Vegetation Encroachment Research Articles

Riparian Vegetated Area in Pre‐Dam, Post‐Dam, and Environmental Flow Periods in Canyonlands National Park From 1940 to 2022

ABSTRACTThe Upper Colorado River Basin is the principal water supply of the western United States and includes a series of canyons that provide habitat for disproportionate numbers of flora and fauna. Following the closing of Flaming Gorge and Blue Mesa dams in 1963 and 1966, decreases in peak flows and elevated base flows allowed extensive vegetation encroachment, channel narrowing, and channel simplification. Since 1992, reservoir releases have been modified to increase the ratio of peak to base flows for environmental reasons, including protection of endangered fish. We used remote imagery from 1940 to 2022 to examine rates of vegetation encroachment along three river reaches in Canyonlands National Park during the pre‐dam (1940–1966), post‐dam (1967–1992), and environmental flows (1993–2022) periods. We found an increase in the vegetated area along the Colorado and Green Rivers upstream of their confluence since 1940. We documented a 6.1% and 4.0% increase in vegetated area in the post‐dam period and a 19.5% and 6.5% increase in vegetated area in the environmental flows period on the Colorado and Green Rivers, respectively. The Cataract Canyon reach (Colorado River below the confluence) has been stable since 1966. All three river reaches showed the slowest period of vegetation encroachment, indicative of channel narrowing, in the last 16 years of environmental flows that included a large peakflow in 2011. Environmental flows that mimic the natural hydrograph have not reversed decreases in peak flow and channel width, due in part to decreasing runoff and increasing flow diversion. Flow alterations that reduce the spring peak could cause further narrowing.

Inferring on fluvial resilience from multi-temporal high-resolution topography and geomorphic unit diversity

The resilience of a river corridor represents its ability to withstand and recover from disturbances. Quantifying fluvial resilience in the face of various stressors is essential for integrating ecology and geomorphology in a context of river management. Geomorphic unit diversity analysis emerges as a valuable tool for characterizing and quantifying fluvial resilience to disturbances due to its inherent connection with fluvial dynamics. This paper aims to analyse and quantify the fluvial resilience of a wandering gravel-bed river affected by natural (e.g., floods) and human-induced (e.g., instream gravel mining) stressors. To achieve this goal, we use multi-temporal high-resolution topographic surveys of the Upper River Cinca (South-Central Pyrenees) spanning from 2014 to 2020. By employing the Geomorphic Unit Tool (GUT) on these surveys, we can map geomorphic units over time, quantify their diversity, and study geomorphic adjustments through morphodynamic signatures, altogether allowing inferring fluvial resilience.Our findings reveal that topographic changes (i.e., erosion and sedimentation) correlate with the type of stressor: maintenance works and gravel mining lead to degradation, while floods induce aggradation. Geomorphic Unit Diversity decreases following channel disturbances caused by gravel mining but rebounds after periods primarily stressed by floods, returning to pre-impact levels within six years. Geomorphic adjustments, such as channel incision and mid bar development, reflect erosion and sedimentation processes respectively, with channel maintenance works and floods being the main drivers. Despite the recovery of the geomorphic unit diversity within the reach, the deficit resulting from gravel extraction remains unresolved, perpetuating a degrading trend that poses risks in reducing lateral connectivity and could potentially catalyse future vegetation encroachment in bars and floodplains, causing changes on flood conveyance and hydraulics. Monitoring the river's geomorphic diversity provides crucial insights for effective conservation and management decisions regarding land use, development, and conservation along riverbanks, thereby sustaining or enhancing fluvial system resilience.

UAV Visual and Thermographic Power Line Detection Using Deep Learning.

Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images.

