Stand-volume estimation from multi-source data for coppiced and high forest Eucalyptus spp. silvicultural systems in KwaZulu-Natal, South Africa

摘要

Forest stand volume is one of the crucial stand parameters, which influences the ability of these forests to provide ecosystem goods and services. This study thus aimed at examining the potential of integrating multispectral SPOT 5 image, with ancillary data (forest age and rainfall metrics) in estimating stand volume between coppiced and planted Eucalyptus spp. in KwaZulu-Natal, South Africa. To achieve this objective, Partial Least Squares Regression (PLSR) algorithm was used. The PLSR algorithm was implemented by applying three tier analysis stages: stage I: using ancillary data as an independent dataset, stage II: SPOTS spectral bands as an independent dataset and stage III: combined SPOTS spectral bands and ancillary data. The results of the study showed that the use of an independent ancillary dataset better explained the volume of Eucalyptus spp. growing from coppices (adjusted R2 (R-Adj(2)) = 0.54, RMSEP = 44.08 m(3)/ha), when compared with those that were planted (R-Adj(2) = 0.43, RMSEP = 53.29 m(3)/ha). Similar results were also observed when SPOT 5 spectral bands were applied as an independent dataset, whereas improved volume estimates were produced when using combined dataset. For instance, planted Eucalyptus spp. were better predicted adjusted R2 (R-Adj(2)) = 0.77, adjusted R-Adj(2) = 0.59, RMSEP = 36.02 rn(3)/ha) when compared with those that grow from coppices (R-2 = 0.76, R-Adj(2) = 0.46, RMSEP = 40.63 m(3)/ha). Overall, the findings of this study demonstrated the relevance of multi-source data in ecosystems modelling. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.