The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 879–882, 2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 879–882, 2016

  23 Jun 2016

23 Jun 2016


H. J. Hernández1, T. Acuña1, P. Reyes2, M. Torres2, and E. Figueroa3 H. J. Hernández et al.
  • 1Geomatics and Landscape Ecology Lab, Forestry and Nature Conservation Faculty, Santa Rosa 11315, Santiago, Chile
  • 2Centro de Economía de los Recursos Naturales, Facultad de Economía de la Universidad de Chile, Chile
  • 3Departamento de Economía, Facultad de Economía de la Universidad de Chile, Chile

Keywords: SAVI, EVI, above-ground biomass, random forest

Abstract. The Atacama Desert in northern Chile is known as the driest place on Earth, with an average rainfall of about 15 mm per year. Despite these conditions, it contains a rich variety of flora with hundreds of species characterised by their extraordinary ability to adapt to this extreme environment. These biotic components have a direct link to important ecosystem services, especially those related to carbon storage and sequestration. No quantitative assessment is currently available for these services and the role of the desert in this matter remains unclear. We propose an approach to estimate above-ground biomass (AGB) using Landsat-8 data, which we tested in the Taparacá region, located in the northern section of the desert. To calibrate and validate the models, we used field data from 86 plots and several spectral indexes (NDVI, EVI and SAVI) obtained from the provisional Landsat-8 Surface-reflectance products. We applied randomised branch sampling and allometry principles (non-destructive methods) to collect biomass samples for all plant biological types: wetlands, steppes, shrubs and trees. All samples were dried in an oven until they reached constant weight and the final values were used to extrapolate dry matter content (AGB) to each plot in terms of kg m-2. We used all available scenes from September 2014 to August 2015 to calculate the maximum, minimum and average value for each index in each pixel within this period. For modeling, we used the method based on classification and regression trees called random forest (RF), available in the statistical software R-Project. The explained variance obtained by the RF algorithm was around 80-85%, and it improved when a wetland vector layer was used as the predictive factor in the model to reach the range 85-90%. The mean error was 1.45 kg m-2 of dry matter. The best model was obtained using the maximum and mean values of SAVI and EVI indexes. We were able to estimate total biomass storage of around 8 million tons (~ 4 million tons of C) for the whole region.