Identifying Areas of High-Risk Vegetation Encroachment on Electrical Powerlines Using Mobile and Airborne Laser Scanned Point Clouds

Identifying Areas of High-Risk Vegetation Encroachment on Electrical Powerlines Using Mobile and Airborne Laser Scanned Point Clouds

Research on intelligent monitoring technology for roof damage of traditional Chinese residential buildings based on improved YOLOv8: taking ancient villages in southern Fujian as an example

In the process of preserving historical buildings in southern Fujian, China, it is crucial to provide timely and accurate statistical data to classify the damage of traditional buildings. In this study, a method based on the improved YOLOv8 neural network is proposed to select aerial photographs of six villages in Xiamen and Quanzhou cities in Fujian Province as the dataset, which contains a total of 3124 photographs. Based on the high-resolution orthophotographs obtained from UAV tilt photography, the YOLOv8 model was used to make predictions. The main task in the first stage is to select the buildings with historical value in the area, and the model's mAP (Mean Accuracy Rate) can reach 97.2% in the first stage task. The second stage uses the YOLOv8 model to segment the images selected in the first stage, detecting possible defects on the roofs, including collapses, missing tiles, unsuitable architectural additions, and vegetation encroachment. In the second stage of the segmentation task, the mAP reaches 89.4%, which is a 1.5% improvement in mAP50 (mean accuracy) compared to the original YOLOv8 model, and the number of parameters and GFLOPs are reduced by 22% and 15%, respectively. This method can effectively improve the disease detection efficiency of historical built heritage in southern Fujian under complex terrain and ground conditions.

Wood you believe it? Experimental addition of nonnative wood enhances instream habitat for native dryland fishes

AbstractFlow alteration and riparian vegetation encroachment are causing habitat simplification with severe consequences for native fishes. To assess the effectiveness of enhancing simplified habitat in a large dryland river, we experimentally added invasive wood at 19 paired treatment and reference (no wood added) subreaches (50–100 m) within the main channel of the San Juan River. Using a before‐after‐control‐impact design, we sampled fishes and macroinvertebrates, and quantified habitat complexity. After wood addition, total native fish densities were 2.2× higher in treatments compared with references, whereas total nonnative fish densities exhibited no response. Macroinvertebrate densities were 6.8× higher, and habitat complexity increased in treatments. Counts of geomorphic features in treatments increased from 1 to a maximum of 11 following wood addition, while the number of features in references remained unchanged. Wood addition has potential to instigate natural riverine processes, ultimately enhancing native fish habitat by increasing macroinvertebrate densities and habitat complexity in dryland rivers. Water overallocation and increasing aridity will continue to challenge efforts to improve habitat conditions with environmental flows alone, and managers might consider integrating non‐flow alternatives like addition of abundant, invasive wood to reduce habitat simplification.

Invasive vegetation encroachment modulates dual threats faced by island-endemic scaly crickets

The mechanisms by which non-native species impact island-endemic populations are varied and often convoluted. For effective invertebrate conservation where capacity is limited, it is vital to understand both direct and indirect species interactions. Those interactions define specific threats and therefore highlight possible solutions. We quantified several of the non-native species threats faced by an endemic scaly cricket, Discophallus ascension (family: Mogoplistidae), found on Ascension Island’s naturally barren coastlines. We hypothesized that encroaching non-native Mexican thorn Prosopis juliflora (1) directly reduced microclimate suitability, (2) indirectly increased resource competition with non-native species, and (3) indirectly increased non-native predation. Our aim was to assess how rapidly spreading Mexican thorn might reduce endemic cricket populations. Lab trials first confirmed the temperature and diet preferences of D. ascension. Mexican thorn significantly increased ground-level temperature but not above the estimated optimum for D. ascension within key habitat. Observed interspecific resource competition was significantly greater in habitat degraded by Mexican thorn and was driven by non-native ants, house crickets Gryllodes sigillatus and cockroaches Periplaneta americana. Recorded cricket predation was almost entirely by non-native rats Rattus rattus in barren habitat but primarily by ants in Mexican thorn habitat. Threats to D. ascension were therefore modulated by invasive vegetation, but driven by non-native insects and rats at all sites. We conclude that removal of vegetation alone is likely to reduce resource competition but not predation. Managing multiple invasive species is likely necessary for successful species conservation.

