Non-Urbanised Areas (NUAs) are part of agricultural and green infrastructures that provide ecosystem services. Their role is fundamental for the minimization of urban pollution and adaptation to climate change. Like all natural ecosystems, NUAs are endangered by urban sprawl. The regulation of sprawl is a key issue for land-use planning. We propose a land use suitability strategy model to orient Land Uses of NUAs, based on integration of Land Cover Analysis (LCA) and Fragmentation Analysis (FA). With LCA the percentage of evapotranspiring surface is defined for each land use. Dimensions and densities of NUAs patches are assessed in FA. The model has been developed with Geographical Information Systems, using an extensive set of geodatabases, including orthophotos, vectorial cartographies and field surveys. The case of the municipality of Mascalucia in Catania metropolitan area (Italy), characterized by a considerable urban sprawl, is presented.

The use of Unmanned Aerial Systems (UAS, aka drones) images for mapping macro-litter in the environment have been exponentially increasing in the recent years. In this work, we developed a multi-class Neural Network (NN) to automatically identify stranded plastic litter categories on an UAS-derived orthophoto.The best results were assessed for items that did not have substantial intra-class colour variability, such as octopus pots and fishing ropes (F-score = 61%, on average). Instead, performance was poor (37%) for plastic bottles and fragments, due to their changing intra-class colours. On average, the performance improved 24% when the binary detection (litter/non-litter, F-Score = 73%) was considered, however this approach did not discriminate the litter categories.This work gives a new perspective for the automated litter detection on drone images, suggesting that colour-based approach can be used to improve the categorization of stranded litter on UAS orthophoto.

Recent works have shown the feasibility of Unmanned Aerial Systems (UAS) for monitoring marine pollution.We provide a comparison among techniques to detect and map marine litter objects on an UAS-derived orthophoto of a sandy beach-dune system. Manual image screening technique allowed a detailed description of marine litter categories. Random forest classifier returned the best-automated detection rate (F-score 70%), while convolutional neural network performed slightly worse (F-score 60%) due to a higher number of false positive detections.We show that automatic methods allow faster and more frequent surveys, while still providing a reliable density map of the marine litter load. Image manual screening should be preferred when the characterization of marine litter type and material is required.Our analysis suggests that the use of UAS-derived orthophoto is appropriate to obtain a detailed geolocation of marine litter items, requires much less human effort and allows a wider area coverage.

The presented study shows that the delivery of marine macrodebris to a high-energy coastal environment has been abundant enough over the last three decades as to allow a spatial reconstruction of morphological change based on production-date prints. A dataset of >110 spatially discrete samples has been collected in an area affected by overwashing on the Skallingen peninsula, SW Denmark. A conceptual model for the chronological interpretation of the date prints is proposed and cross-compared with a dense time-series of satellite images and orthophotos. It appears that the litter-derived ages are capable of reproducing information on both the timing and the extent of overwash occurrence. Despite the usefulness of the method as a tool for rapidly assessing the approximate age of recent coastal deposits, the study shows the alarming degree and long-standing of marine-litter pollution on the eastern board of the southern North Sea.

With high groundwater levels, coal–grain overlap areas (CGOAs) are vulnerable to subsidence and water logging during mining activities, thereby impacting crop yields adversely. Such damage requires full reports of disturbed boundaries for agricultural reimbursement and ongoing reclamation, but because direct measurements are difficult in such cases because of vast unreachable areas, it is necessary to be able to identify out-of-production boundaries (OBs) and reduced-production boundaries (RBs) in the corresponding region. In this study, an OB was extracted by setting a threshold via the characteristics of the cultivated-land elevation based on a digital surface model and a digital orthophoto map generated using an unmanned aerial vehicle (UAV). Meanwhile, the above-ground biomass (AGB), the soil plant analysis development (SPAD) value of chlorophyll contents, and leaf area index (LAI) were used to select the appropriate vegetation indices (VIs) to produce a reduced-production map (RM) based on power regression (PR), exponential regression (ER), multiple linear regression (MR), and random forest (RF) algorithms. Finally, an improved Otsu segmentation algorithm was used to extract mild and severe RBs. The results showed the following. (1) Crop growth heights in a typical ponding basin of the CGOA rendered a fast and efficient approach to distinguishing the OB. (2) In subsequent sample modeling, the red-edge microwave VI (MVIᵣₑdgₑ), the normalized difference VI (NDVI), and the red-edge modified simple ratio index (MSRᵣₑdgₑ) combined with RF were shown to be optimal estimators for AGB (R² = 0.83, RMSE = 0.114 kg·m⁻²); the red-edge NDVI (NDVIᵣₑdgₑ), the green NDVI (GNDVI), and the red-edge chlorophyll index (CIᵣₑdgₑ) acted as strong tools in SPAD prediction using RF (R² = 0.83, RMSE = 0.152 SPAD); the red-edge modified simple ratio index (MSRᵣₑdgₑ), the GNDVI, and the green chlorophyll index (CIgᵣₑₑₙ) via MR were more accurate when conducting the inversion of LAI (R² = 0.88, RMSE = 1.070). (3) With the improved Otsu algorithm, multiple degrees of RB extraction can be achieved in RM. This study provides reference methods and theoretical support for determining disturbed boundaries in CGOAs with high groundwater levels for further agricultural compensation and reclamation processes.