Next Article in Journal
MiRTaW: An Algorithm for Atmospheric Temperature and Water Vapor Profile Estimation from ATMS Measurements Using a Random Forests Technique
Previous Article in Journal
Sensitivity of Sentinel-1 Backscatter to Vegetation Dynamics: An Austrian Case Study
Previous Article in Special Issue
Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(9), 1397; https://doi.org/10.3390/rs10091397

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

1
School of Environmental and Forest Sciences, University of Washington, Box 352100, Seattle, WA 98195, USA
2
Department for Innovation in Biological, Agro-Food and Forest System, Università degli Studi della Tuscia, S. Camillo de Lellis snc, 01100 Viterbo, Italy
3
Department of Agricultural, Food and Forestry System, Università degli Studi di Firenze, Via San Bonaventura 13, 50145 Florence, Italy
*
Author to whom correspondence should be addressed.
Received: 11 July 2018 / Revised: 24 August 2018 / Accepted: 29 August 2018 / Published: 2 September 2018
(This article belongs to the Special Issue UAV Applications in Forestry)
Full-Text   |   PDF [3157 KB, uploaded 2 September 2018]   |  

Abstract

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. View Full-Text
Keywords: Unmanned Aerial systems (UAS); RGB high resolution imagery; forest canopy gaps; understory; vertical species diversity; microhabitat-bearing trees; contrast split segmentation; drone Unmanned Aerial systems (UAS); RGB high resolution imagery; forest canopy gaps; understory; vertical species diversity; microhabitat-bearing trees; contrast split segmentation; drone
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Bagaram, M.B.; Giuliarelli, D.; Chirici, G.; Giannetti, F.; Barbati, A. UAV Remote Sensing for Biodiversity Monitoring: Are Forest Canopy Gaps Good Covariates? Remote Sens. 2018, 10, 1397.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top