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The easy implementation of a nature-based solution (NBS) to address a prompt stabilisation of burnt areas may be crucial in the Mediterranean forest environment. A plot scale hydrological and ecological survey was conducted in a pine forest of the Aspromonte Massif (southern Italy) affected by a high fire severity in summer 2021. The hydrological response to 30 rainfall event groups is analysed by monitoring nine sloping plots distributed into three plot blocks (forested according to the pre-fire situation, burnt with randomly directed felled logs and burnt with manually felled logs redirected along contour lines). The hydrological response of bare soil is mitigated by about 30% through the combined effects of the NBS and the vegetation cover by pioneer species. Although the regeneration of Pinus radiata is scarcer in the arranged plots, the spontaneous vegetation, once gone, prepares better edaphic conditions for the triggering of forest dynamics assured by the auto-succession of Pinus radiata. In terms of woody mechanical resistance, NBS durability is compatible with the time required for seedlings to regenerate the forest ecosystem. The results achieved so far encourage further research on higher slopes and complementary aspects (vegetal and animal biodiversity, economic factors, etc.). [ABSTRACT FROM AUTHOR]

Durum wheat (Triticum durum Desf.) is one of the most widely cultivated cereal species in the Mediterranean basin, supporting pasta, bread and other typical food productions. Considering its importance for the nutrition of a large population and production of high economic value, its supply is of strategic significance. Therefore, an early and accurate crop yield estimation may be fundamental to planning the purchase, storage, and sale of this commodity on a large scale. Multispectral (MS) remote sensing (RS) of crops using unpiloted aerial vehicles (UAVs) is a powerful tool to assess crop status and productivity with a high spatial–temporal resolution and accuracy level. The object of this study was to monitor the behaviour of thirty different durum wheat varieties commonly cultivated in Italy, taking into account their spectral response to different vegetation indices (VIs) and assessing the reliability of this information to estimate their yields by Pearson's correlation and different machine learning (ML) approaches. VIs allowed us to separate the tested wheat varieties into different groups, especially when surveyed in April. Pearson's correlations between VIs and grain yield were good (R2 > 0.7) for a third of the varieties tested; the VIs that best correlated with grain yield were CVI, GNDVI, MTVI, MTVI2, NDRE, and SR RE. Implementing ML approaches with VIs data highlighted higher performance than Pearson's correlations, with the best results observed by random forest (RF) and support vector machine (SVM) models. [ABSTRACT FROM AUTHOR]

In Italy, historic agri-food buildings can be considered a relevant material expression and testimony of century-old agriculture and food processing practices handed down by generations. Recently they have gained ever-growing importance as a part of the wider architectural heritage. As such, they deserve dedicated general surveys to build a thorough knowledge of their distinctive characteristics and investigate their current condition, setting the basis for the implementation of planning and management actions for their sustainable valorisation. To this end, building information modelling can be considered an efficient strategy to preserve construction information by creating 3D models based on surveys of the built heritage. To acquire in a fast and accurate way geometric, reflectance, and colour data of rural buildings as a 3D point cloud, the terrestrial laser scanner (TLS) represents a powerful tool. The traditional TLS-based survey methods, in the context of historic agricultural buildings, have several limitations, mainly due to the presence of inaccessible parts and bulky machinery once used for processing and storage. In the present research, to overcome these issues and thus have a complete survey, we describe a proposal of an integrated methodology for obtaining 3D point-cloud data of existing rural agri-food buildings based on the integrated use of TLS, hand-held scanner, and unmanned aerial vehicles instruments. The proposed methodology was tested in surveying three historic agri-food buildings, and the accuracy of the obtained 3D point cloud was calculated using the root mean square error (RMSE) on the X, Y, and Z alignment of the two different 3D point clouds in correspondence of the used B/W target. Moreover, a measure of the distance between two merged 3D point clouds in their overlap area has been performed using the multi-scale model to model cloud comparison (M3C2). RMSE analysis always shows values lesser than 1 cm, and M3C2 shows values between 0 and about 6 cm. [ABSTRACT FROM AUTHOR]

This study analyzes, through remote sensing techniques and innovative clouding services, the recent land use dynamics in the North-Roman littoral zone, an area where the latest development has witnessed an important reconversion of purely rural areas to new residential and commercial services. The survey area includes five municipalities and encompasses important infrastructure, such as the "Leonardo Da Vinci" Airport and the harbor of Civitavecchia. The proximity to the metropolis, supported by an efficient network of connections, has modified the urban and peri-urban structure of these areas, which were formerly exclusively agricultural. Hereby, urban expansion has been quantified by classifying Landsat satellite images using the cloud computing platform "Google Earth Engine" (GEE). Landsat multispectral images from 1985 up to 2020 were used for the diachronic analysis, with a five-yearly interval. In order to achieve a high accuracy of the final result, work was carried out along the temporal dimension of the images, selecting specific time windows for the creation of datasets, which were adjusted by the information related to the NDVI index variation through time. This implementation showed interesting improvements in the model performance for each year, suggesting the importance of the NDVI standard deviation parameter. The results showed an increase in the overall accuracy, being from 90 to 97%, with improvements in distinguishing urban surfaces from impervious surfaces. The final results highlighted a significant increase in the study area of the "Urban" and "Woodland" classes over the 35-year time span that was considered, being 67.4 km2 and 70.4 km2, respectively. The accurate obtained results have allowed us to quantify and understand the landscape transformations in the area of interest, with particular reference to the dynamics of urban development. [ABSTRACT FROM AUTHOR]

