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

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.

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

“…The field of remote sensing is no exception [79]. Intelligent interpretation of remote sensing is crucial to study in many areas, including environmental monitoring, land resources [80], crop monitoring [81] and yield estimation, forest carbon sink estimation [82], and national defense security [83]. Intelligent remote sensing interpretation is also an important requirement for national strategic development [8].…”

Identification of Araucaria angustifolia trees in an urban forest fragment using UAV images and YOLOv7 structure

“…Additionally, computer vision techniques used to analyze images from remote sensors, particularly those based on deep learning, more speci cally Convolutional Neural Networks (CNNs), have shown their potential in recognizing tree species in forest regions (KNAUER et al, 2019;SANTOS et al, 2019). These techniques involve applying machine learning models capable of extracting characteristics from images and classifying objects, such as trees, according to their species (ALBUQUERQUE et al, 2022;WAGNER et al, 2022). Such techniques offer the advantage of processing images in real-time and with high precision, outperforming traditional methods based on manual or statistical analysis.…”

Active Actions in the Extraction of Urban Objects for Information Quality and Knowledge Recommendation with Machine Learning

“…Both structural and deterministic clues must be taken into account when constructing the solution. Up-to-date and accurate data are essential for municipalities, applied at the Smart Cities concept, that use disruptive technology to solve some specifics problems, in this case, to solve this problem, the solution is the use of satellite and UAV imagery [ 13 ] in combination with machine learning techniques [ 14 , 15 ].…”