Simulated Land Cover Research Articles

The impacts of modelling prescribed vs. dynamic land cover in a high-CO2 future scenario – greening of the Arctic and Amazonian dieback

Abstract. Terrestrial biosphere models are a key tool in investigating the role played by land surface in the global climate system. However, few models simulate the geographic distribution of biomes dynamically, opting instead to prescribe them using remote sensing products. While prescribing land cover still allows for the simulation of the impacts of climate change on vegetation growth and the impacts of land use change, it prevents the simulation of climate-change-driven biome shifts, with implications for the projection of future terrestrial carbon sink. Here, we isolate the impacts of prescribed vs. dynamic land cover implementations in a terrestrial biosphere model. We first introduce a new framework for evaluating dynamic land cover (i.e., the spatial distribution of plant functional types across the land surface), which can be applied across terrestrial biosphere models alongside standard benchmarking of energy, water, and carbon cycle variables in model intercomparison projects. After validating simulated land cover, we then show that the simulated terrestrial carbon sink differs significantly between simulations with dynamic vs. prescribed land cover for a high-CO2 future scenario. This is because of important range shifts that are only simulated when dynamic land cover is implemented: tree expansion into the Arctic and Amazonian transition from forest to grassland. In particular, the projected change in net land–atmosphere CO2 flux at the end of the 21st century is twice as large in simulations with dynamic land cover than in simulations with prescribed land cover. Our results illustrate the importance of climate-change-driven biome shifts for projecting future terrestrial carbon sink.

Modeling Land Cover Change Using MOLUSCE in Kahayan Tengah Forest Management Unit, Kalimantan Tengah

A management unit-based land cover change analysis was examined in Kahayan Tengah Forest Management Unit (FMU) to understand past, present, and future land cover to assist forest management planning in Kahayan Tengah FMU. This study aims to model land cover change in 2011 and 2016, predict 2021, and simulate land cover in 2026 in Kahayan Tengah FMU. Modeling land cover prediction and simulation using MOLUSCE from the QGIS plugin. The results revealed that agricultural land experienced significant increase in total area during 2011–2016. Modeling potential land cover transitions in 2011 and 2016 with the Artificial Neural Network method showed a Kappa coefficient of 0.701 in the good category, and simulation of land cover in 2021 with the Cellular Automata method showed a Kappa coefficient of 0.672 in the good category. By 2026, the agricultural land will continue to increase while forest land tends to remain stable in its total area. This study managed to predict land cover in 2021 and simulated 2026 with good accuracy. Thus, this data and information can support forest management planning in Kahayan Tengah FMU. Keywords: forest management unit, Kahayan Tengah, land cover change, land cover prediction, MOLUSCE

Quo vadis, smallholder forest landscape? An introduction to the LPB-RAP model.

The impacts of the Anthropocene on climate and biodiversity pose societal and ecological problems that may only be solved by ecosystem restoration. Local to regional actions are required, which need to consider the prevailing present and future conditions of a certain landscape extent. Modeling approaches can be of help to support management efforts and to provide advice to policy making. We present stage one of the LaForeT-PLUC-BE model (Landscape Forestry in the Tropics-PCRaster Land Use Change-Biogeographic & Economic model; in short: LPB) and its thematic expansion module RAP (Restoration Areas Potentials). LPB-RAP is a high-resolution pixel-based scenario tool that relies on a range of explicit land use types (LUTs) to describe various forest types and the environment. It simulates and analyzes future landscape configurations under consideration of climate, population and land use change long-term. Simulated Land Use Land Cover Change (LULCC) builds on dynamic, probabilistic modeling incorporating climatic and anthropogenic determinants as well as restriction parameters to depict a sub-national regional smallholder-dominated forest landscape. The model delivers results for contrasting scenario settings by simulating without and with potential Forest and Landscape Restoration (FLR) measures. FLR potentials are depicted by up to five RAP-LUTs. The model builds on user-defined scenario inputs, such as the Shared Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP). Model application is here exemplified for the SSP2-RCP4.5 scenario in the time frame 2018-2100 on the hectare scale in annual resolution using Esmeraldas province, Ecuador, as a case study area. The LPB-RAP model is a novel, heuristic Spatial Decision Support System (SDSS) tool for smallholder-dominated forest landscapes, supporting near-time top-down planning measures with long-term bottom-up modeling. Its application should be followed up by FLR on-site investigations and stakeholder participation across all involved scales.

