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.

Detection of pitted morningglory (Ipomoea lacunosa) by hyperspectral remote sensing. I. Effects of tillage and cover crop residue

Weed Science ◽  
2004 ◽  
Vol 52 (2) ◽  
pp. 222-229 ◽  
Author(s):  
Clifford H. Koger ◽  
David R. Shaw ◽  
Krishna N. Reddy ◽  
Lori M. Bruce
Keyword(s):  
Cover Crop ◽  
Crop Residue ◽  
Field Experiments ◽  
Leaf Growth ◽  
Plant Size ◽  
Hyperspectral Reflectance ◽  
Discrimination Accuracy ◽  
Reflectance Measurement ◽  
Pitted Morningglory ◽  
Residue Systems

Field experiments were conducted to evaluate the potential of hyperspectral reflectance data collected with a hand-held spectroradiometer to discriminate soybean intermixed with pitted morningglory and weed-free soybean in conventional till and no-till plots containing rye, hairy vetch, or no cover crop residue. Pitted morningglory was in the cotyledon to six-leaf growth stage. Seven 50-nm spectral bands (one ultraviolet, two visible, four near-infrared) derived from each hyperspectral reflectance measurement were used as discrimination variables. Pitted morningglory plant size had more influence on discriminant capabilities than tillage or cover crop residue systems. Across all tillage and residue systems, discrimination accuracy was 71 to 95%, depending on the size of pitted morningglory plants at the time of data acquisition. The versatility of the seven 50-nm bands was tested by using a discriminant model developed for one experiment location to test discriminant capabilities for the other experiment, with discrimination accuracy across all tillage and residue systems of 55 to 73%, depending on pitted morningglory plant size.

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.

scholarly journals Utilizing Hyperspectral Remote Sensing for Soil Gradation

2020 ◽  
Vol 12 (20) ◽  
pp. 3312
Author(s):  
Jordan Ewing ◽  
Thomas Oommen ◽  
Paramsothy Jayakumar ◽  
Russell Alger
Keyword(s):  
Remote Sensing ◽  
Fine Particles ◽  
Soil Mechanics ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Controlled Environment ◽  
Sieve Analysis ◽  
Field Sample ◽  
Specific Objective ◽  
Soil Gradation

Soil gradation is an important characteristic for soil mechanics. Traditionally soil gradation is performed by sieve analysis using a sample from the field. In this research, we are interested in the application of hyperspectral remote sensing to characterize soil gradation. The specific objective of this work is to explore the application of hyperspectral remote sensing to be used as an alternative to traditional soil gradation estimation. The advantage of such an approach is that it would provide the soil gradation without having to obtain a field sample. This work will examine five different soil types from the Keweenaw Research Center within a laboratory-controlled environment for testing. Our study demonstrates a correlation between hyperspectral data, the percent gravel and sand composition of the soil. Using this correlation, one can predict the percent gravel and sand within a soil and, in turn, calculate the remaining percent of fine particles. This information can be vital to help identify the soil type, soil strength, permeability/hydraulic conductivity, and other properties that are correlated to the gradation of the soil.

scholarly journals Mapping Ground Cover Using Hyperspectral Remote Sensing after the 2003 Simi and Old Wildfires in Southern California

Fire Ecology ◽  
2007 ◽  
Vol 3 (1) ◽  
pp. 109-128 ◽  
Author(s):  
Sarah A. Lewis ◽  
Leigh B. Lentile ◽  
Andrew T. Hudak ◽  
Peter R. Robichaud ◽  
Penelope Morgan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Southern California ◽  
Ground Cover ◽  
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.

Ensemble Learning on Hyperspectral Remote Sensing Image Classification

2012 ◽  
Vol 546-547 ◽  
pp. 508-513 ◽  
Author(s):  
Qiong Wu ◽  
Ling Wei Wang ◽  
Jia Wu
Keyword(s):  
Remote Sensing ◽  
Ensemble Learning ◽  
Classification Accuracy ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Remote Sensing Image Classification ◽  
Hyperspectral Remote Sensing Image

The characteristics of hyperspectral data with large number of bands, each bands have correlation, which has required a very high demand of solving the problem. In this paper, we take the features of hyperspectral remote sensing data and classification algorithms as the background, applying the ensemble learning to image classification.The experiment based on Weka. I compared the classification accuracy of Bagging, Boosting and Stacking on the base classifiers J48 and BP. The results show that ensemble learning on hyperspectral data can achieve higher classification accuracy. So that it provide a new method for the classification of hyperspectral remote sensing image.

