In the last decades, invasive plant species have rapidly settled on the islands in the Danube River. This process is closely related to anthropogenic activities, such as felling and cultivation of soil for affor-estation of Euro-American poplar trees throughout the Danube River basin. In this regard, it is im-portant to: (i) determine the speed and scope of invasive vegetation establishment and (ii) determine more precisely the ongoing successional dynamics. Monitoring in permanent plots is needed to track the changes and assist in decision-making processes for the management of protected areas on the islands in the Danube River. The aim of the study is to describe and justify an innovative approach to analyse the dynamic of the invasion species occurrence and development as related to site conditions and management across the islands in the Danube River. The following methodological approach was considered: (1) selection of islands with well-preserved and sufficient in area covered by natural forest vegetation; (2) differentiation of strata by forest type, age and canopy cover; (3) setting up permanent sample plots for long-term monitoring in natural riparian forests.

In the last decades, invasive plant species have rapidly settled on the islands in the Danube River. This process is closely related to anthropogenic activities, such as felling and cultivation of soil for affor-estation of Euro-American poplar trees throughout the Danube River basin. In this regard, it is im-portant to: (i) determine the speed and scope of invasive vegetation establishment and (ii) determine more precisely the ongoing successional dynamics. Monitoring in permanent plots is needed to track the changes and assist in decision-making processes for the management of protected areas on the islands in the Danube River. The aim of the study is to describe and justify an innovative approach to analyse the dynamic of the invasion species occurrence and development as related to site conditions and management across the islands in the Danube River. The following methodological approach was considered: (1) selection of islands with well-preserved and sufficient in area covered by natural forest vegetation; (2) differentiation of strata by forest type, age and canopy cover; (3) setting up permanent sample plots for long-term monitoring in natural riparian forests.

International audience | Forest conservation in human-dominated tropical landscapes ensures provision of major ecosystem services. However, conservation goals are threatened by growing demands for agricultural products. As the expansion of agricultural frontiers continues to exert increasing pressure on forest cover, it is crucial to provide indicators on forest vulnerability to improve our understanding of forest dynamics and prioritize management actions by local decision-makers. The purpose of this study is to develop a rigorous methodological framework to assess forest ecological vulnerability. We aim at evaluating the potential of remote sensing to characterize forest landscape dynamics in spatial and temporal dimensions. We present an innovative method that spatially integrates current landscape mosaic mapping with 45 years of landscape trajectories using Sentinel-2 and Landsat imagery. We derive indicators of exposure to cropland expansion, sensitivity linked with forest degradation and fragmentation, and forest capacity to respond based on forest landscape composition in Di Linh district in the Central Highlands of Vietnam. We map current forest-agricultural mosaics with high accuracy to assess landscape intensification (kappa index = 0.78). We also map the expansion of the agricultural frontier and highlighted heterogeneous agricultural encroachment on forested areas (kappa index = 0.72-0.93). Finally, we identify degradation and fragmentation trajectories that affect forest cover at different rates and intensity. Combined, these indicators pinpoint hotspots of forest vulnerability. This study provides tailored management responses and levers for action by local decision makers. The accessibility of multi-dimensional remote sensing data and the developed landscape approach open promising perspectives for continuously monitoring agricultural frontiers.

Forest conservation in human-dominated tropical landscapes ensures provision of major ecosystem services. However, conservation goals are threatened by growing demands for agricultural products. As the expansion of agricultural frontiers continues to exert increasing pressure on forest cover, it is crucial to provide indicators on forest vulnerability to improve our understanding of forest dynamics and prioritize management actions by local decision-makers. The purpose of this study is to develop a rigorous methodological framework to assess forest ecological vulnerability. We aim at evaluating the potential of remote sensing to characterize forest landscape dynamics in spatial and temporal dimensions. We present an innovative method that spatially integrates current landscape mosaic mapping with 45 years of landscape trajectories using Sentinel-2 and Landsat imagery. We derive indicators of exposure to cropland expansion, sensitivity linked with forest degradation and fragmentation, and forest capacity to respond based on forest landscape composition in Di Linh district in the Central Highlands of Vietnam. We map current forest-agricultural mosaics with high accuracy to assess landscape intensification (kappa index = 0.78). We also map the expansion of the agricultural frontier and highlighted heterogeneous agricultural encroachment on forested areas (kappa index = 0.72-0.93). Finally, we identify degradation and fragmentation trajectories that affect forest cover at different rates and intensity. Combined, these indicators pinpoint hotspots of forest vulnerability. This study provides tailored management responses and levers for action by local decision makers. The accessibility of multi-dimensional remote sensing data and the developed landscape approach open promising perspectives for continuously monitoring agricultural frontiers.

