**7. References**


The approach to this scale of work, using medium spatial resolution sensors, is to estimate AGB independently of species or plant community types and then overlap stands boundaries, or any other administrative division, to obtain the associated total AGB stocks. Coregionalization models (direct and cross variograms) are simplifications of reality; in particular, they may not detect anisotropies when having a too small amount of field data, allowing only for an omnidirectional inference. The number of field data should be such that the inference of AGB variogram is feasible, which is usually achieved with more than 100 data in each stratum. In order for this number to be reduced, we recommend the sampling of the area (site) to be as regular as possible, i.e. avoiding samples clustered in

In the future, we suggest identifying other covariates correlated with AGB, for example, an indicator of the amount of sunlight available or a local indicator of the ground geometry (concave, convex or plane) that is related to the availability of water. These indicators could be calculated from a digital elevation model, already available at several spatial resolutions.

We would like to express our thanks to Biocomsa Consortium, and therefore INNOVA CORFO CHILE, for funding most of the field work, data acquisition and processing. Also, we thank Miss Paz Acuña and Miss Lissette Cortés from GEP Lab. at Universidad de Chile, for their support in reviewing the text and for providing the reference cartography. Also we

Anuchin, N.P. (1960). Forest Mensuration. Second edition. Goslesbumizdat. Moskova-

Ardo, J. (1992). Volume quantification of coniferous forest compartments using spectral

Ares & Braener (2005). Above ground biomass partitioning in loblolly pine silvopastoral

Avery, Th. & Burkhart, H. (1994). Forest Measurements. Fourth edition. McGraw-Hill. ISBN

Baffetta, F.; Fattorini, L.; Franceschi, S. & Corona, P. (2009). Design-based approach to k-

Baker, T.; Attiwill, P. & Stewart H. (1984). Biomass Equations for Pinus radiate in Gippsland,

Bertini, R.; Chirici, G.; Corona, P. & Travaglini, D. (2007). Comparison between parametric

Birdsey R. (2004). Data gaps for monitoring forest carbon in the United States: An inventory

perspective. Environmental Management 33 supplement 1, S1-S8.

surveys. *Remote Sensing of Environment*, 113(3):463-475

Victoria. New Zealand Journal of Forestry Science 14(1): 89-96

radiance record by LANDSAT Thematic Mapper. *International Journal of Remote* 

stands: Spatial configuration and pruning effects. Forest Ecology and Management

nearest neighbours technique for coupling field and remotely sensed data in forest

and non-parametric methods for the spatialization of forest standing volume by integrating eld measures, remote sensing data and ancillary data. *Forest@*,

space.

**6. Acknowledgment** 

**7. References** 

thank Helen Grover for proof reading the text.

Leningrado.454 pp

*Sensing,* 13, 1779–1786

0-07-002556-8. 408 pp

4110−117:1082−1089

219 (2005) 176-184


Geostatistical Estimation of Biomass Stock in Chilean Native Forests and Plantations 293

Horler, D. & Ahern, F. (1986). Forestry information content of Thematic Mapper data.

Husch, B.; Miller, C. & Beers, T. (1993). Forest Mensuration. Krieger Publishing Company,

INFOR. (2010). Wisdom, Plataforma de Información de la Oferta de Dendrocombustibles en

Isaaks, E.H. & Srivastava, R.M. (1989). *An Introduction to Applied Geostatistics*, Oxford

Ishii, T. & Tateda, Y. (2004). Leaf Area Index and Biomass Estimation for Mangrove

Jakubauskas, M. & Price, K. (1997). Empirical relationships between structural and spectral

Keith, H.; Barret, D. & Keenan, R. (2000). Review of Allometric Relationships for Estimating

Ketterings, Q.; Coe, R.; van Noordwijk M.; Ambagau, Y. & Pal, Ch. A. (2001). Reducing

Kimes, D.; Holben, B.; Nickeson, J. & Mckee, W. (1996). Extracting forest ages in a pacific

Koch, B. & Dees, M. (2008). Forestry applications. In: Advances in Photogrammetry, Remote

Li, Chen & Baltsavias (eds). Taylor & Francis Group, London. pp 439-465. Labrecque, S.; Fournier, R.; Luther, J. & Piercy, D. (2006). A comparison of four methods to

Lathrop, R. & Pierce, L. ( 1991). Ground-based canopy transmittance and satellite remotely

Lavery, P. (1986). Plantation Forestry with Pinus radiata. Review Paper Nº 12. School of Forestry. University of Canterbury, Christchurch, New Zealand 255 pp Lewis N. & Ferguson, I. (1993) in association with Sutton, W.; Donald, D. & Lisboa, H.

