The wildland–urban interface in the United States based on 125 million building locations

The wildland–urban interface (WUI) is the focus of many important land management issues, such as wildfire, habitat fragmentation, invasive species, and human–wildlife conflicts. Wildfire is an especially critical issue, because housing growth in the WUI increases wildfire ignitions and the number of homes at risk. Identifying the WUI is important for assessing and mitigating impacts of development on wildlands and for protecting homes from natural hazards, but data on housing development for large areas are often coarse. We created new WUI maps for the conterminous United States based on 125 million individual building locations, offering higher spatial precision compared to existing maps based on U.S. census housing data. Building point locations were based on a building footprint data set from Microsoft. We classified WUI across the conterminous United States at 30-m resolution using a circular neighborhood mapping algorithm with a variable radius to determine thresholds of housing density and vegetation cover. We used our maps to (1) determine the total area of the WUI and number of buildings included, (2) assess the sensitivity of WUI area included and spatial pattern of WUI maps to choice of neighborhood size, (3) assess regional differences between building-based WUI maps and censusbased WUI maps, and (4) determine how building location accuracy affected WUI map accuracy. Our building-based WUI maps identified 5.6%–18.8% of the conterminous United States as being in the WUI, with larger neighborhoods increasing WUI area but excluding isolated building clusters. Building-based maps identified more WUI area relative to census-based maps for all but the smallest neighborhoods, particularly in the north-central states, and large differences were attributable to high numbers of non-housing structures in rural areas. Overall WUI classification accuracy was 98.0%. For wildfire risk mapping and for general purposes, WUI maps based on the 500-m neighborhood represent the original Federal Register definition of the WUI; these maps include clusters of buildings in and adjacent to wildlands and exclude remote, isolated buildings. Our approach for mapping the WUI offers flexibility and high spatial detail and can be widely applied to take advantage of the growing availability of high-resolution building footprint data sets and classification methods.

File: Carlson-Ecological-Applications-2022-Carlson-The-wildland-urban-interface-in-the-United-States-based-on-125-million-building.pdf

Rural land abandonment is too ephemeral to provide major benefits for biodiversity and climate

Hundreds of millions of hectares of cropland have been abandoned globally since 1950 due to demographic, economic, and environmental changes. This abandonment has been seen as an important opportunity for carbon sequestration and habitat restoration; yet those benefits depend on the persistence of abandonment, which is poorly known. Here, we track abandonment and recultivation at 11 sites across four continents using annual land-cover maps for 1987–2017. We find that abandonment is largely fleeting, lasting on average only 14.22 years (SD = 1.44). At most sites, we project that >50% of abandoned croplands will be recultivated within 30 years, precluding the accumulation of substantial amounts of carbon and biodiversity. Recultivation resulted in 30.84% less abandonment and 35.39% less carbon accumulated by 2017 than expected without recultivation. Unless policy-makers take steps to reduce recultivation or provide incentives for regeneration, abandonment will remain a missed opportunity to reduce biodiversity loss and climate change.

File: Crawford_SciAdv_2022.pdf

Mapping forest types over large areas with Landsat data partially affected by clouds and SLC gaps

The ecosystem services that forests provide depend on tree species composition. Therefore, it is important to map not only forest extent and its dynamics, but also composition. Open access to Landsat has resulted in considerable improvements in remote sensing methods for mapping tree species, but most approaches fail to perform when there is a shortage of clear observations. Our main goal was to map forest composition with Landsat imagery in various data availability conditions, and to investigate how the missing data, either due to clouds or scan line problems affect classification accuracy. We tested a data driven approach that is based on multi-temporal analysis of the tree species’ spectral characteristics making it applicable to regional-scale mapping even when the gap-free imagery is not available. Our study area consisted of one Landsat footprint (26/28) located in Northern Wisconsin, USA. We selected this area because of numerous tree species (23), heterogenic composition of forests where the majority of stands are mixed, and availability of high-quality reference data. We quantified how classification accuracy at the species level was affected by a) the amount of missing data due to cloud cover and Scanning Line Corrector (SLC) gaps, b) the number of acquisitions, and c) the seasonal availability of images. We applied a decision tree classifier, capable of handling missing data to both single- and a three-year Landsat-7 and Landsat-8 observations. We classified the dominant tree species in each pixel and grouped results to forest stands to match our reference data. Our results show four major findings. First, producer’s and user’s accuracies range from 46.2% to 96.2% and from 59.9% to 93.7%, respectively for the most abundant forest types in the study area (all types covering greater than 2% of the forest area). Second, all tree species were mapped with overall accuracy above 70% even in when we restricted our data set to images having gaps larger than 30% of the study area. Third, the classification accuracy improved with more acquisitions, especially when images were available for the fall, spring, and summer. Finally, producer’s accuracies for pure-stands were higher than those for mixed stands by 10 to 30 percentage points. We conclude that inclusion of Landsat imagery with missing data allows to map forest types with accuracies that previously could be achieved only for those rare years for which several gap-free images were available. The approach presented here is directly applicable to Landsat-like observations and derived products such as seasonal composites and temporal statistics that miss 30% or more of the data for any single date to develop forest composition maps that are important for both forest management and ecology.

