Tree Recognition on the Plantation Using UAV Images with Ultrahigh Spatial Resolution in a Complex Environment

Abstract

The survival rate of seedlings is a decisive factor of afforestation assessment. Generally, ground checking is more accurate than any other methods. However, the survival rate of seedlings can be higher in the growing season, and this can be estimated in a larger area at a relatively lower cost by extracting the tree crown from the unmanned aerial vehicle (UAV) images, which provides an opportunity for monitoring afforestation in an extensive area. At present, studies on extracting individual tree crowns under the complex ground vegetation conditions are limited. Based on the afforestation images obtained by airborne consumer-grade cameras in central China, this study proposes a method of extracting and fusing multiple radii morphological features to obtain the potential crown. A random forest (RF) was used to identify the regions extracted from the images, and then the recognized crown regions were fused selectively according to the distance. A low-cost individual crown recognition framework was constructed for rapid checking of planted trees. The method was tested in two afforestation areas of 5950 m2 and 5840 m2, with a population of 2418 trees (Koelreuteria) in total. Due to the complex terrain of the sample plot, high weed coverage, the crown width of trees, and spacing of saplings vary greatly, which increases both the difficulty and complexity of crown extraction. Nevertheless, recall and F-score of the proposed method reached 93.29%, 91.22%, and 92.24% precisions, respectively, and 2212 trees were correctly recognized and located. The results show that the proposed method is robust to the change of brightness and to splitting up of a multi-directional tree crown, and is an automatic solution for afforestation verification.

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

“…Recently, these indices have been used as input data for prediction and classification purposes alike, the spectrum of tree canopies can be considered a distinctive feature of the specific vegetation, thus making VIs useful for both vegetation identification in aerial photographs and for tree classification (Abdollahnejad and Panagiotidis, 2020;Imangholiloo et al, 2020;Yang and Kan, 2020;Guo et al, 2021;Arevalo-Ramirez et al, 2022;Cabrera-Ariza et al, 2022;Shovon et al, 2022). Photosynthetic pigments have a distinctive reflectance in some bands, thus the prediction of chlorophyll content and other pigments is suitable with the appropriate VI (Watt et al, 2020;Kopackova-Strnadováet al, 2021;Lou et al, 2021;Lu et al, 2021;Raddi et al, 2021;Raj et al, 2021;Zhuo et al, 2022).…”

“…Its importance relies on the fact that tree recognition is a key factor when evaluating biodiversity evolution due to external factors such as climate change and natural disasters (Hologa et al, 2021). Another important application for tree identification is to evaluate the survival rate of seedlings, which is vital to assess the efforts of afforestation, identifying seedlings across several seasons is a difficult task, given the fact that each individual tree crown needs to be identified in a complex vegetation environment (Guo et al, 2021). Forest inventory and mapping are crucial for forest managers, to ensure the preservation of the different habitats (Neuville et al, 2021).…”

“…Tree structure identification is related to the measurement of parameters that help to characterize both individual trees and forests alike. The most common parameters used to characterize tree structure are tree height, diameter at breast height, basal area, total stem volume, crown cover, crown height, and crown area (Lin and Herold, 2016;Shin et al, 2018;Fraser and Congalton, 2021b;Guo et al, 2021;Hologa et al, 2021;Neuville et al, 2021;Terryn et al, 2022). These parameters are strong indicators of forest vigor and forest health when facing stress due to climate change (Fraser and Congalton, 2021b).…”

“…Another task that can be accomplished using ML methods is tree classification, which is important for forest inventory and mapping. The most common classifiers are random forests, support vector machines, and artificial neural networks (Feng et al, 2020;Guo et al, 2021;Hologa et al, 2021). Another use for classifiers in forestry health assessment is the identification of live trees and snags, the ratio between these two is an important parameter to evaluate forest health (Shovon et al, 2022).…”

Weed Detection in Maize Fields by UAV Images Based on Crop Row Preprocessing and Improved YOLOv4

“…The application of UAV technology in agriculture has developed rapidly [6][7][8] as it provides unique features that humankind cannot implement on the ground [9], thus facilitating the detection of weeds in maize fields. UAVs can obtain complete field images with high resolution [10,11]. The advantage of UAV images is that they have a wide collection area and can quickly obtain complete field data for statistics and analysis [12].…”