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Understanding fine‐scale assemblage composition patterns of mammals is important to increase ecological knowledge and support conservation actions. We investigated whether β‐diversity of medium and large‐sized mammals and its components (replacement and richness difference) are related to distance to major rivers, height above the nearest drainage (HAND), and vegetation height; and whether species interactions shape their spatiotemporal distribution. We used camera traps to monitor the mammal assemblage of the Tumucumaque Mountains National Park, Northeastern Amazon. The difference (Δ) in distance to major rivers and geographic distance between sites affected positively the total β‐diversity and both its components. ΔVegetation height affected total β‐diversity and species replacement, while ΔHAND affected richness differences, and to a lower extent, species replacement. We found evidence that spotted pacas (Cuniculus paca) and ocelots (Leopardus pardalis) adjust their spatiotemporal dynamics to escape from predation and to overlap with abundant prey, respectively. However, we did not find evidence that competition affects the spatiotemporal distribution of any other species. Vegetation height, distance to rivers and HAND influenced mammal assemblages probably through shaping the quality and amount of resources. Predation risk seems to be important in shaping the use of time and space by prey, while the distribution of prey is important for the use of time and space for predators. Furthermore, other niche differentiation mechanisms may explain why competition did not affect the species' spatiotemporal dynamics. We highlight the need to include different environmental contexts within protected areas to promote mammal diversity and conservation. [ABSTRACT FROM AUTHOR]

Affected by the insufficient information of single baseline observation data, the three-stage method assumes the Ground-to-Volume Ratio (GVR) to be zero so as to invert the vegetation height. However, this assumption introduces much biases into the parameter estimates which greatly limits the accuracy of the vegetation height inversion. Multi-baseline observation can provide redundant information and is helpful for the inversion of GVR. Nevertheless, the similar model parameter values in a multi-baseline model often lead to ill-posed problems and reduce the inversion accuracy of conventional algorithm. To this end, we propose a new step-by-step inversion method applied to the multi-baseline observations. Firstly, an adjustment inversion model is constructed by using multi-baseline volume scattering dominant polarization data, and the regularized estimates of model parameters are obtained by regularization method. Then, the reliable estimates of GVR are determined by the MSE (mean square error) analysis of each regularized parameter estimation. Secondly, the estimated GVR is used to extracts the pure volume coherence, and then the vegetation height parameter is inverted from the pure volume coherence by least squares estimation. The experimental results show that the new method can improve the vegetation height inversion result effectively. The inversion accuracy is improved by 26% with respect to the three-stage method and the conventional solution of multi-baseline. All of these have demonstrated the feasibility and effectiveness of the new method.

Vegetation height, and its spatial and temporal changes, is an important environmental parameter required for understanding natural habitats, estimating carbon stores and monitoring forestry activities. Recent satellite LiDAR altimetry sensors have discontinuous spatial coverage but can be combined with spatially complete remote sensing data to extrapolate to large regions. Earlier studies have focused on producing a single spatially continuous vegetation height product. This research builds on past studies, using Landsat (annual surface reflectance and fractional cover products) and the Global Ecosystem Dynamics Investigation (GEDI) data to generate annual vegetation height layers from 1988 to 2022. GEDI data for 2019 were used to train and validate the model, resulting in a root mean square error (RMSE) of 5.45 m, mean absolute error (MAE) of 3.82 m, and coefficient of determination (R2) of 0.63. This accuracy reduces when the modelled height for 2020, 2021, and 2022 is compared to GEDI data for the same years (RMSE = 6.08–6.29 m, MAE = 4.36–4.73 m, and R2 = 0.48–0.54). Validation with independent field measurements across Australia from 2011 to 2021 shows an RMSE, MAE, and R2 of 8.2 m, 5.2 m, and 0.48, respectively. One source of error is the saturation of the Landsat signal in tall, closed canopy vegetation. While model accuracy is correlated with plot-based vegetation height measurements, results indicate that accuracy reduces for the years outside of the model calibration year (i.e. 2019). When compared to other vegetation height products (also produced using GEDI and spatial remote sensing data) from three independent published studies (one for 2009, one for 2019, and one for 2020), the model developed here tends to estimate 2–4 m taller than the first two studies and around 5 m shorter when compared to the third study. This investigation demonstrates the potential to produce multiyear vegetation height at a continental scale but also highlights the large uncertainty in modelled estimates especially when extrapolating to years other than the model calibration year. [ABSTRACT FROM AUTHOR]

An important consideration for UAV-based (unmanned aerial vehicle) object detection in the natural environment is vegetation height and foliar cover, which can visually obscure the items a machine learning model is trained to detect. Hence, the accuracy of aerial detection of objects such as surface landmines and UXO (unexploded ordnance) is highly dependent on the height and density of vegetation in a given area. In this study, we develop a model that estimates the detection accuracy (recall) of a YOLOv8 object's detection implementation as a function of occlusion due to vegetation coverage. To solve this function, we developed an algorithm to extract vegetation height and coverage of the UAV imagery from a digital surface model generated using structure-from-motion (SfM) photogrammetry. We find the relationship between recall and percent occlusion is well modeled by a sigmoid function using the PFM-1 landmine test case. Applying the sigmoid recall-occlusion relationship in conjunction with our vegetation cover algorithm to solve for percent occlusion, we mapped the uncertainty in detection rate due to vegetation in UAV-based SfM orthomosaics in eight different minefield environments. This methodology and model have significant implications for determining the optimal location and time of year for UAV-based object detection tasks and quantifying the uncertainty of deep learning object detection models in the natural environment. [ABSTRACT FROM AUTHOR]

The tropical region is one of the regions that has a high potential for carbon reserves. 3D carbon of above-ground biomass (AGB) is a way to estimate potential carbon reserves using a vegetation height approach. Vegetation height is extracted from the height difference between DSM and DTM, updated with vertical deformation. This study aims to estimate 3D carbon stock (AGB) in Indonesia using the vegetation height approach. Carbon stocks were classified based on the land cover class of primary forest, mangroves, other vegetation, and all vegetation with a height of 1-40 m. Then, we identified which province had the highest carbon stock potential. The results obtained show that Papua Province had the largest AGB potential in Indonesia. The 3D carbon (AGB) volume based on mangrove class was 68,856,487,188 Mg³ with an area of 8266.6 km² and a perimeter of 16882 km. The total 3D carbon volume (AGB) based on the primary forest was 3,549,448,346,598 Mg³, with an area of 195,327 km² and a perimeter of 102,230 km. The total volume of 3D carbon (AGB) based on other vegetation was 17,031,593,026,337.00 Mg³ with an area of 112,210.40 km². Papua Province had a 3D carbon (AGB) volume based on the vegetation of 1-40 m with a volume of 20,649,897,860,123.00 Mg³, an area of 315,804.00 km², and a perimeter of 6,810.60 km. The largest 3D carbon (AGB) volume comparison value to the province's area was in Papua Province (64.46%). This 3D carbon (AGB) mapping can be done in a low-cost and time-efficient manner and produces precise and accurate spatial information. The estimation results can be used for a preliminary survey of various thematic information and policy applications related to carbon stocks. [ABSTRACT FROM AUTHOR]

