Automatic Extraction of Tree Crown for the Estimation of Biomass from UAV Imagery Using Neural Networks

Kolanuvada, Srinivasa Raju; Ilango, Kishore Kowtham

doi:10.1007/s12524-020-01242-0

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…The crown recognition of broad-leaved saplings may be affected by many factors, including the color difference between the crown and the background, the shape of the crown, the density of the crown, and the interference of complex background on ITC recognition. Recent studies have discussed the relationship between multiple data sources and enhancing ITC recognition capability [44,47,49], LiDAR point cloud analysis [39], multispectral analysis [41], convolutional neural network [46], and other methods that improve the effect of ITC recognition by collecting data such as RE and LiDAR to obtain more broad-leaved sapling features [24,39], using CNN and other algorithms to improve the recognition effect of crowns [9,45]. Therefore, in future studies, more data sources should be considered to extract potential features of tree crowns to further enhance the accuracy of ITC recognition.…”

Section: Future Studiesmentioning

confidence: 99%

“…Although palm crowns in the study area often overlap, with the support of large-scale multi-class samples collected, the detection accuracy of oil palm trees based on the deep convolutional neural network DCNN has reached 92-97% [48]. The tree crown in simple scenes can also be automatically extracted from UAV images using neural networks to estimate biomass [49] and tree species classification [50].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Future Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Among the sensors installed on UAVs, multispectral cameras have attracted experts' interest, especially in the field of precision agriculture (PA) due to their very high spatial and spectral resolution. In recent years, these images have been widely used in research to investigate the water stress of trees , pest contamination (Näsi et al 2015), separation of healthy and unhealthy trees (Garcia-Ruiz et al 2013) and(DadrasJavan et al 2019), classification of tree species (Franklin et al 2017), Evaluation of natural indicators such as leaf area index and amount of nitrogen content (Vega et al 2015) and (Caturegli et al 2016), identification and extraction of tree canopy (Kolanuvada and Ilango 2021). Most of the researches that had used UAV based multispectral imagery are mainly focused on the process of radiometric outputs to identify the disease, and little research has been done on the extraction of tree crowns using geometric analysis.…”

Section: Introductionmentioning

confidence: 99%

Improved Watershed Segmentation Algorithm for Tree Crowns Extraction From Multi-Spectral Uav-Based Aerial Images

Amlashi

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Due to many problems such as diseases and pests, low fertility, and dehydration, trees need immediate actions to be taken in time of need. Since they are an important source of fruit, food, and nutrients consumed by humans, keeping track of the trees in orchards is a crucial issue in recent years. Today, drones equipped with multispectral cameras are used in precision agriculture, especially for monitoring and controlling trees. For this cause, two citrus orchards in Iran with an area of 9.2 and 2.67 hectares and a resolution of 3.6 and 0.68 cm were selected for the study area. In this study, First, tree extraction was conducted using four algorithms namely Local maxima, Image binarization, valley following, and watershed segmentation, and a proposed method that is based on the improvement of the watershed algorithm. This method achieved an overall accuracy of 87% and 81% in the two study regions which was higher than common methods. Secondly, the effect of the number of spectral bands on the accuracy of tree extraction was investigated. As a result, by adding of Red-Edge and NIR bands, the accuracy increased by about 5% and 7%. Therefore, experts suggested using NIR and Red-Edge bands besides RGB bands.

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Deep convolutional neural networks and Swin transformer-based frameworks for individual date palm tree detection and mapping from large-scale UAV images

Gibril

Shafri

Shanableh

et al. 2022

Geocarto International

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Automatic Extraction of Tree Crown for the Estimation of Biomass from UAV Imagery Using Neural Networks

Cited by 9 publications

References 17 publications

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

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

Improved Watershed Segmentation Algorithm for Tree Crowns Extraction From Multi-Spectral Uav-Based Aerial Images

Deep convolutional neural networks and Swin transformer-based frameworks for individual date palm tree detection and mapping from large-scale UAV images

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