Hyperspectral vs. Multispectral data: Comparison of the spectral differentiation capabilities of Natura 2000 non-forest habitats

Identification of the Natura 2000 habitats using remote sensing techniques is one of the most important challenges of nature conservation. In this study, the potential for differentiating non-forest Natura 2000 habitats from the other habitats was examined using hyperspectral data in the scope of VNIR (0.4–1 µm), SWIR (1–2.5 µm) and simulated multispectral data (Sentinel-2). The aim of the research was also to determine the most informative spectral ranges from the optical range. Five different Natura 2000 habitats common in Central Europe were analysed: heaths (code 4030), mires (code 7140), grasslands (code 6230) and meadows (codes 6410 and 6510). In order to guarantee the objectivity and transferability of the results each habitat was tested in two areas and in three campaigns (spring, summer, autumn). Hyperspectral data was acquired using HySpex VNIR-1800 and SWIR-384 scanners. The Sentinel-2 data was resampled based on HySpex spectral reflectance. The overflights were performed simultaneously with ground reference data – habitats and background polygons. The Linear Discriminant Analysis was performed in iterative mode based on spectral reflectance acquired from hyperspectral and multispectral data. This resulted in distribution of correctness rate values and information about the most differentiating spectral bands for each habitat. Based on the results of our experiments we conclude that: (i) hyperspectral data (both VNIR and SWIR) obtained from May to September was useful for differentiation of habitats from background with efficiency reaching over 90%, regardless of the area; (ii) the most useful spectral ranges are: in VNIR − 0.416–0.442 µm and 0.502–0.522 µm, in SWIR − 1.117–1.165 µm and 1.290–1.361 µm; (iii) the potential of multispectral data (Sentinel-2) in distinguishing Natura 2000 habitats from the background is diverse; higher for heaths and mires (comparable to hyperspectral data) lower for meadows (6410, 6510) and grasslands (6230); (iv) in case of meadows and grasslands, the correctness rate for the Sentinel-2 data was on average about 20% lower compared to the hyperspectral data

Study of fauna population changes on Penguin Island and Turret Point Oasis (King George Island, Antarctica) using an unmanned aerial vehicle

An unmanned aerial vehicle (UAV) as an alternative to manned aircrafts is an excellent, less invasive, safe tool, especially in sensitive polar regions. Here we used a fixed-wing UAV to collect data on seabird and pinniped populations in hardly accessible Antarctic areas. The implementation of an auto-piloted UAV equipped with a digital camera (Canon EOS 700D, 35 mm f/2.0 lens) allowed us to collect high-quality material applicable to a quantitative analysis of the fauna populations. A successful photogrammetric mission, at an altitude of 550 m above sea level, was accomplished during one Beyond Visual Line of Sight flight above hard-to-access Penguin Island and Turret Point Oasis (King George Island). Obtained selected RGB images were processed to generate a panoramic image stitch with resolution of 0.07 m ground sampling distance. A total of 4290 (SD = 33.08) breeding individuals of two penguin species, Adélie (Pygoscelis adeliae) and chinstrap (Pygoscelis antarcticus), 426 (SD = 7.78) individuals of the southern elephant seal (Mirounga leonina) and 6 individuals of the Weddell seal (Leptonychotes weddellii) were identified in both study areas. Additionally, 222 (SD = 2.0) individuals of the southern giant petrel (Macronectes giganteus) and 76 (SD = 1.0) of the Antarctic shag (Phalacrocorax atriceps bransfieldensis) in the Turret Point area were recognized. The presented observations on the natural history of the investigated fauna together with the available literature may be useful in future research on population trends. A comparison with available historical data for both investigated areas suggests a decrease of 68.29% in both penguin species in the 1980–2016 period. The presented results confirmed that UAVs are useful for remote census work for Antarctic seabirds

Feasibility of hyperspectral vegetation indices for the detection of chlorophyll concentration in three high Arctic plants: Salix polaris, Bistorta vivipara, and Dryas octopetala