Simulating Land Cover Changes and It’s Impacts on Land Surface Temperature: A Case Study in Rajshahi, Bangladesh

Simulating Land Cover Changes and It’s Impacts on Land Surface Temperature: A Case Study in Rajshahi, Bangladesh

Hydrological Modelling to Inform Forest Management: Moving Beyond Equivalent Clearcut Area

Forest disturbance can alter the hydrologic conditions of a watershed, including the frequency, magnitude, and timing of peak and low flows. Equivalent Clearcut Area (ECA) has been routinely used in watershed assessments to estimate hydrologic alteration due to forest disturbance. ECA analyses typically rely on broad regional assumptions, qualitative observations, and/or expert judgement, making it difficult to provide accurate quantitative estimates of hydrologic change. Process-based hydrological models offer an improved approach since they replicate watershed processes, can simulate land cover and climate change scenarios, and provide quantitative estimates of hydrologic change, including at ungauged points of interest. A workflow using a regionally calibrated hydrological model to investigate forest disturbance and future climate change scenarios is demonstrated. Results are contrasted with ECA-based outputs and emphasize that in addition to the amount of forest disturbance, watershed physical characteristics and the location of disturbance within a watershed influence the hydrologic response. This approach provides forest managers with quantitative outputs that support risk-based forest management decisions and presents a substantial improvement over ECA-based methods. Keywords: hydrological modelling, ECA-based analysis, watershed assessment, forest disturbance, cumulative effects, risk-based forest management

Assessing and Enhancing Predictive Efficacy of Machine Learning Models in Urban Land Dynamics: A Comparative Study Using Multi-Resolution Satellite Data

Reliable and accurate land-use/land cover maps are vital for monitoring and mitigating urbanization impacts. This necessitates evaluating machine learning simulations and incorporating valuable insights. We used four primary models, logistic regression (LR), support vector machine, random decision forests, and artificial neural network (ANN), to simulate land cover maps for Tsukuba City, Japan. We incorporated an auxiliary input that used multinomial logistic regression to enhance the ANN and obtained a fifth model (ANN was run twice, with and without the new input). Additionally, we developed a sixth simulation by integrating the predictions of ANN and LR using a fuzzy overlay, wherein ANN had an additional new input alongside driving forces. This study employed six models, using classified maps with three different resolutions: the first involved 15 m (ASTER) covering a study area of 114.8 km2, for the second and third, 5 and 0.5 m (derived from WorldView-2 and GeoEye-1) covering a study area of 14.8 km2, and the models were then evaluated. Due to a synergistic effect, the sixth simulation demonstrated the highest kappa in all data, 86.39%, 72.65%, and 70.65%, respectively. The results indicate that stand-alone machine learning-based simulations achieved satisfactory accuracy, and minimalistic approaches can be employed to improve their performance.

Integrating climate change adaptation policies in spatial development planning in hyperarid regions of Kerman province, Iran

In recent years, lifestyle changes and urbanization of societies, as well as macro-environmental changes, i.e. climate changes (CCs), have caused changes in the land spatial structure and the transfer of resources between different economic sectors of the land. The development of long-term spatial development plans (SDPs) needs to be compatible with CCs, especially in hyperarid areas with low supplies and high demands. In this research, machine learning methods; including Cellular Automata (CA), Random Forest (RF) and regression models through PLUS model were used to simulate the amount of supplies and demands based on land cover (LC) maps during the years 2000, 2010 and 2020 in the hyperarid areas of Kerman, Iran. Then, the best predicted model (Kappa = 0.94, overall accuracy = 0.98) was used to simulate changes in LC classes under climate change scenarios (CCSs) for 2050. The results showed the efficiency of machine learning in simulating land cover changes (LCCs) under CCSs. Findings revealed that SDPs of these areas are not compatible under any possible consideration of CCSs. The modeling results showed that spatial development plans under CCSs is not environmentally efficient and there is no compatibility between supplies, based on agricultural lands, and demands, based on increased population, by 2050. Overall, under the scenario of RCP 8.5, man-made, agriculture and natural LC classes with 106.9, 2.9, and 18.6% changes, respectively, showed the greatest changes compared to 2020. Population control, adjustment of infrastructures, and changes in LC plans can reduce socio-economical and socio-environmental problems in the future of hyperarid areas to some extent.

