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

Extracting flowering phenology from grassland species mixtures using time-lapse cameras

Understanding the impacts of climate change on plant phenology is crucial for predicting ecosystem responses. However, accurately tracking the flowering phenology of individual plant species in grassland species mixtures is challenging, hindering our ability to study the impacts of biotic and abiotic factors on plant reproduction and plant-pollinator interactions. Here, we present a workflow for extracting flowering phenology from grassland species mixtures using near-surface time-lapse cameras. We used 89 image series acquired in plots with known species composition at the Jena trait-based experiment (Germany) to develop random forest classifiers, which were used to classify images and compute time series of flower cover for each species. The high temporal resolution of time-lapse cameras allowed to select images in proper light conditions, and to extract vegetation indices and texture metrics to improve discrimination among flowering species. The random forest classifiers showed a high accuracy in predicting the cover of Leucanthemum vulgare, Ranunculus acris, and Knautia arvensis flowers, whereas graminoid flowers were harder to predict due to their green-to-brownish colours. The proposed workflow can be applied in climate change studies, ecosystem functioning, plant community ecology, and biodiversity change research, including the investigation of effects of species richness on individual species' flowering phenology. Our method could be a valuable tool for understanding the impacts of climate change on plant reproduction and ecosystem dynamic

Notulae to the Italian alien vascular flora: 11

In this contribution, new data concerning the distribution of vascular flora alien to Italy are presented. It includes new records, confirmations, exclusions, and status changes for Italy or for Italian administrative regions. Nomenclatural and distribution updates published elsewhere are provided as Suppl. material 1

Notulae to the Italian alien vascular flora: 14

In this contribution, new data concerning the distribution of vascular flora alien to Italy are presented. It includes new records, confirmations, and status changes for Italy or for Italian administrative regions. Nomenclatural and distribution updates, published elsewhere, and corrections are provided as Suppl. materia

Notulae to the Italian alien vascular flora: 14

In this contribution, new data concerning the distribution of vascular flora alien to Italy are presented. It includes new records, confirmations, and status changes for Italy or for Italian administrative regions. Nomenclatural and distribution updates, published elsewhere, and corrections are provided as Suppl. material

Spectral separability of bark beetle infestation stages: A single-tree time-series analysis using Planet imagery

Bark beetles cause severe damage to European forests leading to impacts on many sectors, from the environmental to the economical. Timely mapping of the different stages of an attack is very important. Remote sensing has been widely used to map bark beetle damage using both airborne and satellite data. Newly available satellite multispectral data with a daily revisit time and high spatial resolution has the potential to monitor an attack in all its phases. This study explores the spectral separability of bark beetle infestation stages using the Planet imagery at individual tree level. Multi-temporal spectral analysis of 78 trees in different stages of a spruce bark beetle attack was carried out. Bands and vegetation indexes derived from 42 multispectral images were compared to eleven field surveys over a time span of approximately four months. The spectral separability analysis was done considering three criteria exploring: 1) the significance of the differences, 2) the magnitude of the differences and 3) the separability in a supervised classification context. The field surveys reported different effects depending on the season of the bark beetle attack - spring vs. summer. Spectral bands and indexes extracted from trees in the healthy and red-stage were significantly different. Trees in the green-attack stage at the end of the summer showed a statistically significant difference from healthy trees. The separability measured with a supervised classifier showed that it is possible to separate healthy, green-attack and red-stage trees with high accuracy values (kappa accuracy above 0.9)

Characteristics and trends of grassland degradation research

Purpose Grasslands are the largest type of terrestrial ecosystem on the earth, providing rich and unique ecosystem services. However, climate change and human activities have triggered a global degradation of grasslands, which has become a major ecological crisis. In this study, a scientometric analysis was performed to explore the hotspots and frontiers of global grassland degradation research. Materials and methods Two methods involving visualization were used to analyze these data: document co-citation analysis and burst analysis based on the papers indexed in the Web of Science (WOS) during 1970–2020. Results and discussion A total of 3580 research papers related to grassland degradation research and 54,666 references were included. The results showed that Harris’s paper in 2010 had the strongest burst value of 26.2, far larger than any other, which shows that this paper was a turning point in the research process. The document co-citation network was divided into 14 main theme clusters. The most influential and emerging research theme clusters were including alpine meadow, grazing exclusion, alpine region, and human activities. Alpine meadow was the largest cluster lasting from 2010 to 2020, indicating that this topic is still active in grassland degradation research. Furthermore, research focus has transferred toward grasslands in Qinghai-Tibetan Plateau. The topic of grazing exclusion is both classic and currently active as it lasted as a research hotspot for 15 years (2004–2018). However, the extent and state of grazing effects research are unclear. Conclusions As the first scientometric review on grassland degradation research, our study identified the research hotspots and their shifts over the past 50 years, pointing to some potential research frontiers in the future. The scientometric analysis is a useful tool for a quantitative evaluation of research hotspots and trends of global grassland degradation