Hyperspectral remote sensing for foliar nutrient detection in forestry: A near-infrared perspective

2022 ◽  
Vol 25 ◽  
pp. 100676
Author(s):  
L. Singh ◽  
O. Mutanga ◽  
P. Mafongoya ◽  
K. Peerbhay ◽  
J. Crous
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Hyperspectral Remote Sensing ◽  
Foliar Nutrient

Spectral characteristics of leafy spurge (Euphorbia esula) leaves and flower bracts

Weed Science ◽  
2004 ◽  
Vol 52 (4) ◽  
pp. 492-497 ◽  
Author(s):  
E. Raymond Hunt ◽  
James E. McMurtrey ◽  
Amy E. Parker Williams ◽  
Lawrence A. Corp
Keyword(s):  
Remote Sensing ◽  
High Performance ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Leafy Spurge ◽  
Hyperspectral Remote Sensing ◽  
Total Carotenoids ◽  
Physiological Basis ◽  
Infrared Wavelengths ◽  
Pigment Concentrations

Leafy spurge can be detected during flowering with either aerial photography or hyperspectral remote sensing because of the distinctive yellow-green color of the flower bracts. The spectral characteristics of flower bracts and leaves were compared with pigment concentrations to determine the physiological basis of the remote sensing signature. Compared with leaves of leafy spurge, flower bracts had lower reflectance at blue wavelengths (400 to 500 nm), greater reflectance at green, yellow, and orange wavelengths (525 to 650 nm), and approximately equal reflectances at 680 nm (red) and at near-infrared wavelengths (725 to 850 nm). Pigments from leaves and flower bracts were extracted in dimethyl sulfoxide, and the pigment concentrations were determined spectrophotometrically. Carotenoid pigments were identified using high-performance liquid chromatography. Flower bracts had 84% less chlorophylla, 82% less chlorophyllb, and 44% less total carotenoids than leaves, thus absorptance by the flower bracts should be less and the reflectance should be greater at blue and red wavelengths. The carotenoid to chlorophyll ratio of the flower bracts was approximately 1:1, explaining the hue of the flower bracts but not the value of reflectance. The primary carotenoids were lutein, β-carotene, and β-cryptoxanthin in a 3.7:1.5:1 ratio for flower bracts and in a 4.8:1.3:1 ratio for leaves, respectively. There was 10.2 μg g−1fresh weight of colorless phytofluene present in the flower bracts and none in the leaves. The fluorescence spectrum indicated high blue, red, and far-red emission for leaves compared with flower bracts. Fluorescent emissions from leaves may contribute to the higher apparent leaf reflectance in the blue and red wavelength regions. The spectral characteristics of leafy spurge are important for constructing a well-documented spectral library that could be used with hyperspectral remote sensing.

Hyperspectral remote sensing monitoring of cyanobacteria blooms in a large South American reservoir: high- and medium-spatial resolution satellite algorithm simulation

2020 ◽  
Vol 71 (5) ◽  
pp. 593 ◽  
Author(s):  
A. Drozd ◽  
P. de Tezanos Pinto ◽  
V. Fernández ◽  
M. Bazzalo ◽  
F. Bordet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Monitoring Program ◽  
Hyperspectral Remote Sensing ◽  
Coefficient Of Determination ◽  
South American ◽  
Cell Abundance ◽  
Hyperspectral Signatures ◽  
Sentinel 2

We used hyperspectral remote sensing with the aim of establishing a monitoring program for cyanobacteria in a South American reservoir. We sampled at a wide temporal (2012–16; 10 seasons) and spatial (30km) gradient, and retrieved 111 field hyperspectral signatures, chlorophyll-a, cyanobacteria densities and total suspended solids. The hyperspectral signatures for cyanobacteria-dominated situations (n=75) were used to select the most suitable spectral bands in seven high- and medium-spatial resolution satellites (Sentinel 2, Landsat 5, 7 and 8, SPOT-4/5 and -6/7, WorldView 2), and for the development of chlorophyll and cyanobacteria cell abundance algorithms (λ550 – λ650+λ800) ÷ (λ550+λ650+λ800). The best-performing chlorophyll algorithm was Sentinel 2 ((λ560 – λ660+λ703) ÷ (λ560+λ660+λ703); R2=0.80), followed by WorldView 2 ((λ550 – λ660+λ720) ÷ (λ550+λ660+λ720); R2=0.78), Landsat and the SPOT series ((λ550 – λ650+λ800) ÷ (λ550+λ650+λ800); R2=0.67–0.74). When these models were run for cyanobacteria abundance, the coefficient of determination remained similar, but the root mean square error increased. This could affect the estimate of cyanobacteria cell abundance by ~20%, yet it still enable assessment of the alert level categories for risk assessment. The results of this study highlight the importance of the red and near-infrared region for identifying cyanobacteria in hypereutrophic waters, demonstrating coherence with field cyanobacteria abundance and enabling assessment of bloom distribution in this ecosystem.

