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Individuals, communities, and societies ascribe a diverse array of values to landscapes. These values are shaped by the aesthetic, cultural, and recreational benefits and services provided by those landscapes. However, across the globe, processes such as urbanization, agricultural intensification, and abandonment are threatening landscape integrity, altering the personally meaningful connections people have toward specific places. Existing methods used to study landscape values, such as social surveys, are poorly suited to capture dynamic landscape-scale processes across large geographic extents. Social media data, by comparison, can be used to indirectly measure and identify valuable features of landscapes at a regional, continental, and perhaps even worldwide scale. We evaluate the usefulness of different social media platforms—Panoramio, Flickr, and Instagram—and quantify landscape values at a continental scale. We find Panoramio, Flickr, and Instagram data can be used to quantify landscape values, with features of Instagram being especially suitable due to its relatively large population of users and its functional ability of allowing users to attach personally meaningful comments and hashtags to their uploaded images. Although Panoramio, Flickr, and Instagram have different user profiles, our analysis revealed similar patterns of landscape values across Europe across the three platforms. We also found variables describing accessibility, population density, income, mountainous terrain, or proximity to water explained a significant portion of observed variation across data from the different platforms. Social media data can be used to extend our understanding of how and where individuals ascribe value to landscapes across diverse social, political, and ecological boundaries.

Ecological memory is central to how ecosystems respond to disturbance and is maintained by two types of legacies -information and material. Species life-history traits represent an adaptive response to disturbance and are an information legacy; in contrast, the abiotic and biotic structures (such as seeds or nutrients) produced by single disturbance events are material legacies. Disturbance characteristics that support or maintain these legacies enhance ecological resilience and maintain a \"safe operating space\" for ecosystem recovery. However, legacies can be lost or diminished as disturbance regimes and environmental conditions change, generating a \"resilience debt\" that manifests only after the system is disturbed. Strong effects of ecological memory on post-disturbance dynamics imply that contingencies (effects that cannot be predicted with certainty) of individual disturbances, interactions among disturbances, and climate variability combine to affect ecosystem resilience. We illustrate these concepts and introduce a novel ecosystem resilience framework with examples of forest disturbances, primarily from North America. Identifying legacies that support resilience in a particular ecosystem can help scientists and resource managers anticipate when disturbances may trigger abrupt shifts in forest ecosystems, and when forests are likely to be resilient.

In spite of increasing point density and accuracy, airborne lidar point clouds often exhibit point density variations. Some of these density variations indicate issues with point clouds, potentially leading to errors in derived products. To highlight these issues, we provide an overview of point density variations and show examples in six airborne lidar point cloud datasets that we used in our topographic and geospatial modeling research. Using the published literature, we identified sources of point density variations and issues indicated or caused by these variations. Lastly, we discuss the reduction in point density variations using decimations, homogenizations, and their applicability.

Context Expansion of urban settlements has caused observed declines in ecosystem services (ES) globally, further stressing the need for informed urban development and policies. Incorporating ES concepts into the decision making process has been shown to support resilient and functional ecosystems. Coupling land change and ES models allows for insights into the impacts and anticipated trade-offs of specific policy decisions. The spatial configuration of urbanization likely influences the delivery and production of ES. Objective When considering multiple ES simultaneously, improving the production of one ecosystem service often results in the decrease in the provision of other ES, giving rise to trade-offs. We examine the impact of three urban growth scenarios on several ES to determine the degree to which spatial configuration of urbanization and the development of natural land cover impacts these services over 25 years. Methods We couple land change and ES models to examine impacts to carbon sequestration, surface water-run off, nitrogen and phosphorus export, organic farming and camping site suitability, to determine trade-offs among the six ES associated with each spatial configuration for western North Carolina. Results Consequences of urban configurations are dramatic, with degraded ES across all scenarios and substantial variation depending on urban pattern, revealing trade-offs. Counter-intuitive trade-offs between carbon sequestration and lands available for organic farming and camping were observed, suggesting that no configurations result in mutual benefits for all ES. Conclusions By understanding trade-offs associated with urban configurations, decision makers can identify ES critical to an area and promote configurations that enhance those.

