scholarly journals Monitoring of Canopy Stress Symptoms in New Zealand Kauri Trees Analysed with AISA Hyperspectral Data

2020 ◽  
Vol 12 (6) ◽  
pp. 926 ◽  
Author(s):  
Jane J. Meiforth ◽  
Henning Buddenbaum ◽  
Joachim Hill ◽  
James Shepherd

The endemic New Zealand kauri trees (Agathis australis) are under threat by the deadly kauri dieback disease (Phytophthora agathidicida (PA)). This study aimed to identify spectral index combinations for characterising visible stress symptoms in the kauri canopy. The analysis is based on an aerial AISA hyperspectral image mosaic and 1258 reference crowns in three study sites in the Waitakere Ranges west of Auckland. A field-based assessment scheme for canopy stress symptoms (classes 1–5) was further optimised for use with RGB aerial images. A combination of four indices with six bands in the spectral range 450–1205 nm resulted in a correlation of 0.93 (mean absolute error 0.27, RMSE 0.48) for all crown sizes. Comparable results were achieved with five indices in the 450–970 nm region. A Random Forest (RF) regression gave the most accurate predictions while a M5P regression tree performed nearly as well and a linear regression resulted in slightly lower correlations. Normalised Difference Vegetation Indices (NDVI) in the near-infrared / red spectral range were the most important index combinations, followed by indices with bands in the near-infrared spectral range from 800 to 1205 nm. A test on different crown sizes revealed that stress symptoms in smaller crowns with denser foliage are best described in combination with pigment-sensitive indices that include bands in the green and blue spectral range. A stratified approach with individual models for pre-segmented low and high forest stands improved the overall performance. The regression models were also tested in a pixel-based analysis. A manual interpretation of the resulting raster map with stress symptom patterns observed in aerial imagery indicated a good match. With bandwidths of 10 nm and a maximum number of six bands, the selected index combinations can be used for large-area monitoring on an airborne multispectral sensor. This study establishes the base for a cost-efficient, objective monitoring method for stress symptoms in kauri canopies, suitable to cover large forest areas with an airborne multispectral sensor.

2021 ◽  
Vol 13 (15) ◽  
pp. 2967
Author(s):  
Nicola Acito ◽  
Marco Diani ◽  
Gregorio Procissi ◽  
Giovanni Corsini

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.


NIR news ◽  
2014 ◽  
Vol 25 (7) ◽  
pp. 15-17 ◽  
Author(s):  
Y. Dixit ◽  
R. Cama ◽  
C. Sullivan ◽  
L. Alvarez Jubete ◽  
A. Ktenioudaki

2010 ◽  
Vol 16 (1) ◽  
Author(s):  
J. Tamás

Nowadays airborne remote sensing data are increasingly used in precision agriculture. The fast space-time dependent localization of stresses in orchards, which allows for a more efficient application of horticultural technologies, could lead to improved sustainable precise management. The disadvantage of the near field multi and hyper spectroscopy is the spot sample taking, which can apply independently only for experimental survey in plantations. The traditional satellite images is optionally suitable for precision investigation because of the low spectral and ground resolution on field condition. The presented airborne hyperspectral image spectroscopy reduces above mentioned disadvantages and at the same time provides newer analyzing possibility to the user. In this paper we demonstrate the conditions of data base collection and some informative examination possibility. The estimating of the board band vegetation indices calculated from reflectance is well known in practice of the biomass stress examinations. In this method the N-dimension spectral data cube enables to calculate numerous special narrow band indexes and to evaluate maps. This paper aims at investigating the applied hyperspectral analysis for fruit tree stress detection. In our study, hyperspectral data were collected by an AISADUAL hyperspectral image spectroscopy system, with high (0,5-1,5 m) ground resolution. The research focused on determining of leaves condition in different fruit plantations in the peach orchard near Siófok. Moreover the spectral reflectance analyses could provide more information about plant condition due to changes in the absorption of incident light in the visible and near infrared range of the spectrum.


