UAV Remote Sensing for Biodiversity Monitoring: Are Forest Canopy Gaps Good Covariates?

Abstract

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<adjusted R 2 <0.79) with highest values found for density of trees with microhabitats and 31 vertical species profile. In conclusion, this research highlights that UAV remote sensing can 32 potentially provide covariate surfaces of variables of interest for forest biodiversity monitoring, 33 conventionally collected in forest inventory plots. By integrating the two sources of data, these 34 variables can be mapped over small forest areas with satisfactory levels of accuracy, at a much 35 higher spatial resolution than would be possible by field-based forest inventory solely. 36 37

Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements

“…One of the dimensions could be summarized as indication of canopy complexity and height expressed by PC3 which relate to gaps in the canopy. It is known that gaps are related to forest biodiversity [63] and the size influences forest dwelling species, for instance ants [64] which are in turn related to TreMs. In addition remote sensing techniques have been used to detect habitat thresholds for indicator species as the three-toed woodpecker (Picoides tridactylus) based on canopy properties such as the amount of deadwood crown size [19], which will eventually result in canopy gaps and points in a similar direction as our results.…”

Estimation of Carbon Fluxes from Eddy Covariance Data and Satellite-Derived Vegetation Indices in a Karst Grassland (Podgorski Kras, Slovenia)

“…In order to compare the performance of the different vegetation indices as proxies of carbon fluxes in the different regressions, three accuracy metrics were computed, namely the coefficient of determination (R 2 ), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Additionally, in order to validate our models, we applied a bootstrap simulation with 1000 iterations to obtain the mean coefficient of determination (R 2 boot ) with a 95% confidence interval for each regression [61][62][63]. The best models are the ones with a high value of R 2 and a low value of RMSE and AIC.…”

Evaluating the Contribution of Trees outside Forests and Small Open Areas to the Italian Landscape Diversification during the Last Decades

“…Particularly, we distinguished between: (i) small patches with trees within land use classes other than forests, namely TOF, and (ii) small patches without trees within forest, namely SOA. In general terms, the latter can be principally associated with: (i) human activities (i.e., clearings; [44]); (ii) natural disturbances (i.e., treefall causing tree canopy gaps [42]); (iii) the tree crown architecture typical of certain forest types due to the so-called "canopy shyness" phenomenon [52]; (iv) the traditional extensive grazing activities (e.g., the so-called "maggenghi" meadows in the Alps; [62]); (v) the canopy cover gaps due to the ongoing encroaching process in new forest stands [25]. Taking into account forest dynamics and forest ecosystem functions and related services (e.g., biodiversity conservation), in this study we divided SOA into two main sub-types, namely enclosed small open areas (encld-SOA) and encroaching small open areas (encrg-SOA).…”

“…SOA due to forest harvesting or natural treefall are not considered in the present study because of their temporary nature (i.e., short-term natural evolution processes). "canopy shyness" phenomenon [52]; (iv) the traditional extensive grazing activities (e.g., the so-called "maggenghi" meadows in the Alps; [62]); (v) the canopy cover gaps due to the ongoing encroaching process in new forest stands [25]. Taking into account forest dynamics and forest ecosystem functions and related services (e.g., biodiversity conservation), in this study we divided SOA into two main sub-types, namely enclosed small open areas (encld-SOA) and encroaching small open areas (encrg-SOA).…”

“…Therefore, assessing the spatial configuration of small patches in fragmented landscapes and the relative influence on biodiversity and ecosystem services provision remains an open issue [46,47]. Recently, different methodological approaches and techniques for small patches monitoring were developed and implemented using both inventory and cartographic approaches [48][49][50][51][52][53]. The inventory approaches are convenient in terms of costs and are usually adopted to estimate the total coverage of small patches and additional quantitative attributes, such as trees characteristics and biodiversity.…”