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
Climate change is affecting terrestrial and maritime ecosystems of Mono transboundary biosphere reserve and the whole Togolese coast. This research focuses both on the dynamics of the barrier beach and on spatial and temporal changes that affected the ocean coastline and its adjacent Lake Togo and Lake Boko during the period 1988-2018. The methodological approach adopted is based on a combination of optical and radar remote sensing. As results, the Lake Togo widened on average by 1.55 m/year while the coastline of the Lake Boko shrank by 1.25 m/year. For the coastline of the Ocean, the regression ranged from 1.66 to 5.25 m/year. The barrier beach experienced an average immersion of 9.25 ha/year. Predictions on the basis of the average rate of immersion of the barrier beach of 9.25 ha/year showed that the latter is exposed to a continuous immersion hazard that would affect more than 7% of its current area (6557.33 ha) by the year 2070.
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.
Maâmora is considered the most important cork-oak forest in the world with regard to surface. Therefore, anthropic pressure, including cork harvesting, grazing and soft acorn picking up by local communities, has harmful consequences on forest regeneration and the forest become older exceeding harvesting age. Thus, its sustainability depends on the managers' ability to succeed cork oak plantations. This work presents an assessment approach to evaluate Quercus suber suitability to its plantation which is based on a random forest algorithm (RF). In fact, this suitability has been assessed through analyzing management data related to previous plantation success rates (SR). Then a relationship between SR and a set of environmental and social factors has been investigated using the RF. Application of the fitted model to continuous maps of all involved factors enabled establishment of suitability maps which would help managers to make more rational decisions in terms of cork oak regeneration, ensuring Maâmora forest sustainability.
Forest planners have traditionally used expected growth and yield coefficients to predict future merchantable timber volumes. However, because climate change affects forest growth, the typical forest planning methods using expected value of forest growth can lead to sub-optimal harvest decisions. In this paper, we propose to formulate the harvest planning with growth uncertainty due to climate change problem as a multistage stochastic optimization problem and use sample average approximation (SAA) as a tool for finding the best set of forest units that should be harvested in the first period even though we have a limited knowledge of what future climate will be. The objective of the harvest planning model is to maximize the expected value of the net present value (NPV) considering the uncertainty in forest growth and thus in revenues from timber harvest. The proposed model was tested on a small forest with 89 stands and the numerical results showed that the approach allows to have superior solutions in terms of net present value and robustness in face of different growth scenarios compared to the approach using the expected growth and yield. The SAA method requires to generate samples from the distribution of the random parameter. Our results suggested that a sampling scheme that focuses on generating high number of samples in distant future stages is favorable compared to having large sample sizes for the near future stages. Finally, we demonstrated that, depending on the level of forest growth change, ignoring this uncertainty can negatively affect forest resources sustainability.
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