Forest Biodiversity and Its Assessment by Remote Sensing

John L. Innes; Barbara Koch

doi:10.2307/2997712

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

10.20944/preprints201807.0209.v1 ◽

2018 ◽

Cited By ~ 2

Author(s):

Martin B. Bagaram ◽

Diego Giuliarelli ◽

Gherardo Chirici ◽

Francesca Giannetti ◽

Anna Barbati

Keyword(s):

Remote Sensing ◽

Species Diversity ◽

Forest Inventory ◽

Forest Canopy ◽

Mixed Forest ◽

Canopy Gaps ◽

Forest Biodiversity ◽

Biodiversity Monitoring ◽

Forest Types ◽

Turkey Oak

Forest canopy gaps are important for the ecosystem dynamics. Depending on tree species, small canopy openings might be also associated to intra-crown porosity and to space between crowns. Yet, little is known on the relationships between the fine-scaled pattern of canopy openings and biodiversity features. This research explored the possibility of i)- mapping forest canopy gaps from a very high resolution orthomosaic (10 cm), processed from a versatile imaging platform such as unmanned aerial vehicles (UAV), ii)- to derive patch metrics that can be tested as covariates of variables of interest for forest biodiversity monitoring. This is attempted in a test area of 240 ha covered by temperate deciduous forest types in Central Italy and containing 50 forest inventory plots of about 530 m2. Correlation and linear regression techniques were used to explore relationships between patch metrics and understorey (density, development and species diversity) or forest habitat biodiversity variables (density of micro-habitat bearing trees, vertical species profile, tree species diversity). The results revealed that small openings in the canopy cover (75% smaller than 7 m2) can be faithfully extracted from UAV RGB imagery, using the red band and contrast split segmentation. Highest correlations were observed in the mixed forest (beech and turkey oak), while beech forest had the poorest ones and turkey oak forest displayed intermediate results. Moderate to strong linear relationships were found between gap metrics and understorey variables in mixed forest type, with adjusted R2 from linear regression ranging from 0.52 to 0.87. Equally good results, in the same forest types, were observed for forest habitat biodiversity variables (0.52<adjusted R2<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.

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