Técnicas no destructivas para la estimación de la biomasa forestal aérea

Abstract

Anthropic consumption of hydrocarbons and deforestation have increased the concentration of CO2 in the atmosphere, accelerating global climate change. However, in the face of this problem, vegetation presents itself as a natural barrier to climate change mitigation, since it stores large amounts of carbon in its aboveground forest biomass (AFB). Therefore, it is necessary to estimate the AFB through accurate and non-destructive techniques with nature. In this context, this work aimed to describe and compare non-destructive techniques: allometric models and remote sensing to estimate the AFB. For this purpose, a systematic review of the existing literature on these techniques was carried out. Allometric models are the most accurate non-destructive technique for estimating the AFB. This technique is based on regression models between the AFB and the dasometric variables of the vegetation. On the other hand, the estimation of the AFB by remote sensing is based on the application of satellite images and LIDAR. The use of satellite images is aimed at obtaining vegetation indices, which are used to estimate the AFB by simple linear regression methods, multiple regression, random forest, support vector regression, among others. On the other hand, the use of LIDAR images has the purpose of obtaining the three-dimensional structure of the forest, which is used to estimate the BFA using the regression methods mentioned above. It is concluded that, although allometric models are the most accurate non-destructive technique for estimating the AFB, the application of remote sensing has greater advantages in terms of temporal and spatial resolution and free availability for biomass studies, depending on the platform used.