File: Turlej_IJAEOG_2022.pdf

Localized versus wide-ranging effects of the post-Soviet wars in the Caucasus on agricultural abandonment

Wars are frequent and can affect land use substantially, but the effects of wars can vary greatly depending on their characteristics, such as intensity or duration. Furthermore, the spatial scale of the effects can differ. The effects of wars may be localized and thus close to conflict locations if direct mechanisms matter most (e.g., abandonment because active fighting precludes farming), or wide-ranging, e.g., farther away from conflict locations, if indirect mechanisms predominate (e.g., no access to agricultural inputs). Our goal was to quantify how the very different wars in the Caucasus region during post-Soviet times most likely affected agricultural abandonment at different scales. We analyzed data on conflict locations plus Landsat-derived land-cover data from 1987 to 2015, and applied matching statistics, difference-in-differences estimators, and logistic panel regressions. We examined the localized versus wide-ranging effects of the different wars on permanent agricultural abandonment and inferred to direct and indirect mechanisms that may have resulted in agricultural abandonment. While permanent agricultural abandonment was overall surprisingly limited across the Caucasus, up to one third of abandonment was most likely related to the wars. Among the wars, the war in Chechnya was by far the most intense and longest, but its effect on abandonment was similar to the less intense and relatively short war in Abkhazia. 47 % and 45 % of agricultural abandonment was related to each war, respectively. The reason was that the effect of the war in Chechnya was more localized, and abandonment occurred near conflict locations, in contrast to Abkhazia, where the effect was wide-ranging and abandonment occurred farther away from conflict locations. In contrast, the war in South Ossetia showed no significant effect on abandonment, and the war in Nagorno-Karabakh had the surprising pattern that abandonment was higher where no war had occurred. For each of the wars, abandonment was predominately related to the nearest conflict locations, but in Abkhazia additional conflict locations within 10 km further increased the probability of abandonment. We infer that the direct mechanisms of the war such as bombing, and active fighting most likely resulted in a localized effect close to conflict locations in Chechnya and in Nagorno-Karabakh. However, in Nagorno-Karabakh subsidies for new settlers after the war, (i.e., a positive wide-ranging effect), potentially reduced the amount of abandonment there. In contrast, negative wide-ranging effects such as refugee movements and post-war restrictions on their return is related to broad-scale abandonment in Abkhazia. In summary, permanent agricultural abandonment was not necessarily higher in a war with a high overall intensity. Instead, the effect of a given war varied in scale, and was related to the relative importance of direct and localized versus indirect and wide-ranging mechanisms, including postwar events and policies, which is likely the case for other wars, too.

File: Buchner-GCB_Buchner_2022.pdf

Growth of the wildland-urban interface within and around U.S. National Forests and Grasslands, 1990-2010

The wildland-urban interface (WUI), where housing is in close proximity to or intermingled with wildland vegetation, is widespread throughout the United States, but it is unclear how this type of housing development affects public lands. We used a national dataset to examine WUI distribution and growth (1990–2010) in proximity to National Forests and created a typology to characterize each National Forest’s combination of WUI area and housing growth. We found that National Forests are hotspots for WUI growth, with a 38% increase in WUI area and 46% growth in WUI houses from 1990 to 2010, in excess of WUI growth for the conterminous U.S. Growth within National Forests was higher than the surrounding area. Diffuse intermix WUI, where houses are intermingled with wildland vegetation, is common within National Forests, but WUI houses around National Forests were primarily in denser interface WUI areas, which lack substantial wildland vegetation. WUI was more prevalent within and around National Forests in the East, while National Forests in the West experienced higher rates of WUI growth. National Forests with the most challenging WUI issues—extensive WUI area and rapid growth in intermix and interface—were found primarily in the South and interior West. Given the diversity of WUI landscapes, effectively responding to current and future WUI challenges will require both engagement with individual homeowners dispersed throughout National Forests, as well as increased emphasis on mitigating denser interface development around National Forests. At a time when wildfire risks are expected to intensify due to climate change, and 75% of privately owned land within and around National Forests is not yet WUI, understanding WUI growth patterns in proximity to public lands is vital for land management and human wellbeing.

