Estimation of aboveground forest biomass in Galicia (NW Spain) by the combined use of LiDAR, LANDSAT ETM+ and National Forest Inventory data

doi:10.3832/ifor1989-010

Estimation of aboveground forest biomass in Galicia (NW Spain) by the combined use of LiDAR, LANDSAT ETM+ and National Forest Inventory data

Enrique Jiménez⁽¹⁾ , José A Vega⁽¹⁾, José M Fernández-Alonso⁽¹⁾, Daniel Vega-Nieva⁽²⁾, Luis Ortiz⁽³⁾, Pablito M López-Serrano⁽²⁾, Carlos A López-Sánchez⁽²⁾

iForest - Biogeosciences and Forestry, Volume 10, Issue 3, Pages 590-596 (2017)
doi: https://doi.org/10.3832/ifor1989-010
Published: May 15, 2017 - Copyright © 2017 SISEF

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Geo-mapping

Assessing biomass is critical for accounting bioenergy potentials and monitoring forest ecosystem responses to global change and disturbances. Remote sensing, especially Light Detection and Ranging (LiDAR) data combined with field data, is being increasingly used for forest inventory purposes. We evaluated the feasibility of the combined use of freely available data, both remote sensing (LiDAR data provided by the Spanish National Plan for Aerial Ortophotography - PNOA - and Landsat vegetation spectral indices) and field data (from the National Forest Inventory) to estimate stand dendrometric and aboveground biomass variables of the most productive tree species in a pilot area in Galicia (northwestern Spain). The results suggest that the models can accurately predict dendrometric and biomass variables at plot level with an R² ranging from 0.49 to 0.65 for basal area, from 0.65 to 0.95 for dominant height, from 0.48 to 0.68 for crown biomass and from 0.55 to 0.82 for stem biomass. Our results support the use of this approach to reduce the cost of forest inventories and provide a useful tool for stakeholders to map forest stand variables and biomass stocks.

Keywords

Biomass Maps, Forest Inventory, LiDAR, Landsat Vegetation Indices

Authors’ address

(1)

Centro de Investigación Forestal - Lourizán, PO Box 127, 36080 Pontevedra (Spain)

(2)

Facultad de Ciencias Forestales - Universidad Juárez del Estado de Durango (México) Río Papaloapan, Valle del Sur, 34120 Durango, Dgo. (México)

(3)

Departmento de Ingeniería de Recursos Naturales y Medio Ambiente, Universidad de Vigo, Campus A Xunqueira, Pontevedra, 36005 (Spain)

Corresponding author

Enrique Jiménez
cordogaita@gmail.com

Citation

Jiménez E, Vega JA, Fernández-Alonso JM, Vega-Nieva D, Ortiz L, López-Serrano PM, López-Sánchez CA (2017). Estimation of aboveground forest biomass in Galicia (NW Spain) by the combined use of LiDAR, LANDSAT ETM+ and National Forest Inventory data. iForest 10: 590-596. - doi: 10.3832/ifor1989-010

Academic Editor

Piermaria Corona

Paper history

Received: Jan 20, 2016
Accepted: Mar 12, 2017

First online: May 15, 2017
Publication Date: Jun 30, 2017
Publication Time: 2.13 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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List of the papers citing this article based on CrossRef Cited-by.

(1)

Balboa-Murias MA (2005)
Biomasa arbórea y estabilidad nutricional de los sistemas forestales de Pinus pinaster Ait., Eucalyptus globulus Labill. y Quercus robur L. en Galicia [Aboveground biomass and nutritional stability of Pinus pinaster Ait., Eucalyptus globulus Labill. and Quercus robur L. forest systems in Galicia]. PhD Thesis, Universidad de Santiago de Compostela, Spain, pp. 256. [in Spanish]
Gscholar

(2)

Balboa-Murias M, Rodríguez-Soalleiro R, Merino A, Alvarez-González JG (2006)
Temporal variations and distribution of carbon stocks in aboveground of radiata pine and maritime pine pure stands under different silvicultural alternatives. Forest Ecology and Management 237: 29-38.
CrossRef | Gscholar

(3)

Baret F, Guyot G (1991)
Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment 35: 161-173.
CrossRef | Gscholar

(4)

Belsley DA (1991)
Conditioning diagnostics, collinearity and weak data regression. Wiley Series in Probability, John Wiley and Sons, New York, USA, pp. 396.
Gscholar

(5)

Brañas J, González-Río F, Merino A (2000)
Contenido y distribución de nutrientes en plantaciones de Eucalyptus globulus del Nordeste de la Península Ibérica [Nutrients content and distribution in Eucalyptus globulus plantations in northwestern Iberian peninsula]. Investigación Agraria: Sistemas y Recursos Forestales 9: 316-335. [in Spanish]
Gscholar

(6)