Forest conservation in human-dominated tropical landscapes ensures provision of major ecosystem services. However, conservation goals are threatened by growing demands for agricultural products. As the expansion of agricultural frontiers continues to exert increasing pressure on forest cover, it is crucial to provide indicators on forest vulnerability to improve our understanding of forest dynamics and prioritize management actions by local decision-makers. The purpose of this study is to develop a rigorous methodological framework to assess forest ecological vulnerability. We aim at evaluating the potential of remote sensing to characterize forest landscape dynamics in spatial and temporal dimensions. We present an innovative method that spatially integrates current landscape mosaic mapping with 45 years of landscape trajectories using Sentinel-2 and Landsat imagery. We derive indicators of exposure to cropland expansion, sensitivity linked with forest degradation and fragmentation, and forest capacity to respond based on forest landscape composition in Di Linh district in the Central Highlands of Vietnam. We map current forest-agricultural mosaics with high accuracy to assess landscape intensification (kappa index = 0.78). We also map the expansion of the agricultural frontier and highlighted heterogeneous agricultural encroachment on forested areas (kappa index = 0.72-0.93). Finally, we identify degradation and fragmentation trajectories that affect forest cover at different rates and intensity. Combined, these indicators pinpoint hotspots of forest vulnerability. This study provides tailored management responses and levers for action by local decision makers. The accessibility of multi-dimensional remote sensing data and the developed landscape approach open promising perspectives for continuously monitoring agricultural frontiers.

The Landsat archive offers great potential for monitoring forest cover change, and new approaches moving from categorical towards continuous change products emerge rapidly. Most approaches, however, require vast amounts of high-quality reference data, limiting their applicability across space and time. We here propose the use of a generalized regression-based unmixing approach to overcome this limitation. The unmixing approach relies on temporally generalized machine learning regression models (random forest regression [RFR] and support vector regression [SVR]), which are trained on synthetically mixed data from a multi-year library of pure and hence easy to identify image spectra. We apply the model to three decades of Landsat data, mapping both overall forest cover and broadleaved/coniferous forest cover fractions across space and time. The resulting maps well represented the spatial-temporal patterns of forest (change) in our study region. The SVR model outperformed the RFR model, yielding accuracies of r² = 0.74/RMSE = 0.18 for the forest cover fraction maps, r² = 0.50/RMSE = 0.24 for the broadleaved forest cover fraction maps, and r² = 0.59/RMSE = 0.23 for coniferous forest cover fraction maps, respectively. Highest map errors were found in mature stands, residential areas, and recently disturbed forests. We also found some variability in forest cover fractions for stable forest pixels over time, which were explained by variation in Landsat image acquisition dates. We conclude that regression-based unmixing using synthetically mixed training data from a multi-year spectral library offers an innovative strategy for mapping forest cover fractions and forest types throughout the Landsat archive that likely can be extended to large areas.