File: Mockrin_LUP_2022.pdf

Conservation responsibility for bird species in tropical logged forests

Unprotected lands can help prevent the extinctions of species if managed carefully. Over half of the tropical forest is leased by logging companies, whereas only 6%–18% is protected. This makes the timber industry, institutions that regulate it, and consumers of its products important actors in conservation. We assessed the conservation responsibility, the proportion of a species’ range that tropical timber industry concessions overlap with, for bird species that decline after selective logging. Up to 32% of the global range and up to 100% of the national range of sensitive species within our study countries are leased by logging companies. Individual concessions overlap with the ranges of up to 25 sensitive and more than 500 total bird species, with a particularly high density in Borneo. Our results can inform governments, forest managers, sustainability certifiers, and consumers so that they can turn this responsibility into a conservation opportunity through interventions at multiple scales.

File: Burivalova_Conservation-Letters-2022-Conservation-responsibility-for-bird-species-in-tropical-logged-forests.pdf

Early warning sign of forest loss in protected areas

As humanity is facing the double challenge of species extinctions and climate change, designating parts of forests as protected areas is a key conservation strategy.1–4 Protected areas, encompassing 14.9% of the Earth’s land surface and 19% of global forests, can prevent forest loss but do not do so perfectly everywhere. 5–12 The reasons why protection only works in some areas are difficult to generalize: older and newer parks, protected areas with higher and lower suitability for agriculture, and more and less strict protection can be more effective at preventing forest loss than their counterparts.6,8,9,12–16 Yet predicting future forest loss within protected areas is crucial to proactive conservation. Here, we identify an early warning sign of subsequent forest loss, based on forest loss patterns in strict protected areas and their surrounding landscape worldwide, from 2000 to 2018.17,18 We found that a low level in the absolute forest cover immediately outside of a protected area signals a high risk of future forest loss inside the protected area itself. When the amount of forest left outside drops to <20%, the protected area is likely to experience rates of forest loss matching those in the wider landscape, regardless of its protection status (e.g., 5% loss outside will be matched by 5% loss inside). This knowledge could be used to direct funding to protected areas threatened by imminent forest loss, helping to proactively bolster protection to prevent forest loss, especially in countries where detailed information is lacking.

File: mmc3.pdf

Effect of forest logging on food availability, suitable nesting habitat, nest density and spatial pattern of a Neotropical parrot

Secondary cavity nesters, bird species that rely on the presence of existing cavities, are highly vulnerable to anthropogenic and stochastic processes that reduce the availability of cavity bearing trees. The most common logging practice in Neotropical forests is selective logging, where a few valuable tree species are logged, primarily old, large trees that are the most prone to develop cavities and produce larger amounts of fruits and seeds. Tucuman Amazon, Amazona tucumana, is a threatened parrot that relies on the tree-cavities and food provided by large, old trees. Our objective was to evaluate how logging affects 1) stand and nest plot forest structure, 2) nesting site selection, 3) food availability, 4) density of suitable cavities, 5) nest density, and 6) nest spatial pattern of Tucuman Amazon by comparing a mature undisturbed forest in a National Park (NP) vs a logged forest (LF). We determined the availability of suitable cavities and food resources consumed by Tucuman Amazon, and we compared nest density and spatial pattern of nests between NP vs LF. The Index of food availability for all tree species consumed by Tucuman Amazon and for P. parlatorei were significantly higher in NP than in LF (34.5 ±13.3 m ha− 1 vs. 3.5 ± 1.0 m ha− 1 and 5.6 ± 2.3 m ha− 1 vs. 1.2 ± 1.0 m ha− 1, respectively). Density of suitable cavities for nesting in the NP was significantly higher than in the LF: 4.6 cavities ha− 1 [C.I. 95 %: 3.07 – 7.04 cavities ha− 1] vs. 1.1 cavities ha− 1 [C.I. 95 %: 0.73 – 1.66 cavities ha− 1], respectively. Mean density of Tucuman Amazon nests was significantly higher in the NP than in LF (0.25 ± 0.04 vs. 0.06 ± 0.04 nest ha− 1, respectively). Food availability is an important factor that affects Tucuman Amazon populations and when food is not limiting, the availability of suitable cavities and territorial behavior could play a role in regulating nest density. When evaluating the limiting factors for secondary cavity-nesting species of conservation concern it is important to evaluate the interplay of a set of potential limiting factors to propose sound forest management recommendations.

