Estimating grassland vegetation cover with remote sensing: a comparison between Landsat-8, Sentinel-2 and PlanetScope imagery

Grassland fractional vegetation cover (FVC) accurate mapping on a large scale is crucial, since degraded grasslands contribute less to provisioning services, carbon storage, water purification, erosion control and biodiversity conservation. The spatial and temporal resolution of Sentinel-2 (S2) and PlanetScope (PS) data has never been explored for grassland FVC estimation so far and will enable researchers and agencies to quantify and map timelier and more precisely grassland processes. In this paper we compare FVC estimation models developed from Landsat-8 (L8), S2 and PS imagery. The reference grassland FVC dataset was obtained on the Paganella ski runs (46.15°N, 11.01°E, Italy) applying unsupervised classification to nadir grassland RGB photographs taken from 1.35 m above the soil. Fractional Response Models between reference FVC and 18 vegetation indices (VIs) extracted from satellite imagery were fitted and analysed. Then, leave-one-out cross validation and spatiotemporal change analysis were also performed. Our study confirms the robustness of the commonly used VIs based on the difference between NIR and the red wavelength region (R2 = 0.91 for EVI using S2 imagery) and indicate that VIs based on the red-edge spectral region are the best performing for PS imagery (R2 = 0.89 for RECI). Only medium to high spatial resolution imagery (S2 and PS) precisely mapped spatial patterns at the study site, since grasslands FVC varies at a fine scale. Previously available imagery at medium to low spatial and temporal resolution (e.g., L8) may still be interesting for analysis requiring long time-series of dat

Detection of grassland mowing frequency using time series of vegetation indices from Sentinel-2 imagery

5openInternationalItalian coauthor/editorManagement intensity deeply influences meadow structure and functioning, therefore affecting grassland ecosystem services. Conservation and management measures, including European Common Agricultural Policy subsidies, should therefore be based on updated and publicly available data about management intensity. The mowing frequency is a crucial trait to describe meadows management intensity, but the potential of using vegetation indices from Sentinel-2 imagery for its retrieval has not been fully exploited. In this work we developed on the Google Earth Engine platform a four-phases algorithm to identify mowing frequency, including i) vegetation index time-series computing, ii) smoothing and resampling, iii) mowing detection, and iv) majority analysis. Mowing frequency during 2020 of 240 ha of grassland fields in the Italian Alps was used for algorithm optimization and evaluation. Six vegetation indexes (EVI, GVMI, MTCI, NDII, NDVI, RENDVI783.740) were tested as input to the proposed algorithm. The Normalized Difference Infrared Index (NDII) showed the best performance, resulting in mean absolute error of 0.07 and 93% overall accuracy on average at the four sites used for optimization, at pixel resolution. A slightly lower accuracy (mean absolute error = 0.10, overall accuracy = 90%) was obtained aggregating the maps to management parcels. The algorithm showed a good generalization ability, with a similar performance between global and local optimization and an average mean absolute error of 0.12 and an overall accuracy of 89% on average on the sites not used for parameters optimization. The lowest accuracies occurred in intensively managed grasslands surveyed by one satellite orbit only. This study demonstrates the suitability of the proposed algorithm to monitor very fragmented grasslands in complex mountain ecosystems. Google Earth Engine was used to develop the model and will enable researchers, agencies and practitioners to easily and quickly apply the code to map grassland mowing frequency for extensive grasslands protection and conservation, for mowing event verification, or for forage system characterization.openAndreatta, Davide; Gianelle, Damiano; Scotton, Michele; Vescovo, Loris; Dalponte, MicheleAndreatta, D.; Gianelle, D.; Scotton, M.; Vescovo, L.; Dalponte, M

A method for continuous sub-annual mapping of forest disturbances using optical time series

Forest disturbances have a major impact on ecosystem dynamics both at local and global scales. Accordingly, it is important to acquire objective information about the location, nature and timing of such events to improve the understanding of their impact, update forest management policies and disturbance mitigation strategies. To this date, remotely sensed data have been widely used for the detection of stand replacing disturbances (SRD) such as windthrows and wildfires. In contrast, less effort has been devoted to the detection of non-stand replacing disturbances (NSRD), typically characterized by slower and gradual temporal dynamics. To address this gap, we propose a method for the automated detection of both SRD and NSRD. The proposed method can detect both past and recent disturbances, with a monthly temporal resolution, in a near real-time fashion by processing new images as they are acquired. Differently from existing approaches that handle the time series as a one-dimensional (1D) temporal trajectory, the method analyzes the sequence of images by organizing them in a two-dimensional (2D) grid-like structure. This representation allows us to model both the intra- and inter-annual variations of the time series taking advantage of the annual cyclical nature of the plant phenology. The method has been tested on study areas attacked by bark beetles achieving a user’s accuracy and producer’s accuracy of 0.91±0.08 and 0.81±0.07 (with 95% confidence intervals) for the disturbed areas, respectively

Mapping a European spruce bark beetle outbreak using sentinel-2 remote sensing data