2020 ◽  
Vol 12 (12) ◽  
pp. 1906 ◽  
Author(s):  
Jane J. Meiforth ◽  
Henning Buddenbaum ◽  
Joachim Hill ◽  
James D. Shepherd ◽  
John R. Dymond

New Zealand kauri trees are threatened by the kauri dieback disease (Phytophthora agathidicida (PA)). In this study, we investigate the use of pan-sharpened WorldView-2 (WV2) satellite and Light Detection and Ranging (LiDAR) data for detecting stress symptoms in the canopy of kauri trees. A total of 1089 reference crowns were located in the Waitakere Ranges west of Auckland and assessed by fieldwork and the interpretation of aerial images. Canopy stress symptoms were graded based on five basic stress levels and further refined for the first symptom stages. The crown polygons were manually edited on a LiDAR crown height model. Crowns with a mean diameter smaller than 4 m caused most outliers with the 1.8 m pixel size of the WV2 multispectral bands, especially at the more advanced stress levels of dying and dead trees. The exclusion of crowns with a diameter smaller than 4 m increased the correlation in an object-based random forest regression from 0.85 to 0.89 with only WV2 attributes (root mean squared error (RMSE) of 0.48, mean absolute error (MAE) of 0.34). Additional LiDAR attributes increased the correlation to 0.92 (RMSE of 0.43, MAE of 0.31). A red/near-infrared (NIR) normalised difference vegetation index (NDVI) and a ratio of the red and green bands were the most important indices for an assessment of the full range of stress symptoms. For detection of the first stress symptoms, an NDVI on the red-edge and green bands increased the performance. This study is the first to analyse the use of spaceborne images for monitoring canopy stress symptoms in native New Zealand kauri forest. The method presented shows promising results for a cost-efficient stress monitoring of kauri crowns over large areas. It will be tested in a full processing chain with automatic kauri identification and crown segmentation.


2021 ◽  
Author(s):  
Gianrico Filacchione ◽  
Marco Tarabini ◽  
Elena Mazzotta Epifani ◽  
Mauro Ciarniello ◽  
Giuseppe Piccioni ◽  
...  

