Identification of species of the genus Acer L. using vegetation indices calculated from the hyperspectral images of leaves

Explicit information of tree species composition provides valuable materials for the management of forests and urban greenness. In recent years, scholars have employed multiple features in tree species classification, so as to identify them from different perspectives. Most studies use different features to classify the target tree species in a specific growth environment and evaluate the classification results. However, the data matching problems have not been discussed; besides, the contributions of different features and the performance of different classifiers have not been systematically compared. Remote sensing technology of the integrated sensors helps to realize the purpose with high time efficiency and low cost. Benefiting from an integrated system which simultaneously acquired the hyperspectral images, LiDAR waveform, and point clouds, this study made a systematic research on different features and classifiers in pixel-wised tree species classification. We extracted the crown height model (CHM) from the airborne LiDAR device and multiple features from the hyperspectral images, including Gabor textural features, gray-level co-occurrence matrix (GLCM) textural features, and vegetation indices. Different experimental schemes were tested at two study areas with different numbers and configurations of tree species. The experimental results demonstrated the effectiveness of Gabor textural features in specific tree species classification in both homogeneous and heterogeneous growing environments. The GLCM textural features did not improve the classification accuracy of tree species when being combined with spectral features. The CHM feature made more contributions to discriminating tree species than vegetation indices. Different classifiers exhibited similar performances, and support vector machine (SVM) produced the highest overall accuracy among all the classifiers.

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Influence of atmospheric correction methods at the calculation of vegetation indices on hyperspectral images

Geodesy and Cartography ◽

10.22389/0016-7126-2015-84-87 ◽

2016 ◽

Vol 906 (13) ◽

pp. 84-87

Author(s):

K.I. Zubkova ◽

◽

L.I. Permitina ◽

L.N. Chaban ◽

◽

...

Keyword(s):

Vegetation Indices ◽

Atmospheric Correction ◽

Hyperspectral Images

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Wheat Yellow Rust Detection Using UAV-Based Hyperspectral Technology

Remote Sensing ◽

10.3390/rs13010123 ◽

2021 ◽

Vol 13 (1) ◽

pp. 123

Author(s):

Anting Guo ◽

Wenjiang Huang ◽

Yingying Dong ◽

Huichun Ye ◽

Huiqin Ma ◽

...

Keyword(s):

Spatial Resolution ◽

Yellow Rust ◽

Vegetation Indices ◽

Hyperspectral Images ◽

Disease Monitoring ◽

Least Squares Regression ◽

Field Scale ◽

Near Surface ◽

Infection Period ◽

Monitoring Accuracy

Yellow rust is a worldwide disease that poses a serious threat to the safety of wheat production. Numerous studies on near-surface hyperspectral remote sensing at the leaf scale have achieved good results for disease monitoring. The next step is to monitor the disease at the field scale, which is of great significance for disease control. In our study, an unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor was used to obtain hyperspectral images at the field scale. Vegetation indices (VIs) and texture features (TFs) extracted from the UAV-based hyperspectral images and their combination were used to establish partial least-squares regression (PLSR)-based disease monitoring models in different infection periods. In addition, we resampled the original images with 1.2 cm spatial resolution to images with different spatial resolutions (3 cm, 5 cm, 7 cm, 10 cm, 15 cm, and 20 cm) to evaluate the effect of spatial resolution on disease monitoring accuracy. The findings showed that the VI-based model had the highest monitoring accuracy (R2 = 0.75) in the mid-infection period. The TF-based model could be used to monitor yellow rust at the field scale and obtained the highest R2 in the mid- and late-infection periods (0.65 and 0.82, respectively). The VI-TF-based models had the highest accuracy in each infection period and outperformed the VI-based or TF-based models. The spatial resolution had a negligible influence on the VI-based monitoring accuracy, but significantly influenced the TF-based monitoring accuracy. Furthermore, the optimal spatial resolution for monitoring yellow rust using the VI-TF-based model in each infection period was 10 cm. The findings provide a reference for accurate disease monitoring using UAV hyperspectral images.