Identification of Key Areas and Early-Warning Points for Ecological Protection and Restoration in the Yellow River Source Area Based on Ecological Security Pattern

The Yellow River Source Area is located in the ecological protection and restoration area of the Sanjiangyuan in China, which has been deteriorating as a result of human-caused overgrazing and the grassland destruction caused by plateau rats and rabbits, as well as the influence of other natural factors. The Yellow River Source Area is an important area that implements ecological restoration and protection in the Three Rivers Source Region, where the ecologically vulnerable areas that are of great significance to the implementation of ecological restoration in national land space should be identified and the early warning should be extended. This measure can help us identify potential ecological problems and take timely and targeted protection measures to ensure the health and sustainable development of the ecosystem in the Yellow River Source Area. Therefore, an in-depth understanding of the ecological vulnerability of the Yellow River Source Area and the establishment of an early warning mechanism are of vital importance for the protection and restoration of the ecology of the national space in the Yellow River Source Area. Aiming to comprehensively delimit ecological restoration areas in the Yellow River Source Area and provide an early warning, this study applies the MSPA method to identify ecological source areas and uses a patch importance index to identify the importance of ecological source areas, along with extracting potential ecological corridors by using the MCR model, screening important ecological corridors by using the gravity model as a criterion, and simulating land cover changes in 2030 under the limitations of ecological security pattern by using the PLUS model. The results show that: (1) 154 potential ecological corridors were extracted based on the MCR model, with a total length of 7891.90. (2) The total area of the landscape types in the Yellow River Source Area was 12,301 based on MSPA analysis, including the 6899.57 of core area. (3) Based on the PLUS model, the area of farmland and forest in the study area will decrease, while the grassland area will increase. According to the studies, the grassland in the study area expands in a circular pattern towards the surrounding wetland and water area, threatening the ecological sources and corridors. In summary, the study delimits one protection zone in the ecological source land and two core ecological restoration zones and points out the early warning points in the ecological protection space. The delimitation and instruction provide specific spatial guidance for the protection and restoration of ecosystems in order to promote ecological sustainability.

Land cover modelling for tropical forest vulnerability prediction in Kalimantan, Indonesia

This study aimed to estimate the vulnerability of tropical forest in Kalimantan, Indonesia, from simulated land cover changes using the Weighted Normalised Likelihood–Markov Chain model (WNL-MC) for the period 2018–2050 under a forest conservation and a no-conservation scenario. Predictions of future vulnerability and forest change were based on land cover maps for 2010 and 2014 developed using the integration of Landsat, ALOS PALSAR, and Sentinel-1 data. The four main land cover classes in the region were oil palm and rubber plantations, native forests, and non-forested areas. The performance of the WNL–MC model was evaluated by comparing the simulated and actual land cover maps for 2018 and validating them against high-resolution images. Kappa accuracy for the simulated maps was >85%, and overall accuracy was >90%. Under the scenario of no forest conservation, native forest showed approximately 50% loss over the next 30 years, while the forest conservation scenario showed a slowing of current deforestation by 18% from 2018 to 2030 and by 5.95% from 2030 to 2050. Native forest outside forest conservation zones was most vulnerable to conversion to other land uses, particularly in lowland areas close to settlements and roads. Estimations of native forest loss, particularly under the no-conservation scenario, emphasise the need for policies to preserve and conserve remaining native forest areas.