Combining terrestrial lidar with single line transects to investigate geomorphic change: A case study on the Upper Verde River, Arizona

The Upper Verde River in northern Arizona, USA is a vital resource for the wildlife and humans that rely on its waters. We characterize the riparian corridor topography using terrestrial laser scanner (TLS) data from 2021 to 2022. We also quantify geomorphic changes associated with human and climate-driven alterations in river flow and vegetation changes by combining the contemporary lidar surveys with legacy measurements from single line geomorphology transects measured by the United States Forest Service (USFS) in 2009. Seventeen plots along the Upper Verde River were surveyed with the TLS and the data were coregistered within individual plots with a Root Mean Square Error of <0.03 m among scan positions. Digital Elevation Models (DEM) were derived for each plot from the TLS data at 10 cm resolution and compared to the 2009 USFS cross-section data to quantify elevation changes. In areas with statistically significant change, we detected maximum changes in elevation due to erosion and deposition of −0.37 m and + 0.97 m, respectively. Topographic changes over the 13-year period were predominately aggradation and associated with sediment deposition, which we hypothesize might have resulted from altered river flow and vegetation encroachment. This study also demonstrates a quantitative and statistical methodology to fuse traditional single line cross-section data with contemporary lidar data to quantify geomorphic change. The novel approach demonstrated here is broadly applicable to natural resource managers for integrating and contextualizing legacy topographic data for understanding past, present, and future landscape and habitat changes.

Satellite Imagery System for Pruning Vegetation Interference in Power Transmission Lines

Vegetation encroachment in power transmission lines can cause outages, which may result in severe impact on electricity board as well as the consumer. Vegetation detection and monitoring along the power lines are implemented to protect power transmission lines from vegetation interference. There were various methods used to monitor the vegetation interference with power transmission lines, however, most of them were too expensive and time consuming. Satellite images can play a pivotal role in vegetation monitoring, because it can cover high spatial area with relatively low cost. The current methods depend usually on setting manually threshold values and parameters which make the detection process very static. Machine Learning (ML) and deep learning (DL) algorithms can provide a very high accuracy with flexibility in the detection process. Hence the potential of using Deep Learning based algorithms are also included. The input data were derived from satellite images, UAV images and other aerial images. This work is significant because it shows how satellite images that are already commercially available can be used for the large-scale assessment of vegetation encroachment on transmission lines

Recent changes in riparian and floodplain vegetation in England and Wales and its geomorphic implications

AbstractRecent river studies in the United Kingdom have observed an increase in riparian vegetation and its potential impact on river channel change and sedimentation. Here, we quantify changes in floodplain and riparian vegetation during the first two decades of the 21st century along reaches of eight gravel‐bed rivers in England and Wales that exhibit varied active and stable, single and multi‐thread planforms. The analysis employs information drawn from open‐access sources including national LiDAR surveys and the photographic analysis of repeat aerial and satellite imagery. Most reaches show an increase in woody vegetation across their floodplains reflecting both natural colonisation and deliberate planting, and all but one of the reaches show an increase in woody riparian vegetation cover along their riverbanks. Of the six reaches that have exposed riverine sediments, five show a reduction in exposed sediment area as a result of vegetation encroachment. Contrasts in the rate, extent and location of riparian change were associated with the stability of the channel planform, with differences seen between stable and more active reaches. The significance of riparian woodland in promoting riverbank stability is demonstrated. Riparian vegetation development varies between river reaches, largely in response to differing rates of channel movement but also because of multiple and diverse local decisions regarding the management of in‐channel and channel‐adjacent vegetation by landowners rather than a single national policy. We consider the relevance of our findings and the value of remote monitoring for future river management interventions.