Crown fire damage is a mixture of three principal fire-related components: charred material, scorched foliage, and unaltered green canopy. This study estimated the abundance of these physical alterations in two immediate post-fire Mediterranean forest contexts (Portugal and Italy) by applying linear spectral mixture analysis (LSMA) on Sentinel-2 imagery. The tree crowns fire damage was subsequently mapped, integrating fractional abundance information in a random forest (RF) algorithm, comparing the accuracy resulting from the adoption of generic or image spectral libraries as the primary investigative goal. Although image-derived endmembers resulted in more effectiveness in terms of fire-related components abundance quantification (LMSA-derived RMSE < 0.1), the F-scores always were ≥ 90% whether generic endmembers or image endmembers derived information was employed. The environmental heterogeneity of the two study areas affected the fire severity gradients, with a prevalence of the charred (PT) (45–46%) and green class (IT) (44–53%). Post-fire temporal monitoring was initialized by applying the proposed strategies, and the preliminary results showed a positive recovery trend in forest vegetation from the first year following the fire event, with a reduced charcoal predominance and an increasing proportion of green components. [ABSTRACT FROM AUTHOR]

Among landscapes, rural landscapes are important because they simultaneously present functional and cultural aspects. Rural landscapes are often created to modify the Earth's surface according to different human needs and, among these modifying activities, terracing has significant anthropogenic effect. A multitemporal approach that covers a significant time interval is crucial for monitoring the integrity and cultural value of historical rural landscapes. The present research aims to study the fragmentation dynamics detectable in an active agricultural terraced system of historical and cultural relevance over a considerable time interval, by conducting a morphological spatial pattern analysis (MSPA). We analysed a period of about 60 years, from 1955 to 2014, considering five intermediate years (1976, 1989, 1998, 2008, and 2012) and investigated the dynamics that occurred. We detected a trend of abandonment of agricultural terraces, with a reduction in area from 813.25 ha (in 1955) to 118.79 ha (in 2014). The MSPA results showed a decrease in core areas, the most stable pattern, and an increase in the relative importance of other less stable classes. Moreover, we highlighted two different fragmentation dynamics, i.e., one between 1955 and 1976 and the other between 1998 and 2008. [ABSTRACT FROM AUTHOR]

This study aimed to compare and assess different Geographic Object-Based Image Analysis (GEOBIA) and machine learning algorithms using unmanned aerial vehicles (UAVs) multispectral imagery. Two study sites were provided, a bergamot and an onion crop located in Calabria (Italy). The Large-Scale Mean-Shift (LSMS), integrated into the Orfeo ToolBox (OTB) suite, the Shepherd algorithm implemented in the Python Remote Sensing and Geographical Information Systems software Library (RSGISLib), and the Multi-Resolution Segmentation (MRS) algorithm implemented in eCognition, were tested. Four classification algorithms were assessed: K-Nearest Neighbour (KNN), Support Vector Machines (SVM), Random Forests (RF), and Normal Bayes (NB). The obtained segmentations were compared using geometric and non-geometric indices, while the classification results were compared in terms of overall, user's and producer's accuracy, and multi-class F-scoreM. The statistical significance of the classification accuracy outputs was assessed using McNemar's test. The SVM and RF resulted as the most stable classifiers and less influenced by the software used and the scene's characteristics, with OA values never lower than 81.0% and 91.20%. The NB algorithm obtained the highest OA in the Orchard-study site, using OTB and eCognition. NB performed in Scikit-learn results in the lower (73.80%). RF and SVM obtained an OA>90% in the Crop-study site. [ABSTRACT FROM AUTHOR]

