Forest canopy gaps are important to ecosystem dynamics. Depending on tree species, small canopy openings may be associated with intra-crown porosity and with space among crowns. Yet, literature on the relationships between very fine-scaled patterns of canopy openings and biodiversity features is limited. This research explores the possibility of: (1) mapping forest canopy gaps from a very high spatial resolution orthomosaic (10 cm), processed from a versatile unmanned aerial vehicle (UAV) imaging platform, and (2) deriving patch metrics that can be tested as covariates of variables of interest for forest biodiversity monitoring. The orthomosaic was imaged from a test area of 240 ha of temperate deciduous forest types in Central Italy, containing 50 forest inventory plots each of 529 m² in size. Correlation and linear regression techniques were used to explore relationships between patch metrics and understory (density, development, and species diversity) or forest habitat biodiversity variables (density of micro-habitat bearing trees, vertical species profile, and tree species diversity). The results revealed that small openings in the canopy cover (75% smaller than 7 m²) can be faithfully extracted from UAV red, green, and blue bands (RGB) imagery, using the red band and contrast split segmentation. The strongest correlations were observed in the mixed forests (beech and turkey oak) followed by intermediate correlations in turkey oak forests, followed by the weakest correlations in beech forests. Moderate to strong linear relationships were found between gap metrics and understory variables in mixed forest types, with adjusted R² from linear regression ranging from 0.52 to 0.87. Equally strong correlations in the same forest types were observed for forest habitat biodiversity variables (with adjusted R² ranging from 0.52 to 0.79), with highest values found for density of trees with microhabitats and vertical species profile. In conclusion, this research highlights that UAV remote sensing can potentially provide covariate surfaces of variables of interest for forest biodiversity monitoring, conventionally collected in forest inventory plots. By integrating the two sources of data, these variables can be mapped over small forest areas with satisfactory levels of accuracy, at a much higher spatial resolution than would be possible by field-based forest inventory solely.

The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m². This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.

The aim of this work is to test the potential of SPOT5 satellite images for monitoring coppice clearcuts. The clearcuts, delineated by on screen interpretation of the satellite images, are compared with the administrative statistics reported for a sample of 230 administrative units. Administrative statistics result significantly lower than those by satellite images, with an average ratio between clearcut area observed by SPOT5 images within each sample unit and the corresponding administrative statistics equal to 1.45. The underestimation by the administrative data is likely due to the simplified process of authorization of coppice clearcuts as well as the eventual lack of timeliness and/or loss of data that sometimes hinder the information flow from administrative bodies to National Forest Service, that is in charge of feeding the official forest statistics. In the light of the experimentation carried out, an operational procedure by SPOT5 images is proposed for the statistical calibration of administrative data about coppice clearcuts.

In the Mediterranean basin human activity has modified landscapes for millennia, nevertheless there are few remote forest areas relatively untouched long enough from direct anthropogenic disturbance to develop old-growth attributes. The aim of this note is to assess the potential of QuickBird (QB) satellite multispectral imagery for detecting old-growth forest stands, considering as case study a Mediterranean beech forest in central Italy. The segmentation-based analysis of QB image proved to be a promising tool to detect scaledependent pattern of forest structural heterogeneity. Values of remotely sensed attributes are compared in old-growth and not-old-growth stands: the statistical analysis showed that oldgrowthness is associated to the variability of multispectral reflectance from the image objects (polygons). Green band variability, notably, expressed by Ratio_band_2 has proven to be helpful for predicting old-growthness.

This work aims to assess the potential of segmentation and object oriented classification techniques of satellite images with medium spatial resolution, for land use/cover (Corine Land Cover, CLC) mapping. The tested procedures are assessed both in term of thematic accuracy and working time, with reference to a study area of about 4000 km2 in central Italy. The automatic procedure is carried out by segmentation of the pan-sharpened image and by subsequent classification using membership and standard nearest neighbour functions. Results are evaluated by sample circular photoplots taken from digital IT2000 orthophotos coverage. In terms of overall accuracy, object oriented classification achieves better results than conventional on screen interpretation. The classification shows difficulties for the identification of the “mixed” classes of CLC nomenclature system; however, even in these cases the object oriented techniques provide higher producer and user accuracy than on screen interpretation. On the whole, since they are able to produce more objective and more accurate cartographic products at similar costs, the application of the tested automatic techniques seems to be preferred to the conventional on screen interpretation for satellite images with medium spatial resolution.