Spatio-temporal analysis of geomorphic recovery along an altered ephemeral stream using automated image processing

Ephemeral rivers in the Mediterranean region have been exposed to significant human disturbance over the last century. Recently, there has been a growing interest in restoring their morpho-sedimentary condition, backed by the European Water Framework Directive. Previous research has highlighted the severe geomorphic impacts of sediment extraction, such as reduced channel width and riverbed incision, but the recovery of these channels during annual flows remains poorly understood. This study presents a methodological framework to investigate geomorphologic recovery (signs of landform change) and constraints in the Rambla de Cervera (eastern Spain) from 2018 to 2021. Aerial imagery, LiDAR topography, and field surveys were used for stream segmentation, fluvial landform classification, temporal landform change, and comparison with flow characteristics from a 2D hydraulic model. The study identified three groups of segments with different channel types and recovery mechanisms. Upstream (G1), unconfined valley settings showed minor changes, indicating morphological stability. Middle segments in confined valleys (G2) showed greater alluvial channel recovery and vegetation encroachment in response to flood/no-flood periods. Downstream segments (G3), incised on cemented gravel, were highly responsive to floods but lacked landform stability due to high energy conditions. The findings suggest that channel recovery during annual floods is slow and occurs mainly downstream in degraded areas (G3). Increased sediment flux from upstream and additional sediment storage in new forms are required to recover these channels. This analysis underscores the challenge of interpreting short-term morpho-dynamic changes for long-term river recovery, but provides insight into the morpho-sedimentary conditions required to accelerate recovery of ephemeral streams.

Numerical Modeling Prediction of Vegetation Trajectories Under Different Flow Regimes in New Zealand Braided Rivers

AbstractWe use two‐dimensional physics‐based numerical modeling to study multi‐decadal evolution of vegetation and morphology under different flow regimes in real‐world gravel‐bed braided rivers. To assess model realism, we focus on two rivers in Canterbury (New Zealand) that, despite having been subjected to the introduction of similar invasive vegetation species in the last ∼100 years, show very different trajectories of vegetation presence due to their different flow regimes: the Lower Waitaki River and the Waimakariri River. The former, featuring a naturally damped flow regime—and having experienced further artificial flow damping due to hydropower generation from the 1930s, experienced vegetation encroachment; while the latter, featuring a flashy flow regime, retains an unvegetated braided planform. We propose an innovative calibration and validation procedure to determine an optimal setup of vegetation parameters that allows the model to robustly reproduce the trajectories of both rivers, thereby proving that the model responds sensibly to different hydrological conditions. Then, we isolate the impact of hydropower‐related flow regime modifications on vegetation encroachment in the Lower Waitaki by running the calibrated model with a natural flow regime that does not feature the effect of hydropower generation, and find that vegetation encroachment would have happened even without flow alteration, albeit to a milder degree. Finally, we apply to simulation results a conceptual framework based on a synthetic parameter that compares the relative strength of hydrological and vegetation controls, and discuss the use of this parameter as a predictor of vegetation presence across flow regimes.

VEPL-Net: A Deep Learning Ensemble for Automatic Segmentation of Vegetation Encroachment in Power Line Corridors Using UAV Imagery

Vegetation encroachment in power line corridors remains a major challenge for modern energy-dependent societies, as it can cause power outages and lead to significant financial losses. Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution for monitoring infrastructure, owing to their ability to acquire high-resolution overhead images of these areas quickly and affordably. However, accurate segmentation of the vegetation encroachment in this imagery is a challenging task, due to the complexity of the scene and the high pixel imbalance between the power lines, the vegetation and the background classes. In this paper, we propose a deep learning-based approach to tackle this problem caused by the original and different geometry of the objects. Specifically, we use DeepLabV3, U-Net and a modified version of the U-Net architecture with VGG-16 weights to train two separate models. One of them segments the dominant classes, the vegetation from the background, achieving an IoU of 0.77. The other one segments power line corridors from the background, obtaining an IoU of 0.64. Finally, ensembling both models into one creates an “encroachment” zone, where power lines and vegetation are intersected. We train our models using the Vegetation Encroachment in Power Line Corridors dataset (VEPL), which includes RGB orthomosaics and multi-label masks for segmentation. Experimental results demonstrate that our approach outperforms individual networks and original prominent architectures when applied to this specific problem. This approach has the potential to significantly improve the efficiency and accuracy of vegetation encroachment monitoring using UAV, thus helping to ensure the reliability and sustainability of power supply.