<p>With the introduction of visible and infrared Imaging Spectrometers about 20 years ago, we have assisted to a dramatic enhancement of the scientific return achieved by space missions launched for the exploration of the Solar System. This innovative class of instruments are in fact able to conjugate together the imaging capabilities of the cameras with the spectral resolution accomplished by spectrographs. By exploiting such capabilities, planetary scientists can retrieve and map the physical and chemical properties of a target body and correlate them with surface morphological features or with dynamical structures of the atmospheres. It is not by chance that such instruments have become an essential payload on many planetary exploration missions since they have demonstrated their capabilities to allow a better understanding of the composition, processes and evolution of Solar System bodies. In the meanwhile, ongoing photonic research has made available new devices whose technology is mature enough to allow to investigate new optical concepts for color cameras and imaging spectrometers architectures to be implemented in a single integrated instrument (⨏ISPEx), which we present here. The ⨏ISPEx camera design uses a Liquid Crystal Tunable Filter (LCTF) to ensure high flexibility in the selection of the bandpass (between 0.42-0.73 µm) and bandwidth (35 nm at 0.55 µm) of the color filters, which can then be adapted to very different scientific targets. The camera has a broad 3.3°x1.6° FOV and an IFOV of 14 µm allowing to reach a spatial resolution of 0.14 m/px from 10 km distance. Conversely, the ⨏ISPEx VIS (0.4-1.05 µm spectral range, 3.2 nm/band sampling) and IR (0.95-5.0 µm, 8 nm/band) integral field imaging spectrometers use Coded Mask Optical Reformatters (CMOR) based on optical fibers bundles to collect the full hyperspectral cube through a single acquisition resulting in a great reduction of the acquisition time with respect to traditional whiskbroom and pushbroom instruments. The FOV/IFOV are 0.4° (circular)/100 µrad and 0.28°x0.28° (square)/225 µrad for the VIS and IR channel spectrometers, respectively. These values correspond to a 70 m-wide circular hyperspectral image made of 4500 pixels at 1 m/px resolution for the VIS channel and to a 50x50 m image made of 484 pixels at 2.25 m/px for the IR channel for acquisitions made from a distance of 10 Km. These requirements are optimized for remote sensing of asteroids and comets from close distances. Camera’s and spectrometers’ FOV are co-located on the same boresight thanks to the use of a common Three Mirror Afocal Telescope operating with a 2.4X beam reduction factor (entrance pupil diameter 84 mm). By means of a beamsplitter and a dichroic filter the collimated beam generated by the telescope is split to the camera and spectrometers’ pupils. Thanks to the solutions we are developing, the camera and imaging spectrometers will operate at the same time on the same boresight, resulting in a great simplification of their operations. In this respect, the ⨏ISPEx will offer the advantage to allow a better exploitation of the data collected by the three channels resulting in a tremendous advantage for many scientific investigations. With this synergic approach it will be possible to analyze high resolution images to constrain morphology interpretation of a target, while hyperspectral data collected at the same time allow the retrieval of composition and physical properties. The availability of camera images makes possible to apply sharpening algorithms to spectrometer’s ones. Apart this, an integral-field spectrometer will keep the capabilities of more traditional whiskbroom and pushbroom spectrometers but it will overcome them when the target scene is rapidly evolving and changing during the acquisition: traditional instruments are limited by the fact that the cube acquisition process may take a time longer than the temporal scale of the investigated event. This is the case of observations enquiring into the dynamical evolution of planetary atmospheres, lightning events, outbursts, hypervelocity impact or fast-moving targets. By operating with fast readout detectors, an Integral Field spectrometer can adequately resolve the four dimensions of data (2D spatial, spectral and temporal) opening the possibility to perform time-resolved hyperspectral movies. Another substantial advantage of Integral Field spectrometers is their better operability during fast flyby phases, where the distance from the target and illumination geometry are rapidly changing, resulting in limited time periods suitable to observe the target with optimal conditions. By collecting the entire hyperspectral cube in a fraction of the time necessary to complete the scan for a traditional scanning spectrometer, an Integral Field spectrometer can reach a level of imaging flexibility similar to the one achieved by a camera. Within this study we are defining the configuration of the ⨏ISPEx space model operating in the 0.4-5.0 µm spectral range including optical performance analyses, and thermomechanical and electronic architecture. Moreover, we are realizing a development breadboard limited to the 0.4-1.0 µm spectral range to conduct performance tests at system level on LCTF and CMOR devices. We gratefully acknowledge financial contribution from the Agreement ASI-INAF n.2018-16-HH.0.</p>


2019 ◽  
Vol 11 (23) ◽  
pp. 2865 ◽  
Author(s):  
Jane J. Meiforth ◽  
Henning Buddenbaum ◽  
Joachim Hill ◽  
James Shepherd ◽  
David A. Norton

