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Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models

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  • Viana, H.
  • Aranha, J.
  • Lopes, D.
  • Cohen, Warren B.

Abstract

Spatially crown biomass of Pinus pinaster stands and shrubland above-ground biomass (AGB) estimation was carried-out in a region located in Centre-North Portugal, by means of different approaches including forest inventory data, remotely sensed imagery and spatial prediction models. Two cover types (pine stands and shrubland) were inventoried and biomass assessed in a total of 276 sample field plots. We compared AGB spatial predictions derived from Direct Radiometric Relationships (DRR) of remotely sensed data; and the geostatistical method Regression-kriging (RK), using remotely sensed data as auxiliary variables. Also, Ordinary Kriging (OK), Universal Kriging (UK), Inverse Distance Weighted (IDW) and Thiessen Polygons estimations were performed and tested. The comparison of AGB maps shows distinct predictions among DRR and RK; and Kriging and deterministic methods, indicating the inadequacy from these later ones to map AGB over large areas. DRR and RK methods produced lower statistical error values, in pine stands and shrubland, when compared to kriging and deterministic interpolators. Since forest landscape is not continuous variable, the tested forest variables showed low spatial autocorrelation, which makes kriging methods unsuitable to these purposes. Despite the geostatistical method RK did not increase the accuracy of estimates developed by DRR, denser sampling schemes and different auxiliary variables should be explored, in order to test if the accuracy of predictions is improved.

Suggested Citation

  • Viana, H. & Aranha, J. & Lopes, D. & Cohen, Warren B., 2012. "Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models," Ecological Modelling, Elsevier, vol. 226(C), pages 22-35.
    • Handle: RePEc:eee:ecomod:v:226:y:2012:i:c:p:22-35
    DOI: 10.1016/j.ecolmodel.2011.11.027
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    References listed on IDEAS

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    1. Viana, H. & Cohen, Warren B. & Lopes, D. & Aranha, J., 2010. "Assessment of forest biomass for use as energy. GIS-based analysis of geographical availability and locations of wood-fired power plants in Portugal," Applied Energy, Elsevier, vol. 87(8), pages 2551-2560, August.
    2. Sales, Marcio H. & Souza, Carlos M. & Kyriakidis, Phaedon C. & Roberts, Dar A. & Vidal, Edson, 2007. "Improving spatial distribution estimation of forest biomass with geostatistics: A case study for Rondônia, Brazil," Ecological Modelling, Elsevier, vol. 205(1), pages 221-230.
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    1. Steen MAGNUSSEN, 2018. "An estimation strategy to protect against over-estimating precision in a LiDAR-based prediction of a stand mean," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 64(12), pages 497-505.
    2. Lin Zhuo & Hong Tao & Hong Wei & Wu Chengzhen, 2016. "Compatible Models of Carbon Content of Individual Trees on a Cunninghamia lanceolata Plantation in Fujian Province, China," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-21, March.

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