Evaluation of Forestry Component Survival in Plots of the Program “Sembrando Vida” (Sowing Life) Using Drones

Abstract

Reforestation is one of the main actions undertaken to mitigate the effects of climate change. In Mexico, the Federal Government program “Sembrando Vida” (Sowing Life) is currently the most important reforestation effort. It aims to recoup forest cover and achieve food self-sufficiency through the establishment of agroforestry systems. The evaluation of tree survival in reforested areas helps to identify achievements and failures, as well as aspects of the program that require improvement. However, given the magnitude of this program, evaluation using traditional methodologies is labor-intensive and costly. In this context, drones equipped with high-resolution cameras are a promising tool. The objective of this study was to evaluate the feasibility of using drones to monitor tree survival in reforested areas. This study was conducted in 12 randomly chosen plots, benefited by the “Sembrando Vida” program, located on the Purépecha Plateau in the state of Michoacán, in central–western Mexico. Field surveys with GPS were conducted to record the total number of live and dead forest-tree seedlings. Simultaneously, high-resolution images were captured using a DJI Phantom 4 Pro drone equipped with an RGB camera for subsequent visual interpretation in a geographic information system to determine the status of each seedling and calculate the rates of survival. ANOVA was performed to compare the survival calculated using the drone images compared to that recorded in the field. No significant difference was found between survival estimated using the drone and that recorded directly in the field in any of the study plots, although the drone overestimated survival by an average of 6%, mostly due to the presence of dead seedlings that had already lost their foliage and were thus missed when scoring the RGB image. It is therefore concluded that the estimation of survival using drones is a reliable method. For future research, it is recommended to evaluate machine-learning algorithms in terms of detecting both living and dead trees in reforested sites. It is also recommended to use multispectral thermal cameras and LiDAR technology to broaden the knowledge of the different levels of vigor/stress present in the vegetation.