scholarly journals Hyperspectral Remote Sensing for Determining Water and Nitrogen Stress in Maize during Rabi Season

2020 ◽  
pp. 1-9
Author(s):  
H. R. Naveen ◽  
B. Balaji Naik ◽  
G. Sreenivas ◽  
Ajay Kumar ◽  
J. Adinarayana ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Hyperspectral Remote Sensing ◽  
Nitrogen Stress ◽  
Fixed Amount ◽  
Nitrogen Levels ◽  
Stress Environment

Aims/Objectives: Is to examine the use of spectral reflectance characteristics and explore the effectiveness of spectral indices under water and nitrogen stress environment. Study Design: Split-plot. Place and Duration of Study: Agro Climate Research Center, A.R.I., P.J.T.S. Agricultural University, Rajendranagar, Hyderabad, India in 2018-19. Methodology: Fixed amount of 5 cm depth of water was applied to each plot when the ratio of irrigation water and cumulative pan evaporation (IW/CPE) arrives at pre-determined levels of 0.6, 0.8 & 1.2 as main-plot and 3 nitrogen levels viz. 100, 200 & 300 kg N ha-1 as a subplot to create water and nitrogen stress environment. Spectral reflectance from each treatment was measured using Spectroradiometer and analyzed using statistical software package SPSS 17, SAS and trial version of UNSCRABLER. Results: At tasseling and dough stages, the reflectance pattern of maize was found to be higher in visible light spectrum of 400 to700 nm whereas lower in near-infrared region (700 to 900) in both underwater (IW/CPE ratio of 0.6) and nitrogen stress (100 kg N ha-1) environment as compared to moderate and no stress irrigation (IW/CPE ratio of 0.8 & 1.2) and nitrogen (200 and 300 kg N ha-1) treatments. The discriminant analysis of NDVI, GNDVI, WBI and SR indicated that 72.2% and 66.7% of the original grouped cases and 55.6% and 38.9% of the cross-validated grouped cases under irrigation and nitrogen levels, respectively were correctly classified. Conclusion: Hyperspectral remote sensing can be used as a tool to detect and quantify the water and nitrogen stress in maize non-destructively. Spectral vegetation indices viz. Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were found effective to distinguish water and nitrogen stress severity in maize.

scholarly journals Application of UAV-Based Multi-Angle Hyperspectral Remote Sensing in Fine Vegetation Classification

2019 ◽  
Vol 11 (23) ◽  
pp. 2753 ◽  
Author(s):  
Yan ◽  
Deng ◽  
Liu ◽  
Zhu
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Hot Spot ◽  
Hyperspectral Remote Sensing ◽  
Vegetation Classification ◽  
Principal Plane ◽  
Dark Spot ◽  
Infrared Band ◽  
Blue Band ◽  
Better Than

To obtain a high-accuracy vegetation classification of high-resolution UAV images, in this paper, a multi-angle hyperspectral remote sensing system was built using a six-rotor UAV and a Cubert S185 frame hyperspectral sensor. The application of UAV-based multi-angle remote sensing in fine vegetation classification was studied by combining a bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods. This method can not only effectively reduce the classification phenomena that influence different objects with similar spectra, but also benefit the construction of a canopy-level BRDF. Then, the importance of the BRDF characteristic parameters are discussed in detail. The results show that the overall classification accuracy (OA) of the vertical observation reflectance based on BRDF extrapolation (BRDF_0°) (63.9%) was approximately 24% higher than that based on digital orthophoto maps (DOM) (39.8%), and kappa using BRDF_0° was 0.573, which was higher than that using DOM (0.301); a combination of the hot spot and dark spot features, as well as model features, improved the OA and kappa to around 77% and 0.720, respectively. The reflectance features near hot spots were more conducive to distinguishing maize, soybean, and weeds than features near dark spots; the classification results obtained by combining the observation principal plane (BRDF_PP) and on the cross-principal plane (BRDF_CP) features were best (OA = 89.2%, kappa = 0.870), and especially, this combination could improve the distinction among different leaf-shaped trees. BRDF_PP features performed better than BRDF_CP features. The observation angles in the backward reflection direction of the principal plane performed better than those in the forward direction. The observation angles associated with the zenith angles between −10° and −20° were most favorable for vegetation classification (solar position: zenith angle 28.86°, azimuth 169.07°) (OA was around 75%–80%, kappa was around 0.700–0.790); additionally, the most frequently selected bands in the classification included the blue band (466 nm–492 nm), green band (494 nm–570 nm), red band (642 nm–690 nm), red edge band (694 nm–774 nm), and the near-infrared band (810 nm–882 nm). Overall, the research results promote the application of multi-angle remote sensing technology in vegetation information extraction and provide important theoretical significance and application value for regional and global vegetation and ecological monitoring.