Integrating Copernicus land cover data into the i-Tree Cool Air model to evaluate and map urban heat mitigation by tree cover

ABSTRACT Cities host more than half of the world’s population and due to global warming and land use change their vulnerability to deadly heat waves has increased. A healthy vegetated landscape can abate heat wave severity and diminish the related urban heat island through the process of evapotranspiration. This research aimed to develop a methodology for cities to use publicly available Copernicus land cover maps within the i-Tree Cool Air water and energy balance model to map air temperature and humidity. The manuscript presents proof of concept using Naples, Italy with its Mediterranean climate characterized by limited soil water for cooling via evapotranspiration. The approach achieved strong correlations between predicted and observed air temperatures across the city (r ≥ 0.89). During the warm season of 2020, forested land cover was 5°C cooler than land cover dominated by impervious cover. Simulated land cover change, limited to a 10% increase or decrease in tree cover, generated an inverse change of 0.2°C in maximum hourly air temperature, with more trees obtaining cooler air. Soil water limited the cooling, with the generally wetter spring season enabling greater cooling of air temperatures, and summer droughts without irrigation had constrained cooling. Sustainable urban design will likely require an increase in plant cover along with a reduction of impervious surfaces that absorb and reradiate heat in order to improve community resilience to heat waves.

Simulating thaw-induced land cover change in discontinuous permafrost landscapes

Permafrost thaw is causing a rapid evolution of the lowland discontinuous permafrost regions of the Taiga Plains in Northern Canada and elsewhere in the Northern Hemisphere. Notably, this thaw is changing the spatial distribution of the dominant hydrologic land cover types (permafrost plateaus, fens, and isolated bogs) in parts of the Northwest Territories (NWT), Canada. Here, we develop a multinomial time series land cover model (TSLCM) to simulate historical land cover transitions, model spatial patterns of transition, and predict the long term evolution of land cover in the areas surrounding the Scotty Creek Research Station (SCRS), NWT, and similar discontinuous permafrost landscapes. The machine learning-based TSLCM is informed by a set of observed spatio-temporal variables. The independent variables represent driving factors of change, and include the estimated summertime land surface temperature anomaly (LST), the distance and a custom cost distance to land cover interfaces, time increment between initial and final states, and time-accumulated temperature; the dependent variable is classified land cover maps from 1970 to 2008. First, we applied both random forest (RF) and Multinomial Log-Linear Regression (MLR) methods to train a synthetic data model; the model which improves the performance of the TSLCM in extrapolating time series change by adding new data instances to the initial data set. We boosted the initial data by combining the predicted land cover change maps from synthetic data model and the real data set. Then, we evaluated a MLR, RF, and an extreme gradient boosting (XGBoost) model in their ability to simulate land cover change.The final results of this study show that the Ensemble Learning (EL) based approaches are capable of effectively representing historical land cover change and can produce physically consistent and plausible future land cover scenarios. Deterministic predictions from the TSLCM indicate that the permafrost plateaus’ coverage will continue to decrease, with corresponding decreases in isolated bogs’ coverage and their secondary runoff contributing areas.

Predicting land change trends and water consumption in typical arid regions using multi-models and multiple perspectives

Water scarcity has emerged as a major impediment to the long-term development of inland arid region basins. Increased human activity has produced land degradation in arid areas and water stress in recent years. The objective of this research was to expose patterns in land cover changes in inland arid basin and the mechanisms driving them, as well as to optimize the socioeconomic and ecological water consumption structure of water resources. Therefore, this research used Google Earth Engine to map land cover year by year from 2000 to 2020, simulated future land cover changes and driving force analysis using a Patch-generating land use simulation model, and finally established the relationship between water consumption of different systems and different land cover types using linear equations, and scenario simulation method were used to estimates the ecological and agricultural water consumption in 2035. The results showed (1) Over the previous 20 years, there has been an increase in the amount of saline, agricultural land, and natural vegetation (grassland, scrub, and forest), as well as a considerable drop around permanent glaciers and snow. (2) Under the Markov-2035 scenario, saline land decreases to 32.92 km2, natural vegetation decreases to 381.89 km2, arable land shrinks to 135.69 km2, and the permanent glacier shrinks to 30.03 km2. Ecological water consumption would be 0.127 × 108 m3, while agricultural water consumption would be 0.394 × 108 m3. (3) Elevation influenced the variety of all land cover types. Saline land was more sensitive to temperature, railroad proximity, and pH; agricultural land was more sensitive to population density and gross regional product; and natural vegetation was more sensitive to soil organic matter, railroad proximity, and temperature.

