From Remote Sensing to Species Distribution Modelling: An Integrated Workflow to Monitor Spreading Species in Key Grassland Habitats

Predicting the responses of European grassland communities to climate and land cover change

European grasslands are among the most species-rich ecosystems on small spatial scales. However, human-induced activities like land use and climate change pose significant threats to this diversity. To explore how climate and land cover change will affect biodiversity and community composition in grassland ecosystems, we conducted joint species distribution models (SDMs) on the extensive vegetation-plot database sPlotOpen to project distributions of 1178 grassland species across Europe under current conditions and three future scenarios. We further compared model accuracy and computational efficiency between joint SDMs (JSDMs) and stacked SDMs, especially for rare species. Our results show that: (i) grassland communities in the mountain ranges are expected to suffer high rates of species loss, while those in western, northern and eastern Europe will experience substantial turnover; (ii) scaling anomalies were observed in the predicted species richness, reflecting regional differences in the dominant drivers of assembly processes; (iii) JSDMs did not outperform stacked SDMs in predictive power but demonstrated superior efficiency in model fitting and predicting; and (iv) incorporating co-occurrence datasets improved the model performance in predicting the distribution of rare species. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Utilising random forests in the modelling of Eragrostis curvula presence and absence in an Australian grassland system

Eragrostis curvula is an agronomically and ecologically undesirable perennial tussock grass dispersed across Australia. The objective of this study is to investigate relationships of ecologically relevant abiotic variables with the presence of E. curvula at a landscape scale in the Snowy Monaro region, Australia. Through vegetation surveys across 21 privately owned properties and freely available ancillary data on E. curvula presence, we used seven predictor variables, including Sentinel 2 NDVI reflectance, topography, distance from roads and watercourses and climate, to predict the presence or absence of E. curvula across its invaded range using a random forest (RF) algorithm. Assessment of performance metrics resulted in a pseudo-R squared of 0.96, a kappa of 0.97 and an R squared for out-of-bag samples of 0.67. Temperature had the largest influence on the model's performance, followed by linear features such as highways and rivers. Highways' high importance in the model may indicate that the presence or absence of E. curvula is related to the density of human transit, thus as a vector of E. curvula propagule dispersal. Further, humans' tendency to reside adjacent to rivers may indicate that E. curvula's presence or absence is related to human density and E. curvula's potential to spread via water courses.

Robotic monitoring of grasslands: a dataset from the EU Natura2000 habitat 6210* in the central Apennines (Italy)

Despite the remarkable growth of the global market for robotics, robotic monitoring of habitats is still an understudied topic. This is true, among others, for the species-rich EU Annex I habitat "6210 - Semi-natural grasslands and scrubland facies on calcareous substrates". This habitat is typically surveyed by human operators. In this work, we present a dataset concerning relevés performed through the quadrupedal robot ANYmal C. The dataset contains information from three plots, which include the robot state, videos, and images acquired to assess the habitat conservation status. Additionally, a collection of videos and pictures about two typical and one early warning species of habitat 6210 is also presented. This database is publicly available in the provided Zenodo repository and will aid researchers in several fields. Robot state information can be used by engineers to validate their algorithms, while data gathered by the robot can be used to design new methodologies and new metrics to assess the habitat conservation status or train/test classifiers (e.g. neural networks) for plant classification.

Double down on remote sensing for biodiversity estimation: a biological mindset

In the light of unprecedented planetary changes in biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential for informing policy and sustainable development. Biodiversity monitoring is a challenge, especially for large areas such as entire continents. Nowadays, spaceborne and airborne sensors provide information that incorporate wavelengths that cannot be seen nor imagined with the human eye. This is also now accomplished at unprecedented spatial resolutions, defined by the pixel size of images, achieving less than a meter for some satellite images and just millimeters for airborne imagery. Thanks to different modeling techniques, it is now possible to study functional diversity changes over different spatial and temporal scales. At the heart of this unifying framework are the “spectral species”—sets of pixels with a similar spectral signal—and their variability over space. The aim of this paper is to summarize the power of remote sensing for directly estimating plant species diversity, particularly focusing on the spectral species concept.

