•Integrated hyperspectral/LiDAR data yielded better results than their individual use.•Classifications based on hyperspectral data only were negatively affected by shadows.•Using LiDAR for water body ...extraction successfully eliminated problems with shadows.
Inland water bodies are globally threatened by environmental degradation and climate change. On the other hand, new water bodies can be designed during landscape restoration (e.g. after coal mining). Effective management of new water resources requires continuous monitoring; in situ surveys are, however, extremely time-demanding. Remote sensing has been widely used for identifying water bodies. However, the use of optical imagery is constrained by accuracy problems related to the difficulty in distinguishing water features from other surfaces with low albedo, such as tree shadows. This is especially true when mapping water bodies of different sizes. To address these problems, we evaluated the potential of integrating hyperspectral data with LiDAR (hereinafter “integrative approach”). The study area consisted of several spoil heaps containing heterogeneous water bodies with a high variability of shape and size. We utilized object-based classification (Support Vector Machine) based on: (i) hyperspectral data; (ii) LiDAR variables; (iii) integration of both datasets. Besides, we classified hyperspectral data using pixel-based approaches (K-mean, spectral angle mapper). Individual approaches (hyperspectral data, LiDAR data and integrative approach) resulted in 2–22.4 % underestimation of the water surface area (i.e, omission error) and 0.4–1.5 % overestimation (i.e., commission error).The integrative approach yielded an improved discrimination of open water surface compared to other approaches (omission error of 2 % and commission error of 0.4 %). We also evaluated the success of detecting individual ponds; the integrative approach was the only one capable of detecting the water bodies with both omission and commission errors below 10 %. Finally, the assessment of misclassification reasons showed a successful elimination of shadows in the integrative approach. Our findings demonstrate that the integration of hyperspectral and LiDAR data can greatly improve the identification of small water bodies and can be applied in practice to support mapping of restoration process.
There is a lack of guidance on the choice of the spatial grain of predictor and response variables in species distribution models (SDM). This review summarizes the current state of the art with ...regard to the following points: (i) the effects of changing the resolution of predictor and response variables on model performance; (ii) the effect of conducting multi-grain versus single-grain analysis on model performance; and (iii) the role of land cover type and spatial autocorrelation in selecting the appropriate grain size. In the reviewed literature, we found that coarsening the resolution of the response variable typically leads to declining model performance. Therefore, we recommend aiming for finer resolutions unless there is a reason to do otherwise (e.g. expert knowledge of the ecological scale). We also found that so far, the improvements in model performance reported for multi-grain models have been relatively low and that useful predictions can be generated even from single-scale models. In addition, the use of high-resolution predictors improves model performance; however, there is only limited evidence on whether this applies to models with coarser-resolution response variables (e.g. 100 km2 and coarser). Low-resolution predictors are usually sufficient for species associated with fairly common environmental conditions but not for species associated with less common ones (e.g. common vs rare land cover category). This is because coarsening the resolution reduces variability within heterogeneous predictors and leads to underrepresentation of rare environments, which can lead to a decrease in model performance. Thus, assessing the spatial autocorrelation of the predictors at multiple grains can provide insights into the impacts of coarsening their resolution on model performance. Overall, we observed a lack of studies examining the simultaneous manipulation of the resolution of predictor and response variables. We stress the need to explicitly report the resolution of all predictor and response variables.
Several global digital elevation models (DEMs) have been developed in the last two decades. The most recent addition to the family of global DEMs is the TanDEM-X DEM. The original version of the ...TanDEM-X DEM is, however, a nonedited product (i.e., it contains local artefacts such as voids, spikes, and holes). Therefore, subsequent identification of local artefacts and their editing is necessary. In this study, we evaluated the accuracy of the original TanDEM-X DEM and its improved edited version, the Copernicus DEM, in three major European mountain ranges (the Alps, the Carpathians, and the Pyrenees) using a digital surface model derived from airborne laser scanning data as a reference. In addition, to evaluate the applicability of data acquisition characteristics (coverage map, consistency mask, and height error map) and terrain characteristics (slope, aspect, altitude) to the localization of problematic sites, we modeled their associations with the TanDEM-X DEM error. We revealed local occurrences of large errors in the TanDEM-X DEM that were typically found on steep ridges or in canyons, which were largely corrected in the Copernicus DEM. The editing procedure used for the Copernicus DEM construction was evidently successful as the RMSE for the TanDEM-X and Copernicus DEMs at the 90 m resolution improved from 45 m to 12 m, from 16 m to 6 m, and from 24 m to 9 m for the Alps, the Pyrenees, and the Carpathians, respectively. The Copernicus DEM at the 30 m resolution performed similarly well. The boosted regression trees showed that acquisition characteristics provided as auxiliary data are useful for locating problematic sites and explained 28–50% of deviance of the absolute vertical error. The absolute vertical error was strongly related to the height error map. Finally, up to 26% of cells in the Copernicus DEM were filled using DEMs from different time periods and, hence, users performing multitemporal analysis or requiring data from a specific time period in the mountain environment should be wary when using TanDEM-X and its derivations. We suggest that when filling problematic sites using alternative DEMs, more attention should be paid to the period of their collection to minimize the temporal displacement in the final products.