Floods are the leading cause of natural disaster damages in the United States, with billions of dollars incurred every year in the form of government payouts, property damages, and agricultural losses. The Federal Emergency Management Agency oversees the delineation of floodplains to mitigate damages, but disparities exist between locations designated as high risk and where flood damages occur due to land use and climate changes and incomplete floodplain mapping. We harnessed publicly available geospatial datasets and random forest algorithms to analyze the spatial distribution and underlying drivers of flood damage probability (FDP) caused by excessive rainfall and overflowing water bodies across the conterminous United States. From this, we produced the first spatially complete map of FDP for the nation, along with spatially explicit standard errors for four selected cities. We trained models using the locations of historical reported flood damage events ( n = 71 434) and a suite of geospatial predictors (e.g. flood severity, climate, socio-economic exposure, topographic variables, soil properties, and hydrologic characteristics). We developed independent models for each hydrologic unit code level 2 watershed and generated a FDP for each 100 m pixel. Our model classified damage or no damage with an average area under the curve accuracy of 0.75; however, model performance varied by environmental conditions, with certain land cover classes (e.g. forest) resulting in higher error rates than others (e.g. wetlands). Our results identified FDP hotspots across multiple spatial and regional scales, with high probabilities common in both inland and coastal regions. The highest flood damage probabilities tended to be in areas of low elevation, in close proximity to streams, with extreme precipitation, and with high urban road density. Given rapid environmental changes, our study demonstrates an efficient approach for updating FDP estimates across the nation.

Coastal forests sequester and store more carbon than their terrestrial counterparts but are at greater risk of conversion due to sea level rise. Saltwater intrusion from sea level rise converts freshwater-dependent coastal forests to more salt-tolerant marshes, leaving 'ghost forests' of standing dead trees behind. Although recent research has investigated the drivers and rates of coastal forest decline, the associated changes in carbon storage across large extents have not been quantified. We mapped ghost forest spread across coastal North Carolina, USA, using repeat Light Detection and Ranging (LiDAR) surveys, multi-temporal satellite imagery, and field measurements of aboveground biomass to quantify changes in aboveground carbon. Between 2001 and 2014, 15% (167 km2) of unmanaged public land in the region changed from coastal forest to transition-ghost forest characterized by salt-tolerant shrubs and herbaceous plants. Salinity and proximity to the estuarine shoreline were significant drivers of these changes. This conversion resulted in a net aboveground carbon decline of 0.13 ± 0.01 TgC. Because saltwater intrusion precedes inundation and influences vegetation condition in advance of mature tree mortality, we suggest that aboveground carbon declines can be used to detect the leading edge of sea level rise. Aboveground carbon declines along the shoreline were offset by inland aboveground carbon gains associated with natural succession and forestry activities like planting (2.46 ± 0.25 TgC net aboveground carbon across study area). Our study highlights the combined effects of saltwater intrusion and land use on aboveground carbon dynamics of temperate coastal forests in North America. By quantifying the effects of multiple interacting disturbances, our measurement and mapping methods should be applicable to other coastal landscapes experiencing saltwater intrusion. As sea level rise increases the landward extent of inundation and saltwater exposure, investigations at these large scales are requisite for effective resource allocation for climate adaptation. In this changing environment, human intervention, whether through land preservation, restoration, or reforestation, may be necessary to prevent aboveground carbon loss.

