Assessment of Invasive and Weed Species by Hyperspectral Imagery in Agrocenoses Ecosystem

Dmitriev, Pavel; Kozlovsky, Boris L.; Kupriushkin, Denis P.; Dmitrieva, Anastasia A.; Rajput, Vishnu D.; Chokheli, Vasiliy; Tarik, Ekaterina P.; Kapralova, Olga A.; Tokhtar, Valeriy K.; Minkina, Tatiana; Varduni, Tatiana V.

doi:10.3390/rs14102442

Cited by 12 publications

(5 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, irrespective of their cause (which is beyond the scope of this study), the observed shifts can serve as indicators of light stress and can be taken into account when calculating VIs. The light-stress index (LSI = mean(R 666:682 )/mean(R 522:594 ) calculated on the basis of these ranges appeared to be the most sensitive to light stress compared to the SB and 80 VIs [28] selected for testing. If it is possible to develop and verify a scale for LSI, it would be possible to significantly simplify the identification of light stress.…”

Section: Discussionmentioning

confidence: 99%

Indication of Light Stress in Ficus elastica Using Hyperspectral Imaging

Dmitriev,

Kozlovsky,

Dmitrieva

et al. 2023

AgriEngineering

Self Cite

View full text Add to dashboard Cite

Hyperspectral imaging techniques are widely used to remotely assess the vegetation and physiological condition of plants. Usually, such studies are carried out without taking into account the light history of the objects (for example, direct sunlight or light scattered by clouds), including light-stress conditions (photoinhibition). In addition, strong photoinhibitory lighting itself can cause stress. Until now, it is unknown how light history influences the physiologically meaningful spectral indices of reflected light. In the present work, shifts in the spectral reflectance characteristics of Ficus elastica leaves caused by 10 h exposure to photoinhibitory white LED light, 200 μmol photons m−2 s−1 (light stress), and moderate natural light, 50 μmol photons m−2 s−1 (shade) are compared to dark-adapted plants. Measurements were performed with a Cubert UHD-185 hyperspectral camera in discrete spectral bands centred on wavelengths from 450 to 950 nm with a 4 nm step. It was shown that light stress leads to an increase in reflection in the range of 522–594 nm and a decrease in reflection at 666–682 nm. The physiological causes of the observed spectral shifts are discussed. Based on empirical data, the light-stress index (LSI) = mean(R666:682)/mean(R552:594) was calculated and tested. The data obtained suggest the possibility of identifying plant light stress using spectral sensors that remotely fix passive reflection with the need to take light history into account when analysing hyperspectral data.

show abstract

Section: Discussionmentioning

confidence: 99%

Indication of Light Stress in Ficus elastica Using Hyperspectral Imaging

Dmitriev,

Kozlovsky,

Dmitrieva

et al. 2023

AgriEngineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, 78 more VIs were calculated. Details regarding these VIs can be found in Dmitriev et al [55].…”

Section: Ndvi Pri CCImentioning

confidence: 99%

Vegetation and Dormancy States Identification in Coniferous Plants Based on Hyperspectral Imaging Data

Dmitriev,

Kozlovsky,

Dmitrieva

2024

Horticulturae

Self Cite

View full text Add to dashboard Cite

Conifers are a common type of plant used in ornamental horticulture. The prompt diagnosis of the phenological state of coniferous plants using remote sensing is crucial for forecasting the consequences of extreme weather events. This is the first study to identify the “Vegetation” and “Dormancy” states in coniferous plants by analyzing their annual time series of spectral characteristics. The study analyzed Platycladus orientalis, Thuja occidentalis and T. plicata using time series values of 81 vegetation indices and 125 spectral bands. Linear discriminant analysis (LDA) was used to identify “Vegetation” and “Dormancy” states. The model contained three to four independent variables and achieved a high level of correctness (92.3 to 96.1%) and test accuracy (92.1 to 96.0%). The LDA model assigns the highest weight to vegetation indices that are sensitive to photosynthetic pigments, such as the photochemical reflectance index (PRI), normalized PRI (PRI_norm), the ratio of PRI to coloration index 2 (PRI/CI2), and derivative index 2 (D2). The random forest method also diagnoses the “Vegetation” and “Dormancy” states with high accuracy (97.3%). The vegetation indices chlorophyll/carotenoid index (CCI), PRI, PRI_norm and PRI/CI2 contribute the most to the mean decrease accuracy and mean decrease Gini. Diagnosing the phenological state of conifers throughout the annual cycle will allow for the effective planning of management measures in conifer plantations.

show abstract

“…The pre-processing of hyperspectral data acquired by UAV mainly includes three parts: radiation correction, imagery mosaicking, and spectral stitching. The radiation correction was completed by Matlab based on the center wavelength and half-width wavelength of the UHD185 [34]. Considering the limited hardware configuration of the data processing ground station and the large memory of the stitched full-band hyperspectral imagery, the data were firstly exported by JPG and cue formats based on Cubert-PILOT provided by Cubert.…”

Section: Hyperspectral Imagery Pre-processingmentioning

confidence: 99%

Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery

Lyu,

Du,

Zhang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Crop phenotype observation techniques via UAV (unmanned aerial vehicle) are necessary to identify different winter wheat cultivars to better realize their future smart productions and satisfy the requirement of smart agriculture. This study proposes a UAV-based hyperspectral remote sensing system for the fine classification of different winter wheat cultivars. Firstly, we set 90% heading overlap and 85% side overlap as the optimal flight parameters, which can meet the requirements of following hyperspectral imagery mosaicking and spectral stitching of different winter wheat cultivars areas. Secondly, the mosaicking algorithm of UAV hyperspectral imagery was developed, and the correlation coefficient of stitched spectral curves before and after mosaicking reached 0.97, which induced this study to extract the resultful spectral curves of six different winter wheat cultivars. Finally, the hyperspectral imagery dimension reduction experiments were compared with principal component analysis (PCA), minimum noise fraction rotation (MNF), and independent component analysis (ICA); the winter wheat cultivars classification experiments were compared with support vector machines (SVM), maximum likelihood estimate (MLE), and U-net neural network ENVINet5 model. Different dimension reduction methods and classification methods were compared to get the best combination for classification of different winter wheat cultivars. The results show that the mosaicked hyperspectral imagery effectively retains the original spectral feature information, and type 4 and type 6 winter wheat cultivars have the best classification results with the classification accuracy above 84%. Meanwhile, there is a 30% improvement in classification accuracy after dimension reduction, the MNF dimension reduction combined with ENVINet5 classification result is the best, its overall accuracy and Kappa coefficients are 83% and 0.81, respectively. The results indicate that the UAV-based hyperspectral remote sensing system can potentially be used for classifying different cultivars of winter wheat, and it provides a reference for the classification of crops with weak intra-class differences.

show abstract

Assessment of Invasive and Weed Species by Hyperspectral Imagery in Agrocenoses Ecosystem

Cited by 12 publications

References 81 publications

Indication of Light Stress in Ficus elastica Using Hyperspectral Imaging

Indication of Light Stress in Ficus elastica Using Hyperspectral Imaging

Vegetation and Dormancy States Identification in Coniferous Plants Based on Hyperspectral Imaging Data

Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery

Contact Info

Product

Resources

About