Leyton, J. (2009). Tenencia Forestal en Chile. Available from http://www.fao.org/forestry/17192-0422df95bf58b971d853874bb7c5755f7.pdf Loetsch, F., Zoehrer, F. & Haller, K.E. (1973). Forest Inventory. Vol. 2 B.L.V.

Chile, In: Seminario Centro de Energías Renovables "Energía sustentable de la

Plantation in Thailand. In: Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International, 20-24 Sept. 2004. Vol 4: 2323 –

factors of Yellowstone lodgepole pine forests. *Photogrammetric Engineering and* 

Woody Biomass for New South Wales, the Australian Capital Territory, Victoria, Tasmania and South Australia. National Carbon Accounting System Technical

uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. Forest Ecology and Management 146: 199-

northwest forest from Thematic Mapper and topographic data. *Remote Sensing of* 

Sensing and Spatial Information Sciences: 2008 ISPRS Congress Book, Chapter 32 -

maps biomass from LANDSAT–TM and inventory data in western Newfoundland.

sensed measurements for estimation of coniferous forest canopy structure. *Remote* 

Management of radiata Pine.ISBN 0-909605-79-3 Langley Editing, Melbourne,

*International Journal of Remote Sensing*, 7, 405–428

biomasa: oportunidades y desafíos"

*Remote Sensing*, 63 (12), 1375–1381

*Environment*, 56, 133–140

Report Nº 5B. Australian Greenhouse Office 121 pp.

*Forest Ecology and Management*, 226, 129–144

*Sensing of Environment*, 36, 179–188

Verlagsgesellschaft. München. 469 pp

Australia 404 pp

University Press, New York

2326

2009

Third Edition. ISBN 0-89464-821-7, Malabar, Florida. 402 pp


Foody, G.; Boyd, D. & Cutler, M. (2003). Predictive relations of tropical forest biomass from

Fournier, R. A.; Luther, J. E.; Guindon, L.; Lambert, M.C.; Piercey, D.; Hall, R.J. & Wulder,

Franklin, J. (1986). Thematic Mapper analysis of coniferous forest structure and composition.

Fuenzalida, H. (1965). Clima. Biogeografía. In: Geografía Económica de Chile, Texto

Fundación Chile. (2005). Tablas Auxiliares de Producción. Simulador de Árbol Individual

Gajardo, R. (1994). La Vegetación Natural de Chile. Clasificación y Distribución Geográfica.

Garfias, R. (1994). Crecimiento y biomasa en renoval raleado de *Nothofagus alpina* (Poepp, et

Gayoso, J.; Guerra, J. & Alarcón, D. (2002). Contenido de carbono y funciones de biomasa en

Gemmell, F. (1995). Effects of forest cover, terrain, and scale on timber volume estimation

Gerding, V. (1991). Manejo de las plantaciones de Pinus radiata D. Don en Chile. BOSQUE

Gilabert, H.; Meza, F.; Cabello, H. & Aurtenenchea, M. (2010). Estimación del carbono

Goulard, M. & Voltz, M. (1992). Linear Coregionalization Model: Tools for Estimation and Choice of Cross-Variogram Matrix. *Mathematical Geology*, Vol.24,No.3, pp. 269-286 Gringarten, E. & Deutsch, C.V. (2001). Variogram Interpretation and Modeling, *Mathematical* 

Hall, R.; Skakun, R.; Arsenault, E. & Case, B. (2006). Modeling forest stand structure

biomass and stand volume. *Forest Ecology and Management*, 225, 378–390 Herrera, S.&Waisberg, R. (2002). Estimación de carbono almacenado en el Tipo Forestal

Facultad de Ingeniería, Universidad de Santiago de Chile.