With the aim at developing a landscape dynamics framework for environmental planning and management and testing the effectiveness of protected areas in achieving the 2030 Agenda of the United Nations sustainability goals, we characterized the historical transformation trajectories of forest area changes from 1936 to 2010 in the Metropolitan City of Rome Capital (Italy). Remote sensing‐based products coupled with landscape pattern metrics and fragmentation analysis have been implemented, comparing different historical forest maps. The results show a remarkable forest area gain – from 17.6% to 25.5% – thanks to 68,299 ha of recently established forest. Statistical descriptors showed that the highest relative gain occurred in mountain zones, confirming a wide European forest recovery pattern in marginal areas from past deforestation and overexploitation. Deforestation mainly occurred in the flat and hilly areas where almost 26,000 ha of forests were lost since 1936. In summary, two main forest landscape dynamics were reconstructed: (I) the increase of forest cover fragmentation in the lowland areas; and (II) the rise in the forest area in the interior sectors of the mountain landscape, mainly within protected areas. Restoring the forest ecosystem's bioecological integrity has been highlighted as an urgent action for biodiversity conservation and carbon mitigation. In lowland areas, the study revealed the urgent need to establish new protected areas and rewilding spaces as landscape metrics are relatively below the sustainability targets for healthy forest ecosystems. The proposed framework can be used for testing the effectiveness of environmental planning and management in other forest landscapes to achieve the Agenda 2030 goals. [ABSTRACT FROM AUTHOR]

This paper presents the results of a change-detection study of the historical agricultural terraced landscape in 'Costa Viola' (Calabria, South Italy). During the last century, because of the loss of economic competitiveness, it has undergone progressive abandonment, followed by landscape degradation. Taking into consideration the very steep slopes of Costa Viola and the need to analyse with high precision the historical evolution of the terraced landscape, research methods were implemented coupling advanced geomatic techniques with in situ detailed surveys. Based on historical aerial photographs, orthophotos, and numeric cartography, we analysed the land use/land cover change in the period 1955-2014 using photogrammetric and geoprocessing techniques, focusing particularly on trajectories in agricultural terraces. Area covered by active terraces decreased dramatically between 1955 and 2014, from 813.25 to 118.79 ha (−85.4%). The implemented spatial database was built in a free open-source software taking into consideration spatial accuracies and completeness. Spatial comparison among land use/land cover maps was carried out using a postclassification comparison technique that can provide complete cross-tabulation matrices. These data were compared with socio-economic statistics concerning demography and trends of farms with vineyards. The evolutionary dynamics of the active agricultural terraces were also analysed trough the definition of 6 types of spatio-temporal patterns recognised in the analysed period. These methods allowed to highlight the ongoing dynamics of abandonment of agricultural terraces in relation to their main causes and effects. Although tailored for the specific case study, they can be applied to many other terraced agricultural landscapes presenting similar characteristics and problems. [ABSTRACT FROM AUTHOR]

The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively. [ABSTRACT FROM AUTHOR]

• A UAV-based framework to monitor forest canopy structure in a primary beech forest. • Canopy structure patterns were used as indicator of forest natural process integrity. • Canopy profile revealed a complex forest structure with low gap disturbance regime. • Proposed method can be adopted for monitoring high-value natural forest ecosystems. In front of climate change scenarios and global loss of biodiversity, it is essential to monitor the structure of old-growth forests to study ecosystem status and dynamics to inform future conservation and restoration programmes. We propose an Unmanned Aerial Vehicle (UAV)-based framework to monitor fine-grained forest top canopy structure in a primary old-growth beech (Fagus sylvatica L.) forest in Pollino National Park, Italy, which belongs to the UNESCO World Heritage (UNESCO WH) serial site "Ancient and Primeval beech forests of the Carpathians and other regions of Europe". Canopy profile, gap properties and their spatial distribution patterns were analysed using the canopy height model (CHM) derived from UAV surveys. Very high-resolution orthomosaic images coupled with direct field measurement data were used to assess gap detection accuracy and CHM validation. Forest canopy properties along with the vertical layering of the canopy were further explored using second-order statistics. The reconstructed canopy profile revealed a bimodal top height frequency distribution. The upper canopy layer (h > 14 m) was the most represented canopy height, with the remaining 50% split between the medium and lowest layer; 551 gaps were identified within 11.5 ha. Gap size varied between 2 m2 and 353 m2, and 19 m2was the mean gap size; the gap size-frequency relationship reflected a power-law probability distribution. About 97 % of the gaps were <100 m2 in size, showing a significant tendency to cluster. Most gaps were located in the upper and medium canopy layers; however, the highest relative gap area was found in the lowest layer. These results confirmed the high natural integrity of the ecosystem processes that distinguish the old-growth beech stands in respect to managed woodlands. Our findings demonstrate that the low-cost UAV-DAP (Digital Aerial Photogrammetry) workflow has the potential to generate realistic old-growth forest canopy attributes at a very fine scale. The proposed protocol can be adopted for monitoring the structural dynamics of high-value natural forest ecosystems as in the case of UNESCO WH sites or other old-growth stands. This approach is also helpful for mapping and deriving spatially explicit canopy structure information over confined forest areas and determining where conservation actions should be directed to preserve or restore natural ecosystem function. [ABSTRACT FROM AUTHOR]