Remote Sensing (RS) is a useful tool for detecting and mapping Invasive Alien Plants (IAPs). IAPs mapping on dynamic and heterogeneous landscapes, using satellite RS data, is not always feasible. Unmanned aerial vehicles (UAV) with ultra-high spatial resolution data represent a promising tool for IAPs detection and mapping. This work develops an operational workflow for detecting and mapping Acacia saligna invasion along Mediterranean coastal dunes. In particular, it explores and tests the potential of RGB (Red, Green, Blue) and multispectral (Green, Red, Red Edge, Near Infra—Red) UAV images collected in pre-flowering and flowering phenological stages for detecting and mapping A. saligna. After ortho—mosaics generation, we derived from RGB images the DSM (Digital Surface Model) and HIS (Hue, Intensity, Saturation) variables, and we calculated the NDVI (Normalized Difference Vegetation Index). For classifying images of the two phenological stages we built a set of raster stacks which include different combination of variables. For image classification, we used the Geographic Object-Based Image Analysis techniques (GEOBIA) in combination with Random Forest (RF) classifier. All classifications derived from RS information (collected on pre-flowering and flowering stages and using different combinations of variables) produced A. saligna maps with acceptable accuracy values, with higher performances on classification derived from flowering period images, especially using DSM + HIS combination. The adopted approach resulted an efficient method for mapping and early detection of IAPs, also in complex environments offering a sound support to the prioritization of conservation and management actions claimed by the EU IAS Regulation 1143/2014.

Remote sensing (RS) has been widely adopted as a tool to investigate several biotic and abiotic factors, directly and indirectly, related to biodiversity conservation. European grasslands are one of the most biodiverse habitats in Europe. Most of these habitats are subject to priority conservation measure, and several human-induced processes threaten them. The broad expansions of few dominant species are usually reported as drivers of biodiversity loss. In this context, using Sentinel-2 (S2) images, we investigate the distribution of one of the most spreading species in the Central Apennine: Brachypodium genuense. We performed a binary Random Forest (RF) classification of B. genuense using RS images and field-sampled presence/absence data. Then, we integrate the occurrences obtained from RS classification into species distribution models to identify the topographic drivers of B. genuense distribution in the study area. Lastly, the impact of B. genuense distribution in the Natura 2000 (N2k) habitats (Annex I of the European Habitat Directive) was assessed by overlay analysis. The RF classification process detected cover of B. genuense with an overall accuracy of 94.79%. The topographic species distribution model shows that the most relevant topographic variables that influence the distribution of B. genuense are slope, elevation, solar radiation, and topographic wet index (TWI) in order of importance. The overlay analysis shows that 74.04% of the B. genuense identified in the study area falls on the semi-natural dry grasslands. The study highlights the RS classification and the topographic species distribution model’s importance as an integrated workflow for mapping a broad-expansion species such as B. genuense. The coupled techniques presented in this work should apply to other plant communities with remotely recognizable characteristics for more effective management of N2k habitats.

Aim: The ongoing global change makes landscape planning and management of ecological corridors crucial to preserve biodiversity. We propose a workflow optimizing the use of different data sources to convert ecological niche models (ENMs) into landscape‐focused species distribution models (SDMs), using these latter to compute ecological corridors. We infer corridors connecting present occurrence localities to future climatic refugia as well as to localities where extinct populations occurred. Also, a continuous connectivity change index is proposed to assess current–future differences. Finally, we discuss possible applications of our workflow to conservation, assessing the capability of established protected areas to preserve ecological corridors.Location: Europe.Methods: As case study to illustrate our framework, we use a database comprising occurrence localities of Vipera ursinii, one of the most endangered European reptiles. We obtain weighted SDMs for each of the four V. ursinii subspecies by coupling climate‐based ENMs with standardized occurrence frequencies along land use and altitude gradients through weighted averaging in GIS. We calculate current and future landscape connectivity for each subspecies based on the corresponding weighted SDM. We compare predictive performance of “traditional” ENMs, including climate, land use and topography as predictors and weighted SDMs.Results: Weighted SDMs outperform ENMs, according to Boyce index. SDMs are used to infer connectivity, predicted to decrease in all future scenarios for V. ursinii, and assess where connections may favour movements of individuals to, for example, future suitable areas. Generally, protected areas are predicted to cover low‐connectivity territories.Main conclusions: The proposed “couple‐and‐weigh” approach could represent a helpful tool to investigate biogeography, conservation and landscape planning topics, as it permits to capitalize on occurrence records and accessible environmental predictors by narrowing the target species’ potential distribution, estimated within “traditional” ENMs, to the realized one through post‐modelling GIS analyses, which in turn improves estimation of friction maps used to infer connectivity.