Predicting catchment suitability for biodiversity at national scales

Biomonitoring of water quality and catchment management are often disconnected, due to mismatching scales. Considerable effort and money are spent each year on routine reach-scale surveying across many sites, particularly in countries like the UK, where nationwide sampling has been conducted using standardised techniques for many decades. Most of these traditional freshwater biomonitoring schemes focus on pre-defined indicators of organic pollution to compare observed vs expected subsets of common macroinvertebrate indicator species. Other taxa, including many threatened species, are often ignored due to their rarity, as are many invasive species, which are seen as undesirable despite becoming increasingly common and widespread in freshwaters, especially in urban ecosystems. Both these types of taxa are often monitored separately for reasons related to biodiversity concerns rather than for gauging water quality. Repurposing such data could therefore provide important new biomonitoring tools that can help catchment managers to directly link the water quality they aim to control with the biodiversity they are trying to protect. Here we used extensive data held in the England Non-Native and Rare/Protected species records that track these two groups of species as a proof-of-concept for linking catchment scale management of freshwater ecosystems and biodiversity to a range of potential drivers across England. We used national land use (Centre for Ecology and Hydrology land cover map) and water quality indicator (Environment Agency water quality data archive) datasets to predict, at the catchment scale, the presence or absence of 48 focal threatened or invasive species of concern routinely sampled by the English Environment Agency, with a median accuracy of 0.81 area under the receiver operating characteristic curve. A variety of water quality indicators and land-use types were useful in predictions, highlighting that future biomonitoring schemes could use such complementary measures to capture a wider spectrum of drivers and responses. In particular, the percentage of a catchment covered by freshwater was the single most important metric, reinforcing the need for space/habitat to support biodiversity, but we were also able to resolve a range of key environmental drivers for particular focal species. We show how our method could inform new catchment management approaches, by highlighting how key relationships can be identified and how to understand, visualise and prioritise catchments that are most suitable for restoration or water quality interventions. The scale of this work, in terms of number of species, drivers and locations, represents a significant step towards forging a new approach to catchment management that enables managers to link drivers they can control (water quality and land use) to the biota they are trying to protect (biodiversity).

Functional Analysis for Habitat Mapping in a Special Area of Conservation Using Sentinel-2 Time-Series Data

The mapping and monitoring of natural and semi-natural habitats are crucial activities and are regulated by European policies and regulations, such as the 92/43/EEC. In the Mediterranean area, which is characterized by high vegetational and environmental diversity, the mapping and monitoring of habitats are particularly difficult and often exclusively based on in situ observations. In this scenario, it is necessary to automate the generation of updated maps to support the decisions of policy makers. At present, the availability of high spatiotemporal resolution data provides new possibilities for improving the mapping and monitoring of habitats. In this work, we present a methodology that, starting from remotely sensed time-series data, generates habitat maps using supervised classification supported by Functional Data Analysis. We constructed the methodology using Sentinel-2 data in the Mediterranean Special Area of Conservation “Gola di Frasassi” (Code: IT5320003). In particular, the training set uses 308 field plots with 11 target classes (five forests, two shrubs, one grassland, one mosaic, one extensive crop, and one urban land). Starting from vegetation index time-series data, Functional Principal Component Analysis was applied to derive FPCA scores and components. In particular, in the classification stage, the FPCA scores are considered as features. The obtained results out-performed a previous map derived from photo-interpretation by domain experts. We obtained an overall accuracy of 85.58% using vegetation index time-series, topography, and lithology data. The main advantages of the proposed approach are the capability to efficiently compress high dimensional data (dense remote-sensing time series) providing results in a compact way (e.g., FPCA scores and mean seasonal time profiles) that: (i) facilitate the link between remote sensing with habitat mapping and monitoring and their ecological interpretation and (ii) could be complementary to species-based approaches in plant community ecology and phytosociology.

Superpixel-Based Regional-Scale Grassland Community Classification Using Genetic Programming with Sentinel-1 SAR and Sentinel-2 Multispectral Images

Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and utilization of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimized classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimized classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21% and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale.