5 October 2017 Discriminating the Mediterranean Pinus spp. using the land surface phenology extracted from the whole MODIS NDVI time series and machine learning algorithms
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Abstract
Land surface phenology (LSP) can improve the characterisation of forest areas and their change processes. The aim of this work was: i) to characterise the temporal dynamics in Mediterranean Pinus forests, and ii) to evaluate the potential of LSP for species discrimination. The different experiments were based on 679 mono-specific plots for the 5 native species on the Iberian Peninsula: P. sylvestris, P. pinea, P. halepensis, P. nigra and P. pinaster. The entire MODIS NDVI time series (2000–2016) of the MOD13Q1 product was used to characterise phenology. The following phenological parameters were extracted: the start, end and median days of the season, and the length of the season in days, as well as the base value, maximum value, amplitude and integrated value. Multi-temporal metrics were calculated to synthesise the inter-annual variability of the phenological parameters. The species were discriminated by the application of Random Forest (RF) classifiers from different subsets of variables: model 1) NDVI-smoothed time series, model 2) multi-temporal metrics of the phenological parameters, and model 3) multi-temporal metrics and the auxiliary physical variables (altitude, slope, aspect and distance to the coastline). Model 3 was the best, with an overall accuracy of 82%, a kappa coefficient of 0.77 and whose most important variables were: elevation, coast distance, and the end and start days of the growing season. The species that presented the largest errors was P. nigra, (kappa= 0.45), having locations with a similar behaviour to P. sylvestris or P. pinaster.
Conference Presentation
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Victor Rodriguez-Galiano, Victor Rodriguez-Galiano, David Aragones, David Aragones, Jose A. Caparros-Santiago, Jose A. Caparros-Santiago, Rafael M. Navarro-Cerrillo, Rafael M. Navarro-Cerrillo, } "Discriminating the Mediterranean Pinus spp. using the land surface phenology extracted from the whole MODIS NDVI time series and machine learning algorithms", Proc. SPIE 10428, Earth Resources and Environmental Remote Sensing/GIS Applications VIII, 104280U (5 October 2017); doi: 10.1117/12.2278557; https://doi.org/10.1117/12.2278557
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