Potential of machine learning and WorldView-2 images for recognizing endangered and invasive species in the Atlantic Rainforest

Abstract

& Key message We found high accuracy classification (F measure = 95%, on cross-validation) of Araucaria angustifolia (Bertol.) Kuntze, an endangered native species, and Hovenia dulcis Thunb. an aggressive, invasive alien species in WorldView-2 multispectral images. In applying machine learning algorithms, the spectral attributes mainly related to the near-infrared band were the most important for the models. & Context It is difficult to classify tree species in tropical rainforests due to the high spectral response's diversity of existing species, as well as to adjust efficient machine learning techniques and orbital image resolution. & Aims To explore the spectral and textural response of an endangered species (A. angustifolia) and an invasive species (H. dulcis) in WorldView-2 multispectral images, testing its recognition capability by machine learning techniques. & Methods We used a WordView-2 (2016) image with 0.5-m spatial resolution. Then we manually clipped the canopy area of the two species in this image using two compositions: True color composition (R=660 nm, G=545 nm, B=480 nm) and near-infrared composition (NIR-2=950 nm, G=545 nm, B=480 nm). Thus, we applied spectral and textural descriptors (pyramid histogram of oriented gradients-PHOG and Edge Filter), which selects the most representative features of the dataset. Finally, we used artificial neural networks (ANN) and random forest (RF) for tree species classification. & Results The species classification was performed with high accuracy (F measure = 95%, on cross-validation), essentially for spectral attributes using the near-infrared composition. RF surpassed the ANN classification rates and also proved to be more stable and faster for training and testing.Handling Editor: Barry A. Gardiner Contribution of the co-authors Crisigiovanni E. L. designed the methods, performed the experiments, processed the data, analyzed the results, and wrote most of the manuscript. Figueiredo Filho A. idealized the article, provided the materials (satellite image), and formulated the research framework. Pesck V. A. contributed to the geoprocessing and remote sensing analysis and cooperated in the methodology's design. De Lima V. A. cooperated in the methodology design and performed the machine learning analyses and data processing.

Detection of the invasive tree species Hovenia dulcis Thumb. (Rhamnaceae) in an urban remnant of Atlantic Forest through Remote Sensing techniques combined with RGB images obtained by an unmanned aerial vehicle (UAV)

“…A small DJI Mini 2 drone was used to obtain RGB images, employing the Drone Harmony application for autonomous ight operations. A frontal overlap of 70% and a lateral overlap of 60% were used, with a ight speed of 7 m/s, following the methodology described by Lima et al, (2021). Image processing was done using the Open Drone Map software, generating georeferenced highresolution mosaics, which were subsequently analyzed and overlaid in QGIS 3.28.7 software.…”

“…During this period, the leaves of the invader exhibit a light green color, distinguishing them from native species (Fig. 2) and facilitating their cognitive recognition in high-resolution images (Crisigiovanni et al, 2021). After the initial hygrophytic phase, the invader's leaves darken, hindering identi cation within forested areas.…”

Construction and optimization of vending machine decision support system based on improved C4.5 decision tree

“…The computational complexity is reduced, and the efficiency of building decision trees is improved. In addition, there are many decision tree algorithms, such as the Rain Forest algorithm [ 23 ] and the fuzzy decision tree method [ 24 ]. These algorithms all improve C4.5 to a certain extent, and each has its advantages and limitations.…”

Assessing the Efficacy of Phenological Spectral Differences to Detect Invasive Alien Acacia dealbata Using Sentinel-2 Data in Southern Europe

“…IAP monitoring has been an objective widely addressed with remote sensing [28]. Among the multiple multispectral sources of information are Landsat [28][29][30], Pléiades [31,32], Worldview [33][34][35][36], Ikonos [37,38], and Sentinel [39][40][41][42]. Particularly, multispectral and hyperspectral data have been used to discriminate Acacia species in Hawaiian rainforests [19], in Mediterranean Dune Ecosystems [43,44], and in South Africa [40].…”