International audience | Forest conservation in human-dominated tropical landscapes ensures provision of major ecosystem services. However, conservation goals are threatened by growing demands for agricultural products. As the expansion of agricultural frontiers continues to exert increasing pressure on forest cover, it is crucial to provide indicators on forest vulnerability to improve our understanding of forest dynamics and prioritize management actions by local decision-makers. The purpose of this study is to develop a rigorous methodological framework to assess forest ecological vulnerability. We aim at evaluating the potential of remote sensing to characterize forest landscape dynamics in spatial and temporal dimensions. We present an innovative method that spatially integrates current landscape mosaic mapping with 45 years of landscape trajectories using Sentinel-2 and Landsat imagery. We derive indicators of exposure to cropland expansion, sensitivity linked with forest degradation and fragmentation, and forest capacity to respond based on forest landscape composition in Di Linh district in the Central Highlands of Vietnam. We map current forest-agricultural mosaics with high accuracy to assess landscape intensification (kappa index = 0.78). We also map the expansion of the agricultural frontier and highlighted heterogeneous agricultural encroachment on forested areas (kappa index = 0.72-0.93). Finally, we identify degradation and fragmentation trajectories that affect forest cover at different rates and intensity. Combined, these indicators pinpoint hotspots of forest vulnerability. This study provides tailored management responses and levers for action by local decision makers. The accessibility of multi-dimensional remote sensing data and the developed landscape approach open promising perspectives for continuously monitoring agricultural frontiers.

The amount of marine litter, mainly composed by plastic materials, has become a global environmental issue in coastal environments. Traditional monitoring programs are based on in-situ visual census, which require human effort and are time-demanding. Therefore, it is crucial to implement innovative mapping strategies to improve the environmental monitoring of marine litter on the coast.This work presents a procedure for an automated Unmanned Aerial System (UAS)-based marine litter mapping on a beach-dune system. A multidisciplinary framework, which comprises photogrammetry, geomorphology, machine learning and hydrodynamic modelling, was developed to process a block of UAS images. The work shows how each of these scientific methodologies can be complementary to improve and making more efficient the mapping of marine litter items with UAS on coastal environment. The very high-resolution orthophoto produced from UAS images was automatically screened by random forest machine learning method, in order to characterize the marine litter load on beach and dune areas, distinctively. The marine litter objects were identified with a F-test score of 75% when compared to manual procedure. The location of major marine litter loads within the monitored area was found related to beach slope and water level dynamics on the beach profiles, suggesting that UAS flight deployment and post-processing for beach litter mapping can be optimized based on these environmental parameters.The described UAS-based marine litter detection framework is intended to support scientists, engineers and decision makers aiming at monitoring marine and coastal pollution, with the additional aim of optimizing and automating beach clean-up operations.

The forests of Northwest lower Michigan are experiencing an increasing variety and level of ecological disturbance, exacerbated by the ongoing and impending effects of climate change. These forests occur within a predominately privately-owned landscape, making responsive and innovative forest regeneration practices difficult to actualize. Assisted range expansion (ARE) is one of many forest regeneration tools being employed in response to disturbances and climate change; however, it is understudied and rarely implemented within the State of Michigan. Here, a citizen science project is presented as a method for addressing this set of difficult circumstances and exploring the feasibility of ARE as a forest regeneration tool within a tri-county area in Northwest lower Michigan. The Assisted Tree Range Expansion Project (ATREP) leverages the unique position of a county-based Conservation District to conduct a community-based planting and monitoring program. ATREP is designed to increase the level of forest regeneration efforts on private and community lands while measuring the success of tree species whose ranges reach their northern limits in mid-Michigan but are predicted to thrive in northern Michigan under future climate models. The organizational structure and working components of the project, as well as the research and methods required for their development are outlined in the following report. A website (ATREP.net) was created to facilitate participant engagement and illustrate project outcomes overtime; it includes an overview of the rationale behind the project, a data collection instruction and submission area, an interactive map of all trees planted through the project, and a community forum that hosts relevant educational materials and discussion. Future directions for the project and expansion of this model beyond the initial tri-county area are also discussed. Additionally, an assessment of the perception of ARE among a subset of Michigan’s professional foresters is presented. This assessment was conducted in order to gauge the level of familiarity and willingness to implement ARE among Michigan’s Conservation District Foresters in particular. The results of the assessment demonstrated strong support for implementing assisted tree range expansion in Michigan.