scholarly journals SPECTRAL PREPROCESSING FOR HYPERSPECTRAL REMOTE SENSING OF HEAVY METALS IN WATER

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1869-1873
Author(s):  
M. Lee ◽  
X.-Y. Chen ◽  
H.-C. Lee
Keyword(s):  
Heavy Metals ◽  
Remote Sensing ◽  
Prediction Model ◽  
Predictive Model ◽  
Spectral Data ◽  
Near Infrared ◽  
Hyperspectral Remote Sensing ◽  
Blue Color ◽  
Remote Sensing Technique ◽  
First Derivative

<p><strong>Abstract.</strong> This study aims to investigate the feasibility of using hyperspectral remote sensing technique by visible-near infrared spectroradiometer (VNIR, FieldSpec HandHeld 2) for rapid water monitoring of heavy metal, followed by comparison of different spectral preprocessing methods for the development of quantitative predictive model. The water samples evaluated in this study were prepared in our laboratory by dilution of stock solutions. Heavy metals of lead (Pb), zinc (Zn) and copper (Cu) in the range of concentration between 100 to 2000&amp;thinsp;mg/L were selected as the target samples in this study. The sensitive bands for the target metals were characterized in the range from 800&amp;thinsp;nm to 1075&amp;thinsp;nm, based on the reflectance spectral data. Spectral data for developing of the quantitative predictive model was first preprocessed with first derivative and logarithm transformation, followed by establishing of the prediction model using multivariate linear regression (MLR). It was observed that increase in the number of sensitive bands for the MLR can significantly improve the adjusted R<sup>2</sup> for the model. The prediction model for Cu was found to have the highest adjusted R<sup>2</sup> of 0.92 and least normalized root mean square error (NRMSE) of 0.065, while using the reflectance values of 7 sensitive bands. This result could be attributed to the blue color characteristic of the solution, whereas the others remain clear. Additionally, the first derivative transformation was determined as the best method for predicting Pb, whereas the logarithm transformation provided the best outcomes for predicting Cu and Zn.</p>

Reflectance and emission spectroscopy study of four groups of phyllosilicates: smectites, kaolinite-serpentines, chlorites and micas

Clay Minerals ◽  
2008 ◽  
Vol 43 (1) ◽  
pp. 35-54 ◽  
Author(s):  
J. L. Bishop ◽  
M. D. Lane ◽  
M. D. Dyar ◽  
A. J. Brown
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Hyperspectral Remote Sensing ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Bending Mode ◽  
Formation Conditions ◽  
Remote Sensing Techniques ◽  
Combination Bands ◽  
Emissivity Spectra

AbstractCoordinated visible/near-infrared reflectance/mid-infrared reflectance and emissivity spectra of four groups of phyllosilicates were undertaken to provide insights into the differences within and among groups of smectites, kaolinite-serpentines, chlorites and micas. Identification and characterization of phyllosilicates via remote sensing on Earth and Mars can be achieved using the OH combination bands in the 2.2–2.5 μm region and the tetrahedral SiO4 vibrations from ~8.8–12 μm (~1140–830 cm–1) and ~20–25 μm (500–400 cm–1). The sharp and well resolved OH combination bands in the 2.2–2.5 μm region provide unique fingerprints for specific minerals. Al-rich phyllosilicates exhibit OH combination bands near 2.2 μm, while these bands are observed near 2.29–2.31, 2.33–2.34 μm and near 2.35–2.37 μm for Fe3+-rich, Mg-rich and Fe2+-rich phyllosilicates, respectively. When a tetrahedral substitution of Al or Fe3+ for Si occurs, the position of the Si(Al,Fe)O4 stretching mode absorption shifts. Depending on the size of the cation, the Si(Al,Fe)O4 bending mode near 500 cm–1 is split into multiple bands that may be distinguished via hyperspectral remote sensing techniques. The tetrahedral SiO4 vibrations are also influenced by the octahedral cations, such that Al-rich, Fe-rich and Mg-rich phyllosilicates can be discriminated in reflectance and emissivity spectra based on diagnostic positions of the stretching and bending bands. Differences among formation conditions for these four groups of phyllosilicates are also discussed. Hyperspectral remote sensing can be used to identify specific phyllosilicates using electronic and vibrational features and thus provide constraints on the chemistry and formation conditions of soils.

