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Proceeding Paper

Remote Sensing Assessment of Biomass Evolution Depending on Forest Management †

ITACA Institute, Universitat Politècnica de València, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Presented at the II Congress in Geomatics Engineering, Madrid, Spain, 26–27 June 2019.
Proceedings 2019, 19(1), 3; https://doi.org/10.3390/proceedings2019019003
Published: 12 July 2019
(This article belongs to the Proceedings of The II Geomatics Engineering Conference)

Abstract

Remote sensing and advanced statistical models have been used to assess actual biomass and to analyze the biomass growth in a mature forest of Pinus halepensis Mill. Biomass maps using Landsat-5 in 2011 and Landsat-8 imagery in 2017 and non-parametric models were achieved. Afterwards, a fast and affordable methodology has been developed to monitor the evolution depending on forest management or its abandonment. Significative statistical evidences have been found between two types of management, demonstrating that managed areas have bigger growth potential. This methodology spares the efforts of exhaustive inventories and encourage forest managers to maximize their forest allowances.
Keywords: remote sensing; biomass evolution; forest management; statistics remote sensing; biomass evolution; forest management; statistics

Share and Cite

MDPI and ACS Style

Vinué-Visús, D.; Coll-Aliaga, E.; Oliver-Villanueva, J.-V. Remote Sensing Assessment of Biomass Evolution Depending on Forest Management. Proceedings 2019, 19, 3. https://doi.org/10.3390/proceedings2019019003

AMA Style

Vinué-Visús D, Coll-Aliaga E, Oliver-Villanueva J-V. Remote Sensing Assessment of Biomass Evolution Depending on Forest Management. Proceedings. 2019; 19(1):3. https://doi.org/10.3390/proceedings2019019003

Chicago/Turabian Style

Vinué-Visús, David, Eloína Coll-Aliaga, and José-Vicente Oliver-Villanueva. 2019. "Remote Sensing Assessment of Biomass Evolution Depending on Forest Management" Proceedings 19, no. 1: 3. https://doi.org/10.3390/proceedings2019019003

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