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Forest canopy gaps are important for the ecosystem dynamics. Depending on tree species, 13 small canopy openings might be also associated to intra-crown porosity and to space between 14 crowns. Yet, little is known on the relationships between the fine-scaled pattern of canopy openings 15 and biodiversity features. This research explored the possibility of i)-mapping forest canopy gaps 16 from a very high resolution orthomosaic (10 cm), processed from a versatile imaging platform such 17 as unmanned aerial vehicles (UAV), ii)-to derive patch metrics that can be tested as covariates of 18 variables of interest for forest biodiversity monitoring. This is attempted in a test area of 240 ha 19 covered by temperate deciduous forest types in Central Italy and containing 50 forest inventory 20 plots of about 530 m 2 . Correlation and linear regression techniques were used to explore 21 relationships between patch metrics and understorey (density, development and species diversity) 22 or forest habitat biodiversity variables (density of micro-habitat bearing trees, vertical species 23 profile, tree species diversity). The results revealed that small openings in the canopy cover (75% 24 smaller than 7 m 2 ) can be faithfully extracted from UAV RGB imagery, using the red band and 25 contrast split segmentation. Highest correlations were observed in the mixed forest (beech and 26 turkey oak), while beech forest had the poorest ones and turkey oak forest displayed intermediate 27 results. Moderate to strong linear relationships were found between gap metrics and understorey 28 variables in mixed forest type, with adjusted R 2 from linear regression ranging from 0.52 to 0.87. 29Equally good results, in the same forest types, were observed for forest habitat biodiversity variables 30 (0.52

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FABIO LOMBARDI (*) (°)-GHERARDO CHIRICI (*)-MARCO MARCHETTI (*) ROBERTO TOGNETTI (*)-BRUNO LASSERRE (*)-PIERMARIA CORONA (**) ANNA BARBATI (**)-BARBARA FERRARI (**)-SILVIA DI PAOLO (**) DIEGO GIULIARELLI (**)-FRANCO MASON (***)

Reliable assessment of forest resource stock, productivity and harvesting is a commonly agreed objective of environmental monitoring programs. Distinctively, the assessment of wood harvesting has become even more relevant to evaluate the sustainability of forest management and to quantify forest carbon budget. This paper presents the development and testing of procedures for assessing forest harvesting in coppice forests by very high resolution (VHR) satellite imagery. The study area is located in central Italy over approximately 34,000 km 2 . A set of SPOT5 HRG multispectral images was acquired for the study years (2002)(2003)(2004)(2005)(2006)(2007). Official administrative statistics of coppice clearcuts were also acquired. More than 9500 clearcuts were mapped and dated by on-screen interpretation of the SPOT5 images. In a subset of the study area various methods for semi-automatic clearcut mapping were tested by pixel-and object-oriented approaches. The following results are presented: (i) clearcut map developed by visual interpretation of the SPOT5 images resulted in high thematic (overall accuracy of 0.99) and geometric (root mean square error of clearcut boundary delineation of 5.3 m) reliability; (ii) object-oriented approach achieved significantly better accuracy than pixel-based methods for semi-automatic classification of the coppice clearcuts; (iii) comparison between mapped clearcut area and official forest harvesting statistics proved a significant underestimation by the latter (65% of the total mapped clearcut area). A sample-based procedure exploiting VHR satellite imagery is finally proposed to correct the official statistics of coppice clearcuts.

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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.

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.