Remote sensing, which is based on a reflected electromagnetic spectrum, offers a wide range of research methods. It allows for the identification of plant properties, e.g., chlorophyll, but a registered signal not only comes from green parts but also from dry shoots, soil, and other objects located next to the plants. It is, thus, important to identify the most applicable remote-acquired indices for chlorophyll detection in polar regions, which play a primary role in global monitoring systems but consist of areas with high and low accessibility. This study focuses on an analysis of in situ-acquired hyperspectral properties, which was verified by simultaneously measuring the chlorophyll concentration in three representative arctic plant species, i.e., the prostrate deciduous shrub Salix polaris, the herb Bistorta vivipara, and the prostrate semievergreen shrub Dryas octopetala. This study was conducted at the high Arctic archipelago of Svalbard, Norway. Of the 23 analyzed candidate vegetation and chlorophyll indices, the following showed the best statistical correlations with the optical measurements of chlorophyll concentration: Vogelmann red edge index 1, 2, 3 (VOG 1, 2, 3), Zarco-Tejada and Miller index (ZMI), modified normalized difference vegetation index 705 (mNDVI 705), modified normalized difference index (mND), red edge normalized difference vegetation index (NDVI 705), and Gitelson and Merzlyak index 2 (GM 2). An assessment of the results from this analysis indicates that S. polaris and B. vivipara were in good health, while the health status of D. octopetala was reduced. This is consistent with other studies from the same area. There were also differences between study sites, probably as a result of local variation in environmental conditions. All these indices may be extracted from future satellite missions like EnMAP (Environmental Mapping and Analysis Program) and FLEX (Fluorescence Explorer), thus, enabling the efficient monitoring of vegetation condition in vast and inaccessible polar areas

BVLOS UAV missions for vegetation mapping in maritime Antarctic

Polar areas are among the regions where climate change occurs faster than on most of the other areas on Earth. To study the effects of climate change on vegetation, there is a need for knowledge on its current status and properties. Both classic field observation methods and remote sensing methods based on manned aircraft or satellite image analysis have limitations. These include high logistic operation costs, limited research areas, high safety risks, direct human impact, and insufficient resolution of satellite images. Fixed-wing unmanned aerial vehicle beyond the visual line of sight (UAV BVLOS) missions can bridge the scale gap between field-based observations and full-scale airborne or satellite surveys. In this study the two operations of the UAV BVLOS, at an altitude of 350m ASL, have been successfully performed in Antarctic conditions. Maps of the vegetation of the western shore of Admiralty Bay (King George Island, South Shetlands, Western Antarctic) that included the Antarctic Specially Protected Area No. 128 (ASPA 128) were designed. The vegetation in the 7.5 km2 area was mapped in ultra-high-resolution(<5cm and DEM of 0.25m GSD), and from the Normalized Difference Vegetation Index (NDVI), four broad vegetation units were extracted: “dense moss carpets” (covering 0.14 km2 ,0.8%ofASPA128), “Sanionia uncinata moss bed” (0.31 km2 , 1.7% of ASPA 128), “Deschampsia antarctica grass meadow” (0.24 km2,1.3% of ASPA 128), and “Deschampsia antarctica–Usnea antarctica heath” (1.66 km2,9.4% of ASPA 128). Our results demonstrate that the presented UAV BVLOS–based surveys are time-effective (single flight lasting 2.5 h on a distance of 300 km) and cost-effective when compared to classical field-based observations and are less invasive for the ecosystem. Moreover, unmanned airborne vehicles significantly improve security, which is of particular interest in polar region research. Therefore, their development is highly recommended for monitoring areas in remote and fragile environments. KEYWORD

Reviews and syntheses:Remotely sensed optical time series for monitoring vegetation productivity

International audienceAbstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time; reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include e.g., gross primary productivity, net primary productivity, biomass or yield. To summarize current knowledge, in this paper, we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVM). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS-data derived productivity metrics: (1) using in situ measured data, such as yield, (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras, and (3) inter-comparison of different productivity products or modelled estimates. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully-integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and also enhances the accuracy of vegetation productivity monitoring

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions

The Impact of Tourist Traffic on the Condition and Cell Structures of Alpine Swards

This research focuses on the effect of trampling on vegetation in high-mountain ecosystems through the electromagnetic spectrum’s interaction with plant pigments, cell structure, water content and other substances that have a direct impact on leaf properties. The aim of the study was to confirm with the use of fluorescence methods of variability in the state of high-mountain vegetation previously measured spectrometrically. The most heavily visited part of the High Tatras in Poland was divided into polygons and, after selecting the dominant species within alpine swards, a detailed analysis of trampled and reference patterns was performed. The Analytical Spectral Devices (ASD) FieldSpec 3/4 were used to acquire high-resolution spectral properties of plants, their fluorescence and the leaf chlorophyll content with the difference between the plant surface temperature (ts), and the air temperature (ta) as well as fraction of Absorbed Photosynthetically Active Radiation (fAPAR) used as reference data. The results show that, along tourist trails, vegetation adapts to trampling with the impact depending on the species. A lower chlorophyll value was confirmed by a decrease in fluorescence, and the cellular structures were degraded in trampled compared to reference species, with a lower leaf reflectance. In addition, at the extreme, trampling can eliminate certain species such as Luzula alpino-pilosa, for which significant changes were noted due to trampling

Assessment of the dominant alpine sward species condition of the Tatra National Park using hyperespectral remote sensing