Modelling the dynamics of land cover by 2050 in the north-western Atacora (Benin, West Africa)

In the north-western Atacora region, the rugged terrain limits access to agricultural land and creates a particular dynamic of land cover. The objective of this research is to simulate land cover in the 2050s in the mountainous area of northwestern Atacora in Benin. Landsat 5 TM images from 1988 and Landsat 8 OLI-TIRS from 2020 are the main data used. Supervised classification and modelling were performed with Idrisi Selva software. The annual regression rate is 18.30 % and the projection to 2050 predicts the disappearance of dense dry forests and a reduction of forest galleries and woodlands in favour of field and fallow mosaics (56.03 %) and tree and shrub savannas (25.23 %).

Multi-temporal analysis of past and future land cover change in the highly urbanized state of Selangor, Malaysia

BackgroundThis study analysed the multi-temporal trend in land cover, and modelled a future scenario of land cover for the year 2030 in the highly urbanized state of Selangor, Malaysia. The study used a Decision Forest-Markov chain model in the land change modeller (LCM) tool of TerrSet software. Land cover maps of 1999, 2006 and 2017 were classified into 5 classes, namely water, natural vegetation, agriculture, built-up land and cleared land. A simulated land cover map of 2017 was validated against the actual land cover map 2017. The Area Under the Curve (AUC) value of 0.84 of Total Operating Characteristics (TOC) and higher percentage of components of agreement (Hits + Correct rejection) compared to components of disagreement (Misses + False alarm + Wrong hits) indicated successful validation of the model.ResultsThe results showed between the years 1999 to 2017 there was an increase in built-up land cover of 608.8 km2 (7.5%), and agricultural land 285.5 km2 (3.5%), whereas natural vegetation decreased by 831.8 km2 (10.2%). The simulated land cover map of 2030 showed a continuation of this trend, where built-up area is estimated to increase by 723 km2 (8.9%), and agricultural land is estimated to increase by 57.2 km2 (0.7%), leading to a decrease of natural vegetation by 663.9 km2 (8.1%) for the period 2017 to 2030. The spatial trend of land cover change shows built-up areas mostly located in central Selangor where the highly urbanized and populated cities of Kuala Lumpur and Putrajaya and the Klang valley are located.ConclusionThe future land cover modelling indicates that built-up expansion mostly takes place at edges of existing urban boundaries. The results of this study can be used by policy makers, urban planners and other stakeholders for future decision making and city planning.

The impact of land cover changes on temperature parameters in new capital of Indonesia (IKN)

North Penajam Paser Regency and Kutai Kartanegara Regency which are located in East Kalimantan Province are two locations that are planned as the New Capital of Indonesia (IKN). This has become one of the factors changing land cover from vegetation land to urban land, so that it can contribute to temperature changes. In this work, we analyze impacts of land use change on temperature in the new capital city. The change will simulate land cover changes using the Weather Research and Forecasting (WRF) model with two scenarios of land cover change from vegetation land to 547 % and 1,222 % urban land. Scenarios I and II will increase the temperature to 1.17 and 1.77 °C, respectively. This could means that the addition of more urban areas results in an increase in temperature. The quantitative values of this connection will be beneficial for urban planners to manage the development of new capitals without having a significant impact on climate change.