Microclimate and vegetation structure control evapotranspiration (ET) from land surfaces at stand and landscape scales. Tropical rainforests are among the most diverse and complex terrestrial ecosystems, harbouring vast plant and animal species throughout their dense multistory canopy. They contribute substantially to global precipitation through their high ET. However, there is little information about ET influences at very small spatial scales under given climatic conditions. In a tropical rainforest on Sumatra, we studied the relationship between pixel‐level ET as derived from high‐resolution (~10 cm), near‐surface thermography from an unmanned aerial vehicle (UAV) and canopy structure as derived from red–green–blue (RGB) image and three‐dimensional (3D) point cloud analyses. The 16 derived potential predictors encompassed vegetation height, height variability, vegetation density and reflectance variables. Using regression models, several of the studied variables had a significant linear relationship with ET, but the explained variance was only marginal. However, applying a random forest algorithm including forward feature selection and target oriented cross validation explained substantial parts of the pixel‐level variance in ET (R2 = 0.56–0.65), thus indicating multiple non‐linear relationships with interactions among predictor variables. Therein, green leaf index, leaf area density and vegetation height were often the most important variables for the prediction outcome, but their sequence varied among the four study plots. Overall, combining canopy structure variables derived from RGB photogrammetry explained relatively large parts of spatial ET variations. Our study thus indicates the large potential of combining UAV‐based thermography and photogrammetry techniques with machine learning approaches to better understand ET but also suggests that more work remains to be done in explaining ET patterns at very small spatial scales. [ABSTRACT FROM AUTHOR]

Afforestation affects Mediterranean farmland biodiversity due to loss and fragmentation of grassland habitats. While the influence of landscape context and plantation edges on farmland bird responses to afforestation is well-documented, less is known about the influence of grassland vegetation height and how it interacts with afforestation to influence bird communities. Here, we examined how changes in grassland vegetation height affect bird responses to afforestation in a farmland region in southern Portugal, and how these are affected by plantation type and edge. This region has experienced afforestation with eucalyptus, pine and oak stands, agricultural intensification, and frequent dry periods. To capture local and landscape-level changes, we collected data in two periods (2005 and 2014–15). Grassland vegetation height varied between sampling periods, emerging as a key factor affecting changes observed. Ground-nesting and cereal-associated species increased in abundance with taller vegetation in 2014–15, while in 2005, with drier weather and shorter vegetation, the species associated with ploughed fields were more abundant. Vegetation height effects on bird assemblages depended on plantation type and distance to plantation edges. Farmland bird abundance, including ground-nesting and cereal crops-associated species, increased with taller vegetation, particularly near oak and pine plantations. Conversely, species associated with ploughed fields declined with taller vegetation, especially near eucalyptus plantations. Results highlight complex interactions between vegetation height, plantation type, and edge proximity shaping avian assemblages. This study supports the importance of field and landscape-level management with special focus on grassland vegetation height and landscape heterogeneity for preserving open-farmland birds in fast-changing Mediterranean farmland landscapes.

An important consideration for UAV-based (unmanned aerial vehicle) object detection in the natural environment is vegetation height and foliar cover, which can visually obscure the items a machine learning model is trained to detect. Hence, the accuracy of aerial detection of objects such as surface landmines and UXO (unexploded ordnance) is highly dependent on the height and density of vegetation in a given area. In this study, we develop a model that estimates the detection accuracy (recall) of a YOLOv8 object’s detection implementation as a function of occlusion due to vegetation coverage. To solve this function, we developed an algorithm to extract vegetation height and coverage of the UAV imagery from a digital surface model generated using structure-from-motion (SfM) photogrammetry. We find the relationship between recall and percent occlusion is well modeled by a sigmoid function using the PFM-1 landmine test case. Applying the sigmoid recall-occlusion relationship in conjunction with our vegetation cover algorithm to solve for percent occlusion, we mapped the uncertainty in detection rate due to vegetation in UAV-based SfM orthomosaics in eight different minefield environments. This methodology and model have significant implications for determining the optimal location and time of year for UAV-based object detection tasks and quantifying the uncertainty of deep learning object detection models in the natural environment.

Measuring plant heights one by one is an important way to explore the height structure of grassland plant communities, and can be used to analyze the universal rules and regional variations of vegetation growth under environmental change. We chose grasslands in three plateaus, namely Tibetan Plateau (TP), Mongolian Plateau (MP) and Loess Plateau (LP), and set up three grassland transects along the precipitation gradients from meadow, steppe to desert. The mean height of grassland vegetation was 30.38 ± 22.44 cm, and the mean height from TP, MP to LP increased significantly. The aggregation of grassland vegetation exhibited a positive skew distribution (0.91, 3.60), and the community aggregation from TP, MP to LP tended to a normal distribution (0, 3). However, for TP, there was an exponential distribution in community aggregation of meadows, and a uniform distribution in that of desert. The explanatory effect of climate and soil nutrients on the variation of mean height in each region was more than 70%, while the explanatory effect on the community aggregation was the highest at TP, only 29%. From TP, MP to LP, response intensity of mean height to environmental changes increased significantly, and the influence of temperature and precipitation gradually increased, the influence of radiation, wind speed, and nutrients gradually weakened, and the synergy among environmental factors strengthened. Our study shows that normal distribution is a universal rule of grassland height construction, and that the synergy of environmental factors varies from region to region. [ABSTRACT FROM AUTHOR]

Climate change is destabilizing permafrost landscapes, affecting infrastructure, ecosystems, and human livelihoods. The rate of permafrost thaw is controlled by surface and subsurface properties and processes, all of which are potentially linked with each other. However, no standardized protocol exists for measuring permafrost thaw and related processes and properties in a linked manner. The permafrost thaw action group of the Terrestrial Multidisciplinary distributed Observatories for the Study of the Arctic Connections (T-MOSAiC) project has developed a protocol, for use by non-specialist scientists and technicians, citizen scientists, and indigenous groups, to collect standardized metadata and data on permafrost thaw. The protocol introduced here addresses the need to jointly measure permafrost thaw and the associated surface and subsurface environmental conditions. The parameters measured along transects include: snow depth, thaw depth, vegetation height, soil texture, and water level. The metadata collection includes data on timing of data collection, geographical coordinates, land surface characteristics (vegetation, ground surface, water conditions), as well as photographs. Our hope is that this openly available dataset will also be highly valuable for validation and parameterization of numerical and conceptual models, and thus to the broad community represented by the T-MOSAiC project.