*International Journal of Remote Sensing,* 7 (10), 1287–1301

*Environment,* 85, 463–474

Research 2003, 33, 1846-1863.

Madera. 100pp

Santiago, Chile. 64pp

*Environment*, 51, 291–305

157 pp

12(2):3-10

refundido: pp. 98-152; 228-67. CORFO

Editorial Universitaria, Santiago, Chile. 165 pp

Ministerio de Agricultura. Gobierno de Chile.

*Geology*, Vol.33, No.4, pp. 507-534

LANDSAT TM data and their transferability between regions. *Remote Sensing of* 

M.A. 2003, Mapping above-ground tree biomass at the stand level from inventory information: test cases in Newfoundland and Québec. Canadian Journal of Forest

para Pino Radiata (*Pinus radiata* D. Don): Arquitectura de Copa y Calidad de

Endl) Oerst, en la provincia de Bío-Bío, VIII Region. Tesis Ingeniería Forestal. Universidad Chile. Facultad de Ciencias Forestales y conservación de la naturaleza.

especies nativas y exóticas. Proyecto FONDEF D98I1076 Medición de la capacidad de captura de carbono en bosques de Chile y promoción en el mercado mundial.

with Thematic Mapper data in the rocky mountain site. *Remote Sensing of* 

capturado en las plantaciones de pino radiata y eucaliptos relacionados con el DL 701 de 1974. Informe final. Oficina de Estudios y Políticas Agrarias. ODEPA.

attributes using LANDSAT ETM+ data: Aplication to mapping of above ground

Roble-Raulí–Coigüe (*Nothofagus obliqua-Nothofagus alpina–Nothofagus dombeyi*), para determinar los beneficios ambientales de someterlo a sumidero. Memoria de título Ingeniería de Ejecución en ambiente. Departamento de Ingeniería Geográfica,


Geostatistical Estimation of Biomass Stock in Chilean Native Forests and Plantations 295

Roy, P. & Ravan, S. (1996). Biomass estimation using satellite remote sensing data: an

Saez, M. (1991). Biomasa y contenido de nutrientes e renovales no intervenidos de roble-

Schlatter, J. (1977). La Relación entre suelo y Plantaciones de *Pinus radiata* D.DON en Chile

Schlegel, B. (2001). Estimación de la biomasa y carbono en bosques del tipo forestal

natural regeneration in the Chilean Patagonia. Ann. For. Sci. 66 (5):315-323. Stein, M.L. (2002). The Screening Effect in Kriging, *Annals of Statistics*, Vol.30, No.1, pp. 298-

Steininger, M. (2000). Satellite estimation of tropical secondary forest above-ground

Subramanyam, A. & Pandalai, H.S. (2008). Data Configurations and the Cokriging System: Simplification by Screen Effects, *Mathematical Geosciences*, Vol.40, No.4, pp. 425-443 Thessler, S.; Sesnie, S. & Ramosbendana, Z. (2008).Using k-NN and discriminant analyses to

Tomppo, E. & Halme, M. 2004. Using coarse scale forest variables as ancillary information

Tomppo, E.; Gagliano, C.; Denatale, F.; Katila, M. & McRoberts, R. (2009). Predicting

Trotter, C.; Dymond, J. & Goulding, C. (1997). Estimation of timber volume in a coniferous

Tucker, C. (1979). Red and photographic infrared linear combinations for monitoring

Turner, D.; Cohen, W.; Kennedy, R.; Fassnacht, K. & Briggs, J. (1999). Relationships between

LANDSAT imagery. *Remote Sensing of Environment*, 113(3):500-517 Toro, J. & Gessel, S. (1999). Radiata pine plantations in Chile. New Forests 18: 33–44

