Aim of the study: Acacia dealbata is an alien invasive species that is widely spread in Portugal. The main goal of this study was to produce an accurate and detailed map for this invasive species using ASTER multispectral imagery.Area of study: The central-eastern zone of Portugal was used as study area. This whole area is represented in an ASTER scene covering about 321.1 x 103 ha.Material and methods: ASTER imagery of two dates (flowering season and dry season) were classified by applying three supervised classifiers (Maximum Likelihood, Support Vector Machine and Artificial Neural Networks) to five different land cover classifications (from most generic to most detailed land cover categories). The spectral separability of the land cover categories was analyzed and the accuracy of the 30 produced maps compared.Main results: The highest classification accuracy for acacia mapping was obtained using the flowering season imagery, the Maximum Likelihood classifier and the most detailed land cover classification (overall accuracy of 86%; Kappa statistics of 85%; acacia class Kappa statistics of 100%). As a result, the area occupied by acacia was estimated to be approximated 24,770 ha (i.e. 8% of the study area).Research highlights: The methodology explored proved to be a cost-effective solution for acacia mapping in central-eastern of Portugal. The obtained map enables a more accurate and detailed identification of this species’ invaded areas due to its spatial resolution (minimum mapping unit of 0.02 ha) providing a substantial improvement comparably to the existent national land cover maps to support monitoring and control activities.Keywords: remote sensing; invasive alien species; land cover mapping; vegetation mapping.

Wildfires are a major environmental issue that have an impact on land degradation. Remote sensing spectral indices provide valuable information for short-term mitigation and rehabilitation after wildfires. A study area in the Centre inland of Portugal occupied with Maritime pine and Eucalypts forests and affected by wildfires in 2003, 2017 and 2020 was used. The aims of the study were twofold: (1) to compute the Normalized Difference Vegetation Index (NDVI) and with forest inventory data derivate a Maritime pine production model, differentiate evergreen coniferous forests (e.g., Maritime pine), evergreen broadleaved forests (e.g., Eucalypts), and shrubland, and monitor vegetation and its post-fire recovery; and (2) to compute the Normalized Burn Ratio (NBR) difference between pre-fire and post-fire dates for burn severity levels assessment. The plots of a previous forest inventory were used to follow the NDVI values in 2007 and from 2020 to 2022. An aerial coverage in 2007 and the Sentinel-2 imagery in 2020–2022 were used. Linear models fitted maritime pine production with the transformed NDVI by age, showing a fitting efficiency of 60%. The stratification of cover types by stand development stage and fire occurrence was possible using the NDVI time curve, which also showed the impact of fire and of low precipitation. Cover types were ranked by decreasing NDVI values as follows: mature Eucalypts plantations, young Maritime pine regeneration, mature Maritime pine, young Eucalypts plantations, Strawberry tree shrubland, Eucalypts plantations post-fire, Maritime pine post-fire, tall shrubland, and short shrubland. Vegetation post-fire recovery was lower in higher burn severity level areas. Maritime pine areas have lost their natural regeneration capability due to the wildfires’ short cycles. Spectral indices were effective tools to differentiate cover types and assist in the evaluation of forest and shrubland conditions.

Research Highlights: Modelling species’ distribution and productivity is key to support integrated landscape planning, species’ afforestation, and sustainable forest management. Background and Objectives: Maritime pine (Pinus pinaster Aiton) forests in Portugal were lately affected by wildfires and measures to overcome this situation are needed. The aims of this study were: (1) to model species’ spatial distribution and productivity using a machine learning (ML) regression approach to produce current species’ distribution and productivity maps; (2) to model the species’ spatial productivity using a stochastic sequential simulation approach to produce the species’ current productivity map; (3) to produce the species’ potential distribution map, by using a ML classification approach to define species’ ecological envelope thresholds; and (4) to identify present and future key factors for the species’ afforestation and management. Materials and Methods: Spatial land cover/land use data, inventory, and environmental data (climate, topography, and soil) were used in a coupled ML regression and stochastic sequential simulation approaches to model species’ current and potential distributions and productivity. Results: Maritime pine spatial distribution modelling by the ML approach provided 69% fitting efficiency, while species productivity modelling achieved only 43%. The species’ potential area covered 60% of the country’s area, where 78% of the species’ forest inventory plots (1995) were found. The change in the Maritime pine stands’ age structure observed in the last decades is causing the species’ recovery by natural regeneration to be at risk. Conclusions: The maps produced allow for best site identification for species afforestation, wood production regulation support, landscape planning considering species’ diversity, and fire hazard mitigation. These maps were obtained by modelling using environmental covariates, such as climate attributes, so their projection in future climate change scenarios can be performed.

Seven silvicultural scenarios for pure and even aged maritime pine stands were simulated using a diameter class growth and yield model, for three levels of site index (low, medium and high). Total and merchantable yield along rotation and mean annual increment at rotation age were simulated. Revenue obtained from sale of standing timber (three commercial thinning and final harvest) was estimated. Costs paid for stand establishment (site preparation and plantation) and treatments (release, pre-commercial thinning and pruning) were also estimated. For each scenario, a cash-flow was simulated and economic efficiency was assessed using the net present value of an infinitive series of rotations (NPVIS) and the internal rate of return (IRR). The economic analysis results proved the selection of an intensive management stand prescription that is used as a guide for reforestation projects, using artificial regeneration by plantation, when the main goal is round wood yield. For areas of natural regeneration, a fully stocked stand prescription, pulp wood yield oriented, was selected. This prescription enables saving in site preparation and plantation costs, being at the moment the most suitable for the existing naturally regenerated maritime pine stands of Portuguese private forest areas.