Information on breeding habits of grey crowned crane (Balearica regulorum) in Kenya is currently lacking. Investigations were therefore carried out between 2017 and 2020 to determine its nesting habits in Lake Ol' Bolossat basin including nest‐site features, number and distribution of territorial pairs, clutch size, eggs, and duration of breeding cycle. While 103 pairs were observed in the lake marshlands (mean density 5.00 pairs/km2), only 20 were found in upland wetlands. The number of pairs and densities (pairs/km2) in each of the lake's sections were as follows: north (24; 3.23), middle (47; 5.42), and south (32; 6.24). During this study, 61% (n = 63) of the lake marshland pairs attempted to breed compared to 80% (n = 16) in the upland wetlands. A mean of 2.23 eggs/clutch and a modal clutch of three eggs was recorded in 33 accessible nests. The range of influential factors in nest‐site selection were as follows: water depth (50–100 cm), vegetation height around the nest (60–90 cm), and an offshore distance of >100 m. Breeding cycle showed phenotypic plasticity in response to variations in local weather conditions. The number and density of cranes in this study area show its importance in the recovery and survival of the species, and which should subsequently be accorded effective protection. [ABSTRACT FROM AUTHOR]

At present, the mainstream radar sensors are difficult to meet the demands of the light-weighted and small unmanned aerial vehicle (UAV), which usually need high cost of data acquisition and complicate models of algorithm. A highly integrated, lightweight and reliable radar system is deigned, which can be carried on the light-weighted and small UAV. Based on this system, a novel method has been developed for vegetation height information inversion. The inversion algorithm suppress the direct coupled wave by two-dimensional filtering, settle the unstable selection of principle component by the adaptive principle component analysis denoting method with residual image entropy, further enhance the target signal by the back-projection algorithm based on cross-correlation information and finally extract the vegetation height by implementing Sobel operator. The experiment results show that the correlation between the vegetation height derived from the proposed method and the verified data is 0. 92, and the root mean square error is 1. 28 m. [ABSTRACT FROM AUTHOR]

Abstract Livestock farming in Great Britain (GB) faces multiple pressures. Yet, grassland managed for livestock is the most extensive habitat in GB and is key to cultural landscapes and their biodiversity and soil health. This study analysed a nationally representative dataset of over 940 large (200 m2) Neutral (agriculturally semi‐improved) and (agriculturally) Improved Grassland plots from the GB Countryside Survey (CS) to assess relationships between key grassland sward and soil variables. Analysis also looked at how these variables changed over time as plots switched between these grassland types. Data from grassland plots managed by Pasture‐Fed Livestock Association (PFLA) farmers were compared to CS plot data to assess the impacts of their practices on these variables. Plant species richness in Neutral grassland types in CS plots was positively associated with total soil invertebrate abundance (total taxa) and soil N and C and negatively associated with soil P. There were negative relationships between the covers of Lolium sp. (Neutral only), legumes and forbs and soil C and moisture variables. Grassland swards on PFLA member farms were characteristic of Neutral grassland. PFLA plots were more species rich and contained more legume and forb species and lower proportions of Lolium perenne than those on Improved Grassland. Vegetation height was greater in PFLA plots than in CS plots of either Improved or Neutral Grassland. Unlike CS Neutral Grassland plots, soil properties in PFLA plots were not significantly different from those for Improved Grassland for any measured variable (soil carbon concentration [C], bulk density, pH, nitrogen [N], phosphorus [P]). Higher species richness in grasslands is associated with positive measures of soil health. PFLA plant communities contain relatively high species richness and tall vegetation, which is positive for biodiversity. Lack of positive measures of soil health associated with higher species richness recorded in PFLA grassland (as opposed to CS grassland) may reflect time lags in soil responses to management, as evidenced through an analysis of the impacts of land‐use change over time on CS plot characteristics. Our findings indicate that pasture‐fed livestock approaches may be beneficial for grassland and wider ecosystems.

The Baligówka peat bog is one of the peat bogs of the Orawa-Nowy Targ Basin—the largest complex of wetlands in the Polish Carpathians. Its area has declined in the past as a result of drainage and peat exploitation, which caused a bad hydrological condition and it is gradually overgrown by non-peat bog medium and high vegetation. The research uses models derived from airborne laser scanning (ALS) and an orthophotomap to delimit the bog and divide it into parts and assess the range of drainage ditches and vegetation. The area of the peat dome along with 3 sites of peat exploitation is currently 159.6 ha, while the ecotone zone is 105.9 ha. Both sections are separated by a steep post-mining slope. The medium and high vegetation areas cover 44% of the peat bog; its location is related to the dense drainage system in the southern part of the dome. The parameters of the Baligówka peat bog: area, size and extent of drainage system, and the degree of overgrowth by high vegetation, are the subject of research towards the protection under the Natura 2000 network (PLH120016) and the establishment of a plan for restoration activities. [ABSTRACT FROM AUTHOR]

In this article, L-band vegetation optical depth (L-VOD) retrieved by Soil Moisture and Ocean Salinity (SMOS) in four large continents is compared against vegetation parameters (RH100 and PAI) retrieved by Global Ecosystem Dynamics Investigation (GEDI) LIDAR instrument, recently launched by NASA. In order to manage the different spatial resolutions, GEDI parameters were averaged within SMOS pixels and a threshold to the minimum number of GEDI samples per SMOS pixel was applied. Spatial correlations between monthly averages were investigated from May 2019 to April 2020. For continents mostly covered by tropical vegetation (Africa and South America), the Pearson correlation coefficients between L-VOD and RH100 are higher than 0.8 in all months of the year. Conversely, seasonal effects are observed in North America and Asia, producing a lower correlation in colder months. RMS differences between L-VODs retrieved by SMOS and the ones obtained using a linear regression over RH100 are lower than 0.2 for all cases, and close to 0.1 for most cases. Using PAI in place of RH100 slightly lower spatial correlations are generally achieved. Overall, the obtained results confirm the good potential of L-VOD to monitor vegetation height in different environments.

