Mapping Key Indicators of Forest Restoration in the Amazon Using a Low-Cost Drone and Artificial Intelligence

Rafael Walter Albuquerque,Lucas Pedrosa Soares,Manuel Eduardo Ferreira,Søren Ingvor Olsen,Julio Ricardo Caetano Tymus,Carlos Henrique Grohmann,Luciana Spinelli Araujo,Luiz Eduardo Vicente,Cintia Palheta Balieiro,Daniel Luis Mascia Vieira,Marcelo Hiromiti Matsumoto

doi:10.3390/rs14040830

Abstract

Monitoring the vegetation structure and species composition of forest restoration (FR) in the Brazilian Amazon is critical to ensuring its long-term benefits. Since remotely piloted aircrafts (RPAs) associated with deep learning (DL) are becoming powerful tools for vegetation monitoring, this study aims to use DL to automatically map individual crowns of Vismia (low resilience recovery indicator), Cecropia (fast recovery indicator), and trees in general (this study refers to individual crowns of all trees regardless of species as All Trees). Since All Trees can be accurately mapped, this study also aims to propose a tree crown heterogeneity index (TCHI), which estimates species diversity based on: the heterogeneity attributes/parameters of the RPA image inside the All Trees results; and the Shannon index measured by traditional fieldwork. Regarding the DL methods, this work evaluated the accuracy of the detection of individual objects, the quality of the delineation outlines and the area distribution. Except for Vismia delineation (IoU = 0.2), DL results presented accurate values in general, as F1 and IoU were always greater than 0.7 and 0.55, respectively, while Cecropia presented the most accurate results: F1 = 0.85 and IoU = 0.77. Since All Trees results were accurate, the TCHI was obtained through regression analysis between the canopy height model (CHM) heterogeneity attributes and the field plot data. Although TCHI presented robust parameters, such as p-value < 0.05, its results are considered preliminary because more data are needed to include different FR situations. Thus, the results of this work show that low-cost RPA has great potential for monitoring FR quality in the Amazon, because Vismia, Cecropia, and All Trees can be automatically mapped. Moreover, the TCHI preliminary results showed high potential in estimating species diversity. Future studies must assess domain adaptation methods for the DL results and different FR situations to improve the TCHI range of action.

Highlights

Forest restoration (FR) projects [1] aim for benefits, such as the provision of ecosystem services [2] and social well-being [3]
Vismia area distribution was accurate, which means that its contour errors were somehow compensated, for instance, by projecting a shape part on the left where it should be on the right
Mask R-CNN is capable of detecting the crowns of Vismia and Cecropia, as well as the crowns of all kinds of trees, regardless of species in low-cost remotely piloted aircrafts (RPAs) images

Summary

Introduction

Forest restoration (FR) projects [1] aim for benefits, such as the provision of ecosystem services [2] and social well-being [3]. FR monitoring is a must to ensure a proper provision of such benefits [4,5,6,7] When it comes to the Brazilian Amazon, which is a threatened biome [8], and has increased deforestation in the last years [9], the success of FR projects is considerably relevant to ensure the forest structure and species composition that mitigate climate changes [10]. Canopy dominance indicates low resilience; monitoring these two species is significantly relevant to FR in the Amazon [11,15]. Active restoration with high species diversity presents a more heterogeneous canopy in general when compared to the Cecropia and Vismia natural regeneration routes due to a greater species diversity [14]

Objectives

Methods

Results

Discussion

Conclusion