and the availability of associated lands vary spatially and temporally across the United States. Here, we present a nationwide, 30 m resolution map of croplands abandoned throughout the period of 1986–2018 for the conterminous United States (CONUS). We mapped the location and time of abandonment from annual cropland layers we created in Google Earth Engine from 30 m resolution Landsat imagery using an automated classification method and training data from the U.S. Department of Agriculture Cropland Data Layer. Our abandonment map has overall accuracies of 0.91 and 0.65 for the location and time of abandonment, respectively. From 1986 to 2018, 12.3 (±2.87) million hectares (Mha) of croplands were abandoned across CONUS, with areas of greatest change over the Ogallala Aquifer, the southern Mississippi Alluvial Plain, the Atlantic Coast, North Dakota, northern Montana, and eastern Washington state. The average annual nationwide abandoned area across our study period was 0.51 Mha per year. Annual abandonment peaked between 1997 and 1999 at a rate of 0.63 Mha year−1 , followed by a continuous decrease to 0.41 Mha year−1 in 2009–2011. Among the abandoned croplands, 53% (6.5 Mha) changed to grassland and pasture, 18.6% (2.28 Mha) to shrubland and forest, 8.4% (1.03 Mha) to wetlands, and 4.6% (0.56 Mha) to non-vegetated lands. Of the areas that we mapped as abandoned, 19.6% (2.41 Mha) were enrolled in the Conservation Reserve Program as of 2020. Our new map highlights the long-term dynamic nature of agricultural land use and its relation to various competitive pressures and land use policies in the United States.
File: Xie_2024_Environ._Res._Lett._19_044009.pdf
Habitat loss and fragmentation in tropical regions are major threats to the persistence of endangered Malay tapir (Tapirus indicus). The Malay tapir distribution is largely constrained to fragmented habitats inside protected areas. However, it is unclear how the spatial patterns of habitat fragmentation affect its relative abundance. Here, we investigated the effects of habitat fragmentation on Malay tapir relative abundance in Thailand. We first quantified the spatial patterns of habitat fragmentation within nine of Thailand’s protected areas. Second, we assessed the relationship of fragmentation metrics and relative abundance of Malay tapirs. Third, we identified the relative importance of the fragmentation metrics in explaining relative abundance. We found that tapir abundance remained unexpectedly high in the Southern forest complex despite the fact that tapir habitats were significantly more fragmented there than in the protected area in the western forest complex (p < 0.05). Additionally, we found a significantly negative relation with clumpiness index (R2 = 0.51, p < 0.05). This suggests that other factors may also be influencing their populations, so that the Southern protected areas provide preferred habitat with higher relative proportions of moist evergreen forest, large habitat patch size, precipitation, and elevation. It highlights the importance of interconnected habitat for tapirs, and the benefit of conservation efforts in small, less recognized protected areas.
File: Suwannaphong-s2.0-S2351989424003901-main.pdf
Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. For example, classifications based on 30-m Landsat data do not capture urban or other heterogeneous environments well. This limitation may be overcome by quantifying the subpixel fractions of different land cover types. However, the selection process and transferability of models designed for subpixel land cover mapping across biomes is yet challenging. We asked to what extent (a) locally trained models can be used for sub-pixel land cover fraction estimates in other biomes, and (b) training data from different regions can be combined into spatially generalized models to quantify fractions across global biomes. We applied machine learning regression-based fraction mapping to quantify land cover fractions of 18 regions in five biomes using Landsat data from 2022. We used spectral-temporal metrics to incorporate intra-annual temporal information and compared the performance of local, spatially transferred, and spatially generalized models. Local models performed best when applied to their respective sites (average mean absolute error, MAE, 9–18%), and also well when transferred to other sites within the same biome, but not consistently so for out-of-biome sites. However, spatially generalized models that combined input data from many sites worked very well when analyzing sites in many different biomes, and their MAE values were only slightly higher than those of the respective local models. A weighted training data selection approach, preferring training data with a lower spectral distance to the image data to be predicted, further enhanced the performance of generalized models. Our results suggest that spatially generalized regression-based fraction models can support multi-class sub-pixel fraction estimates based on medium resolution satellite images globally. Such products would have great value for environmental monitoring in heterogeneous environments and where land cover varies along spatial or temporal gradients.