Breiman L (2001)
Statistical modeling: the two cultures. Statistical Science 16: 199-301.
CrossRef | Gscholar

(7)

Burnham KP, Anderson DR (1998)
Model selection and inference. Springer-Verlag, New York, USA, pp. 515.
Gscholar

(8)

Chas-Amil ML (2007)
Forest fires in Galicia (Spain): threats and challenges for the future. Journal of Forest Economics 13: 1-5.
CrossRef | Gscholar

(9)

Chen Q, Laurin GV, Battles JJ, Saah D (2012)
Integration of airborne LiDAR and vegetation types derived from aerial photography for mapping aboveground live biomass. Remote Sensing of Environment 121: 108-117.
CrossRef | Gscholar

(10)

Chen Q (2013)
Lidar remote sensing of vegetation biomass. In: “Remote Sensing of Natural Resources” (Weng Q, Wang G eds). CRC Press, Taylor and Francis Group, Boca Raton, FL, USA, pp. 399-420.
Online | Gscholar

(11)

Cortés L, Hernández J, Valencia D, Corvalán P (2014)
Estimation of above-ground biomass using Landsat ETM+, Aster GDEM and LiDAR. Forest Research 3: 117.
Gscholar

(12)

Estornell J, Ruiz LA, Velázquez B, Hermosilla T (2012)
Estimation of biomass and volume of shrub vegetation using LiDAR and spectral data in a Mediterranean environment. Biomass and Bioenergy 46: 710-721.
CrossRef | Gscholar

(13)

Gómez-Vázquez I, Crecente-Campo F, Diéguez-Aranda U, Castedo-Dorado F (2013)
Modelling canopy fuel variables in Pinus pinaster Ait. and Pinus radiata D. Don stands in Northwestern Spain. Annals of Forest Science 70: 161-172.
CrossRef | Gscholar

(14)

González-Ferreiro E, Dieguez-Aranda U, Miranda D (2012)
Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities. Forestry 85: 281-292.
CrossRef | Gscholar

(15)

González-Ferreiro E, Miranda D, Barreiro-Fernández L, Bujan S, García-Gutierrez J, Dieguez-Aranda U (2013)
Modelling stand biomass fractions in Galician Eucalyptus globulus plantations by use of different LiDAR pulse densities. Forest Systems 22: 510-525.
CrossRef | Gscholar

(16)

González-Olabarria JR, Rodríguez F, Fernández-Landa A, Mola-Yudego B (2012)
Mapping fire risk in the model forest of Urbión (Spain) based on airborne LiDAR measurements. Forest Ecology and Management 282: 149-156.
CrossRef | Gscholar

(17)

Huete AR (1988)
A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25: 259-309.
CrossRef | Gscholar

(18)

Jenkins JC, Birdsey RA, Pan Y (2001)
Biomass and NPP estimation for the mid-Atlantic Region (USA) using plot level forest inventory data. Ecological Applications 11: 1174-1193.
CrossRef | Gscholar

(19)

Ji L, Wylie BK, Nossov DR, Peterson B, Waldrop MP, McFarland JW, Rover J, Hollingsworth TN (2012)
Estimating aboveground biomass in interior Alaska with Landsat data and field measurements. International Journal of Applied Earth Observation and Geoinformation 18: 451-461.
CrossRef | Gscholar

(20)

Jiménez E, Vega JA, Fernández-Alonso JM, Vega-Nieva D, González JG, Ruiz-González AD (2013)
Allometric equations for estimating canopy fuel load and distribution of pole-size maritime pine trees in five Iberian provenances. Canadian Journal of Forest Research 43: 149-158.
CrossRef | Gscholar

(21)

Jordan CF (1969)
Derivation of leaf area index from quality of light in the forest floor. Ecology 50: 663-666.
CrossRef | Gscholar

(22)

Kaartinen H, Hyyppa J, Yu X, Vastaranta M, Hyyppa H, Kukko A, Holopainen M, Heipke C, Hirschmugl M, Morsdorf F (2012)
An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sensing 4: 950-974.
CrossRef | Gscholar

(23)

Kraus K, Pfeiffer N (1998)
Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing 53: 193-203.
CrossRef | Gscholar

(24)

Lafiti H, Fassnacht FE, Hartig F, Berger C, Hernández J, Corvalán P, Koch B (2015)
Stratified aboveground forest biomass estimation by remote sensing data. International Journal of Applied Earth Observation and Geoinformation 38: 229-241.
CrossRef | Gscholar

(25)

Laurin GV, Chen Q, Lindsell JA, Coomes DA, Frate F, Guerriero L, Pirotti F, Valentini R (2014)
Above ground biomass estimation in an Africa tropical forest with LiDAR and hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing 89: 49-58.
CrossRef | Gscholar

(26)

