Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery

Kuzmin, Anton; Korhonen, Lauri; Manninen, Terhikki; Maltamo, Matti

doi:10.5721/eujrs20164914

Cited by 24 publications

(27 citation statements)

References 50 publications

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“…On the basis of visual examination of various trials, the scale parameter was set to three; the smoothness and compactness ratio was set to 0.6:0.4 (respectively); and the shape and color parameter ratio was set to 0.1:0.9 (respectively) [6,29,105].…”

Section: Overhead Data Acquisition and Species Classificationmentioning

confidence: 99%

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

et al. 2017

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Abstract:Most recent studies relating to the classification of vegetation species on the individual level use cutting-edge sensors and follow a data-driven approach, aimed at maximizing classification accuracy within a relatively small allocated area of optimal conditions. However, this approach does not incorporate cost-benefit considerations or the ability of applying the chosen methodology for applied mapping over larger areas with higher natural heterogeneity. In this study, we present a phenology-based cost-effective approach for optimizing the number and timing of unmanned aerial vehicle (UAV) imagery acquisition, based on a priori near-surface observations. A ground-placed camera was used in order to generate annual time series of nine spectral indices and three color conversions (red, green and blue to hue, saturation and value) in four different East Mediterranean sites that represent different environmental conditions. After outliers' removal, the time series dataset represented 1852 individuals of 12 common vegetation species and annual herbaceous patches. A feature selection process was used for identifying the optimal dates for species classification in every site. The feature selection can be designed for various objectives, e.g., optimization of overall classification, discrimination between two species, or discrimination of one species from all others. In order to evaluate the a priori findings, a UAV was flown for acquiring five overhead multiband orthomosaics (five bands in the visible-near infrared range based on the five optimal dates identified in the feature selection of the near-surface time series of the previous year. An object-based classification methodology was used for the discrimination of 976 individuals of nine species and annual herbaceous patches in the UAV imagery, and resulted in an average overall accuracy of 85% and an average Kappa coefficient of 0.82. This cost-effective approach has high potential for detailed vegetation mapping, regarding the accessibility of UAV-produced time series, compared to hyper-spectral imagery with high spatial resolution which is more expensive and involves great difficulties in implementation over large areas.

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Section: Overhead Data Acquisition and Species Classificationmentioning

confidence: 99%

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

et al. 2017

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“…As wild pistachio trees with large crown area have been observed in the study area, it seems necessary to reduce color/shape parameter to represent variation of height distribution better. In addition, larger amount of compactness/smoothness parameter (i.e., 0.9 in the present study) makes delineation of tree crowns possible more efficiently (Kuzmin et al, 2016;Piazza et al, 2016;Trang et al, 2016). However, more detailed investigations reveal the optimized amounts of these parameters to recognize wild pistachios and wild almonds due to different canopy structure of these two species.…”

Section: Resultsmentioning

confidence: 67%

“…It takes not only the spectral information, but also form and texture into account. The classification procedure begins with critical step of defining groups of neighboring pixels into significant areas which are called segments (Blaschke et al, 2000;Kuzmin et al, 2016). In this method, single pixels are not classified separately but homogeneous pixels representing objects of interest are extracted.…”

Section: Image Classificationmentioning

confidence: 99%

“…In contrary, relatively few investigations have studied the applicability of orthoimages taken by UAVs for mapping biophysical characteristics of trees and shrubs at single-tree levels. Kuzmin et al (2016) used UAV imagery to recognize tree species in a mixed boreal forest with overall accuracy of 82%. In addition, UAVs are very interesting instruments that hold great potential for mapping individual plants in open woodlands.…”

Section: Introductionmentioning

confidence: 99%

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Woodland Mapping at Single-Tree Levels Using Object-Oriented Classification of Unmanned Aerial Vehicle (Uav) Images

Chenari

Erfanifard

Dehghani

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Remotely sensed datasets offer a reliable means to precisely estimate biophysical characteristics of individual species sparsely distributed in open woodlands. Moreover, object-oriented classification has exhibited significant advantages over different classification methods for delineation of tree crowns and recognition of species in various types of ecosystems. However, it still is unclear if this widely-used classification method can have its advantages on unmanned aerial vehicle (UAV) digital images for mapping vegetation cover at single-tree levels. In this study, UAV orthoimagery was classified using object-oriented classification method for mapping a part of wild pistachio nature reserve in Zagros open woodlands, Fars Province, Iran. This research focused on recognizing two main species of the study area (i.e., wild pistachio and wild almond) and estimating their mean crown area. The orthoimage of study area was consisted of 1,076 images with spatial resolution of 3.47 cm which was georeferenced using 12 ground control points (RMSE=8 cm) gathered by real-time kinematic (RTK) method. The results showed that the UAV orthoimagery classified by object-oriented method efficiently estimated mean crown area of wild pistachios (52.09±24.67 m 2 ) and wild almonds (3.97±1.69 m 2 ) with no significant difference with their observed values (α=0.05). In addition, the results showed that wild pistachios (accuracy of 0.90 and precision of 0.92) and wild almonds (accuracy of 0.90 and precision of 0.89) were well recognized by image segmentation. In general, we concluded that UAV orthoimagery can efficiently produce precise biophysical data of vegetation stands at single-tree levels, which therefore is suitable for assessment and monitoring open woodlands.

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“…Quantitative classification methods applied to the digital imagery have included pixelbased unsupervised clustering (Gini et al 2014), supervised classification using maximum likelihood or linear discriminant probability-based functions (Kuzmin et al 2016), objectbased image analysis (OBIA) with non-parametric classification , and a wide range of combinations and permutations of these different approaches (Singh et al 2015). Successful quantitative image classification requires careful attention to image radiometric concerns during data acquisition (Von Bueren et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Pixel- and object-based multispectral classification of forest tree species from small unmanned aerial vehicles

Franklin

2018

J. Unmanned Veh. Sys.

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Forest inventory, monitoring, and assessment requires accurate tree species identification and mapping. Recent experiences with multispectral data from small fixed-wing and rotary blade unmanned aerial vehicles (UAVs) suggest a role for this technology in the emerging paradigm of enhanced forest inventory (EFI). In this paper, pixel-based and object-based image analysis (OBIA) methods were compared in UAV-based tree species classification of nine commercial tree species in mature eastern Ontario mixedwood forests. Unsupervised clustering and supervised classification of tree crown pixels yielded approximately 50%-60% classification accuracy overall; OBIA with image segmentation to delineate tree crowns and machine learning yielded up to 80% classification accuracy overall. Spectral response patterns and tree crown shape and geometric differences were interpreted in context of their ability to separate tree species of interest with these classification methods. Accuracy assessment was based on field-based forest inventory tree species identification. The paper provides a brief summary of future research issues that will influence the growth of this geomatics innovation in forest tree species classification and forest inventory.

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Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery

Cited by 24 publications

References 50 publications

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

Woodland Mapping at Single-Tree Levels Using Object-Oriented Classification of Unmanned Aerial Vehicle (Uav) Images

Pixel- and object-based multispectral classification of forest tree species from small unmanned aerial vehicles

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