Hyperspectral Remote Sensing and Radiative Transfer Simulation as a Tool for Monitoring Coral Reef Health

2002 ◽  
Vol 36 (1) ◽  
pp. 4-13 ◽  
Author(s):  
Hiroya Yamano ◽  
Masayuki Tamura ◽  
Yoshimitsu Kunii ◽  
Michio Hidaka
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Radiative Transfer ◽  
Feasibility Studies ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Dead Coral ◽  
Hyperspectral Sensors ◽  
Reef Health ◽  
Radiative Transfer Modeling

Recent advances in the remote sensing of coral reefs include hyperspectral remote sensing and radiative transfer modeling. Hyperspectral data can be regarded as continuous and the derivative spectroscopy is effective for extracting coral reef components, including sand, macroalgae, and healthy, bleached, recently dead, and old dead coral. Radiative transfer models are effective for feasibility studies of satellite or airborne remote sensing. Using these techniques, we simulate and analyze the apparent reflectance of coral reef benthic features associated with bleaching events, obtained by hyperspectral sensors on various platforms (ROV, boat, airplane, and satellite), and suggest that the coral reef health on reef flats can be discriminated precisely. Remote sensing using hyperspectral sensors should significantly contribute to mapping and monitoring coral reef health.

scholarly journals MAPPING OF FOLIAR CONTENT USING RADIATIVE TRANSFER MODELING AND VIS-NIR HYPERSPECTRAL CLOSE-RANGE REMOTE-SENSING

2015 ◽  
Vol XL-3/W3 ◽  
pp. 467-472 ◽  
Author(s):  
S. Jay ◽  
R. Bendoula ◽  
X. Hadoux ◽  
N. Gorretta
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Radiative Transfer Model ◽  
Integrating Sphere ◽  
Leaf Angle ◽  
Transfer Model

Most methods for retrieving foliar content from hyperspectral data are well adapted either to remote-sensing scale, for which each spectral measurement has a spatial resolution ranging from a few dozen centimeters to a few hundred meters, or to leaf scale, for which an integrating sphere is required to collect the spectral data. In this study, we present a method for estimating leaf optical properties from hyperspectral images having a spatial resolution of a few millimeters or centimeters. In presence of a single light source assumed to be directional, it is shown that leaf hyperspectral measurements can be related to the directional hemispherical reflectance simulated by the PROSPECT radiative transfer model using two other parameters. The first one is a multiplicative term that is related to local leaf angle and illumination zenith angle. The second parameter is an additive specular-related term that models BRDF effects. <br><br> Our model was tested on visible and near infrared hyperspectral images of leaves of various species, that were acquired under laboratory conditions. Introducing these two additional parameters into the inversion scheme leads to improved estimation results of PROSPECT parameters when compared to original PROSPECT. In particular, the RMSE for local chlorophyll content estimation was reduced by 21% (resp. 32%) when tested on leaves placed in horizontal (resp. sloping) position. Furthermore, inverting this model provides interesting information on local leaf angle, which is a crucial parameter in classical remote-sensing.

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.

Comparison of UAV RGB Imagery and Hyperspectral Remote-sensing Data for Monitoring Winter-wheat Growth

2021 ◽  
Author(s):  
Haikuan Feng ◽  
Huilin Tao ◽  
Chunjiang Zhao ◽  
Zhenhai Li ◽  
Guijun Yang
Keyword(s):  
Remote Sensing ◽  
Spectral Index ◽  
Vegetation Index ◽  
Crop Growth ◽  
Hyperspectral Data ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Estimation Model ◽  
Area Index ◽  
Simple Ratio

Abstract Background: Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unnamed aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. Results: To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into a new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results show that (1) the RGB-imagery indices red reflectance (r), the excess-red index(EXR), the vegetation atmospherically resistant index(VARI), and the modified green-red vegetation index(MGRVI) , as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index(MSR), the simple ratio vegetation index(SR), and the normalized difference vegetation index (NDVI)are more strongly correlated with the CGI than a single growth-monitoring indicator (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stage in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.Conclusions: UAVs carrying RGB cameras and hyperspectral cameras have high inversion CGI accuracy and can judge the overall growth of wheat can provide a reference for monitoring the growth of wheat.

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