Major roads have become significant components of environmental change but their impacts on invertebrate assemblages are insufficiently understood, and therefore rarely considered in road‐planning and management.In the current study, we test (i) whether and how orthopteran assemblages change with distance from a motorway, and (ii) how road‐induced changes in noise level, vegetation height and microclimate affect assemblage metrics and spatial distribution of orthopteran species.In 2018, we sampled orthopterans at five distances from a motorway: 10, 25, 50, 100 and 500 m, in eight locations within homogeneous portions of a grassland habitat in Lika region, Croatia, using two sampling methods: pitfall trapping and sweep‐netting.Orthopteran abundance, species richness, true diversity and conservation value decreased at the sites closest to the motorway in the pitfall dataset, primarily due to negative responses of species with low‐frequency acoustic signals.Road‐influenced vegetation height had a stronger overall impact on orthopteran assemblages than traffic noise and/or microclimate; increased diversity and conservation value at 25 m from the motorway suggest an effect similar to early stages of vegetation succession.This study shows, for the first time, that a major road can induce changes in adjacent orthopteran assemblages, but negative impacts are confined to a narrow zone.Our results indicate that road‐induced changes in orthopteran assemblages could be more efficiently assessed using pitfall trapping than sweep‐netting.Increasing heterogeneity of roadside habitats by appropriate vegetation management could help mitigating negative road impacts on Orthoptera in the study area, while contributing to higher diversity of their assemblages. [ABSTRACT FROM AUTHOR]

Vegetation commonly existing in natural rivers interact with water flow in a strongly nonlinear manner. In this research, vegetation height and a concept of relative submergence are adopted to experimentally investigate the effects of rigid submerged vegetation on flow structure. The results reveal that the relative submergences decrease along the submerged vegetation section. From the perspective of case-average, the linear relationship between water depth and vegetation height demonstrates a decrease trend of average relative submergence with the increase of vegetation height. The differences between the maximum and minimum water depths of submerged cases can be described by a positive linear function with vegetation height and a negative power function with relative submergence, by which, the raised water depths induced by vegetation block can be predicted under similar hydraulic conditions. The findings of the research, especially the introduction of relative submergence and its functional relationship with the water depth alternation induced by submerged vegetation, may improve the comprehensive understandings of the hydraulic behaviors in vegetated open channel and give some guidance to the practices of river ecological restoration. [ABSTRACT FROM AUTHOR]

Analyses of the effects of topography, weather, land management, and fuel on fire severity are increasingly common, and generally apply fire severity indices derived from satellite optical remote sensing. However, these indices are commonly interpreted with insufficient appreciation for their limitations and may be inappropriately invoked as representing physical fire effects and fire behaviour. For a large wildfire in southeast Australia, we investigated three considerations for inferring robust insights from fire severity analyses -- the potentially confounding influences of pre-fire vegetation height and tall vegetation cover, and the choice of fire severity response variable. Using nonparametric regression, we found that different fire severity indices gave rise to substantially different modelled relationships with commonly invoked environmental predictors, as is consistent with dissimilarities in index design. Further, pre-fire vegetation height was a strong control of fire severity, with equivalent importance to weather. Importantly, strong covariation between vegetation height and environmental predictors suggests that modelled fire severity effects are strongly influenced by variation in vertical distance between flames and vegetation, and this confounds fire behaviour insights. To enable more robust and mechanistic insights into the determinants of fire severity, we recommend greater consideration of the meaning and limitations of optical remote sensing indices. [ABSTRACT FROM AUTHOR]

Ecologically-sound management plans for high-altitude grasslands of the Andes depend on an understanding the responses of plants to fire, especially the dominant tussock grasses. This study considers physiognomic responses of tussock grass in 13 sites in northern Ecuador with a known fire history, with time since fire 0.5–10 y, and a control site which had not been burned for at least 40 y. At each site, we assessed vegetation height, basal cover of the tussocks, and the ratio of dead:live leaves in tussocks. We also measured light at ground level. As recovery time increased, tussock cover and number decreased, while tussock height increased. Light levels fell sharply below the tussock canopies as recovery took place, and dead leaves accumulated quickly, reaching 60 – 70% by just two years after fire. The modification of physical tussock structure is likely to influence a much wider ecosystem response to fire, and determines directly the fuel load for future fires. Despite these clear changes in tussock characteristics, they were too variable to be used as a reliable bioindicator of time since fire. However, a better understanding of the responses of tussock grasses to fire and particularly its impact on other species should become the focus of further attention in future.

There is little information about habitat use pattern of river lapwing in India as well as in Southeast Asia. In this regard, we employed the line transect method to quantify the density of river lapwing concerning vegetation height, water depth and soil penetrability. Scan and focal sampling methodology was adapted to investigate the activities (foraging and roosting) and breeding events. The results showed that invertebrates were abundant at 4 cm of soil penetrability, particularly in open unvegetated river bank (OURB) and open unvegetated river island (OURI) habitat types. River lapwings were significant in short vegetation. They occurred in high density in the vicinity of river water at 5 cm of water depth and 4 cm of soil penetrability in OURB and OURI habitat types. Human activities also influenced the density of river lapwing in different habitat types. Results of GLM analysis revealed that all interactions strongly influenced the density of river lapwings except two interactions, e.g. vegetation height*water depth and vegetation height*soil penetrability. Moreover, we found that river lapwing foraged more frequently in OURB and OURI habitat types while less in vegetated river bank (VRB) and crop fields (CF). Roosting activities were more frequently observed in VRB and CF habitat types. Different breeding events (nest seen, chick seen, alarm calling and territory defence) were primarily seen in OURB and OURI habitat types. Finally, developing greater understanding of the river lapwing could help us to design effective conservation measures for this species. [ABSTRACT FROM AUTHOR]

