Determination and accuracy analysis of individual tree crown parameters using UAV based imagery and OBIA techniques

Yurtseven, Hüseyin; Akgül, Mustafa; Çoban, Süleyman; Gülci, Sercan

doi:10.1016/j.measurement.2019.05.092

Cited by 43 publications

(28 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Compared to ULS, the use of SfM point clouds resulted in a threefold increase in the RMSE (0.15 vs. 0.48 m) and RMSE% (5.91 vs. 18.5%). Previous research, that has used SfM point clouds for the measurement of tree heights, demonstrates a wide range of precision between studies, with RMSE% ranging from 1.89 to 19.4% and the coefficient of determination ranging from 0.21 to 0.99 [59,60,[62][63][64][65]69,71,[74][75][76][77][78]80,[83][84][85][86][87][88][109][110][111]. Although our overall results were at the less precise end of the RMSE% range, this was attributable to the inclusion of small trees within the study, which resulted in increases in the percentage error.…”

Section: Discussionmentioning

confidence: 99%

“…The use of photogrammetry and SfM for modelling tree attributes in forestry has been widely researched in recent years. Studies using SfM have covered a diverse range of forest environments including coniferous plantations [59][60][61][62][63][64][65], eucalyptus plantations [63,66], temperate coniferous forests [67][68][69][70][71][72], temperate deciduous forests [73][74][75][76][77][78], boreal forests [79][80][81], tropical rainforests [82], urban trees [83,84] and palm plantations [85,86]. Using SfM data, height has been accurately predicted for both large [60,71,87] and smaller trees [59,63,88].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials

et al. 2020

View full text Add to dashboard Cite

The measurement of forestry trials is a costly and time-consuming process. Over the past few years, unmanned aerial vehicles (UAVs) have provided some significant developments that could improve cost and time efficiencies. However, little research has examined the accuracies of these technologies for measuring young trees. This study compared the data captured by a UAV laser scanning system (ULS), and UAV structure from motion photogrammetry (SfM), with traditional field-measured heights in a series of forestry trials in the central North Island of New Zealand. Data were captured from UAVs, and then processed into point clouds, from which heights were derived and compared to field measurements. The results show that predictions from both ULS and SfM were very strongly correlated to tree heights (R2 = 0.99, RMSE = 5.91%, and R2 = 0.94, RMSE = 18.5%, respectively) but that the height underprediction was markedly lower for ULS than SfM (Mean Bias Error = 0.05 vs. 0.38 m). Integration of a ULS DTM to the SfM made a minor improvement in precision (R2 = 0.95, RMSE = 16.5%). Through plotting error against tree height, we identified a minimum threshold of 1 m, under which the accuracy of height measurements using ULS and SfM significantly declines. Our results show that SfM and ULS data collected from UAV remote sensing can be used to accurately measure height in young forestry trials. It is hoped that this study will give foresters and tree breeders the confidence to start to operationalise this technology for monitoring trials.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The segmentation step plays a crucial role in ITD-based classifications, and needs to be assessed as per the classification results. A qualitative and quantitative assessment based on the works of Persello et al and Yurtseven et al [42,43] was applied, similar to the analysis proposed in Belcore et al [44].…”

Section: Species Classificationmentioning

confidence: 99%

Mapping Riparian Habitats of Natura 2000 Network (91E0*, 3240) at Individual Tree Level Using UAV Multi-Temporal and Multi-Spectral Data

et al. 2021

View full text Add to dashboard Cite

Riparian habitats provide a series of ecological services vital for the balance of the environment, and are niches and resources for a wide variety of species. Monitoring riparian environments at the intra-habitat level is crucial for assessing and preserving their conservation status, although it is challenging due to their landscape complexity. Unmanned aerial vehicles (UAV) and multi-spectral optical sensors can be used for very high resolution (VHR) monitoring in terms of spectral, spatial, and temporal resolutions. In this contribution, the vegetation species of the riparian habitat (91E0*, 3240 of Natura 2000 network) of North-West Italy were mapped at individual tree (ITD) level using machine learning and a multi-temporal phenology-based approach. Three UAV flights were conducted at the phenological-relevant time of the year (epochs). The data were analyzed using a structure from motion (SfM) approach. The resulting orthomosaics were segmented and classified using a random forest (RF) algorithm. The training dataset was composed of field-collected data, and was oversampled to reduce the effects of unbalancing and size. Three-hundred features were computed considering spectral, textural, and geometric information. Finally, the RF model was cross-validated (leave-one-out). This model was applied to eight scenarios that differed in temporal resolution to assess the role of multi-temporality over the UAV’s VHR optical data. Results showed better performances in multi-epoch phenology-based classification than single-epochs ones, with 0.71 overall accuracy compared to 0.61. Some classes, such as Pinus sylvestris and Betula pendula, are remarkably influenced by the phenology-based multi-temporality: the F1-score increased by 0.3 points by considering three epochs instead of two.