Determining the response of riparian vegetation and river morphology to drought using Google Earth Engine and machine learning

Riparian vegetation composition and channel morphology are susceptible to long-term alterations caused by external stressors, including climate-change-induced droughts and engineered infrastructures. The objectives of this study were to (1) quantify trends in riparian vegetation and channel/floodplain morphology over large spatial (∼290 km) and temporal scales (∼30 years) and (2) investigate the relationships between hydroclimatic drivers and changes in riparian vegetation and channel morphology. We implemented a random forest classifier via a machine learning technique in Google Earth Engine. The study area was a 290 km reach of the Rio Grande located in New Mexico, USA. We used the combination of remotely sensed data and products (e.g., Landsat imagery, Normalized Difference Vegetation Index (NDVI), and land cover) to characterize vegetation, vegetation cover changes, and river morphology shifts from 1984 to 2020. The trend analysis revealed increased vegetated areas and NDVI (0.0004/yr) during long-term drought. The channel experienced a reduction in width associated with vegetation encroachment and the formation of stable vegetated islands. The streamflow hydrograph characteristics were positively correlated with vegetation cover and channel morphology. Our study contributes novel insights into the long-term riparian ecosystem dynamics under drought stress, informing drought impact mitigation and ecosystem management in arid and semi-arid regions.

Multi time scale influence of dams on bedload transport

Dams are ones of the main sources of anthropogenic disturbance to the ecology and geomorphology of rivers. The aim of the present study is to understand the mechanisms underlying their influence on downstream bedload transport in three gravel-bed rivers in the Morvan massif, France. The hydrological disturbance caused by four dams is examined at a short (2.4–2.7 years) and longer (21–28 year) time scale. At the short time scale, bedload displacement was monitored (RFID) at 8 study sites around the dams. Morpho-sedimentary characterization of the bed substrate was performed at the study sites and combined with analyses of the long profile evolution and the current cross profile.The flood regime has been to a varying extent durably reduced by the dams depending on their size and purpose: the mean annual maximum flood was reduced by 9 to 40% and the number of flood events by 27 to 73.5% over the 21–28 years period. Sediment availability and loose structures were found above the dams and below medium-sized dams (<10 m) with a null or moderate influence on the flood regime (configuration I). Sediment deficit, consolidated structures, bed coarsening and vegetation encroachment were observed within 20 km downstream of large dams (>15 m) influencing strongly or moderately the flood regime (configuration II). These morpho-sedimentary features significantly affect the current bedload dynamics, creating conditions more or less favorable for the mobility of the present and incoming bedload. The cumulative mean bedload distances of RFID tracers in configuration II are significantly lower (6.8–45 m) than in configuration I (78–315 m). The current flow management of the dams has only a moderate effect on the bedload distances recorded, as shown by the mean virtual bedload velocities, which confirm the different dynamics (0.42–0.91 m/d, and 0.62–6.44 m/d, for II and I respectively). Our results demonstrate how modifications of dams on flow and bedload discharge altered the downstream morphology, but also that this inherited morphology may now be the main controlling factor of bedload transport. These findings invite further discussion about the most appropriate ways to restore rivers downstream of dams when dealing with multi-decadal inherited morphological features.