Amphibian species require specific conditions for reproduction, such as cover structures and shelters, in both aquatic and terrestrial habitats, which directly influence reproductive success. A careful selection of calling sites is an important process, driving life-history strategies and tactics in amphibians. Despite this, a significant knowledge gap on how different variables modulate amphibian microhabitat selection processes is noted. Thus, we aimed to: (1) describing the microhabitat use of adult neotropical Boana punctata males during the breeding season; and (2) quantitatively analyzing the selection process of five resource variables (i.e., vegetation cover, vegetation density, vegetation height, water depth, and distance to the water shore). We then compared the microhabitat selection of males that achieved spawning and that of males that did not achieve spawning. To quantify selection, we used a resource selection function approach, applying a case/control design where the calling site used by each male was paired to eight surrounding, unused locations. We found that males selected microhabitats with higher vegetation than surrounding areas for reproduction, which suggests territorialism, and selected microhabitats slightly (∼ 30 cm) inside the assessed ponds. Males also acted randomly with regard to the other variables, not influencing the males' microhabitat selection. Microhabitat use of males successful in obtaining females to spawn was similar to that of males exhibiting mating failure. In addition to being of ecological interest, our study highlights that preserving tall vegetation and the ponds' shores is important for the conservation of existing B. punctata urban populations. [ABSTRACT FROM AUTHOR]

Abstract Wind speed is one of the most important factors for seed wind dispersal. A wind speed reduction region, which could be influenced by vegetation arrangement, will form in the lee of vegetation and therefore affects the seed dispersal. Here, by taking shrub as an example, quantitative differences in seed dispersals of low vegetation between single element and windbreak‐like clumps are numerically investigated. The local variation of stream‐wise wind speed is focused. Empirically parameterized functions of leeward wind distributions are employed. It reveals that the accumulative probability of dispersed seeds from a point source with considering leeward wind reduction could be well fitted by a logistic function. For a fixed release height or vegetation porosity, accumulative probabilities for single element and those for windbreak‐like clumps would intersect at a leeward location. This intersection location decreases linearly with release height but exponentially with porosity. The fitting parameter r0 (the center of logistic function) for single element increases as the same manner for windbreak‐like clumps, with regard to the increase of release height, porosity, and height. But, the increasing rates for single element are higher than those for windbreak‐like clumps. The fitting parameter p (the power index of logistic function) for single element is generally larger than that for windbreak‐like clumps. With the increase of release height, p decreases at first but increases then for single element, while it shows opposite trend for windbreak‐like clumps. p decreases with porosity for both single element and windbreak‐like clumps. But, the decreasing rate for single element is lower than that for windbreak‐like clumps. p increases exponentially with height for windbreak‐like clumps, while it almost keeps constant for single element. These results suggest the potential importance of vegetation arrangement on seed dispersal and therefore possibly provide additional reason for the disagreement among observed dispersal kernels.

Vegetation height plays a crucial role in various ecological and environmental applications, such as biodiversity assessment and monitoring, landscape characterization, conser- vation planning and disaster management. Its estimation is traditionally based on in situ measurements or airborne Light Detection and Ranging sensors. However, such methods are often proven insufficient in covering large area landscapes due to high demands in cost, labor and time. Since, the emergence of wearable technology, ubiquitous sensors and Internet of Things offers an appealing framework for monitoring environmental parameters at extremely low cost, which, in turn, contributes to the development of affordable real-time vegetation monitoring system. This is especially relevant to rural environments and underdeveloped countries. We proposed a methodology for data acquisition from a ubiquitous sensor wearable platform and developed a machine-learning model to learn vegetation height on the basis attribute associated with pressure sensor. The proposed methods are proven particularly effective in a region where the land has forestry structure. The results of linear regression model (r2 = 0.81 and RSME = 16.73 cm) and multi-regression model (r2= 0.83 and RSME = 15.73 cm), indicate a promising alternative in vegetation height estimation when in situ or Light Detection and Ranging data or wireless sensor network are not available or affordable, thus facilitating and reducing the cost of ecological monitoring and environmental sustainability planning tasks.

Urban areas are characterised by the dominance of impervious surfaces and decreased presence of vegetation compared to their rural surroundings. The resultant increase in temperature is known to amplify global warming, with negative impacts on health and increased energy requirements for cooling. Intra-urban variations in temperature have received less attention than urban–rural variations, although the former can be even larger than the latter. Land cover composition is known to influence surface temperature, while the influence of heights, of buildings and vegetation, is less explored. There are also fewer studies in high-latitude cities although extreme heat events are increasing in frequency and severity in these cities, and high-resolution geospatial datasets are often available for detailed analysis. The aim of this study is therefore to assess the influence of selected land cover variables on the estimated surface temperature in the four largest cities in Denmark—Copenhagen, Aarhus, Odense and Aalborg.Land surface temperatures (LST) of the four cities were estimated using Band 10 (10.60–11.19 μm) from Landsat 8 imagery. Vegetation cover, building cover, vegetation height and building height were estimated using 4-band aerial imagery, building footprints and LiDAR-based elevation models, and their correlations with LST were estimated. Moving average filters, with window sizes from 3 × 3 (90 m × 90 m) to 11 × 11 (330 m × 330 m), were used to understand the area of influence of surrounding land cover on the LST within 30-m cells. When vegetation cover and building cover increased from 0–5% to 95–100%, median values of LST decreased by 4.16 ± 0.76 °C and increased by 4.31 ± 0.69 °C, respectively. Land cover variables within 7 × 7 windows (210 m × 210 m) are shown to have strong correlations with the LST of 30-m cells. The area of influence of building heights on the LST of 30-m cells was the largest in Copenhagen, which also has the tallest buildings among the cities. LST reduced by 4.10 °C when the mean vegetation height within a 30-m cell increased from 0–2 m to 20–22 m, and by 5.75 °C for 210 m × 210 m patches with the same height range. A combination of increased vegetation cover and height could therefore be used to regulate temperature in or close to hot spots in cities depending on the availability of space.

The Baligówka peat bog is one of the peat bogs of the Orawa-Nowy Targ Basin—the largest complex of wetlands in the Polish Carpathians. Its area has declined in the past as a result of drainage and peat exploitation, which caused a bad hydrological condition and it is gradually overgrown by non-peat bog medium and high vegetation. The research uses models derived from airborne laser scanning (ALS) and an orthophotomap to delimit the bog and divide it into parts and assess the range of drainage ditches and vegetation. The area of the peat dome along with 3 sites of peat exploitation is currently 159.6 ha, while the ecotone zone is 105.9 ha. Both sections are separated by a steep post-mining slope. The medium and high vegetation areas cover 44% of the peat bog; its location is related to the dense drainage system in the southern part of the dome. The parameters of the Baligówka peat bog: area, size and extent of drainage system, and the degree of overgrowth by high vegetation, are the subject of research towards the protection under the Natura 2000 network (PLH120016) and the establishment of a plan for restoration activities.

