Drones have emerged as an essential tool in various conservation applications. Despite the great potential, drone use by protected area managers is still scarce or accompanied by scepticism. The ...ongoing debate revolves around whether drones are fancy gadgets or if they can effectively guide management strategies and align with the overarching goals of protected areas.
Here, we present a practical overview of how novel drone applications contribute to the goals of the oldest national park in the Alps, along with the associated challenges. To do so, we review our seven‐year experience as park employees flying drones within the park and its surroundings.
First, we provide background information receiving limited attention in the existing literature such as our motivation behind a drone purchase, costs and an overview of our flight operations.
Second, we show three examples that demonstrate the potential of drones as a valuable tool in addressing the goals of the park: managing the area, researching natural processes and facilitating communication.
Third, we reflect on operational challenges and provide valuable lessons for addressing the specific challenges of flying drones in an alpine and protected environment.
Practical implication: Our experience supports the benefits of drones for protected area management, but it also highlights the need for certain precautions, increased focus on operational challenges and further research on wildlife‐drone interactions.
Drawing on our seven‐year experience flying drones in the Swiss National Park and its surroundings, we provided an overview of how innovative drone applications contribute to the goals and tasks of the oldest national park in the Alps. Our insight offer valuable lessons to assist managers and scientists planning to fly drones in protected areas.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
•Accounting for spatio-temporal spectral dissimilarities between plant communities.•Using Sentinel-2 data to quantify spectral diversity in grasslands.•Improving the estimation of taxonomic beta ...diversity by integrating multi-temporal data.•Identifying the contribution of different spatio-temporal components to spectral diversity.
The increasing availability of remote sensing data allows the quantification of biodiversity in space and time. In particular, spectral diversity, defined as the variability of electromagnetic radiation reflected from plants, can be assessed with remote sensing. Plant traits vary diurnally and seasonally due to plant phenology and land management. This results in strong temporal variation of spectral diversity, which cannot be accurately represented by remotely sensed data collected at a single point in time. However, knowledge of how datasets sampled at multiple points in time should best be used to quantify spectral diversity is scarce. To address this issue, we first introduced a new approach using spatio-temporal spectral diversity based on the dissimilarity measure Rao's quadratic entropy index (RaoQ). Thereby, we demonstrated how RaoQ can be used to partition the total spectral diversity of a region (γSD) into additive alpha (αSD, within communities) and spatio-temporal beta (βSD; between communities) components, allowing the calculation of βSD from community mean spectral features, independent from αSD. Second, we illustrated our methodological approach with a case study in which βSD is calculated from Sentinel-2 satellite data at high temporal resolution for managed grasslands which differ across a large gradient of environmental properties. We were able to show differences in βSD and separate its components into phenological and management effects. Furthermore, the contribution of different plant communities to βSD was assessed, and the results were validated against a dataset of in-situ measured β diversity from plant surveys. Compared to spatial dissimilarities from distinct stages of the growing season, using spatio-temporal dissimilarities between communities produced a more accurate estimation of the uniqueness of a community. This study shows how to account for temporal variations in the spectral diversity of plant communities and demonstrates that this improves the estimation of plant biodiversity through remote sensing. Spectral diversity in space and time makes it possible to assess mechanisms that drive biodiversity and identify plant communities relevant for conservation purposes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•The CSI methodology measures the terrestrial landscape permeability for ecological connectivity at macro-regional scale.•Sensitivity and plausibility analyses provide insights for its ...application.•Different levels of detail and data processing minimally affect CSI results.•The CSI can support nature protection, connectivity conservation measures and landscape planning at macro-regional scale.
