Abstract

Abstract. An assessment on the amount and spatial distribution of forest aboveground biomass (AGB) for the forests in Colombia was generated using in-situ national forest inventory data (IDEAM, 2018), in combination with multispectral optical data and synthetic aperture radar (SAR) satellite imagery. ALOS-2 PALSAR-2 gamma-0 backscatter annual mosaics (2015–2017) provided by JAXA were normalised and corrected using previous ALOS PALSAR annual mosaics (2007–2010) as reference. A multi-temporal Landsat 7 & 8 composite over the whole of Colombia was used for the year 2016 ± 1. The national forest inventory in-situ plots used to train our model consisted of 5-subplots each and were collected during the period 2015–2017 in the main biomes of the country. A sample of permanent 1ha plots (PPMs) were also measured. Nationally developed allometries (Alvarez et al., 2012) were used to estimate AGB. A non-parametric random forests (RF) algorithm was used within a k-fold framework to retrieve AGB at 30 m spatial resolution for the whole of Colombia. The algorithm was trained using forest inventory plots and validated at plot (0.35 ha) and PPM level (1 ha). The accuracy assessment found coefficients of determination (R2) of 0.68 and 0.61, and relative root mean square errors (Rel. RMSE) of 49% and 34% at plot and at PPM level, respectively. The results showed that the average AGB for the country was 118.1 t ha−1 (45.6 t ha−1 for Caribe, 75.4 t ha−1 Andes, 122.5 t ha−1 Pacifico, 32.7 t ha−1 Orinoquia, and 200.5 t ha−1 for the Amazonia, regionally), and that the total carbon stocks for the country were 6.7 Pg C for the period 2015–2017.

Highlights

  • Forest cover area in Colombia amounts to 52% of the national territory

  • The dataset was stratified by three aboveground biomass (AGB) levels, and randomly sampled into the folds to ensure that all folds have similar probability distribution functions of AGB

  • The results of the Jackknife analyses based on root mean square error (RMSE), bias, and coefficient of determination (R2) indicated that the combination of ALOS-2 PALSAR-2 and Landsat variables showed the best balance between the lowest possible bias and RMSE, and the highest possible R2 (Figure 4)

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Summary

Introduction

Forest cover area in Colombia amounts to 52% of the national territory. Forests are one of the most important resources in terms of goods and ecosystem services, which are vital for the sustainable development of the country. The government of Colombia signed the Joint Declaration of Intent in 2015 with the governments of Norway, Germany and the United Kingdom to cooperate in reducing emissions from deforestation and forest degradation (i.e. REDD+) and promoting sustainable development in Colombia. Colombia has strengthened its monitoring capabilities by developing the National Forest Information System, the Forest and Carbon Monitoring System, and the National Forest Inventory (NFI). Those have allowed to improve the monitoring of forest dynamics associated to disturbances leading to greenhouse gas emissions, and to promote the conservation and sustainable management of forests

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