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
Protected areas are cornerstones of conservation efforts worldwide. However, protected areas do not act in isolation because they are connected with surrounding, unprotected lands. Few studies have evaluated the effects of protected areas on wildlife populations inhabiting private lands in the surrounding landscapes. The lowland tapir Tapirus terrestris is the largest terrestrial mammal of the Neotropics and is categorized as Vulnerable on the IUCN Red List. It is necessary to understand the influence of landscape characteristics on the tapir’s habitat use to enable effective conservation management for this species. Our objectives were to () determine the potential distribution of the lowland tapir’s habitat in the Southern Yungas of Argentina, and () evaluate the role of protected areas and other covariates on tapir habitat use in adjacent private lands. We used records of lowland tapirs to model the species’ potential distribution and determined habitat use with occupancy modelling. Based on the covariates found to be significant in our models, we constructed predictive maps of probability of habitat use and assessed the area of potential habitat remaining for the species. Probability of habitat use was higher in the vicinity of two national parks and small households than further away from them. We found that in % of the lowland tapir’s potential distribution the probability of habitat use is high (..). These areas are near the three national parks in the study area. The probability of detecting lowland tapirs increased with distance to roads. We conclude that national parks play a key role in the persistence of lowland tapir populations on adjacent private lands.
File: Rivera-et-al-2021_National-Parks-influence-habitat-use-of-lowland-tapirs_Southern-Yungas-of-Argentina.pdf
Understandingof the vulnerabilityof populationsexposedto wildfires is limited.We usedan index from the U.S.Centersfor DiseaseControl and Preventionto assessthe socialvulnerabilityof populationsexposedto wildfirefrom 2000–2021in California,Oregon,and Washington,whichaccountedfor 90%of exposures in the westernUnitedStates. The numberof peopleexposedto fire from 2000–2010to 2011–2021increasedsubstantially, withthe largest increase,nearly250%,for peoplewithhighsocialvulnerability. In Oregonand Washington,a higherpercentageof exposedpeoplewere highlyvulnerable (>40%)thanin California(~8%).Increasedsocialvulner-abilityof populationsin burnedareas was the primarycontributorto increasedexposure of the highlyvulner-able in California,whereas encroachmentof wildfires on vulnerable populationswas the primarycontributorinOregonand Washington.Our resultsemphasizethe importanceof integrating the vulnerabilityof at-riskpop-ulationsin wildfire mitigation and adaptation plans.
File: sciadv.adh4615.pdf
The Wildland-Urban Interface (WUI) is the area where natural vegetation is close to housing and area of concern due to various negative consequences for humans and the environment including fire ignitions, landscape fragmentation and human-wildlife interactions. The WUI is a global phenomenon, and widespread in many countries but long-term WUI dynamics and the main factors causing WUI growth are unknown. Our goal was to assess WUI changes in the Polish Carpathians since the mid-19th century, based on high-resolution spatial data for 1860s, 1970s and 2013. We found that WUI covered already 30% of the study area in the 1860s but grew to cover nearly half by 2013, especially at lower elevations. Detailed analysis of WUI determinants confirmed the areas closer to regional administrative centres or located on steep slopes were more WUI-prone. Tourist trail density also fostered WUI occurrence. We conclude that in Central Europe, with a long history of human settlements and agricultural activities, WUI has been a persistent landscape feature for centuries, but increased in area in recent decades due to widespread abandonment of agricultural land combined with development of new residential areas.
File: Kaim_AppGeog_2024.pdf
Socioeconomic shocks can cause regime shifts in land use, but even during shocks, and when land use change is widespread, some areas persist in their land use. The question is what makes these areas more resistant. Our research goal was to find out what explains where arable farming persisted despite a major socioeconomic shock of forced post-war displacements. Our study area were 291 villages in the Polish Carpathians where abandonment due to the forced displacement of the Ukrainian population after WWII was widespread. We compared prewar arable land with 1990 CORINE Land Cover data to quantify land-use change throughout the socialist period. We applied logistic regression with economically relevant environmental and access-related variables, and assessed the explanatory power of our models and relative importance of determinants. Forty years after forced displacements, arable farming persisted only in a small portion of what had been farmed in the 1940s (16 %), while the majority of former arable land converted to forests (54 %) or grasslands (22 %). Arable farming persisted mainly in areas with high accessibility that had oak-hornbeam forest as potential natural vegetation, on less steep slopes, and at lower elevations. Our models predicting agricultural abandonment leading to reforestation performed well (R2 = 0.57), but our model of persistent agriculture had low explanatory power (R2 = 0.26) as did models of conversion to grassland (R2 = 0.24). We therefore conclude that agricultural persistence is driven by different factors than agricultural land abandonment. In the long term, after arable farming ceases, areas can either be completely abandoned or convert to less intensive grassland use. These long-term changes have strong effects on biodiversity and ecosystem services, but are not well predicted by environmental and access-related determinants. Our findings can help to develop strategies and policies for areas affected by agricultural land abandonment caused by depopulation, and other socioeconomic shocks, and highlight the need to understand not only why arable land is abandoned, but also what determines its long-term fate.
File: Kaim_LandUsePolicy_2023.pdf