South Africa 30 April 11- May 1984 pp 541-549

*Remote Sensing of Environment*, 112(5):2485-2494

vegetation. *Remote Sensing of Environment*, 8: 127-150

temperate zone sites. *Remote Sensing of Environment*, 70, 52–68

*Remote Sensing of Environment*, 92:1−20

Bosque Vol. 2 N° 1 31 pp

535–561

323

1157

2209–2223

investigation on possible approaches for natural forest. *Journal of Bioscience*, 21 (4),

raulí (*Nothofagus obliqua* (mirb) Oerst. – *Nothofagus alpina* (Poepp. et Ende.) Oerst.) en suelos volcánicos de la precordillera andina, IX Región. Memoria de título Ingeniería Forestal. Facultad de Ciencias Forestales. Universidad de Chile. 96 pp Schlatter, J. & Gerding, V. (1984): Important site factors for Pinus radiata growth in Chile. In:

Grey, D.C., Schonau, A.P.G., Schutz, C.J. & Van Laar, A. (Editors). Symposium on Site and Productivity of Fast Growing Plantations, Pretoria and Pietermaritzburg,

Central Análisis de la Situación Actual y Planteamientos para su Futuro Manejo.

siempreverde. Simposio internacional medición y monitoreo de la captura de carbono en ecosistemas forestales. 18-20 Octubre 2001. Valdivia, Chile. 13 pp Schmidt, A.; Poulain, M.; Klein, D.; Krause, K.; Peña-Rojas, K.; Schmidt, H. & Schulte, A.

(2009). Allometric above-belowground biomass equations for *Nothofagus pumilio*

biomass: data from Brazil and Bolivia. Int. J. Remote Sensing Vol 21 Nº 6 & 7, 1139 –

classify rain forest types in a LANDSAT TM image over northern Costa Rica.

and weighting of variables in k-NN estimation: A genetic algorithm approach.

categorical forest variables using an improved k-Nearest Neighbour estimator and

plantation forest using LANDSAT TM. *International Journal of Remote Sensing*, 18,

leaf area index and LANDSAT TM spectral vegetation indices across three


.

Lu, D. (2005). Aboveground biomass estimation using LANDSAT TM data in the Brazilian

Lu, D.; Mausel, P.; Brondizio, E. & Moran, E. (2004). Relationships between forest stand

Luebert, F.&Pliscoff, P. (2006). Sinopsis bioclimática y vegetacional de Chile. Santiago de

Madgwick, H. (1994). *Pinus radiata* – Biomasa, Form and Growth. H.A.I. Madwick (Ed) ISBN

Maselli, F.; Chirici, G.; Bottai, L.; Corona, P. & Marchetti, M. (2005). Estimation of

McRoberts R. & Tomppo, E. (2007). Remote sensing support for national forest inventories.

McRoberts R.; Tomppo, E.; Finley, A. & Heikkinen, J. (2007). Estimating areal means and

Mickler, R.; Earnhardt, T. & Moore, J. (2002). Regional estimation of current and future

Morales, R.; Weintraub, A.; Peters, R.&García, J. (1979). Modelos de simulación y manejo

Nelson, R.F., Kimes, D.S., Salas, W.A. & Routhier, M. (2000). Secondary forest age and

Peña, M. (2007). Correcciones de una imagen satelital ASTER para estimar parámetros vegetacionales en la cuenca del río Mirta, Aysén. Bosque 28(2): 162-172p. Peterson, D.; Spanner, M.; Running, S. & Teuber, K. (1987). Relationship of Thematic

Peterson, D.; Westman, W.; Stephenson, N.; Ambrosia, V.; Brass, J. & Spanner, M. (1986).

Phua, M. & Saito, H. (2003). Estimation of biomass of a mountainous tropical forest using

Prodan, M.; Peters, R.; Cox, F.&Real, P. (1997). Mensura Forestal. Serie Investigación y

Riaño, D.; Chivieco, E.; Salas, J. & Aguado, I. 2003. Assessment of Different Topographic

Rivoirard, J. (1987). Two Key Parameters When Choosing the Kriging Neighborhood,

Rivoirard, J. (2004). On Some Simplifications of Cokriging Neighborhood, *Mathematical* 