Detection of pitted morningglory (Ipomoea lacunosa) with hyperspectral remote sensing. II. Effects of vegetation ground cover and reflectance properties

Weed Science ◽  
2004 ◽  
Vol 52 (2) ◽  
pp. 230-235 ◽  
Author(s):  
Clifford H. Koger ◽  
David R. Shaw ◽  
Krishna N. Reddy ◽  
Lori M. Bruce
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Leaf Growth ◽  
Ground Cover ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Growth Stages ◽  
Discrimination Accuracy ◽  
Reflectance Measurement ◽  
Pitted Morningglory

Field research was conducted to determine the potential of hyperspectral remote sensing for discriminating plots of soybean intermixed with pitted morningglory and weed-free soybean with similar and different proportions of vegetation ground cover. Hyperspectral data were collected using a handheld spectroradiometer when pitted morningglory was in the cotyledon to two-leaf, two- to four-leaf, and four- to six-leaf growth stages. Synthesized reflectance measurements containing equal and unequal proportions of reflectance from vegetation were obtained, and seven 50-nm spectral bands (one ultraviolet, two visible, and four near-infrared) derived from each hyperspectral reflectance measurement were used as discrimination variables to differentiate weed-free soybean and soybean intermixed with pitted morningglory. Discrimination accuracy was 93 to 100% regardless of pitted morningglory growth stage and whether equal or unequal proportions of reflectance from vegetation existed in weed-free soybean and soybean intermixed with pitted morningglory. Discrimination accuracy was 88 to 98% when using the discriminant model developed for one experiment to discriminate soybean intermixed with pitted morningglory and weed-free soybean plots of the other experiment. Reflectance in the near-infrared spectrum was higher for weed-free soybean compared with soybean intermixed with pitted morningglory, and this difference affected the ability to discriminate weed-free soybean from soybean intermixed with pitted morningglory.

scholarly journals KLASIFIKASI PENUTUP/PENGGUNAAN LAHAN DENGAN DATA SATELIT PENGINDERAAN JAUH HIPERSPEKTRAL (HYPERION) MENGGUNAKAN METODE NEURAL NETWORK TIRUAN

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Dony Kushardono
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Near Infrared ◽  
Back Propagation ◽  
Remote Sensing Data ◽  
Back Propagation Neural Network ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data ◽  
Test Input ◽  
Long Time

Hyperspectral remote sensing data has numerous spectral information for the land-use/land-cover (LULC) classification, but a large number of hyperspectral band data is becoming a problem in the LULC classification. This research proposes the use of the back propagation neural network for LULC classification with hyperspectral remote sensing data. Neural network used in this study is three layers, in which to test input layer has a number of neurons as many as 242 to process all band data, 163 neurons, and 50 neurons to process the data band has a high average digital number, and data bands at wavelengths of visible to near infrared. The results showed the use of all the data band hyperspectral on classification with the neural network has the highest classification accuracy of up to 98% for 18 LULC class, but it takes a very long time. Selecting a number of bands of precise data for classification with a neural network, in addition to speeding up data processing time, can also provide sufficient accuracy classification results.ABSTRAKData penginderaan jauh hiperspektral memiliki informasi spektral yang sangat banyak untuk klasifikasi penutup/penggunaan lahan (LULC), akan tetapi banyaknya jumlah band data hiperspektral menjadi masalah dalam klasifikasi LULC. Penelitian ini mengusulkan penggunaan back propagation neural network untuk klasifikasi LULC dengan data penginderaan jauh hiperspektral. Neural network yang dipergunakan 3 lapis, dimana untuk uji coba lapis masukan memiliki jumlah neuron sebanyak 242 untuk mengolah seluruh band, 163 neuron, dan 50 neuron untuk mengolah data band yang memiliki nilai digital rataan yang tinggi, dan data band pada panjang gelombang cahaya tampak hingga infra merah dekat. Hasil penelitian menunjukkan penggunaan seluruh band data hiperspektral pada klasifikasi dengan neural network memiliki akurasi hasil klasifikasi tertinggi hingga 98% untuk 18 kelas LULC, akan tetapi waktu yang diperlukan sangat lama. Pemilihan sejumlah band data yang tepat untuk klasifikasi dengan neural network, selain mempercepat waktu pengolahan data, juga bisa memberikan akurasi hasil klasifikasi yang mencukupi.

Sign in / Sign up

Export Citation Format

Share Document