Occlusion probability in operational forest inventory field sampling with ForeStereo

Montes, F. and Rubio Cuadrado, Álvaro and Sánchez González, María de la O and Aulló Maestro, Isabel Consuelo and Cabrera, Miguel and Gómez, C. (2019). Occlusion probability in operational forest inventory field sampling with ForeStereo. "Photogrammetric Engineering And Remote Sensing", v. 85 (n. 7); pp. 493-508. ISSN 0099-1112. https://doi.org/10.14358/PERS.85.7.493.

Description

Title: Occlusion probability in operational forest inventory field sampling with ForeStereo
Author/s:
  • Montes, F.
  • Rubio Cuadrado, Álvaro
  • Sánchez González, María de la O
  • Aulló Maestro, Isabel Consuelo
  • Cabrera, Miguel
  • Gómez, C.
Item Type: Article
Título de Revista/Publicación: Photogrammetric Engineering And Remote Sensing
Date: July 2019
ISSN: 0099-1112
Volume: 85
Subjects:
Freetext Keywords: basal area, sampling, number of trees, instrument bias, tree occlusions
Faculty: E.T.S.I. Montes, Forestal y del Medio Natural (UPM)
Department: Sistemas y Recursos Naturales
UPM's Research Group: Genética, Fisiología e Historia Forestal
Creative Commons Licenses: Recognition - Non commercial - Share

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Abstract

Field data in forest inventories are increasingly obtained using proximal sensing technologies, often under fixed-point sampling. Under fixed-point sampling some trees are not-detected due to instrument bias and oclussions, hence involving an underestimation of the number of trees per hectare (N). The aim here is to evaluate various approaches to correct tree occlusions and instrument bias estimates calculated with data from ForeStereo (proximal sensor based on stereoscopic hemispherical images) under a fixed-point sampling strategy. Distance-sampling and the new hemispherical photogrammetric correction (HPC), which combines image segmentation-based correction for instrument bias with a novel approach for estimating the proportion of shadowed sampling area in stereoscopic hemispherical images, best estimated N and basal area (BA). Distance-sampling slightly overestimated N (11% bias and 0.60 Pearson coefficient with the reference measures) and BA (4%, 0.82). HPC provided less biased N estimates (-6%, 0.61) but underestimated BA (-8%, 0.83). The diameter distribution was more accurately retrieved through HPC.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of Spain979S/2013UnspecifiedFernando Montes PitaPasado, presente y futuro de los bosques de montaña: seguimiento y modelización de los efectos del cambio climático y la gestión sobre la dinámica forestal
Government of SpainAGL2016-76769-C2-1-RFORESTCHANGEFernando Montes PitaInfluencia del régimen de perturbaciones y la gestión en el balance de carbono, estructura y dinámica de las masas forestales

More information

Item ID: 56554
DC Identifier: http://oa.upm.es/56554/
OAI Identifier: oai:oa.upm.es:56554
DOI: 10.14358/PERS.85.7.493
Official URL: https://www.ingentaconnect.com/content/asprs/pers/2019/00000085/00000007/art00012
Deposited by: Álvaro Rubio Cuadrado
Deposited on: 25 Sep 2019 10:38
Last Modified: 25 Sep 2019 10:38
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