Różnorodność muraw wysokogórskich jest odzwierciedlona w cechach spektralnych, które można analizować za pomocą narzędzi teledetekcji. Każdy gatunek z racji na swoje strategie rozwoju ma unikatowy zestaw właściwości, które świadczą o jego stanie w poszczególnych zakresach widma elektromagnetycznego. Przedmiotem niniejszej pracy były dominujące gatunki muraw wysokogórskich Tatrzańskiego Parku Narodowego: sit skucina (Juncus trifidus), boimka dwurzędowa (Oreochloa disticha), mietlica skalna (Agrostis rupestris), śmiałek pogięty (Deschampsia flexuosa), kostrzewa niska (Festuca airoides), kostrzewa barwna (Festuca picta), kosmatka brunatna (Luzula alpino-pilosa), bliźniczka psia trawka (Nardus stricta). Obszar badawczy obejmował homogeniczne płaty poszczególnych gatunków znajdujące się w transekcie poprzecznym wzdłuż najważniejszych szlaków. Badania objęły zarówno wydeptywane poligony, jak i referencyjne znajdujące się ponad 10 m od szlaków. Szczególna uwaga zwrócona była na obszary poddane rekultywacji przez TPN, jak i obszary stałego monitoringu PKL i TPN. Większość poligonów zlokalizowana była w okolicach Kasprowego Wierchu, Beskidu, Doliny Gąsienicowej oraz Czerwonych Wierchów. Badania bazowały na terenowych pomiarach spektrometrycznych ASD FieldSpec 3. Jako dane referencyjne wykorzystano pomiary fluorymetryczne oraz bioradiometrycznych w okresie badawczym 2011-2014. Uzyskane dane zostały przeanalizowane statystycznie, a następnie obliczono teledetekcyjne wskaźniki roślinności, które pozwoliły ocenić i porównać kondycję badanych gatunków. Rekultywowana roślinność wykazała się cechami bliższymi właściwościom spektralnym płatów referencyjnych, niż uszkodzonych, które w sposób znaczący i istotny statystycznie potwierdziły negatywny wpływ wydeptywania. Wpływ ten jest różny dla poszczególnych gatunków. Zmiany istotne statystycznie różniły się w zakresie widma opisującym ilość chlorofilu, struktury komórkowe oraz zawartość wody w roślinności. Obniżone wartości wskaźników odnotowano dla poligonów wydeptywanych, zwłaszcza dla wskaźników opisujących chlorofil (np. ratio analysis of reflectance spectra algorithm chlorophyll a), stan ogólny (np. normalized multi-band drought index) i zawartość wody w roślinności (np. water band index). Potwierdzono to także poprzez pomiary fluorescencji (wskaźnik Fv/Fm). Ogółem wartości wskaźników mieściły się w optymalnych przedziałach, stan muraw wysokogórskich określono jako dobry. Zastosowanie metod fluorescencji i teledetekcji potwierdził ich przydatność do analizy gatunkowej na obszarach górskich, oceny jej kondycji i monitoringu.Vegetation through its condition reflects properties of the environment. A variety of alpine plant characteristics can be analyzed with an application of remote sensing tools. The aim of the study was to assess the condition of dominant species of alpine swards (Juncus trifidus, Oreochloa disticha, Agrostis rupestris, Deschampsia flexuosa, Festuca airoides, Festuca picta, Luzula alpino-pilosa and Nardus stricta) using hyperspectral remote sensing techniques. The study area was the Tatra National Park (Poland), in particular, areas where the vegetation of high mountain grasslands was strongly pressed by tourists (the Kasprowy Peak with surroundings hills and the Red Peaks). ASD FieldsSpec 3 spectrometer, fluorometer, and bioradiometric instruments during selected periods of 2011 to 2014 were used. Research polygons were located: along the trail, which depicted trampled vegetation and 10 m away from the trail ˗ as reference polygons. In addition, measurements were taken on areas where the Tatra National Park actively protects remediation of the vegetation. The obtained spectral properties for species of trampled, referenced and remediation patches were statistically significantly different of cellular structures, chlorophyll and water content in the canopy. Worst index values were observed for trampled plants, especially among chlorophyll based indices (e.g. ratio analysis of reflectance spectra algorithm chlorophyll a), general condition (e.g. normalized multi-band drought index) or water content (e.g. water band index). Species of remediation areas are characterised by a similar or sometimes even better properties than the reference areas. This observation was also confirmed by fluorescence measurements (e.g. Fv/Fm index). In general, index values were within optimal ranges, so the condition of the high grasslands was determined as good one. Application of fluorescence analysis and remote sensing tools confirms the suitability of such methods for monitoring of species in mountain areas