Using camera traps to inform reintroduction success and recovery of the Northern Aplomado Falcon (Falco femoralis septentrionalis)

Anthropogenic disturbances, including habitat changes, climate change, and pesticides, contribute significantly to species declines and extinctions worldwide. These disturbances also hinder conservation efforts like species reintroductions by creating novel ecosystems that no longer support the species that is being reintroduced. Using camera traps, we observed Great Horned Owls (Bubo virginianus), which are a dominant avian predator, perching on the nests of Northern Aplomado Falcons (Falco femoralis septentrionalis), a federally endangered species reintroduced to coastal Texas with a current breeding population of 23 pairs. The presence of Great Horned Owls on five of the twenty cameras placed on Northern Aplomado Falcon nest structures led us to consider the impact this interaction may have on occupancy and productivity of the nests. Throughout the study system, fast-growing tree and shrub species have become more prominent, which could be facilitating the interaction. We hypothesize that encroachment of woody vegetation is drawing Great Horned Owls into the open coastal grassland habitat of Northern Aplomado Falcons, which may limit the occupancy and productivity of falcon nests. Based on this hypothesis from the camera trap observations and anecdotal evidence of habitat encroachment, quantifying this relationship is a key next step in the recovery process. This will enable us to define priority areas for targeted restoration to push back woody vegetation, as well as areas best suited to release Northern Aplomado Falcons that will minimize the likelihood of predation by Great Horned Owls.

VEPL Dataset: A Vegetation Encroachment in Power Line Corridors Dataset for Semantic Segmentation of Drone Aerial Orthomosaics

Vegetation encroachment in power line corridors has multiple problems for modern energy-dependent societies. Failures due to the contact between power lines and vegetation can result in power outages and millions of dollars in losses. To address this problem, UAVs have emerged as a promising solution due to their ability to quickly and affordably monitor long corridors through autonomous flights or being remotely piloted. However, the extensive and manual task that requires analyzing every image acquired by the UAVs when searching for the existence of vegetation encroachment has led many authors to propose the use of Deep Learning to automate the detection process. Despite the advantages of using a combination of UAV imagery and Deep Learning, there is currently a lack of datasets that help to train Deep Learning models for this specific problem. This paper presents a dataset for the semantic segmentation of vegetation encroachment in power line corridors. RGB orthomosaics were obtained for a rural road area using a commercial UAV. The dataset is composed of pairs of tessellated RGB images, coming from the orthomosaic and corresponding multi-color masks representing three different classes: vegetation, power lines, and the background. A detailed description of the image acquisition process is provided, as well as the labeling task and the data augmentation techniques, among other relevant details to produce the dataset. Researchers would benefit from using the proposed dataset by developing and improving strategies for vegetation encroachment monitoring using UAVs and Deep Learning.

Resetting the baseline: using machine learning to find lost meadows

ContextMountain meadows occur in specific geomorphological conditions where low-gradient topography promotes fine sediment accumulation and high groundwater tables. Over 150 years of human-caused hydrological degradation of meadows along with fire suppression has resulted in decreased groundwater elevations and encroachment of upland vegetation, greatly diminishing the ecological value of meadows for water storage, baseflow, sediment capture, wildfire resistance, wildlife habitat, and carbon storage.ObjectivesWe aimed to understand where and how frequently meadows historically occurred to reset the baseline condition and provide insight into their restoration potential. We trained machine learning algorithms to identify potential meadow areas with similar hydrogeomorphic conditions to extant meadows while ignoring their unique vegetative characteristics because we hypothesized that vegetation would change but geomorphology would remain.MethodsWe used a publicly available dataset of over 11,000 hand-digitized meadow polygons occurring within a 25,300 km2, 60-watershed region in the Sierra Nevada, California USA to train random forest models to detect meadow-like hydrogeomorphic conditions. Predictor variables represented topographical position, flow accumulation, snowpack, and topographical relief at differing spatial scales. We assessed model performance and produced maps delineating high probability meadow polygons.ResultsOur findings showed that there is nearly three times more potential meadow habitat than currently documented. The predicted area includes a mixture of existing but undocumented meadows, non-meadowlands that may have converted from meadows due to lost function and forest encroachment, and areas with meadow-like geomorphology that may never have been meadow. The polygons encompassing predicted meadows often expanded existing meadows habitats into adjacent areas with continuous topography, but with upland vegetation and incised channels.ConclusionsUsing readily available data and accessible statistical techniques, we demonstrated the accuracy of a tool to detect about three times more historical meadows than currently recognized within a complex, mountainous landscape. This “found” area greatly increased the potential area that could be subject to meadow restoration with benefits for biodiversity, wildfire management, carbon sequestration, and water storage.