File: Schug-1-s2.0-S0034425724002785-main.pdf
As land use intensifies globally, it increasingly exerts pressure on protected areas. Despite open, nonforested landscapes comprising up to 40% of protected areas globally, assessments have predominately focused on forests, overlooking the major pressures on rangelands from livestock overgrazing and land conversion. Across the southern Caucasus, a biodiversity hotspot extending over 5 countries, we conducted a broadscale assessment of the extent to which protected areas mitigate land-use pressure on rangelands in them. Using satellite-based indicators of rangeland vegetation greenness from 1988 to 2019, we assessed the effectiveness of 52 protected areas. This period encompassed the collapse of the Soviet Union, economic crises, armed conflicts, and a major expansion of the protected area network.We applied matching statistics combined with fixed-effects panel regressions to quantify the effectiveness of protected areas in curbing degradation as indicated by green vegetation loss. Protected areas were, overall, largely ineffective. Green vegetation loss was higher inside than outside protected areas in most countries, except for Georgia and Turkey. Multiple-use protected areas (IUCN categories IV–VI) were even more ineffective in reducing vegetation loss than strictly protected areas (I & II), highlighting the need for better aligning conservation and development targets in these areas. Mapping >10,000 livestock corrals from satellite images showed that protected areas with a relatively high density of livestock corrals had markedly high green vegetation loss. Ineffectiveness appeared driven by livestock overgrazing. Our key finding was that protected areas did not curb rangeland degradation in the Caucasus. This situation is likely emblematic of many regions worldwide, which highlights the need to incorporate degradation and nonforest ecosystems into effectiveness assessments.
File: Conservation-Biology-2024-Ghoddousi-Effectiveness-of-protected-areas-in-the-Caucasus-Mountains-in-preventing.pdf
Climate warming enables easier access and operation in the Arctic, fostering industrial and urban development. However, there is no comprehensive pan-Arctic overview of industrial and urban development, which is crucial for the planning of sustainable development of the region. In this study, we utilize satellite-derived artificial light at night (ALAN) data to quantify the hotspots and the development of light-emitting human activity across the Arctic from 1992 to 2013. We find that out of 16.4 million km2 analyzed a total area of 839,710 km2 (5.14%) is lit by human activity with an annual increase of 4.8%. The European Arctic and the oil and gas extraction regions in Russia and Alaska are hotspots of ALAN with up to a third of the land area lit, while the Canadian Arctic remains dark to a large extent. On average, only 15% of lit area in the Arctic contains human settlement, indicating that artificial light is largely attributable to industrial human activity. With this study, we provide a standardized approach to spatially assess human industrial activity across the Arctic, independent from economic data. Our results provide a crucial baseline for sustainable development and conservation planning across the highly vulnerable Arctic region.
File: akandil-et-al-2024-artificial-light-at-night-reveals-hotspots-and-rapid-development-of-industrial-activity-in-the-arctic.pdf
There is ongoing debate among conservationists regarding the value of small habitat patches to sustain wild populations in farmlands. Our goal was to assess bird abundance in riparian forests differing in terms of size, configuration, landscape conditions and degradation level, to both inform the debate and to identify conservation strategies to maintain diverse agricultural landscapes. We conducted bird point-counts in 91 sites in 2016 across an agricultural valley in Chile. Using models that accounted for imperfect detection, we assessed variation in bird densities in riparian forests with different sizes and configuration, landscapes, and habitat characteristics. We found support in univariates models for our prediction that bird densities varied across riparian forest of various sizes and configuration for 10 of 16 bird species. However, when we added landscape and habitat characteristics to the model, we found that the densities of many of the birds were best explained by forest cover around their local (1 ha) and broader (50 ha) landscape combined with forests characteristics (e.g., invasive tree abundance). For example, Black-throated huet-huet and Chucao Tapaculo were positively associated with forest cover at the broader landscape (50 ha), but showed no response to number of patches, patch-size and Euclidean distance. Our results showed no evidence of negative fragmentation effect per se (i.e., after controlling for habitat area). While agricultural landscapes provide habitat for some species that use small forest patches, conservation strategies focusing on maintaining high level of forest cover and native vegetation are required to secure populations of forest affiliated species.
File: Rojas_et_all_BioConservation_2024_Riparian_forest_patches.pdf