The ability of a forest to buffer understory temperature extremes depends on the canopy structure, which is often measured from the ground. However, ground measurements provide only point estimates, ...which cannot be used for spatially explicit microclimate modeling. Canopy structures derived from airborne light detection and ranging (LiDAR) can overcome these limitations, but high point-density LiDAR is expensive and computationally challenging. Therefore, we explored whether unmanned aerial systems (UAS) processed with the structure-from-motion (SfM) algorithm could serve as an alternative source of canopy variables for forest microclimate modeling. Specifically, we compared the performance of the canopy cover and height derived from the ground measurements and passive (UAS-SfM) and active (UAS-LiDAR) remote sensing as predictors of air and soil temperature offsets (i.e. differences between the forest understory and treeless areas).
We found that the maximum air temperatures were substantially lower inside than outside the forest, with differences ranging from 9.0 to 12.5 °C. The soil temperatures under the canopy were also reduced, but the soil temperature offsets were lower and ranged from 1.1 to 2.8 °C. The air and soil temperature offsets both increased with increasing tree height and canopy cover. However, the prediction ability of tree height and canopy cover differed if they were ground-based or remotely sensed. The remotely sensed canopy indices explained air temperature offsets better (UAS-SfM: R2 = 0.59, RMSE = 0.75 °C; UAS-LiDAR: R2 = 0.57, RMSE = 0.76 °C) than ground measurements (R2 = 0.51, RMSE = 0.80 °C). Ground-based metrics explained soil temperature offsets better (R2 = 0.37, RMSE = 0.36 °C) than passive remote sensing approach (UAS-SfM: R2 = 0.27, RMSE = 0.39 °C), but comparably to active one (UAS-LiDAR: R2 = 0.35, RMSE = 0.37 °C).
Our results suggest that both UAS-SfM and UAS-LiDAR can substitute ground canopy measurements for air temperature modeling, but soil temperature modeling is more challenging. Overall, our results show that forest microclimate can be modelled at a very high spatial resolution using UAS equipped with inexpensive optical cameras. The increasingly available UAS-SfM approach can thus provide fine-resolution microclimatic data much needed for biologically relevant predictions of species responses to climate change.
•Forest temperature buffering increases with increasing canopy cover and tree height.•UAS remote sensing explains air temperature buffering better than ground measurements.•UAS-SfM predicts air temperature buffering as well as UAS-LiDAR.
The ICESat-2 ATL08 land and vegetation product includes several flags that can be used for the assessment of LiDAR-environment interactions and can help select data of the highest quality. However, ...the usability of these flags has not been sufficiently studied to date. Here, we aimed to evaluate the effects of atmospheric scattering, the presence of snow, canopy cover, terrain slope, beam strength, and solar angle on the accuracy of terrain and canopy height of the ATL08 product as well as on providing recommendations on how to filter data in order to minimize errors. We evaluated the vertical accuracy of ATL08 terrain and canopy height in European mountains by comparing them with the digital terrain model and canopy height model derived from airborne laser scanning data. Our results indicate that the assessment of atmospheric effects using the cloud confidence flag (cloud_flag_atm; i.e. number of cloud layers) is better than the previously used multiple scattering warning flag (msw_flag). Day acquisitions with more than one layer of clouds yielded a terrain elevation RMSE of 3.22 m in forests while night acquisitions with no more than a single layer of clouds resulted in RMSE of 1.73 m. The increasing atmospheric scattering effects increased the photons' path length, resulting in terrain height underestimation. The presence of snow had a strong positive effect on the number of identified ground photons, independently of the canopy cover, but resulted in an overestimation of terrain height in higher altitudes. Accordingly, the presence of snow cover resulted in a significant underestimation of canopy height in forests. The canopy height in broadleaf/mixed as well as coniferous forests was in summer underestimated on average by 2.1 m (%ME of −15.3%) and 1.2 m (%ME of −8.2%), respectively; in winter, however, the underestimation increased to 8.5 m (%ME of −56.8%) and 5.7 m (%ME of −38.3%), respectively. Canopy height estimates had better accuracy for the strong beam (RMSE of 5.09 m; %RMSE of 35.4%) than for the weak beam (RMSE of 7.03 m; %RMSE of 51.3%). Our results show that the ATL08 terrain height accuracy decreases with uneven distribution of signal photons within individual segments and further deteriorates with increasing terrain slope. Filtering out segments with poor distribution of photons, more than one layer of clouds during the day, and snow cover in high altitudes is the best approach for minimizing the error while maximizing the number of segments left for subsequent analysis.