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INFLUENCE OF THE VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED FROM UAV SNAPSHOT CAMERAS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-7-257-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 257-261 ◽

Cited By ~ 2

Author(s):

Helge Aasen

Keyword(s):

Unmanned Aerial Vehicles ◽

Near Infrared ◽

Vegetation Indices ◽

Hyperspectral Data ◽

Field Of View ◽

Hyperspectral Images ◽

Single Image ◽

Area Of Interest ◽

Vegetation Parameter ◽

Parameter Retrieval

Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sun-surface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles. This study investigates the influence of the different viewing geometries within an image on the apparent hyperspectral reflection retrieved by these sensors. Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences the apparent heterogeneity with an area of interest. The study is carried out for a barley canopy at booting stage. The results show significant influences of the position of the area of interest within the image. The red region of the spectrum is more influenced by the position than the near infrared. The ability of the NDVI to compensate these effects was limited to the capturing positions close to nadir. The apparent heterogeneity of the area of interest is the highest close to a nadir.

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INFLUENCE OF THE VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED FROM UAV SNAPSHOT CAMERAS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-7-257-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 257-261 ◽

Cited By ~ 4

Author(s):

Helge Aasen

Keyword(s):

Unmanned Aerial Vehicles ◽

Near Infrared ◽

Vegetation Indices ◽

Hyperspectral Data ◽

Field Of View ◽

Hyperspectral Images ◽

Single Image ◽

Area Of Interest ◽

Vegetation Parameter ◽

Parameter Retrieval

Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sun-surface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles. This study investigates the influence of the different viewing geometries within an image on the apparent hyperspectral reflection retrieved by these sensors. Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences the apparent heterogeneity with an area of interest. The study is carried out for a barley canopy at booting stage. The results show significant influences of the position of the area of interest within the image. The red region of the spectrum is more influenced by the position than the near infrared. The ability of the NDVI to compensate these effects was limited to the capturing positions close to nadir. The apparent heterogeneity of the area of interest is the highest close to a nadir.

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Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms

Journal of Imaging ◽

10.3390/jimaging4120143 ◽

2018 ◽

Vol 4 (12) ◽

pp. 143 ◽

Cited By ~ 11

Author(s):

Jan Behmann ◽

David Bohnenkamp ◽

Stefan Paulus ◽

Anne-Katrin Mahlein

Keyword(s):

Time Series ◽

Plant Disease ◽

Spectral Characteristics ◽

Vegetation Indices ◽

Reference Points ◽

Transformation Model ◽

Hyperspectral Images ◽

Septoria Tritici Blotch ◽

Septoria Tritici ◽

Disease Symptoms

The characterization of plant disease symptoms by hyperspectral imaging is often limited by the missing ability to investigate early, still invisible states. Automatically tracing the symptom position on the leaf back in time could be a promising approach to overcome this limitation. Therefore we present a method to spatially reference time series of close range hyperspectral images. Based on reference points, a robust method is presented to derive a suitable transformation model for each observation within a time series experiment. A non-linear 2D polynomial transformation model has been selected to cope with the specific structure and growth processes of wheat leaves. The potential of the method is outlined by an improved labeling procedure for very early symptoms and by extracting spectral characteristics of single symptoms represented by Vegetation Indices over time. The characteristics are extracted for brown rust and septoria tritici blotch on wheat, based on time series observations using a VISNIR (400–1000 nm) hyperspectral camera.

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Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat

Remote Sensing ◽

10.3390/rs13183612 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3612

Author(s):

Imran Haider Khan ◽

Haiyan Liu ◽

Wei Li ◽

Aizhong Cao ◽

Xue Wang ◽

...

Keyword(s):

Powdery Mildew ◽

Early Detection ◽

Least Squares ◽

Partial Least Squares ◽

Traditional Method ◽

Vegetation Indices ◽

Hyperspectral Images ◽

Coefficient Of Determination ◽

Least Squares Regression ◽

Estimation Model

Early detection of the crop disease using agricultural remote sensing is crucial as a precaution against its spread. However, the traditional method, relying on the disease symptoms, is lagging. Here, an early detection model using machine learning with hyperspectral images is presented. This study first extracted the normalized difference texture indices (NDTIs) and vegetation indices (VIs) to enhance the difference between healthy and powdery mildew wheat. Then, a partial least-squares linear discrimination analysis was applied to detect powdery mildew with the combined optimal features (i.e., VIs & NDTIs). Further, a regression model on the partial least-squares regression was developed to estimate disease severity (DS). The results show that the discriminant model with the combined VIs & NDTIs improved the ability for early identification of the infected leaves, with an overall accuracy value and Kappa coefficient over 82.35% and 0.56 respectively, and with inconspicuous symptoms which were difficult to identify as symptoms of the disease using the traditional method. Furthermore, the calibrated and validated DS estimation model reached good performance as the coefficient of determination (R2) was over 0.748 and 0.722, respectively. Therefore, this methodology for detection, as well as the quantification model, is promising for early disease detection in crops.

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