Detailed spatial information on the presence and properties of woody vegetation serves many purposes, including carbon accounting, environmental reporting and land management. Here, we investigated whether machine learning can be used to combine multiple spatial observations and training data to estimate woody vegetation canopy cover fraction (‘cover’), vegetation height (‘height’) and woody above-ground biomass dry matter (‘biomass’) at 25-m resolution across the Australian continent, where possible on an annual basis. We trained a Random Forest algorithm on cover and height estimates derived from airborne LiDAR over 11 regions and inventory-based biomass estimates for many thousands of plots across Australia. As predictors, we used annual geomedian Landsat surface reflectance, ALOS/PALSAR L-band radar backscatter mosaics, spatial vegetation structure data derived primarily from ICESat/GLAS satellite altimetry, and spatial climate data. Cross-validation experiments were undertaken to optimize the selection of predictors and the configuration of the algorithm. The resulting estimation errors were 0.07 for cover, 3.4 m for height, and 80 t dry matter ha-1 for biomass. A large fraction (89–94 %) of the observed variance was explained in each case. Priorities for future research include validation of the LiDAR-derived cover training data and the use of new satellite vegetation height data from the GEDI mission. Annual cover mapping for 2000–2018 provided detailed insight in woody vegetation dynamics. Continentally, woody vegetation change was primarily driven by water availability and its effect on bushfire and mortality, particularly in the drier interior. Changes in woody vegetation made a substantial contribution to Australia’s total carbon emissions since 2000. Whether these ecosystems will recover biomass in future remains to be seen, given the persistent pressures of climate change and land use.

Sharp-tailed grouse occur across much of the northern Great Plains of North America where large grassland tracts remain. Though extirpated from areas within the Great Plains, primarily due to habitat loss, sharp-tailed grouse populations have remained relatively stable since European Settlement. Because of their need for large contiguous grasslands, sharp-tailed grouse typically occur in areas managed for or with cattle. In an effort to better understand how land management affects sharp-tailed grouse, we studied hen sharp-tailed grouse during the brooding season to identify habitat use and factors that influenced the daily survival rate of broods. We conducted our study on the Grand River National Grassland in South Dakota where cattle herbivory is the primary form of disturbance. We monitored 42 broods from 2013 to 2015. Using conditional logistic regression, we found brood-rearing hens used areas based on the distance to the nearest drainage and choose sites with less litter cover and bare ground relative to random sites at a microhabitat scale. Variables associated with variation in survival did not align with those shown to influence habitat selection. We found that brood survival decreased as the relative slope of a site increased and that survival increased as the distance a hen kept her chicks from a fence line also increased. At the microhabitat scale, survival was positively related to visual obstruction and vegetation height. Our findings demonstrate the importance of numerous variables across multiple scales to brood-rearing sharp-tailed grouse. Managers of grouse populations should manage for heterogeneity, as many factors appear to shape sharp-tailed grouse brood ecology in the Great Plains. [ABSTRACT FROM AUTHOR]

Vegetation height plays a key role in many environmental applications such as landscape characterization, conservation planning and disaster management, and biodiversity assessment and monitoring. Traditionally, in situ measurements and airborne Light Detection and Ranging (LiDAR) sensors are among the commonly employed methods for vegetation height estimation. However, such methods are known for their high incurred labor, time, and infrastructure cost. The emergence of wearable technology offers a promising alternative, especially in rural environments and underdeveloped countries. A method for a locally designed data acquisition ubiquitous wearable platform has been put forward and implemented. Next, a regression model to learn vegetation height on the basis of attributes associated with a pressure sensor has been developed and tested. The proposed method has been tested in Oulu region. The results have proven particularly effective in a region where the land has a forestry structure. The linear regression model yields (r2 = 0.81 and RSME = 16.73 cm), while the use of a multi-regression model yields (r2 = 0.82 and RSME = 15.73 cm). The developed approach indicates a promising alternative in vegetation height estimation where in situ measurement, LiDAR data, or wireless sensor network is either not available or not affordable, thus facilitating and reducing the cost of ecological monitoring and environmental sustainability planning tasks. [ABSTRACT FROM AUTHOR]

Salt marshes are transitional zones between ocean and land, which act as natural buffers against coastal hazards. The survival of salt marshes is governed by the rate of organic and inorganic deposition, which strongly depends on vegetation characteristics, such as height and density. Vegetation also favours the dissipation of wind waves and storm surges. For these reasons, an accurate description of both ground elevation and vegetation characteristics in salt marshes is critical for their management and conservation. For this purpose, airborne LiDAR (light detection and ranging) laser scanning has become an accessible and cost‐effective tool to map salt marshes quickly. However, the limited horizontal resolution (~1 m) of airborne‐derived point clouds prevents the direct extraction of ground elevation, vegetation height and vegetation density without the coupling with imagery datasets. Instead, due to the lower flight altitude, UAV (unmanned aerial vehicle)‐borne laser scanners provide point clouds with much higher resolution (~5 cm). Although methods for estimating ground level and vegetation characteristics from UAV LiDAR have been proposed for flat ground, we demonstrate that a sloping ground increases prediction errors. Here we derive a new formulation that improves the estimation by employing a correction based on a LiDAR‐derived estimate of local ground slope. Our method directly converts the 3D distribution of UAV LiDAR‐derived points into vegetation density and height, as well as ground elevation, without the support of additional datasets. The proposed formulation is calibrated by using measured density and height of Spartina alterniflora in a marsh in Sapelo Island, Georgia, USA, and successfully tested on an independent dataset. Our method produces high‐resolution (40 × 40 cm2) maps of ground elevation and vegetation characteristics, thus capturing the large gradients in the proximity of tidal creeks. © 2020 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study evaluates the skills of two types of drone-based point clouds, derived from LiDAR and photogrammetric techniques, in estimating ground elevation, vegetation height, and vegetation density on a highly vegetated salt marsh. The proposed formulation is calibrated and tested using data measured on a Spartina alterniflora-dominated salt marsh in Little Sapelo Island, USA. The method produces high-resolution (ground sampling distance = 0.40 m) maps of ground elevation and vegetation characteristics and captures the large gradients in the proximity of tidal creeks. Our results show that LiDAR-based techniques provide more accurate reconstructions of marsh vegetation (height: MAEVH = 12.6 cm and RMSEVH = 17.5 cm; density: MAEVD = 6.9 stems m−2 and RMSEVD = 9.4 stems m−2) and morphology (MAEM = 4.2 cm; RMSEM = 5.9 cm) than Digital Aerial Photogrammetry (DAP) (MAEVH = 31.1 cm; RMSEVH = 38.1 cm; MAEVD = 12.7 stems m−2; RMSEVD = 16.6 stems m−2; MAEM = 11.3 cm; RMSEM = 17.2 cm). The accuracy of the classification procedure for vegetation calculation negligibly improves when RGB images are used as input parameters together with the LiDAR-UAV point cloud (MAEVH = 6.9 cm; RMSEVH = 9.4 cm; MAEVD = 10.0 stems m−2; RMSEVD = 14.0 stems m−2). However, it improves when used together with the DAP-UAV point cloud (MAEVH = 21.7 cm; RMSEVH = 25.8 cm; MAEVD = 15.2 stems m−2; RMSEVD = 18.7 stems m−2). Thus, we discourage using DAP-UAV-derived point clouds for high-resolution vegetation mapping of coastal areas, if not coupled with other data sources.