File: Rivera-et-al-2022_Effect-of-logging-on-Tucumon-Amazon.pdf

Changes in the grasslands of the Caucasus based on Cumulative Endmember Fractions from the full 1987–2019 Landsat record

Grasslands are important for global biodiversity, food security, and climate change analyses, which makes mapping and monitoring of vegetation changes in grasslands necessary to better understand, sustainably manage, and protect these ecosystems. However, grassland vegetation monitoring at spatial and temporal resolution relevant to land management (e.g., ca. 30-m, and at least annually over long time periods) is challenging due to complex spatio-temporal pattern of changes and often limited data availability. Here we assess both shortand long-term changes in grassland vegetation cover from 1987 to 2019 across the Caucasus ecoregion at 30-m resolution based on Cumulative Endmember Fractions (i.e., annual sums of monthly ground cover fractions) derived from the full Landsat record, and temporal segmentation with LandTrendr. Our approach combines the benefits of physically-based analyses, missing data prediction, annual aggregations, and adaptive identification of changes in the time-series. We analyzed changes in vegetation fraction cover to infer the location, timing, and magnitude of vegetation change episodes of any length, quantified shifts among all ground cover fractions (i.e., green vegetation, non-photosynthetic vegetation, soil, and shade), and identified change pathways (i.e., green vegetation loss, desiccation, dry vegetation loss, revegetation green fraction, greening, or revegetation dry fraction). We found widespread long-term positive changes in grassland vegetation (32.7% of grasslands), especially in the early 2000s, but negative changes pathways were most common before the year 2000. We found little association between changes in green vegetation and meteorological conditions, and varied relationships with livestock populations. However, we also found strong spatial heterogeneity in vegetation dynamics among neighboring fields and pastures, demonstrating capability of our approach for grassland management at local levels. Our results provide a detailed assessment of grassland vegetation change in the Caucasus Ecoregion, and present an approach to map changes in grasslands even where availability of Landsat data is limited.

File: 1-s2.0-S2666017221000225-main.pdf

Informing forest conservation planning with detailed human footprint data for Argentina

Conserving the remaining wildest forests is a top priority for conservation, and human footprint maps are a practical way to identify wild areas. However, available global assessments of wild areas are too coarse for land use decisions, especially in countries with high deforestation rates, such as Argentina. Our main goal was to map the human footprint in Argentina’s forested areas to improve conservation planning at regional and country levels. Specifically, we quantified the level of human influence on the environment and mapped the wildest native forests (i) across forest regions, and (ii) in the different land-use categories of the National Forest Plan, which is a key policy instrument for conserving the nation’s native forests through zoning, and (iii) identified wildest forests that are at risk due to human activities. We analyzed detailed spatial data on settlements, transportation, energy, and land use change, and estimated the areal extent to which these various human activities disrupt natural processes. We defined pixels with human footprint index of zero as wildest areas. We found that a substantial portion (43%) of Argentina’s forested area remains wild, which suggests there are opportunities for conservation. However, levels of human influence varied substantially among forest regions, and Atlantic and Chaco forests have the highest levels of human influence. Further, we found that the National Forest Plan does not conserve the wildest forests of the nation, as most (78%) of the wildest native forests are located in zones that allow silvopasture, timber production, and/or forest conversion to crops, thus potentially threatening biodiversity in these areas. Our map of wildest forests is an important, but first, step in identifying wildland forests in Argentina, as available spatial data layers of human activities capture many, but not all, human influences on forests. For instance, small human features, like certain rural roads, trails, and rural settlements exist in our wildest areas. Our study provides new datasets to assist land use planners and conservationists, and identifies areas for conservation attention in Argentina. More broadly, our analyses highlight the value of detailed human footprint data to support conservation decisions in forest landscapes.

File: Martinuzzi-et-al_2021_HF-Argentina.pdf