Wildfires are currently one of the most important environmental problems, as they cause disturbance in ecosystems generating environmental, economic and social costs. The Sentinel-2 from Copernicus Program (Sentinel satellites) offers a great tool for post-fire monitoring. The main objective of this study is to evaluate the potential of Sentinel-2 in a peculiar mountainous landscape by measuring and identifying the burned areas and monitor the short-term response of the vegetation in different ‘burn severity’ classes. A Sentinel-2 dataset was created, and pre-processing operations were performed. Relativized Burn Ratio (RBR) was calculated to identify ‘burn scar’ and discriminate the ‘burn severity’ classes. A two-year monitoring was carried out with areas identified based on different severity classes, using Normalized Difference Vegetation Index (NDVI) to investigate the short-term vegetation dynamics of the burned habitats; habitats refer to Annex I of the European Directive 92/43/EEC. The study area is located in ‘Campo Imperatore’ within the Gran Sasso – Monti della Laga National Park (central Italy). The first important result was the identification and quantification of the area affected by fire. The RBR allowed us to identify even the less damaged habitats with high accuracy. The survey highlighted the importance of these Open-source tools for qualitative and quantitative evaluation of fires and the short-term assessment of vegetation recovery dynamics. The information gathered by this type of monitoring can be used by decision-makers both for emergency management and for possible environmental restoration of the burned areas.

The common ragweed Ambrosia artemisiifolia has spread throughout Europe since the 1800s, infesting croplands and causing severe allergic reactions. Recently, the ragweed leaf beetle Ophraella communa was found in Italy and Switzerland; considering that it feeds primarily on A. artemisiifolia in its invaded ranges, some projects started biological control of this invasive plant through the adventive beetle. In this context of a ‘double’ invasion, we assessed the influence of climate change on the spread of these alien species through ecological niche modelling. Considering that A. artemisiifolia mainly lives in agricultural and urbanized areas, we refined the models using satellite remote-sensing data; we also assessed the co-occurrence of the two species in these patches. A. artemisiifolia is predicted to expand more than O. communa in the future, with the medium and high classes of suitability of the former increasing more than the latter, resulting in lower efficacy for O. communa to potentially control A. artemisiifolia in agricultural and urbanized patches. Although a future assessment was performed through the 2018 land-cover data, the predictions we propose are intended to be a starting point for future assessments, considering that the possibility of a shrinkage of target patches is unlikely to occur.

Aims: Rice is a staple food for many countries, being fundamental for a large part of the worlds’ population. In sub-Saharan Africa, its importance is currently high and is likely to become even more relevant, considering that the number of people and the per-capita consumption are both predicted to increase. The flea beetles belonging to the Chaetocnema pulla species group (pulla group), a harmful rice pest, are an important vector of the Rice Yellow Mottle Virus, a disease which leads even to 80–100% yield losses in rice production. We present a continental-scale study aiming at: (1) locating current and future suitable territories for both pulla group and rice; (2) identifying areas where rice cultivations may occur without suffering the presence of pulla group using an Ecological Niche Modelling (ENM) approach; (3) estimating current and future connectivity among pulla group populations and areas predicted to host rice cultivations, based on the most recent land-use estimates for future agricultural trends; (4) proposing a new connectivity index called “Pest Aggression Index” (PAI) to measure the agricultural susceptibility to the potential future invasions of pests and disease; (5) quantifying losses in terms of production when rice cultivations co‐occur with the pulla group and identifying the SSA countries which, in the future inferred scenarios, will potentially suffer the greatest losses. Location: Sub-Saharan Africa. Methods: Since the ongoing climate and land-use changes affect species’ distributions, we first assess the impact of these changes through a spatially-jackknifed Maxent-based Ecological Niche Modelling in GIS environment, for both the pulla group and rice, in two climatic/socioeconomic future scenarios (SSP_2.45 and 3.70). We then assess the connectivity potential of the pulla group populations towards rice cultivations, for both current and future predictions, through a circuit theory-based approach (Circuitscape implemented in Julia language). We finally measure the rice production and GPD loss per country through the spatial index named “Pest Aggression Index”, based on the inferred connectivity magnitude. Results: The most considerable losses in rice production are observed for Liberia, Sierra Leone and Madagascar in all future scenarios (2030, 2050, 2070). The future economic cost, calculated as USD lost from rice losses/country’s GDP results are high for Central African Republic (−0.6% in SSP_2.45 and −3.0% in SSP_3.70) and Guinea–Bissau (−0.4% in SSP_2.45 and −0.68% in SSP_3.70), with relevant losses also obtained for other countries. Main conclusions: Since our results are spatially explicit and focused on each country, we encourage careful land-use planning. Our findings could support best practices to avoid the future settlement of new cultivations in territories where rice would be attacked by pulla group and the virus, bringing economic and biodiversity losses.