Automatic Tree Crown Extraction from UAS Multispectral Imagery for the Detection of Bark Beetle Disturbance in Mixed Forests

Minařík, Robert; Langhammer, Jakub; Lendzioch, Theodora

doi:10.3390/rs12244081

Cited by 32 publications

(49 citation statements)

References 86 publications

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“…However, a small proportion of ground objects that are not classified as tea in the ground multispectral images can affect the final quality of the parameter monitoring results. In order to reduce the influence of soil and shadow noise and improve the accuracy of the final quality parameter monitoring results, this study used the EXG index to effectively distinguish the background of green vegetation and soil for image enhancement [31,[79][80][81][82]. The Ostu method was used for image segmentation [32,[83][84][85][86] to enable the effective extraction of the tea areas from the original image that contains other features.…”

Section: Ground Multispectral Imagesmentioning

confidence: 99%

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Chen

Zhao

et al. 2021

Remote Sensing

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The production of high-quality tea by Camellia sinensis (L.) O. Ktze is the goal pursued by both producers and consumers. Rapid, nondestructive, and low-cost monitoring methods for monitoring tea quality could improve the tea quality and the economic benefits associated with tea. This research explored the possibility of monitoring tea leaf quality from multi-spectral images. Threshold segmentation and manual sampling methods were used to eliminate the image background, after which the spectral features were constructed. Based on this, the texture features of the multi-spectral images of the tea canopy were extracted. Three machine learning methods, partial least squares regression, support vector machine regression, and random forest regression (RFR), were used to construct and train multiple monitoring models. Further, the four key quality parameters of tea polyphenols, total sugars, free amino acids, and caffeine content were estimated using these models. Finally, the effects of automatic and manual image background removal methods, different regression methods, and texture features on the model accuracies were compared. The results showed that the spectral characteristics of the canopy of fresh tea leaves were significantly correlated with the tea quality parameters (r ≥ 0.462). Among the sampling methods, the EXG_Ostu sampling method was best for prediction, whereas, among the models, RFR was the best fitted modeling algorithm for three of four quality parameters. The R2 and root-mean-square error values of the built model were 0.85 and 0.16, respectively. In addition, the texture features extracted from the canopy image improved the prediction accuracy of most models. This research confirms the modeling application of a combination of multi-spectral images and chemometrics, as a low-cost, fast, reliable, and nondestructive quality control method, which can effectively monitor the quality of fresh tea leaves. This provides a scientific reference for the research and development of portable tea quality monitoring equipment that has general applicability in the future.

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Section: Ground Multispectral Imagesmentioning

confidence: 99%

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Chen

Zhao

et al. 2021

Remote Sensing

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“…PPCs were used in two studies. Minařík et al [100] used PPCs collected through a UAV to identify and delineate canopies in an urban mixed forest. They analyzed the effect of point density on individual tree delineation and found an OA of 82% in crown detection from a PPC.…”

Section: Aerial Imagerymentioning

confidence: 99%

“…Through PPCs generated from UAV flights, metrics, such as tree height and crown data, can be analyzed [99]. Minařík et al [100] found that, when processing PPCs, point density is more important than the selection of a given processing method, recommending a range between 10 and 85 points/m 2 for tree canopy identification and delineation in urban environments, while Birdal et al [99] suggested at least 40 points/m 2 for proper height characterization.…”

Section: Aerial Imagerymentioning

confidence: 99%

“…Meanwhile, although hyperspectral information from aerial images represent a valuable resource for species identification through spectral signatures and shape study, several studies showed that two individuals of the same species may have different spectral and structural characteristics due to the growth form and phenology [93,102]. Another limitation regarding the use of aerial images in the identification of trees in the city is the heterogeneity of the landscape where elements, such as facades, roofs, and others can be mistaken for trees or partly hide them [5], in addition to objects adjacent to the trees that can lead to misclassifications when using PPCs [100,103].…”