Future land cover and climate may drive decreases in snow wind‐scour and transpiration, increasing streamflow at a Colorado, USA headwater catchment

AbstractUnderstanding how land cover change will impact water resources in snow‐dominated regions is of critical importance as these locations produce disproportionate runoff relative to their land area. We coupled a land cover evolution model with a spatially explicit, physics‐based, watershed process model to simulate land cover change and its impact on the water balance in a 5.0 km2 headwater catchment spanning the alpine–subalpine transition on the Colorado Front Range. We simulated two potential futures both with greater air temperature (+4°C/century) and more precipitation (+15%/century, MP) or less precipitation (−15%/century, LP) from 2000 to 2100. Forest cover in the catchment increased from 72% in 2000 to 84% and 83% in 2050 and to 95% and 92% in 2100 for MP and LP, respectively. Surprisingly, increases in forest cover led to mean increases in annual streamflow production of 12 mm (6%) and 2 mm (1%) for MP and LP in 2050 with an annual control streamflow of 208 mm. In 2100, mean streamflow production increased by 91 mm (44%) and 61 mm (29%) for MP and LP. This result counters previous work as runoff production increased with forested area due to decreases in snow wind‐scour and increases in drifting leeward of vegetation, highlighting the need to better understand the impacts of forest expansion on the spatial pattern of snow scour, deposition and catchment effective precipitation. Identifying the hydrologic response of mountainous areas to climate warming induced land cover change is critically important due to the potential water resources impacts on downstream regions.

Climate and Land Cover Trends Affecting Freshwater Inputs to a Fjord in Northwestern Patagonia

Freshwater inputs strongly influence oceanographic conditions in coastal systems of northwestern Patagonia (41–45°S). Nevertheless, the influence of freshwater on these systems has weakened in recent decades due to a marked decrease in precipitation. Here we evaluate potential influences of climate and land cover trends on the Puelo River (640 m3s–1), the main source of freshwater input of the Reloncaví Fjord (41.5°S). Water quality was analyzed along the Puelo River basin (six sampling points) and at the discharge site in the Reloncaví Fjord (1, 8, and 25 m depth), through six field campaigns carried out under contrasting streamflow scenarios. We also used several indicators of hydrological alteration, and cross-wavelet transform and coherence analyses to evaluate the association between the Puelo River streamflow and precipitation (1950–2019). Lastly, using the WEAP hydrological model, land cover maps (2001–2016) and burned area reconstructions (1985–2019), we simulated future land cover impacts (2030) on the hydrological processes of the Puelo River. Total Nitrogen and total phosphorus, dissolved carbon, and dissolved iron concentrations measured in the river were 3–15 times lower than those in the fjord. Multivariate analyses showed that streamflow drives the carbon composition in the river. High streamflow conditions contribute with humic and colored materials, while low streamflow conditions corresponded to higher arrival of protein-like materials from the basin. The Puelo River streamflow showed significant trends in magnitude (lower streamflow in summer and autumn), duration (minimum annual streamflow), timing (more floods in spring), and frequency (fewer prolonged floods). The land cover change (LCC) analysis indicated that more than 90% of the basin area maintained its land cover, and that the main changes were attributed to recent large wildfires. Considering these land cover trends, the hydrological simulations project a slight increase in the Puelo River streamflow mainly due to a decrease in evapotranspiration. According to previous simulations, these projections present a direction opposite to the trends forced by climate change. The combined effect of reduction in freshwater input to fiords and potential decline in water quality highlights the need for more robust data and robust analysis of the influence of climate and LCC on this river-fjord complex of northwestern Patagonia.

Interpolating missing land cover data using stochastic spatial random forests for improved change detection