LANDSAT TM data. *Canadian Journal of Remote Sensing*, 29, 429–440

*Transactions in Geoscience and Remote Sensing*, 24, 113–121

Educación en Desarrollo Sostenible. IICA BMZ/gtz

*in Geoscience and Remote Sensing*, vol. 41(5): 1056:1061

*Mathematical Geology*, Vol.19, No.8, pp. 851-856

*Geology*, Vol.36, No.8, pp. 899-915

parameters and LANDSAT Thematic Mapper spectral responses in the Brazilian

Mediterranean forest attributes by the application of k-NN procedure to multitemporal LANDSAT ETM+ images. *International Journal of Remote Sensing*,

variances of forest attributes using the k-Nearest Neighbors technique and satellite

para plantaciones forestales. FO: DP/CHI/76/003. Documento de trabajo Nº 36.

tropical forest biomass estimation using Thematic Mapper imagery. Bioscience, 50,

Mapper simulator data to leaf area index of temperate coniferous forests. *Remote* 

Analysis of forest structure using Thematic Mapper simulator data. *IEEE* 

Corrections in Landsat-TM Data for Mapping Vegetation Types. *IEEE Transactions* 

Amazon Basin. *International Journal of Remote Sensing*, 26, 2509–2525

Amazon basin. *Forest Ecology and Management*, 198, 149–167

Chile, Editorial Universitaria. ISBN 956-11-1832-7, 316 pp

0-473-02375-X, Rotorua New Zealand. 428pp

*Remote Sensing of Environment*, 110:412−419

forest biomass. *Environmental Pollution*, 116, S7–S16

imagery. *Methods*, 111:466 - 480

*Sensing of Environment*, 22, 323-331

Santiago de Chile. 155pp

.

17:3781−3796

pp. 419–431.


**14** 

*Spain* 

**Using Remote Sensing to Estimate a** 

A. García-Martín, J. de la Riva, F. Pérez-Cabello and R. Montorio

**Renewable Resource: Forest Residual Biomass** 

*Department of Geography and Spatial Management, University of Zaragoza, Zaragoza,* 

Regarding the three objectives of European Union (EU) energy policy (secure supply, competitiveness and environmental protection) the EU Commission published the Communication entitled 'White Paper: Energy for the Future - Renewable Sources of Energy' (EU Commission, 1997; Mourelatou & Smith, 2004). This document, which was the starting point for the European promotion and development of renewable energy, stated the objective that 12% of energy production in 2010 would come from renewable sources. In Spain, this objective was recognized by the government in 1999 in the Plan to the Promotion of Renewable Energies (PPRE). To achieve this objective, the PPRE focused on increasing the use of biomass, identifying forest residues as one of the biomass sources. Specifically, this document established an increase in the use of forest residual biomass to 450 000 tonnes of

Among the different sources of renewable energy, this chapter focuses on forest residual biomass (FRB). This term refers to branches, foliage, and unmerchantable stem tops that are commercially unsuitable in terms of the timber obtained from regular operations in forest management or in timber exploitation (Esteban et al., 2004; IDAE, 1999; Velázquez, 2006). Following the 'complete-tree concept,' the term 'branches' include the wood and bark of live and dead branches; 'foliage' refers to all leaves-needles, new shoots and reproductive organs; and the 'unmerchantable stem top' is the upper section of the stem that is left unutilized in logging operations due to its small diameter and high degree of branching (the bottom stem diameter of this unmerchantable top usually ranges from 5 to 10 cm) (Hakkila & Parikka, 2002). The treatments commonly applied to these residues in Spain include controlled burning, stacking within the forest and, less commonly, splintering to improve

However, this biomass segment can also be used as a source of energy in heating applications (fuel for domestic or industrial stoves and boilers) and in the generation of electricity (replacing fossil fuels in power stations) (Asikainen et al., 2002; IDAE, 2005b), with the majority of the residue currently being utilized for the latter in Spain (IDAE, 2007). The benefits associated with this energy-related use can be divided into two types: environmental and socio-economic. The first benefits are generated in the production phase, as the recovery and elimination of FRB reduces the risk of forest fires and their severity (IDAE, 2005a; Velázquez, 2006), and also because the implementation of silviculture can

petroleum equivalent per year (TPE/year) (IDAE, 1999).

their incorporation into the soil (IDAE, 2005a, 2007; Velázquez, 2006).

**1. Introduction** 