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•Cloud confidence flag is useful for assessing atmospheric effects on ATL08 accuracy.•High number of cloud layers led to low number of photons and terrain underestimation.•Error of canopy height estimates is considerably larger than that of terrain height.•Canopy height is underestimated due to snow-cover and loss of tree foliage in winter.•We provide users with an approach for detecting problematic measurements.
Ecosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species ...distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up-to-date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products.
Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to ...limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs.
Urbanization affects avian community composition in European cities, increasing biotic homogenization. Anthropic pollution (such as light at night and noise) is among the most important drivers ...shaping bird use in urban areas, where bird species are mainly attracted by urban greenery. In this study, we collected data on 127 breeding bird species at 1349 point counts distributed along a gradient of urbanization in fourteen different European cities. The main aim was to explore the effects of anthropic pollution and city characteristics, on shaping the avian communities, regarding species' diet composition. The green cover of urban areas increased the number of insectivorous and omnivorous bird species, while slightly decreasing the overall diet heterogeneity of the avian communities. The green heterogeneity-a measure of evenness considering the relative coverage of grass, shrubs and trees-was positively correlated with the richness of granivorous, insectivorous, and omnivorous species, increasing the level of diet heterogeneity in the assemblages. Additionally, the effects of light pollution on avian communities were associated with the species' diet. Overall, light pollution negatively affected insectivorous and omnivorous bird species while not affecting granivorous species. The noise pollution, in contrast, was not significantly associated with changes in species assemblages. Our results offer some tips to urban planners, managers, and ecologists, in the challenge of producing more eco-friendly cities for the future.
Non‐forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic ...changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non‐forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low‐stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave‐one‐out cross‐validation of 3.9%. Biomass per‐unit‐of‐height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much‐needed information required to calibrate and validate the vegetation models and satellite‐derived biomass products that are essential to understand vulnerable and understudied non‐forested ecosystems around the globe.
Working at sites across the globe, we used a standardized protocol to collect and analyse drone data in order to measure the size of many different plants in non‐forest ecosystems. These measurements of canopy height allowed the prediction of aboveground biomass and carbon storage of different plants accurately across the landscape. This new approach to measuring plants enables detailed monitoring of vegetation dynamics and responses to differences in climate or disturbance that can help us understand the changes happening in important and vulnerable non‐forest ecosystems around the world.
Europe is an urbanized continent characterized by a long history of human-wildlife interactions. This study aimed to assess the effects of specific elements of urbanization and urban pollution on ...complementary avian diversity metrics, to provide new insights on the conservation of urban birds.
Our study recorded 133 bird species at 1624 point counts uniformly distributed in seventeen different European cities. Our results thus covered a large spatial scale, confirming both effects of geographical and local attributes of the cities on avian diversity. However, we found contrasting effects for the different diversity components analyzed. Overall, taxonomic diversity (bird species richness), phylogenetic diversity and relatedness were significantly and negatively associated with latitude, while functional dispersion of communities showed no association whatsoever. At the local level (within the city), we found that urban greenery (grass, bush, and trees) is positively correlated with the number of breeding bird species, while the building cover showed a detrimental effect. Functional dispersion was the less affected diversity metric, while grass and trees and water (rivers or urban streams) positively affected the phylogenetic diversity of avian communities. Finally, the phylogenetic relatedness of species increased with all the main indicators of urbanization (building surface, floors, pedestrian's density and level of light pollution) and was only mitigated by the presence of bushes.
We argue that maintaining adequate levels of avian diversity within the urban settlements can help to increase the potential resilience of urban ecosystems exposed to the stress provoked by rapid and continuous changes. We listed some characteristics of the cities providing positive and negative effects on each facet of urban avian diversity.
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•Four measures of avian diversity were estimated in bird communities of European cities.•Taxonomic and phylogenetic avian diversity were negatively associated with latitude.•Urban greenery increased avian taxonomic diversity, while building decreased it.•Grass, trees, and water positively affected phylogenetic diversity of avian communities.•Phylogenetic relatedness increased with urbanization and was only mitigated by bushes.
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