Over the past decade, ecological connectivity has entered the political agenda, especially within the European transnational context. This evolution has driven the development of structural ecological connectivity and landscape permeability methodologies, such as the Continuum Suitability Index (CSI) presented here, which considers a range of anthropogenic factors that impact ecosystems. Numerous international and national projects have adopted the CSI to assess terrestrial landscape permeability on the macro-regional scale and prioritize areas for the implementation of ecological conservation and restoration measures. Although the CSI methodology has been applied several times, its sensitivity to individual factors, plausibility and ability to maintain consistency and robustness across different data sources and levels of spatial data precision have remained largely unexplored. Here, we presented the conceptual aspects of the CSI methodology, incorporating the outcomes from a literature review and expert workshops, and examined the CSI results for three projects spanning the Alps and Dinaric Mountains. Five key factors—namely, land use, population pressure, landscape fragmentation, environmental protection and topography—were identified as pivotal for analyzing landscape permeability and thus ecological connectivity. Notably, among these factors, population pressure exhibited the highest sensitivity, while fragmentation exerted the least influence on CSI outcomes. When comparing the CSI factors with data on the presence of red-listed species, the environmental protection indicator emerged as the most influential factor. Furthermore, our investigation comparing the different projects indicated that the chosen level of detail and data sources had minimal impact on the CSI results. Collectively, these analyses highlight CSI's adaptability and considerable potential as a versatile and straightforward applicable tool for an initial assessment of ecological connectivity at the macro-regional scale.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract Over the last two decades, considerable research has built on remote sensing of spectral diversity to assess plant diversity. The spectral variation hypothesis (SVH) proposes that spatial ...variation in reflectance data of an area is positively associated with plant diversity. While the SVH has exhibited validity in dense forests, it performs poorly in highly fragmented and temporally dynamic agricultural landscapes covered mainly by grasslands. Such underperformance can be attributed to the mosaic-like spatial structure of human-dominated landscapes with fields in varying phenological and management stages. Therefore, we argued for re-evaluating SVH’s flawed window-based spatial analysis and underutilized temporal component. In particular, we captured the spatial and temporal variation in reflectance and assessed the relationships between spatial and temporal components of spectral diversity and plant diversity at the parcel level as a unit that relates to management patterns. Our investigation spanned three grasslands on two continents covering a wide spectrum of agricultural usage intensities. To calculate different components of spectral diversity, we used multi-temporal spaceborne Sentinel-2 data. We showed that plant diversity was negatively associated with the temporal component of spectral diversity across all sites. In contrast, the spatial component of spectral diversity was related to plant diversity in sites with larger parcels. Our findings highlighted that in agricultural landscapes, the temporal component of spectral diversity drives the spectral diversity-plant diversity associations. Consequently, our results offer a novel perspective for remote sensing of plant diversity globally.
Increasing evidence suggests that remotely sensed spectral diversity is linked to plant species richness. However, a conflicting spectral diversity–biodiversity relationship in grasslands has been ...found in previous studies. In particular, it remains unclear how well the spectral diversity–biodiversity relationship holds in naturally assembled species‐rich grasslands. To address the linkage between spectral diversity and plant species richness in a species‐rich alpine grassland ecosystem, we investigated (i) the trade‐off between spectral and spatial resolution in remote sensing data; (ii) the suitability of three different spectral metrics to describe spectral diversity (coefficient of variation, convex hull volume and spectral species richness) and (iii) the importance of confounding effects of live plant biomass, dead plant biomass and plant life forms on the spectral diversity–biodiversity relationship. We addressed these questions using remote sensing data collected with consumer‐grade cameras with four spectral bands and 10 cm spatial resolution on an unmanned aerial vehicle (UAV), airborne imaging spectrometer data (AVIRIS‐NG) with 372 bands and 2.5 m spatial resolution, and a fused data product of both datasets. Our findings suggest that a fused dataset can cope with the requirement of both high spatial‐ and spectral resolution to remotely measure biodiversity. However, in contrast to several previous studies, we found a negative correlation between plant species richness and spectral metrics based on the spectral information content (i.e. spectral complexity). The spectral diversity calculated based on the spectral complexity was sensitive to live and dead plant biomass. Overall, our results suggest that remote sensing of plant species diversity requires a high spatial resolution, the use of classification‐based spectral metrics, such as spectral species richness, and awareness of confounding factors (e.g. plant biomass), which may be ecosystem specific.