Section: Aerial Imagerymentioning

confidence: 99%

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Remotely Sensed Tree Characterization in Urban Areas: A Review

et al. 2021

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Urban trees and forests provide multiple ecosystem services (ES), including temperature regulation, carbon sequestration, and biodiversity. Interest in ES has increased amongst policymakers, scientists, and citizens given the extent and growth of urbanized areas globally. However, the methods and techniques used to properly assess biodiversity and ES provided by vegetation in urban environments, at large scales, are insufficient. Individual tree identification and characterization are some of the most critical issues used to evaluate urban biodiversity and ES, given the complex spatial distribution of vegetation in urban areas and the scarcity or complete lack of systematized urban tree inventories at large scales, e.g., at the regional or national levels. This often limits our knowledge on their contributions toward shaping biodiversity and ES in urban areas worldwide. This paper provides an analysis of the state-of-the-art studies and was carried out based on a systematic review of 48 scientific papers published during the last five years (2016–2020), related to urban tree and greenery characterization, remote sensing techniques for tree identification, processing methods, and data analysis to classify and segment trees. In particular, we focused on urban tree and forest characterization using remotely sensed data and identified frontiers in scientific knowledge that may be expanded with new developments in the near future. We found advantages and limitations associated with both data sources and processing methods, from which we drew recommendations for further development of tree inventory and characterization in urban forestry science. Finally, a critical discussion on the current state of the methods, as well as on the challenges and directions for future research, is presented.

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“…Remote sensing is a proven technology for pest monitoring and damage assessment over large areas. Many studies have used remotely sensed data with various spatial resolutions to detect and assess forest insect infestations, based on the analysis of spectral responses of trees to the biophysical phenomena such as water stress caused by insect attack [13][14][15][16][17][18][19]. Over the years, a number of spectral indices (such as the Normalised Difference Vegetation Index or NDVI, the Normalised Burn Ratio or NBR, the Moisture Stress Index or MSI, the Enhanced Wetness Difference Index or EWDI, and the Leave Area Index or LAI) have been used for the detection and differentiation of insects and diseases using multispectral and hyperspectral data with different spatial resolutions.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Poplar Looper (Apocheima cinerarius Erschoff) Infestation on Euphrates (Populus euphratica) Using Time-Series MODIS NDVI Data Based on the Wavelet Transform and Discriminant Analysis

et al. 2021

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Poplar looper (Apocheima cinerarius Erschoff) is a destructive insect infesting Euphrates or desert poplars (Populus euphratica) in Xinjiang, China. Since the late 1950s, it has been plaguing desert poplars in the Tarim Basin in Xinjiang and caused widespread damages. This paper presents an approach to the detection of poplar looper infestations on desert poplars and the assessment of the severity of the infestations using time-series MODIS NDVI data via the wavelet transform and discriminant analysis, using the middle and lower reaches of the Yerqiang River as a case study. We first applied the wavelet transform to the NDVI time series data in the period of 2009–2014 for the study area, which decomposed the data into a representation that shows detailed NDVI changes and trends as a function of time. This representation captures both intra- and inter-annual changes in the data, some of which characterise transient events. The decomposed components were then used to filter out details of the changes to create a smoothed NDVI time series that represent the phenology of healthy desert poplars. Next the subset of the original NDVI time series spanning the time period when the pest was active was extracted and added to the smoothed time series to generate a blended time series. The wavelet transform was applied again to decompose the blended time series to enhance and identify the changes in the data that may represent the signals of the pest infestations. Based on the amplitude of the enhanced pest infestation signals, a predictive model was developed via discriminant analysis to detect the pest infestation and assess its severity. The predictive model achieved a severity classification accuracy of 91.7% and 94.37% accuracy in detecting the time of the outbreak. The methodology presented in this paper provides a fast, precise, and practical method for monitoring pest outbreak in dense desert poplar forests, which can be used to support the surveillance and control of poplar looper infestations on desert poplars. It is of great significance to the conservation of the desert ecological environment.

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Automatic Tree Crown Extraction from UAS Multispectral Imagery for the Detection of Bark Beetle Disturbance in Mixed Forests

Cited by 32 publications

References 86 publications

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Remotely Sensed Tree Characterization in Urban Areas: A Review

Assessment of Poplar Looper (Apocheima cinerarius Erschoff) Infestation on Euphrates (Populus euphratica) Using Time-Series MODIS NDVI Data Based on the Wavelet Transform and Discriminant Analysis

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