AbstractForest cover requires large scale and frequent monitoring as an indicator of biodiversity and progress towards United Nations and World Bank Sustainable Development Goal 15. Measuring change in forest cover over time is an essential task in order to track and preserve quality habitats for species around the world. Due to the prohibitive expense and impracticality of mass field data collection to monitor forest cover at regular intervals, satellite images are a key data source for monitoring forest cover globally. A challenge of working with satellite images is missing data due to clouds. Existing methods for interpolating the missing data based on past images, such as compositing, are effective for stable land cover but can be inaccurate for dynamic and substantially changing landscapes. Here we present an adaptation of our recent stochastic spatial random forest (SS‐RF) method, which combines observed data from a prior image and modelled estimates of the current image to produce interpolated land cover values and associated probabilities of those values. Results show our SS‐RF method accurately detected simulated land cover change under both clear felling (0.83 average overall accuracy) and tree thinning (0.85 average overall accuracy). Our method detected forest cover change substantially more accurately than compositing, offering 39% and 12% increases in average overall accuracy for clear felling and tree thinning simulations respectively. However, when natural fluctuation occurs and there is minimal change in land cover, compositing has equivalent or more accurate performance than our method. Overall we find that our SS‐RF method produces accurate estimates under a range of simulated forest clearing scenarios and has a more accurate and robust performance than compositing when modelling noticeably changing landscapes.

Past and future marsh adaptation: Lessons learned from the Ria Formosa lagoon

It is rational to assume that salt marshes in regions where sediment loads are high should remain stable or prograde when facing a range of sea-level scenarios, whereas those in sediment-poor systems may erode or drown. Despite extensive theoretical and laboratory studies, additional marsh ‘persistence’ indicators under human pressures and accelerated sea-level rise rates are still needed. This study investigates the recent lateral and volumetric changes occurring in the lagoon marshes of the Ria Formosa lagoon (south Portugal), under human pressures and sea-level rise. Our analysis assesses the past (1947–2014) geomorphological evolution of marshes based on aerial imagery analysis and estimates its potential future adjustment to sea-level rise (~100 years) based on SLAMM (landscape-based model) simulated land cover changes. We highlight the influence of both stressors on marsh ecosystems and examine how their interactions can contribute to understanding sea-level rise impacts and ecological resilience of lagoon marshes. Salt marshes in the Ria Formosa have slowly expanded over the last 70 years (~0.2 mm/yr), with local erosion in front of tidal inlets and along the main navigable channels, associated with inlet migration and dredging activities. Past evolution shows that the ecosystem was able to maintain its functions and cope with sea-level rise. However, future marsh trajectories under a high sea-level rise rate suggest unbalanced vertical marsh accretion and progressive migration of the tidal flat (and water bodies) towards the salt marsh area. The model results show evidence of non-linearity in marsh response to high sea-level rise rates, which could indicate the presence of a system tipping-point and potential positive (disturbance-reinforcing) feedbacks within the system, with significant implications to marsh resilience.

Rapid Ecosystem Change at the Southern Limit of the Canadian Arctic, Torngat Mountains National Park

Northern protected areas guard against habitat and species loss but are themselves highly vulnerable to environmental change due to their fixed spatial boundaries. In the low Arctic, Torngat Mountains National Park (TMNP) of Canada, widespread greening has recently occurred alongside warming temperatures and regional declines in caribou. Little is known, however, about how biophysical controls mediate plant responses to climate warming, and available observational data are limited in temporal and spatial scope. In this study, we investigated the drivers of land cover change for the 9700 km2 extent of the park using satellite remote sensing and geostatistical modelling. Random forest classification was used to hindcast and simulate land cover change for four different land cover types from 1985 to 2019 with topographic and surface reflectance imagery (Landsat archive). The resulting land cover maps, in addition to topographic and biotic variables, were then used to predict where future shrub expansion is likely to occur using a binomial regression framework. Land cover hindcasts showed a 235% increase in shrub and a 105% increase in wet vegetation cover from 1985/89 to 2015/19. Shrub cover was highly persistent and displaced wet vegetation in southern, low-elevation areas, whereas wet vegetation expanded to formerly dry, mid-elevations. The predictive model identified both biotic (initial cover class, number of surrounding shrub neighbors), and topographic variables (elevation, latitude, and distance to the coast) as strong predictors of future shrub expansion. A further 51% increase in shrub cover is expected by 2039/43 relative to 2014 reference data. Establishing long-term monitoring plots within TMNP in areas where rapid vegetation change is predicted to occur will help to validate remote sensing observations and will improve our understanding of the consequences of change for biotic and abiotic components of the tundra ecosystem, including important cultural keystone species.