We estimated small‐scale plant species richness from spectral diversity using a multi‐sensor and data fusion approach in a species‐rich grassland ecosystem. Our results suggest that remote sensing of plant species diversity requires a high spatial resolution, the use of classification‐based spectral metrics, such as spectral species richness, and awareness of confounding factors, which may be ecosystem specific.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Remotely sensed spectral diversity has emerged as a promising proxy for plant diversity. However, spectral diversity approaches relate image spectra to plant community diversity by only incorporating ...variation among adjacent pixels, considering each pixel as a homogeneous entity composed of one class, and disregarding the within-pixel variability. Although such approaches might work for remotely-sensed data with fine spatial resolution, they might not be viable solutions to estimate plant diversity using coarse-resolution data from forthcoming spaceborne imagers. To address the limitations associated with spectral diversity approaches, we proposed a novel approach, known as endmember diversity, for remote estimation of plant diversity through quantifying spectral diversity at the sub-pixel level and taking into account the within-pixel variability. The approach consisted of deriving the number and abundance of distinct spectral entities within each pixel via spectral unmixing. In doing so, we considered the spectral signature of each pixel as a mixture of distinct spectral entities, commonly known as endmembers. We then used the per-pixel endmembers and their abundance to calculate different spectral diversity metrics for every pixel. We assessed the performance of the endmember diversity approach at estimating plant taxonomic and phylogenetic diversity based on two experiments using a simulated spectral dataset and a real-world spaceborne DESIS (DLR Earth Sensing Imaging Spectrometer) dataset. In both experiments, we found significant associations between endmember diversity and in situ plant diversity. Additionally, our method applied to DESIS data outperformed a conventional spectral diversity metric based on the coefficient of variation when applied to 1-m airborne imaging spectroscopy data. Collectively, our results demonstrate the capability of forthcoming spaceborne imagers to monitor local plant diversity.
•We estimate plant diversity at sub-pixel level using endmember diversity.•Spaceborne imaging spectroscopy captures taxonomic and phylogenetic diversity.•Endmember diversity is better equipped to handle soil exposure effectively.•Choice of diversity metric and plot size affect spectral-plant diversity relationship.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The coronavirus disease 2019 (COVID-19) pandemic changed recreation patterns worldwide. Increases in protected areas' visitor numbers were reported along with associated challenges. Changes in ...visitor numbers, composition, and motivation remain mostly unrecorded due to a lack of baseline records for comparison. We aimed to fill this gap with a study in the Swiss National Park (SNP), an International Union for Conservation of Nature (IUCN) strict nature reserve in the European Alps, where visitor numbers strongly increased in 2020 and 2021 compared to previous years. In summer 2020, we repeated a visitor survey previously conducted in 2006 and 2012, complemented by assessments of COVID-19-related motivations. To deepen our understanding of the COVID-19 context, we conducted semistructured interviews with SNP visitors. In general, COVID-19-related factors were a strong driver of increased visitor numbers. A fifth of survey respondents indicated that they would not have visited the SNP but for the pandemic, with most of them being first-time or infrequent visitors. Furthermore, our data showed that more young, domestic, and less experienced visitors came to the park. We discuss impacts and implications for practitioners and researchers (ie the need to better sensitize newcomers to environmental issues) and argue that our study holds insights for park managers worldwide.
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Assessing the environmental status of Protected Areas (PAs) is a challenging issue. To indicate that status, the identification of a common set of variables that are scientifically sound, and easy to ...assess and monitor by the PA practitioners, is particularly important. In this study, a set of 27 Essential Variables (EVs) for PA management was selected in a bottom-up process from 67 harmonised variables that describe the status of Ecosystem Functions and Structures, Ecosystem Services, and Threats in PAs. This bottom-up process involved 27 internationally recognised PAs, mostly European, with different level of protection, different extent, and a wide range of human-nature interactions. The EVs were selected by more than 120 practitioners, i.e. PA managers and rangers, as well as scientists, working in terrestrial and aquatic PAs. Across both terrestrial and aquatic PAs, scientists and practitioners largely identified the same variables as important. Data availability for these 27 EVs varied between PAs and av
eraged 67% across all studied PAs. As this set of EVs for PAs is defined through a bottom-up approach considering variables already in use both in management and research, it is more than for previous EVs likely to be adopted, applied and developed to record the status and changes in the ecological and socio-economic conditions of PAs and to forecast future changes. Thereby, the EVs for PAs present a common vocabulary and tool to enhance in a uniform way the (inter)national communication, exchange and comparison of information on the status of PAs between policy makers, scientists and PA managers. The perceived status of the EVs, on an average 3.6 on a scale to a maximum of 5, indicates the surveyed PAs are in a moderate to good environmental condition. Moreover, the EVs for PAs form a cost- and time-efficient tool for PA managers to monitor developments in essential elements of their PAs, including the potential for Societal Goods and Benefits (SG&B), and to (pro-)actively tackle the potential threats that may arise in their area. Likewise, for policy makers EVs for PAs may support decision making on ecosystem management, spatial planning, and predictive modelling on the future status and requirements of PAs in their country or region.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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