Using hyperspectral data to detect the responses of masson pine's needle spectral reflectance to acid stress

Studies were conducted in 2001 and 2002 to determine the effect of POST herbicides on the spectral reflectance of corn. POST corn herbicides evaluated included 2,4-D, atrazine, bromoxynil, dicamba + diflufenzopyr, nicosulfuron, and primisulfuron. Multispectral and hyperspectral data were collected and spectral properties were analyzed using SAS procedures and MultiSpec image analysis. Corn treated with POST applications of atrazine and primisulfuron could not be distinguished from nontreated corn regardless of data type or analysis method used. 2,4-D and dicamba + diflufenzopyr were the most readily distinguished from nontreated corn plots using both hyperspectral and multispectral data.

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DETERMINING SPECTRAL REFLECTANCE COEFFICIENTS FROM HYPERSPECTRAL IMAGES OBTAINED FROM LOW ALTITUDES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b7-107-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 107-110

Author(s):

P. Walczykowski ◽

A. Jenerowicz ◽

A. Orych ◽

K. Siok

Keyword(s):

Remote Sensing ◽

Spectral Reflectance ◽

Spectral Characteristics ◽

Research Work ◽

Object Identification ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Post Processing ◽

Ecosystem Studies ◽

Processing Techniques

Remote Sensing plays very important role in many different study fields, like hydrology, crop management, environmental and ecosystem studies. For all mentioned areas of interest different remote sensing and image processing techniques, such as: image classification (object and pixel- based), object identification, change detection, etc. can be applied. Most of this techniques use spectral reflectance coefficients as the basis for the identification and distinction of different objects and materials, e.g. monitoring of vegetation stress, identification of water pollutants, yield identification, etc. Spectral characteristics are usually acquired using discrete methods such as spectrometric measurements in both laboratory and field conditions. Such measurements however can be very time consuming, which has led many international researchers to investigate the reliability and accuracy of using image-based methods. According to published and ongoing studies, in order to acquire these spectral characteristics from images, it is necessary to have hyperspectral data. The presented article describes a series of experiments conducted using the push-broom Headwall MicroHyperspec A-series VNIR. This hyperspectral scanner allows for registration of images with more than 300 spectral channels with a 1.9 nm spectral bandwidth in the 380- 1000 nm range. The aim of these experiments was to establish a methodology for acquiring spectral reflectance characteristics of different forms of land cover using such sensor. All research work was conducted in controlled conditions from low altitudes. Hyperspectral images obtained with this specific type of sensor requires a unique approach in terms of post-processing, especially radiometric correction. Large amounts of acquired imagery data allowed the authors to establish a new post- processing approach. The developed methodology allowed the authors to obtain spectral reflectance coefficients from a hyperspectral sensor mounted on an unmanned aerial vehicle, ensuring a high accuracy of obtained data.

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Estimation of leaf total chlorophyll and nitrogen concentrations using hyperspectral satellite imagery

The Journal of Agricultural Science ◽

10.1017/s0021859607007514 ◽

2007 ◽

Vol 146 (1) ◽

pp. 65-75 ◽

Cited By ~ 26

Author(s):

N. RAMA RAO ◽

P. K. GARG ◽

S. K. GHOSH ◽

V. K. DADHWAL

Keyword(s):