Mapping of tree height is of great importance for management, planning, and research related to agroforestry parklands in Africa. In this paper, we investigate the potential of spotlight-mode data from the interferometric synthetic aperture radar (InSAR) satellite system TanDEM-X (TDM) for mapping of tree height in Saponé, Burkina Faso, a test site characterised by a low average canopy cover (~15%) and a mean tree height of 9.0 m. Seven TDM acquisitions from January–April 2018 are used jointly to create high-resolution (~3 m) maps of interferometric phase height and mean canopy elevation, the latter derived using a new, model-based processing approach compensating for some effects of the side-looking geometry of SAR. Compared with phase height, mean canopy elevation provides a more accurate representation of tree height variations, a better tree positioning accuracy, and better tree height estimation performance when assessed using 915 trees inventoried in situ and representing 15 different species/genera. We observe and discuss two bias effects, and we use empirical models to compensate for these effects. The best-performing model using only TDM data provides tree height estimates with a standard error (SE) of 2.8 m (31% of the average height) and a correlation coefficient of 75%. The estimation performance is further improved when TDM height data are combined with in situ measurements; this is a promising result in view of future synergies with other remote sensing techniques or ground measurement-supported monitoring of well-known trees.

An important function of agri-environmental schemes (AES) is to change management of pastures to better conserve biodiversity. However, the effects of most AES on biodiversity are poorly understood, especially when it comes to effects of AES management over time. The main aim of this study is to investigate if the species richness and abundance of grassland specialists of vascular plants and two important insect pollinator groups (bumblebees and butterflies) differ over time (5 years) in pastures with AES management (two value levels; general values and special values) and pastures without AES management. We also investigate if local vegetation characteristics and landscape composition relate to species richness in semi-natural grasslands. Using data from more than 400 sites we found that species richness of vascular plants (grassland specialists) was higher in pastures with AES management (for special and general values) compared to those without AES, which implies that these schemes do have value of the conservation of plant diversity. However, species richness and abundance of butterflies (grassland specialists) and bumblebees (all species) did not differ significantly among the three AES categories. We found no evidence that the type of AES management caused any changes in species richness of plants, butterflies or bumblebees during the 5 year period of our investigation. It appears that AES management that encourages uniform and minimum levels of grazing can have both positive and negative effects on biodiversity. For example, pollinators may benefit from a lower grazing intensity that could increase flower richness and heterogeneity in vegetation height. However, low grazing intensity may lead to increased cover of trees and shrubs, which can have negative effects for both insect pollinators and vascular plants. The effects of landscape composition were weak and only species richness of bumble bees were associated with landscape composition. Designing management regimes to maintain suitably heterogeneous vegetation layer, and continued long-term monitoring of biodiversity will be critical for safeguarding culturally and functionally important semi-natural grasslands. [ABSTRACT FROM AUTHOR]

Entire-area spring burning is a common practice in many grasslands. Although there are many studies on the effects of entire-area spring burning on insects, the underlying mechanism of insect response is not well known. Herbivorous insect responses to spring burning are expected to be related to hibernating traits and grassland quality (host and nectar plants). I surveyed butterfly assemblages and vegetation characteristics (e.g., nectar flower occurrence, host plant richness, vegetation height, and vegetation coverage) as parameters of grassland quality for butterflies over 21 transects in 3 burned and 3 abandoned grasslands. Butterfly species were classified based on their hibernating traits, with a focus on endangered and grassland species. Although grassland quality as measured by nectar resources and host plants did not differ between the abandoned and burned grasslands, butterfly richness and abundance were lower in the burned grasslands. Each grassland, particularly two burned grasslands, supported a lower number of Red List grassland species, considering the presence of their host plants. Butterfly richness and abundance exhibited a unimodal response to vegetation height and were lower in burned grasslands; however, they were not related to other variables. Species associated with burned grasslands hibernated belowground or on the surface in sparse vegetation, whereas species associated with abandoned grasslands hibernated on the surface or aboveground. Therefore, although burning is an effective method to halt grassland decline, entire-area spring burning is detrimental to grassland butterfly species. Rotational patch fire and grassland restoration would be required to maintain grassland butterfly diversity. [ABSTRACT FROM AUTHOR]

Wind speed is one of the most important factors for seed wind dispersal. A wind speed reduction region, which could be influenced by vegetation arrangement, will form in the lee of vegetation and therefore affects the seed dispersal. Here, by taking shrub as an example, quantitative differences in seed dispersals of low vegetation between single element and windbreak‐like clumps are numerically investigated. The local variation of stream‐wise wind speed is focused. Empirically parameterized functions of leeward wind distributions are employed. It reveals that the accumulative probability of dispersed seeds from a point source with considering leeward wind reduction could be well fitted by a logistic function. For a fixed release height or vegetation porosity, accumulative probabilities for single element and those for windbreak‐like clumps would intersect at a leeward location. This intersection location decreases linearly with release height but exponentially with porosity. The fitting parameter r0 (the center of logistic function) for single element increases as the same manner for windbreak‐like clumps, with regard to the increase of release height, porosity, and height. But, the increasing rates for single element are higher than those for windbreak‐like clumps. The fitting parameter p (the power index of logistic function) for single element is generally larger than that for windbreak‐like clumps. With the increase of release height, p decreases at first but increases then for single element, while it shows opposite trend for windbreak‐like clumps. p decreases with porosity for both single element and windbreak‐like clumps. But, the decreasing rate for single element is lower than that for windbreak‐like clumps. p increases exponentially with height for windbreak‐like clumps, while it almost keeps constant for single element. These results suggest the potential importance of vegetation arrangement on seed dispersal and therefore possibly provide additional reason for the disagreement among observed dispersal kernels. [ABSTRACT FROM AUTHOR]

