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Estimation of Gap Fraction and Foliage Clumping in Forest Canopies

SfM-Based Method to Assess Gorgonian Forests (Paramuricea clavata (Cnidaria, Octocorallia))

Dipartimento di Scienze della Vita e dell’Ambiente (DISVA), Via Brecce Bianche, Monte Dago, 60130 Ancona, Italy
School of Water, Energy and Environment, Cranfield University, Cranfield MK430AL, UK
UBICA srl (Underwater BIo-CArtography), Via San Siro 6 int.1, 16124 Genova, Italy
Istituto Superiore Tecnologia Italiana (ISTI), Consiglio Nazionale delle Ricerche (CNR), Via Giuseppe Moruzzi 1, 56124 Pisa, Italy
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(7), 1154;
Received: 21 June 2018 / Revised: 11 July 2018 / Accepted: 17 July 2018 / Published: 21 July 2018
(This article belongs to the Section Ocean Remote Sensing)
Animal forests promote marine habitats morphological complexity and functioning. The red gorgonian, Paramuricea clavata, is a key structuring species of the Mediterranean coralligenous habitat and an indicator species of climate effects on habitat functioning. P. clavata metrics such as population structure, morphology and biomass inform on the overall health of coralligenous habitats, but the estimation of these metrics is time and cost consuming, and often requires destructive sampling. As a consequence, the implementation of long-term and wide-area monitoring programmes is limited. This study proposes a novel and transferable Structure from Motion (SfM) based method for the estimation of gorgonian population structure (i.e., maximal height, density, abundance), morphometries (i.e., maximal width, fan surface) and biomass (i.e., coenenchymal Dry Weight, Ash Free Dried Weight). The method includes the estimation of a novel metric (3D canopy surface) describing the gorgonian forest as a mosaic of planes generated by fitting multiple 5 cm × 5 cm facets to a SfM generated point cloud. The performance of the method is assessed for two different cameras (GoPro Hero4 and Sony NEX7). Results showed that for highly dense populations (17 colonies/m2), the SfM-method had lower accuracies in estimating the gorgonians density for both cameras (60% to 89%) than for medium to low density populations (14 and 7 colonies/m2) (71% to 100%). Results for the validation of the method showed that the correlation between ground truth and SfM estimates for maximal height, maximal width and fan surface were between R2 = 0.63 and R2 = 0.9, and R2 = 0.99 for coenenchymal surface estimation. The methodological approach was used to estimate the biomass of the gorgonian population within the study area and across the coralligenous habitat between −25 to −40 m depth in the Portofino Marine Protected Area. For that purpose, the coenenchymal surface of sampled colonies was obtained and used for the calculations. Results showed biomass values of dry weight and ash free dry weight of 220 g and 32 g for the studied area and to 365 kg and 55 Kg for the coralligenous habitat in the Marine Protected Area. This study highlighted the feasibility of the methodology for the quantification of P. clavata metrics as well as the potential of the SfM-method to improve current predictions of the status of the coralligenous habitat in the Mediterranean sea and overall management of threatened ecosystems. View Full-Text
Keywords: animal forest; point cloud classification; Good Environmental Status; environmental accounting; conservation animal forest; point cloud classification; Good Environmental Status; environmental accounting; conservation
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MDPI and ACS Style

Palma, M.; Rivas Casado, M.; Pantaleo, U.; Pavoni, G.; Pica, D.; Cerrano, C. SfM-Based Method to Assess Gorgonian Forests (Paramuricea clavata (Cnidaria, Octocorallia)). Remote Sens. 2018, 10, 1154.

AMA Style

Palma M, Rivas Casado M, Pantaleo U, Pavoni G, Pica D, Cerrano C. SfM-Based Method to Assess Gorgonian Forests (Paramuricea clavata (Cnidaria, Octocorallia)). Remote Sensing. 2018; 10(7):1154.

Chicago/Turabian Style

Palma, Marco, Monica Rivas Casado, Ubaldo Pantaleo, Gaia Pavoni, Daniela Pica, and Carlo Cerrano. 2018. "SfM-Based Method to Assess Gorgonian Forests (Paramuricea clavata (Cnidaria, Octocorallia))" Remote Sensing 10, no. 7: 1154.

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