show abstract

“…In recent years, OBIA techniques have reached high levels of automation and adaptability to ultra-high spatial resolution images. Moreover, the use of orthomosaic and DSMs as inputs has allowed to address complicated agronomical studies, i.e., the efficient identification and characterization of individual trees of woody crops, such as olive trees [38,39] and vines [9,22,40], classification of vegetation types [41], plant breeding program applications [42], and plant count estimation [43]. In addition, OBIA techniques using UAV imagery-based geomatic products have enabled the discrimination of weeds and crops in the early vegetative stage between [6] and within crop rows [3,44], and in tilled soils of vineyards without cover crops [22], which makes OBIA one of the most useful methodologies in complex scenarios with spectral similarity [36,45,46].…”

Section: Introductionmentioning

confidence: 99%

Mapping Cynodon Dactylon Infesting Cover Crops with an Automatic Decision Tree-OBIA Procedure and UAV Imagery for Precision Viticulture

et al. 2019

View full text Add to dashboard Cite

The establishment and management of cover crops are common practices widely used in irrigated viticulture around the world, as they bring great benefits not only to protect and improve the soil, but also to control vine vigor and improve the yield quality, among others. However, these benefits are often reduced when cover crops are infested by Cynodon dactylon (bermudagrass), which impacts crop production due to its competition for water and nutrients and causes important economic losses for the winegrowers. Therefore, the discrimination of Cynodon dactylon in cover crops would enable site-specific control to be applied and thus drastically mitigate damage to the vineyard. In this context, this research proposes a novel, automatic and robust image analysis algorithm for the quick and accurate mapping of Cynodon dactylon growing in vineyard cover crops. The algorithm was developed using aerial images taken with an Unmanned Aerial Vehicle (UAV) and combined decision tree (DT) and object-based image analysis (OBIA) approaches. The relevance of this work consisted in dealing with the constraint caused by the spectral similarity of these complex scenarios formed by vines, cover crops, Cynodon dactylon, and bare soil. The incorporation of height information from the Digital Surface Model and several features selected by machine learning tools in the DT-OBIA algorithm solved this spectral similarity limitation and allowed the precise design of Cynodon dactylon maps. Another contribution of this work is the short time needed to apply the full process from UAV flights to image analysis, which can enable useful maps to be created on demand (within two days of the farmer´s request) and is thus timely for controlling Cynodon dactylon in the herbicide application window. Therefore, this combination of UAV imagery and a DT-OBIA algorithm would allow winegrowers to apply site-specific control of Cynodon dactylon and maintain cover crop-based management systems and their consequent benefits in the vineyards, and also comply with the European legal framework for the sustainable use of agricultural inputs and implementation of integrated crop management. efficient use of farm inputs (e.g., pesticides, fertilizers, water, labor, fuel, etc.) and, consequently, reducing potential environmental impacts caused by the over-application of inputs [1]. Moreover, PV strategies enable farmers to fulfill decision making with regard to the European Union's (EU) Directives included in the Common Agricultural Policy concerning both the digitizing of agriculture and the sustainable use of agricultural inputs, which foster the development of alternative strategies that limit or optimize their usage. PV strategy implementation involves identifying the intra-and inter-crop-field spatial variability and the causes that determine such variability, as well as designing optimal site-specific management strategies accordingly [2]. One of the most innovative technologies that can be employed to quantify this variability is the use of Unmanned Aerial Vehic...

show abstract

Determination and accuracy analysis of individual tree crown parameters using UAV based imagery and OBIA techniques

Cited by 43 publications

References 78 publications

An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials

An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials

Mapping Riparian Habitats of Natura 2000 Network (91E0*, 3240) at Individual Tree Level Using UAV Multi-Temporal and Multi-Spectral Data

Mapping Cynodon Dactylon Infesting Cover Crops with an Automatic Decision Tree-OBIA Procedure and UAV Imagery for Precision Viticulture

Contact Info

Product

Resources

About