In the United States, requirements for flood insurance, development restrictions, and federal buyout program eligibility rely on regulatory designation of hazardous zones, i.e., inside or outside the 100-year floodplain. Extensive research has investigated floodplain development patterns across different geographies, times, and scales, yet the impacts, and potential unintended consequences, of floodplain policies beyond their boundaries have not been empirically examined. We posit that the regulatory 100-year floodplain presents a “safe development paradox”, whereby attempts to reduce flood risk paradoxically intensifies it by promoting development in and near flood-prone areas. We conducted the first comprehensive national assessment of historical and future development patterns related to the regulatory 100-year floodplain, examining the spatial distribution of developed land within increasingly distant 250-m zones from floodplain boundaries. We found a disproportionate concentration of developed land (24% or 89,080 km 2 of developed land by 2019) in zones immediately adjacent to the floodplain, a trend observed at the national, state, and county levels. Nationwide projections suggest that approximately 22% of all anticipated growth from 2020 to 2060 is likely to occur within 250 m from the 100-year floodplain, equivalent to 6,900 km 2 of new development (SD = 2,842 km 2 ). Understanding and anticipating the influence of flood management policies on current and future land use decisions is crucial for effective planning and mitigation strategies.

We present a multilevel modeling framework for simulating the emergence of landscape spatial structure in urbanizing regions using a combination of field-based and object-based representations of land change. The FUTure Urban-Regional Environment Simulation (FUTURES) produces regional projections of landscape patterns using coupled submodels that integrate nonstationary drivers of land change: per capita demand, site suitability, and the spatial structure of conversion events. Patches of land change events are simulated as discrete spatial objects using a stochastic region-growing algorithm that aggregates cell-level transitions based on empirical estimation of parameters that control the size, shape, and dispersion of patch growth. At each time step, newly constructed patches reciprocally influence further growth, which agglomerates over time to produce patterns of urban form and landscape fragmentation. Multilevel structure in each submodel allows drivers of land change to vary in space (e.g., by jurisdiction), rather than assuming spatial stationarity across a heterogeneous region. We applied FUTURES to simulate land development dynamics in the rapidly expanding metropolitan region of Charlotte, North Carolina, between 1996 and 2030, and evaluated spatial variation in model outcomes along an urban-rural continuum, including assessments of cell- and patch-based correctness and error. Simulation experiments reveal that changes in per capita land consumption and parameters controlling the distribution of development affect the emergent spatial structure of forests and farmlands with unique and sometimes counterintuitive outcomes.

Impacts of sea level rise will last for centuries; therefore, flood risk modeling must transition from identifying risky locations to assessing how populations can best cope. We present the first spatially interactive (i.e., what happens at one location affects another) land change model (FUTURES 3.0) that can probabilistically predict urban growth while simulating human migration and other responses to flooding, essentially depicting the geography of impact and response. Accounting for human migration reduced total amounts of projected developed land exposed to flooding by 2050 by 5%–24%, depending on flood hazard zone (50%–0.2% annual probability). We simulated various “what-if” scenarios and found managed retreat to be the only intervention with predicted exposure below baseline conditions. In the business-as-usual scenario, existing and future development must be either protected or abandoned to cope with future flooding. Our open framework can be applied to different regions and advances local to regional-scale efforts to evaluate potential risks and tradeoffs.

Models that are both spatially and temporally dynamic are needed to forecast where and when non-native pests and pathogens are likely to spread, to provide advance information for natural resource managers. The potential US range of the invasive spotted lanternfly (SLF, Lycorma delicatula ) has been modeled, but until now, when it could reach the West Coast’s multi-billion-dollar fruit industry has been unknown. We used process-based modeling to forecast the spread of SLF assuming no treatments to control populations occur. We found that SLF has a low probability of first reaching the grape-producing counties of California by 2027 and a high probability by 2033. Our study demonstrates the importance of spatio-temporal modeling for predicting the spread of invasive species to serve as an early alert for growers and other decision makers to prepare for impending risks of SLF invasion. It also provides a baseline for comparing future control options. Process-based modelling reveals the predicted spread of the invasive spotted lanternfly to California by 2033 without controlled management.