Insect outbreaks affect forests, causing the deaths of trees and high economic loss. In this study, we explored the detection of European spruce bark beetle (Ips typographus, L.) outbreaks at the individual tree crown level using multispectral satellite images. Moreover, we explored the possibility of tracking the progression of the outbreak over time using multitemporal data. Sentinel-2 data acquired during the summer of 2020 over a bark beetle–infested area in the Italian Alps were used for the mapping and tracking over time, while airborne lidar data were used to automatically detect the individual tree crowns and to classify tree species. Mapping and tracking of the outbreak were carried out using a support vector machine classifier with input vegetation indices extracted from the multispectral data. The results showed that it was possible to detect two stages of the outbreak (i.e., early, and late) with an overall accuracy of 83.4%. Moreover, we showed how it is technically possible to track the evolution of the outbreak in an almost bi-weekly period at the level of the individual tree crowns. The outcomes of this paper are useful from both a management and ecological perspective: it allows forest managers to map a bark beetle outbreak at different stages with a high spatial accuracy, and the maps describing the evolution of the outbreak could be used in further studies related to the behavior of bark beetle

Tourists and Local Stakeholders’ Perception of Ecosystem Services Provided by Summer Farms in the Eastern Italian Alps

In the Alps, summer farms are temporary units, where cattle are moved during summer to graze on Alpine pastures, which provide multiple ecosystem services (ESs), many of which do not have a market value. This study aimed at understanding and comparing the perceptions of summer farms and of the associated ESs by local stakeholders and tourists in a study area of the province of Trento in the eastern Italian Alps. Thirty-five online questionnaires and two focus groups were realized with local stakeholders involved in the dairy value-chain. Semi-structured interviews were conducted with 405 tourists in two representative summer farms. The perceptions of summer farms diered between local stakeholders, who mainly focused on provisioning ESs, and tourists, who mainly focused on cultural and regulating ESs. Both categories of actors rated positively eight dierent ESs associated with summer farms, but demonstrated a lack of knowledge of specific regulating ESs. This study showed that discussion among the dierent actors is required to increase mutual knowledge and to grasp the diversity of links between summer farms and ESs, in order to support public policies and private initiatives for promoting summer farm products and the sustainable development of mountain regions

Detection of forest windthrows with bitemporal COSMO-SkyMed and Sentinel-1 SAR data

Wind represents a primary source of disturbances in forests, necessitating an assessment of the resulting damage to ensure appropriate forest management. Remote sensing, encompassing both active and passive techniques, offers a valuable and efficient approach for this purpose, enabling coverage of large areas while being costeffective. Passive remote sensing data could be affected by the presence of clouds, unlike active systems such as Synthetic Aperture Radar (SAR) which are relatively less affected. Therefore, this study aims to explore the utilization of bitemporal SAR data for windthrow detection in mountainous regions. Specifically, we investigated how the detection outcomes vary based on three factors: i) the SAR wavelength (X-band or C-band), ii) the acquisition period of the pre- and post-event images (summer, autumn, or winter), and iii) the forest type (evergreen vs. deciduous). Our analysis considers two SAR satellite constellations: COSMO-SkyMed (band-X, with a pixel spacing of 2.5 m and 10 m) and Sentinel-1 (band-C, with a pixel spacing of 10 m). We focused on three study sites located in the Trentino-South Tyrol region of Italy, which experienced significant forest damage during the Vaia storm from 27th to 30th October 2018. To accomplish our objectives, we employed a detailpreserving, scale-driven approach for change detection in bitemporal SAR data. The results demonstrate that: i) the algorithm exhibits notably better performance when utilizing X-band data, achieving a highest kappa accuracy of 0.473 and a balanced accuracy of 76.1%; ii) the pixel spacing has an influence on the accuracy, with COSMO-SkyMed data achieving kappa values of 0.473 and 0.394 at pixel spacings of 2.5 m and 10 m, respectively; iii) the post-event image acquisition season significantly affects the algorithm’s performance, with summer imagery yielding superior results compared to winter imagery; and iv) the forest type (evergreen vs. deciduous) has a noticeable impact on the results, particularly when considering autumn/winter dat

New tree monitoring systems: from Industry 4.0 to Nature 4.0

Recently, Internet of Things (IoT) technologies have grown rapidly and represent now a unique opportunity to improve our environmental monitoring capabilities at extremely low costs. IoT is a new system of thinking in which objects, animals or people are equipped with unique identifiers and transfer data a network without requiring human-to-human or human-to-computer interaction. IoT has evolved from the convergence of wireless technologies, microelectromechanical systems (MEMS) and the Internet. The development of these technologies in environmental monitoring domains allows real-time data transmission and numerous low-cost monitoring points. We have designed a new device, the TreeTalker©, which is capable of measuring water transport in trees, diametrical growth, spectral characteristics of the leaves and microclimatic parameters and transmit data in semi-real time. Here we introduce the device’s features, provide an example of monitored data from a field test site and discuss the application of this new technology to tree monitoring in various contexts, from forest to urban green infrastructures management and ecological research

Continuous monitoring of tree responses to climate change for smart forestry: a cybernetic web of trees