The endemic New Zealand kauri trees (Agathis australis) are of major importance for the forests in the northern part of New Zealand. The mapping of kauri locations is required for the monitoring of the deadly kauri dieback disease (Phytophthora agathidicida (PTA)). In this study, we developed a method to identify kauri trees by optical remote sensing that can be applied in an area-wide campaign. Dead and dying trees were separated in one class and the remaining trees with no to medium stress symptoms were defined in the two classes “kauri” and “other”. The reference dataset covers a representative selection of 3165 precisely located crowns of kauri and 21 other canopy species in the Waitakere Ranges west of Auckland. The analysis is based on an airborne hyperspectral AISA Fenix image (437–2337 nm, 1 m2 pixel resolution). The kauri spectra show characteristically steep reflectance and absorption features in the near-infrared (NIR) region with a distinct long descent at 1215 nm, which can be parameterised with a modified Normalised Water Index (mNDWI-Hyp). With a Jeffries–Matusita separability over 1.9, the kauri spectra can be well separated from 21 other canopy vegetation spectra. The Random Forest classifier performed slightly better than Support Vector Machine. A combination of the mNDWI-Hyp index with four additional spectral indices with three red to NIR bands resulted in an overall pixel-based accuracy (OA) of 91.7% for crowns larger 3 m diameter. While the user’s and producer’s accuracies for the class “kauri” with 94.6% and 94.8% are suitable for management purposes, the separation of “dead/dying trees” from “other” canopy vegetation poses the main challenge. The OA can be improved to 93.8% by combining “kauri” and “dead/dying” trees in one class, separate classifications for low and high forest stands and a binning to 10 nm bandwidths. Additional wavelengths and their respective indices only improved the OA up to 0.6%. The method developed in this study allows an accurate location of kauri trees for an area-wide mapping with a five-band multispectral sensor in a representative selection of forest ecosystems.


2018 ◽  
Vol 58 (8) ◽  
pp. 1488 ◽  
Author(s):  
S. Rahman ◽  
P. Quin ◽  
T. Walsh ◽  
T. Vidal-Calleja ◽  
M. J. McPhee ◽  
...  

The objectives of the present study were to describe the approach used for classifying surface tissue, and for estimating fat depth in lamb short loins and validating the approach. Fat versus non-fat pixels were classified and then used to estimate the fat depth for each pixel in the hyperspectral image. Estimated reflectance, instead of image intensity or radiance, was used as the input feature for classification. The relationship between reflectance and the fat/non-fat classification label was learnt using support vector machines. Gaussian processes were used to learn regression for fat depth as a function of reflectance. Data to train and test the machine learning algorithms was collected by scanning 16 short loins. The near-infrared hyperspectral camera captured lines of data of the side of the short loin (i.e. with the subcutaneous fat facing the camera). Advanced single-lens reflex camera took photos of the same cuts from above, such that a ground truth of fat depth could be semi-automatically extracted and associated with the hyperspectral data. A subset of the data was used to train the machine learning model, and to test it. The results of classifying pixels as either fat or non-fat achieved a 96% accuracy. Fat depths of up to 12 mm were estimated, with an R2 of 0.59, a mean absolute bias of 1.72 mm and root mean square error of 2.34 mm. The techniques developed and validated in the present study will be used to estimate fat coverage to predict total fat, and, subsequently, lean meat yield in the carcass.


Author(s):  
Dipendra J. Mandal ◽  
Sony George ◽  
Marius Pedersen ◽  
Clotilde Boust

Pigment classification of paintings is considered an important task in the field of cultural heritage. It helps to analyze the object and to know its historical value. This information is also essential for curators and conservators. Hyperspectral imaging technology has been used for pigment characterization for many years and has potential in its scientific analysis. Despite its advantages, there are several challenges linked with hyperspectral image acquisition. The quality of such acquired hyperspectral data can be influenced by different parameters such as focus, signal-to-noise ratio, illumination geometry, etc. Among several, we investigated the effect of four key parameters, namely focus distance, signal-to-noise ratio, integration time, and illumination geometry on pigment classification accuracy for a mockup using hyperspectral imaging in visible and near-infrared regions. The results obtained exemplify that the classification accuracy is influenced by the variation in these parameters. Focus distance and illumination angle have a significant effect on the classification accuracy compared to signal-to-noise ratio and integration time.


JETP Letters ◽  
2020 ◽  
Vol 112 (1) ◽  
pp. 31-36
Author(s):  
V. I. Kukushkin ◽  
V. E. Kirpichev ◽  
E. N. Morozova ◽  
V. V. Solov’ev ◽  
Ya. V. Fedotova ◽  
...  

2021 ◽  
Vol 13 (2) ◽  
pp. 268
Author(s):  
Xiaochen Lv ◽  
Wenhong Wang ◽  
Hongfu Liu

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.


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