Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images

A sufficient nitrogen (N) supply is mandatory for healthy crop growth, but negative consequences of N losses into the environment are known. Hence, deeply understanding and monitoring crop growth for an optimized N management is advisable. In this context, remote sensing facilitates the capturing of crop traits. While several studies on estimating biomass from spectral and structural data can be found, N is so far only estimated from spectral features. It is well known that N is negatively related to dry biomass, which, in turn, can be estimated from crop height. Based on this indirect link, the present study aims at estimating N concentration at field scale in a two-step model: first, using crop height to estimate biomass, and second, using the modeled biomass to estimate N concentration. For comparison, N concentration was estimated from spectral data. The data was captured on a spring barley field experiment in two growing seasons. Crop surface height was measured with a terrestrial laser scanner, seven vegetation indices were calculated from field spectrometer measurements, and dry biomass and N concentration were destructively sampled. In the validation, better results were obtained with the models based on structural data (R2 < 0.85) than on spectral data (R2 < 0.70). A brief look at the N concentration of different plant organs showed stronger dependencies on structural data (R2: 0.40–0.81) than on spectral data (R2: 0.18–0.68). Overall, this first study shows the potential of crop-specific across‑season two-step models based on structural data for estimating crop N concentration at field scale. The validity of the models for in-season estimations requires further research.

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Mapping potato crop height and leaf area index through vegetation indices using remote sensing in Cyprus

Journal of Applied Remote Sensing ◽

10.1117/1.3596388 ◽

2011 ◽

Vol 5 (1) ◽

pp. 053526 ◽

Cited By ~ 16

Author(s):

George Papadavid

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Potato Crop ◽

Vegetation Indices ◽

Area Index ◽

Crop Height

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Specific band ratio for vegetation indices calculation in hyperspectral images

2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) ◽

10.1109/whispers.2015.8075383 ◽

2015 ◽

Author(s):

Bus ra Ozbay ◽

Yunus Emre Esin ◽

Furkan Cengel

Keyword(s):

Vegetation Indices ◽

Hyperspectral Images ◽

Band Ratio ◽

Specific Band

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Tree Species Classification by Employing Multiple Features Acquired from Integrated Sensors

Journal of Sensors ◽

10.1155/2019/3247946 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Guang Yang ◽

Yaolong Zhao ◽

Baoxin Li ◽

Yuntao Ma ◽

Ruren Li ◽

...

Keyword(s):

Tree Species ◽

Vegetation Indices ◽

Airborne Lidar ◽

Hyperspectral Images ◽

Support Vector ◽

Textural Features ◽

Integrated Sensors ◽

Species Classification ◽

Multiple Features ◽

Tree Species Classification

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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Evaluation of Soil Properties, Topographic Metrics, Plant Height, and Unmanned Aerial Vehicle Multispectral Imagery Using Machine Learning Methods to Estimate Canopy Nitrogen Weight in Corn

Remote Sensing ◽

10.3390/rs13163105 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3105

Author(s):

Jody Yu ◽

Jinfei Wang ◽

Brigitte Leblon

Keyword(s):

Soil Properties ◽

Unmanned Aerial Vehicle ◽

Vegetation Indices ◽

N Fertilizer ◽

Support Vector ◽

Agricultural Practice ◽

Multispectral Imagery ◽

Crop Height ◽

Aerial Vehicle ◽

Canopy Nitrogen

Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, vegetation indices (VI), crop height, field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m2) of a corn field in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models were evaluated for canopy nitrogen weight prediction from 29 variables. RF consistently had better performance than SVR, and the top-performing validation model was RF using 15 selected height, spectral, and topographic variables with an R2 of 0.73 and Root Mean Square Error (RMSE) of 2.21 g/m2. Of the model’s 15 variables, crop height was the most important predictor, followed by 10 VIs, three MicaSense band reflectance mosaics (blue, red, and green), and topographic profile curvature. The model information can be used to improve field nitrogen prediction, leading to more effective and efficient N fertilizer management.

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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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