6openBothTrees are long-lived organisms that contribute to forest development over centuries and beyond. However, trees are vulnerable to increasing natural and anthropic disturbances. Spatially distributed, continuous data are required to predict mortality risk and impact on the fate of forest ecosystems. In order to enable monitoring over sensitive and often remote forest areas that cannot be patrolled regularly, early warning tools/platforms of mortality risk need to be established across regions. Although remote sensing tools are good at detecting change once it has occurred, early warning tools require ecophysiological information that is more easily collected from single trees on the ground. Here, we discuss the requirements for developing and implementing such a treebased platform to collect and transmit ecophysiological forest observations and environmental measurements from representative forest sites, where the goals are to identify and to monitor ecological tipping points for rapid forest decline. Long-term monitoring of forest research plots will contribute to better understanding of disturbance and the conditions that precede it. International networks of these sites will provide a regional view of susceptibility and impacts and would play an important role in ground-truthing remotely sensed data.openTognetti, Roberto; Valentini, Riccardo; Belelli Marchesini, Luca; Gianelle, Damiano; Panzacchi, Pietro; Marshall, John D.Tognetti, R.; Valentini, R.; Belelli Marchesini, L.; Gianelle, D.; Panzacchi, P.; Marshall, J.D

Assessing plant trait diversity as an indicators of species α and β-diversity in a subalpine grassland of the Italian Alps

As the need for ecosystem biodiversity assessment increases within the climatecrisis framework, more and more studies using spectral variation hypothesis(SVH) are proposed to assess biodiversity at various scales. The SVH impliesoptical diversity (also called spectral diversity) is driven by light absorptiondynamics associated with plant traits (PTs) variability (which is an indicator offunctional diversity) which is, in turn, determined by biodiversity. In this study,we examined the relationship between PTs variability, optical diversity andα-andβ-diversity at different taxonomic ranks at the Monte Bondone grasslands,Trentino province, Italy. The results of the study showed that the PTs variabil-ity, at theαscale, was not correlated with biodiversity. On the other hand, theresults observed at the community scale (β-diversity) showed that the variationof some of the investigated biochemical and biophysical PTs was associatedwith theβ-diversity. We used the Mantel test to analyse the relationshipbetween the PTs variability and speciesβ-diversity. The results showed a corre-lation coefficient of up to 0.50 between PTs variability and speciesβ-diversity.For higher taxonomic ranks such as family and functional groups, a slightlyhigher Spearman’s correlation coefficient of up to 0.64 and 0.61 was observed,respectively. The SVH approach was also tested to estimateβ-diversity and wefound that spectral diversity calculated by Spectral Angle Mapper showed to bea better proxy of biodiversity in the same ecosystem where the spectral diversityapproach failed to estimateα-diversity. These findings suggest that optical andPTs diversity approaches can be used to predict species diversity in the grass-lands ecosystem where the species turnover is high

Towards continuous stem water content and sap flux density monitoring: IoT-based solution for detecting changes in stem water dynamics

Taking advantage of novel IoT technologies, a new multifunctional device, the “TreeTalker”, was developed to monitor real-time ecophysical and biological parameters of individual trees, as well as climatic variables related to their surrounding environment, principally, air temperature and air relative humidity. Here, IoT applied to plant ecophysiology and hydrology aims to unravel the vulnerability of trees to climatic stress via a single tree assessment at costs that enable massive deployment. We present the performance of the TreeTalker to elucidate the functional relation between the stem water content in trees and respective internal/external (stem hydraulic activity/abiotic) drivers. Continuous stem water content records are provided by an in-house-designed capacitance sensor, hosted in the reference probe of the TreeTalker sap flow measuring system, based on the transient thermal dissipation (TTD) method. In order to demonstrate the capability of the TreeTalker, a three-phase experimental process was performed including (1) sensor sensitivity analysis, (2) sensor calibration, and (3) long-term field data monitoring. A negative linear correlation was demonstrated under temperature sensitivity analysis, and for calibration, multiple linear regression was applied on harvested field samples, explaining the relationship between the sample volumetric water content and the sensor output signal. Furthermore, in a field scenario, TreeTalkers were mounted on adult Fagus sylvatica L. and Quercus petraea L. trees, from June 2020 to October 2021, in a beech-dominated forest near Marburg, Germany, where they continuously monitored sap flux density and stem volumetric water content (stem VWC). The results show that the range of stem VWC registered is highly influenced by the seasonal variability of climatic conditions. Depending on tree characteristics, edaphic and microclimatic conditions, variations in stem VWC and reactions to atmospheric events occurred. Low sapwood water storage occurs in response to drought, which illustrates the high dependency of trees on stem VWC under water stress. Consistent daily variations in stem VWC were also clearly detectable. Stem VWC constitutes a significant portion of daily transpiration (using TreeTalkers, up to 4% for the beech forest in our experimental site). The diurnal–nocturnal pattern of stem VWC and sap flow revealed an inverse relationship. Such a finding, still under investigation, may be explained by the importance of water recharge during the night, likely due to sapwood volume changes and lateral water distribution rather than by a vertical flow rate. Overall, TreeTalker demonstrated the potential of autonomous devices for monitoring sap density and relative stem VWC in the field of plant ecophysiology and hydrolog