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  • Ruben Valbuena
    University of CambridgeUniversity of Eastern Finland
  • Ana Hernando
  • Jose Antonio Manzanera
  • Eugenio Martínez-Falero
  • Antonio García-Abril
  • Blas Mola-Yudego
In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis
Iaith wreiddiolSaesneg
Tudalennau (o-i)1205-1218
Nifer y tudalennau14
CyfnodolynInternational Journal of Digital Earth
Cyfrol11
Rhif y cyfnodolyn12
Dyddiad ar-lein cynnar13 Hyd 2017
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 2018
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