Spectral Reflectance ◽

Biochemical Parameters ◽

Vegetation Index ◽

Geographical Location ◽

Past Research ◽

Hyperspectral Data ◽

Agricultural Crops ◽

Total Chlorophyll ◽

Nitrogen Concentrations

SUMMARYRemotely sensed estimates of biochemical parameters of agricultural crops are central to the precision management of agricultural crops (precision farming). Past research using in situ and airborne spectral reflectance measurements of various vegetation species has proved the usefulness of hyperspectral data for the estimation of various biochemical parameters of vegetation. In order to exploit the vast spectral and radiometric resources offered by space-borne hyperspectral remote sensing for the improved estimation of plant biochemical parameters, the relationships observed between spectral reflectance and various biochemical parameters at in situ and airborne levels needed to be evaluated in order to establish the existence of a reliable and stable relationship between spectral reflectance and plant biochemical parameters at the pixel scale. The potential of the EO-1 Hyperion hyperspectral sensor was investigated for the estimation of total chlorophyll and nitrogen concentrations of cotton crops in India by developing regression models between hyperspectral reflectance and laboratory measurements of leaf total chlorophyll and nitrogen concentrations. A comprehensive and rigorous analysis was carried out to identify the spectral bands and spectral indices for accurate retrieval of leaf total chlorophyll and nitrogen concentrations of cotton crop. The performance of these critical spectral reflectance indices was validated using independent samples. A new vegetation index, named the plant biochemical index (PBI), is proposed for improved estimation of the plant biochemicals from space-borne hyperspectral data; it is simply the ratio of reflectance at 810 and 560 nm. Further, the applicability of PBI to a different crop and at a different geographical location was also assessed. The present results suggest the use of space-borne hyperspectral data for accurate retrieval of leaf total chlorophyll and nitrogen concentrations and the proposed PBI has the potential to retrieve leaf total chlorophyll and nitrogen concentrations of various crops and at different geographical locations.

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Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data

Agriculture ◽

10.3390/agriculture12010093 ◽

2022 ◽

Vol 12 (1) ◽

pp. 93

Author(s):

Chenjie Lin ◽

Yueming Hu ◽

Zhenhua Liu ◽

Yiping Peng ◽

Lu Wang ◽

...

Keyword(s):

Spectral Data ◽

Spectral Reflectance ◽

Regional Scale ◽

Hyperspectral Data ◽

Gradient Boosting ◽

Cultivated Land ◽

Least Squares Regression ◽

Land Quality ◽

Land Protection ◽

Dry Soil

Efficient monitoring of cultivated land quality (CLQ) plays a significant role in cultivated land protection. Soil spectral data can reflect the state of cultivated land. However, most studies have used crop spectral information to estimate CLQ, and there is little research on using soil spectral data for this purpose. In this study, soil hyperspectral data were utilized for the first time to evaluate CLQ. We obtained the optimal spectral variables from dry soil spectral data using a gradient boosting decision tree (GBDT) algorithm combined with the variance inflation factor (VIF). Two estimation algorithms (partial least-squares regression (PLSR) and back-propagation neural network (BPNN)) with 10-fold cross-validation were employed to develop the relationship model between the optimal spectral variables and CLQ. The optimal algorithms were determined by the degree of fit (determination coefficient, R2). In order to estimate CLQ at the regional scale, HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data were transformed into dry soil spectral data using the linkage model of original soil spectral reflectance to dry soil spectral reflectance. This study was conducted in the Guangdong Province, China and the Conghua district within the same province. The results showed the following: (1) the optimal spectral variables selected from the dry soil spectral variables were 478 nm, 502 nm, 614 nm, 872 nm, 966 nm, 1007 nm, and 1796 nm. (2) The BPNN was the optimal model, with an R2(C) of 0.71 and a normalized root mean square error (NRMSE) of 12.20%. (3) The results showed the R2 of the regional-scale CLQ estimation based on the proposed method was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional method. Additionally, the NRMSE of the regional-scale CLQ estimation base on dry soil spectral variables from HJ-1A HSI data was 2.00% lower than that of the model base on the original HJ-1A HSI data.

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Quantitative Estimating Salt Content of Saline Soil Using Laboratory Hyperspectral Data Treated by Fractional Derivative

Journal of Spectroscopy ◽

10.1155/2016/1081674 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 10

Author(s):

Dong Zhang ◽

Tashpolat Tiyip ◽

Jianli Ding ◽

Fei Zhang ◽

Ilyas Nurmemet ◽

...