Urban parks can function as a proper sink of noise pollution. However, lack of universally-agreed upon methodologies and differing urban conditions have fueled controversy surrounding the effectuation of this urban park function around the world. Hence, to address this controversy in narrow urban parks (with a mean width of ∼109 m) in Isfahan City, Central Iran, noise levels (Lq30) were measured along two longitudinal transects placed along the interior northern river-and adjacent to the southern edge of the parks bordered by a heavily-conjected road. We used statistical tests and models to determine the association of noise levels measured along the northern transect with the distance to, and the intensity of noise emitted from, the road, vegetation biomass, and vegetation height within two 50 and 100 m buffer rings drawn around northern sites and the richness of bird species. The average Lq30 values differed significantly between the southern (∼73.21 dB) and northern (∼66.43 dB) transects and correlated negatively with species richness (r(98) = −0.324, p < 0.01). Three variables including mean NDVIwithin the 100 m buffer ring, distance from the road and mean Lq30 values of the nearest three southern sites were included to build the best predictive multiple-linear regression model through the step-wise procedure with r2 of 0.52. These findings suggest that further attempts aiming to alleviate the parks' interior noise level should be attentive to distance to road, traffic at the nearest road part, and the interior vegetation characteristics. [ABSTRACT FROM AUTHOR]

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Vegetation height estimation plays a pivotal role in forest mapping, which significantly promotes the study of environment and climate. This paper develops a general forest structure model for vegetation height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR) data. In simple terms, the temporal decorrelation factor of the random volume over ground model with volumetric temporal decorrelation (RVoG-vtd) is first modeled by random motions of forest scatterers to solve the problem of ambiguity. Then, a novel four-stage algorithm is proposed to improve accuracy in forest height estimation. In particular, to compensate for the temporal decorrelation mainly caused by changes between multiple observations, one procedure of temporal decorrelation adaptive estimation via Expectation-Maximum (EM) algorithm is added into the novel method. On the other hand, to extract the features of amplitude and phase more effectively, in the proposed method, we also convert Euclidean distance to a generalized distance for the first time. Assessments of different algorithms are given based on the repeat-pass PolInSAR data of Gabon Lope Park acquired in AfriSAR campaign of German Aerospace Center (DLR). The experimental results show that the proposed method presents a significant improvement of vegetation height estimation accuracy with a root mean square error (RMSE) of 6.23 m and a bias of 1.28 m against LiDAR heights, compared to the results of the three-stage method (RMSE: 8.69 m, bias: 4.81 m) and the previous four-stage method (RMSE: 7.72 m, bias: −2.87 m).

• We propose new wind erosion indices that consider spatial heterogeneity of shrubs. • We used field observations to compare new and extant indices of saltation flux. • The new indices were more robust and better explained saltation fluxes. • The range of the upwind azimuthal angle must be specified for saltation observations. • The new indices are expected to facilitate rangeland management. Conventional indicators of vegetation, such as kinds of vegetation and lateral cover, assume spatially homogeneous distribution of vegetation and are insufficient for predicting wind erosion. Conventional indicators for monitoring wind erosion often focus on dust and are not directly related to soil and vegetation, which prevent practitioners from applying monitoring data to rangeland management. We proposed two new indicators—the Height Area Effect (HAE) and Total Height (TH)—as metrics of wind erosion and that consider the spatial distribution of vegetation. The HAE is the sum of windbreak effects calculated from shrub heights, and TH is the sum of the heights of shrubs within the range of calculation. We used field observation data to compare the ability of these new indicators and existing indicators (vegetation cover, shrub height, lateral cover λ , and canopy gap) to explain saltation fluxes. We conducted saltation and meteorological observations in a Nitraria sibirica community on Tsogt-Ovoo, Mongolia. We surveyed the spatial arrangements and heights of shrubs. Indicators calculated in the upwind direction from the observation point over different calculation ranges were analyzed by piecewise regression and logarithmic regression. Models were compared based on coefficients of determination. The HAE and TH had the highest coefficients of determination and the highest robustness against the different calculation ranges. This result was encouraging because HAE was the most detailed indicator of the effect of shrubs on wind erosion. The TH could be measured easily in the field and is expected to be an effective indicator for rangeland management purposes. [ABSTRACT FROM AUTHOR]

Salt marshes provide important services to coastal ecosystems in the southeastern United States. In many locations, salt marsh habitats are threatened by coastal development and erosion, necessitating large-scale monitoring. Assessing vegetation height across the extent of a marsh can provide a comprehensive analysis of its health, as vegetation height is associated with Above Ground Biomass (AGB) and can be used to track degradation or growth over time. Traditional methods to do this, however, rely on manual measurements of stem heights that can cause harm to the marsh ecosystem. Moreover, manual measurements are limited in scale and are often time and labor intensive. Unoccupied Aircraft Systems (UAS) can provide an alternative to manual measurements and generate continuous results across a large spatial extent in a short period of time. In this study, a multirotor UAS equipped with optical Red Green Blue (RGB) and multispectral sensors was used to survey five salt marshes in Beaufort, North Carolina. Structure-from-Motion (SfM) photogrammetry of the resultant imagery allowed for continuous modeling of the entire marsh ecosystem in a three-dimensional space. From these models, vegetation height was extracted and compared to ground-based manual measurements. Vegetation heights generated from UAS data consistently under-predicted true vegetation height proportionally and a transformation was developed to predict true vegetation height. Vegetation height may be used as a proxy for Above Ground Biomass (AGB) and contribute to blue carbon estimates, which describe the carbon sequestered in marine ecosystems. Employing this transformation, our results indicate that UAS and SfM are capable of producing accurate assessments of salt marsh health via consistent and accurate vegetation height measurements.