Lim K, Treitz P, Wulder MA, St-Onge B, Flood M (2003)
LiDAR remote sensing of forest structure. Progress in Physical Geography 27: 88-106.
CrossRef | Gscholar

(27)

Lin Y, Jaakkola A, Hyyppä J, Kaartinen H (2010)
From TLS to VLS: biomass estimation at individual tree level. Remote Sensing 2: 1864-1879.
CrossRef | Gscholar

(28)

Liu HQ, Huete AR (1995)
A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing 33: 457-465.
CrossRef | Gscholar

(29)

López-Serrano PM, López-Sánchez CA, Díaz-Varela RA, Corral-Rivas JJ, Solís-Moreno R, Vargas-Larreta B, González JG (2015)
Estimating biomass of mixed and unevenaged forests using spectral data and a hybrid model combining regression trees and linear models. iForest - Biogeosciences and Forestry 9 (2): 226-234.
CrossRef | Gscholar

(30)

Manes F, Ricotta C, Salvatori E, Bajocco S, Blasi C (2010)
A multiscale analysis of canopy structure in Fagus sylvatica L. and Quercus cerris L. old-growth forests in the Cilento and Vallo di Diano National Park. Plant Biosystems 144 (1): 202-210.
CrossRef | Gscholar

(31)

MARM (2011)
Cuarto Inventario Forestal Nacional. Comunidad Autónoma de Galicia [Fourth National Forest Inventory. Galicia]. Dirección General del Medio Natural y Política Forestal, Galicia, Madrid, Spain, pp. 52. [in Spanish]
Gscholar

(32)

McGaughey R (2009)
FUSION/LDV: software for LIDAR data analysis and visualization. USDA Forest Service, Pacific Northwest Research Station, Seattle, WA, USA, pp. 123.
Gscholar

(33)

McRoberts RE, Naesset E, Gobakken T (2013)
Inference for lidar-assisted estimation of forest growing stock volume. Remote Sensing of Environment 128: 268-275.
CrossRef | Gscholar

(34)

Merino A, Balboa MA, Rodríguez-Soalleiro R, Alvarez-González JG (2005)
Nutrient exports under different harvesting regimes in fast-growing forest plantations in southern Europe. Forest Ecology and Management 207: 325-339.
CrossRef | Gscholar

(35)

Montero G, Ruiz-Peinado R, Muñoz M (2005)
Producción de biomasa y fijación de CO₂ por los bosques españoles [Spanish forest biomass production and CO₂ fixing]. Monografías INIA, Serie Forestal no. 13, Madrid, Spain, pp. 265. [in Spanish]
Gscholar

(36)

Naesset E, Gobakken T, Solberg S, Gregoire TG, Nelson R, Stahl G, Weydahl D (2011)
Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: a case study from a boreal forest area. Remote Sensing of Environment 115 (12): 3599-3614.
CrossRef | Gscholar

(37)

NASA (2011)
Landsat 7 science data users handbook. Landsat Project Science Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA, pp. 186.
Gscholar

(38)

Ni-Meister W, Lee S, Strahler AH, Woodcock AH, Schaaf C, Ranson J, Sun G, Blair JB (2010)
Assessing general relationships between aboveground biomass and vegetation structure parameters for improved carbon estimate from vegetation lidar. Journal of Geophysical Research 115 (G2): 2156-2202.
CrossRef | Gscholar

(39)

Nord-Larsen T, Schumacher J (2012)
Estimation of forest resources from a country wide laser scanning survey and national inventory data. Remote Sensing of Environment 119: 148-157.
CrossRef | Gscholar

(40)

Pérez-Cruzado C, Rodríguez-Soalleiro R (2011)
Improvement in accuracy of aboveground biomass estimation in Eucalyptus nitens plantations: effect of bole sampling intensity and explanatory variables. Forest Ecology and Management 261: 2016-2028.
CrossRef | Gscholar

(41)

Rouse J, Haas R, Schell J, Deering D (1973)
Monitoring vegetation system in the great plains with ERTS. In: Proceedings of the “3^rd ERTS Symposium”. NASA SP-351, NASA, Washington, DC, USA, pp. 309-317.
Gscholar

(42)

Scaramuzza P, Micijevic E, Chander G (2004)
SLC gap-filled products, phase one methodology. Landsat Technical Notes, pp. 5.
Online | Gscholar

(43)

Shendryk I, Hellström M, Klemedtsson L, Kljun N (2014)
Low-density LiDAR and optical imagery for biomass estimation over boreal forest in Sweden. Forests 5: 992-1010.
Gscholar

(44)

Sprugel DG (1983)
Correcting for bias in log-transformed allometric equations. Ecology 64: 209-210.
CrossRef | Gscholar

(45)

Zhao K, Popescu S, Nelson R (2009)
LiDAR remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers. Remote Sensing of Environment 113: 182-196.
CrossRef | Gscholar

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