Detectability of the Critically Endangered Araucaria angustifolia Tree Using Worldview-2 Images, Google Earth Engine and UAV-LiDAR

Abstract

The Brazilian Atlantic Forest is a global biodiversity hotspot and has been extensively mapped using satellite remote sensing. However, past mapping focused on overall forest cover without consideration of keystone plant resources such as Araucaria angustifolia. A. angustifolia is a critically endangered coniferous tree that is essential for supporting overall biodiversity in the Atlantic Forest. A. angustifolia’s distribution has declined dramatically because of overexploitation and land-use changes. Accurate detection and rapid assessments of the distribution and abundance of this species are urgently needed. We compared two approaches for mapping Araucaria angustifolia across two scales (stand vs. individual tree) at three study sites in Brazil. The first approach used Worldview-2 images and Random Forest in Google Earth Engine to detect A. angustifolia at the stand level, with an accuracy of >90% across all three study sites. The second approach relied on object identification using UAV-LiDAR and successfully mapped individual trees (producer’s/user’s accuracy = 94%/64%) at one study site. Both approaches can be employed in tandem to map remaining stands and to determine the exact location of A. angustifolia trees. Each approach has its own strengths and weaknesses, and we discuss their adoptability by managers to inform conservation of A. angustifolia.

A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species

“…In addition to the presently utilized multispectral sensors, previous research has indicated that hyperspectral sensors [55] and LiDAR [56] could provide supplementary spatial information of predominately structural environmental conditions and could be successfully integrated with machine learning classification methods. UASs have also ensured high prediction accuracy in related studies for nature conservation, such as flora species population count and coverage area prediction [57,58]. Although machine learning classification methods, such as those evaluated in this study, are generally resistant to overfitting which might occur in the process, overfitting still might occur due to exaggerated pruning [59].…”

An Ultra-Resolution Features Extraction Suite for Community-Level Vegetation Differentiation and Mapping at a Sub-Meter Resolution

“…Despite the free accessibility of high-resolution satellite images such as those from Sentinel-2 and Landsat 8, a number of ecological studies have utilized ultra-resolution and very high-resolution satellite images. Review of these studies indicates that ultraresolution and very high-resolution satellite images have been adapted mainly for the detection and mapping of small patches of vegetation such as endangered plants and invasive species [101][102][103][104] or for the differentiation and mapping of mixed vegetation in a heterogeneous environment [105][106][107]. The vegetation mixedness is a matter of the spatial resolution of the imagery.…”

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

“…Therefore, the accurate diagnosis and continuous monitoring of A. angustifolia in forest remnants are crucial for environmental preservation. With technological advances, Unmanned Aerial Vehicles (UAVs), or drones, have increasingly become versatile and accessible tools for research in this eld, enabling the mapping and monitoring of trees and forests (SAAD et al, 2021;ECKE et al, 2022;CUNHA NETO et al, 2023). 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).…”