River restoration works design based on the study of early‐stage vegetation development and alternate bar dynamics

AbstractActive geomorphological interventions, such as reprofiling of river bars, are often used to increase bar dynamics and prevent vegetation encroachment. River restoration management should be planned based on the knowledge of what processes will follow the intervention and on the anticipation of the consequences. However, in many cases, the associated physical processes are not clearly identified whereas their consequences on bar morphodynamics are still not fully understood. This study aims to bring new insights into the biomorphodynamics evolution of the riverbed after restoration works by using a 2D biomorphodynamic model developed in the TELEMAC‐MASCARET system. It seeks to compare and evaluate the performance of five bar reprofiling scenarios in which the bar elevation is lowered to just below the water level at specified design discharges. The study area is located in the channelized and regulated alpine gravel‐bed Isère River (France). Bar dynamics and early stages of vegetation establishment are analyzed for the first 2 years after each restoration scenario. The results indicate that plant colonization would occur in all cases. Overall, maximizing the reduction of bar height is the most effective way to improve the bar dynamics and limit future vegetation encroachment.

Integrated Spatiotemporal Analysis of Vegetation Condition in a Complex Post-Mining Area: Lignite Mine Case Study

The motivation for this study arises from the need to monitor the condition of a rehabilitated post-mining areas even decades after the end of the recovery phase. This can be facilitated with satellite derived spectral vegetation indices and Geographic Information System (GIS) based spatiotemporal analysis. The study area described in this work is located in Western Poland and has unique characteristics, as it was subjected to the combined underground and open pit mining of lignite deposits that had been shaped by glaciotectonic processes. The mining ended in early 1970’ties and the area was subjected to reclamation procedures that ended in the 1980’ties. We used the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) spectral indices derived from Sentinel-2 data for the 2015–2022. period. Then, we applied a combination of GIS-based map algebra statistics (local, zonal and combinatorial) and GI* spatial statistics (hot spot and temporal hot spot) for a complex analysis and assessment of the vegetation cover condition in a post-mining area thought to be in the rehabilitated phase. The mean values of NDVI and EVI for the post-mining study area range from 0.48 to 0.64 and 0.24 to 0.31 and are stable in the analyzed 8 year period. This indicates general good condition of the vegetation and post-recovery phase of the area of interest. However, the combination of spatiotemporal analysis allowed us to identify statistically significant clusters of higher and lower values of the vegetation indices and change of vegetation cover classes on 3% of the study area. These clusters signify the occurrence of local processes such as, the encroachment of aquatic vegetation in waterlogged subsidence basins, and growth of low vegetation in old pits filled with waste material, barren earth zones on external waste dumps, as well as present-day forest management activities. We have confirmed that significant vegetation changes related to former mining occur even five decades later. Furthermore, we identified clusters of the highest values that are associated with zones of older, healthy forest and deciduous tree species. The results confirmed applicability of Sentinel-2 derived vegetation indices for studies of post-mining environment and for the detection of local phenomena related to natural landscaping processes still taking place in the study area. The methodology adopted for this study consisting of a combination of GIS-based data mining methods can be used in combination or separately in other areas of interest, as well as aid their sustainable management.