Keyword(s):

Fractional Derivative ◽

Spectral Reflectance ◽

Salt Content ◽

Hyperspectral Data ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Lake Basin ◽

Soil Salt Content ◽

Soil Salt

Most present researches on estimation of soil salinity by hyperspectral data have focused on the spectral reflectance or their integer derivatives but ignored the fractional derivative information of hyperspectral data. Motivated by this situation, the selected study area is the Ebinur Lake basin located in the southwest border in the Xinjiang Uygur Autonomous Region, China, with severe salinization. The field work was conducted from 15 to 25 October, 2014, and a total of 180 soil samples were collected from 45 sampling sites; after measuring the soil salt content and spectral reflectance in the laboratory, the range from 0 to 2 was divided into 11 orders (interval 0.2) and then the hyperspectral data were treated by 4 kinds of mathematical transformations and 11 orders of fractional derivatives. Combined with the soil salt content, partial least square regression method was applied for model calibrations and predictions and some indexes were used to evaluate the performance of models. The results showed that the retrieval model built up by 250 bands based on 1.2-order derivative of 1/lg⁡R had excellent capacity of estimating soil salt content in the study area (RMSEC=14.685 g/kg, RMSEP=14.713 g/kg, R2C=0.782, R2P=0.768, and RPD = 2.080). This study provides an application reference for quantitative estimations of other land surface parameters and some other applications on hyperspectral technology.

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Crop Discrimination using Non-Imaging Hyperspectral Data

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e2802.0610521 ◽

2021 ◽

Vol 10 (5) ◽

pp. 269-273

Author(s):

Pooja Vinod Janse ◽

Ratnadeep R. Deshmukh

Keyword(s):

Classification Accuracy ◽

Spectral Reflectance ◽

Visual Analysis ◽

Vegetation Indices ◽

Research Work ◽

Hyperspectral Data ◽

Support Vector ◽

Spectral Bands ◽

Crop Type ◽

Crop Discrimination

Crop type discrimination is still very challenging task for researchers using non-imaging hyperspectral data. It is because of spectral reflectance similarity between crops. In this research work we have discriminated between four crops wheat, jowar, bajara and maize. We have tried to overcome the problems which have been faced my researchers. Initially by visual analysis we have selected 22 reflectance band which shows the absorption property of particular molecules and classification technique is applied, but it has given us very poor result of classification. We observed only 24% classification accuracy. So we considered nine vegetation indices along with spectral bands and achieved better classification accuracy. ASD FieldSpec 4 Spectroradiometer device is used for capturing spectral reflectance data. We calculated nine different vegetation indices and some selective reflectance bands are used for crop classification. We have used Support Vector Machine (SVM) for classification.

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Compressed Sensing Technology for Spectral Reconstruction Based on Maximum Entropy Criterion

10.21203/rs.3.rs-783127/v1 ◽

2021 ◽

Author(s):

Shoubo Zhao ◽

Mengyu Yang ◽

Yang Wang ◽

Jianying Fan

Keyword(s):

Compressed Sensing ◽

Maximum Entropy ◽

Spectral Reflectance ◽

Spectral Curve ◽

Hyperspectral Data ◽

Threshold Method ◽

Data Set ◽

Spectral Curves ◽

Sensing Technology ◽

Entropy Criterion

Abstract In order to choose the related sampling ratio in the information-poor and information-rich spectral fragments, this paper attempts to recover the spectral reflectance by compressed sensing technology based on maximum entropy criterion. The maximum entropy threshold method is the criterion that the optimal threshold is determined to segment the information content of spectral curves. The spectral reflectance in each sub-spectral fragment is reconstructed by compressed sensing. The wavelet orthogonal matrix performs a sparse representation of each segmented spectral curve. Undersampling spectral curve be collected by random gaussian measurement matrix. The orthogonal matching pursuit algorithm recovers sparse original signals from undersampling observed signals. In this paper, the four standard color blocks of Munsell and the spectral curves of five types of ground objects in the hyperspectral data set are used as the exper-imental objects. The reconstructed results are evaluated by spectral curve reconstruction, root mean square error and information entropy difference. The experimental results show that our approach improves the reconstruction accuracy of spectral reflectance effectively, compared with the traditional method.

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DETERMINING SPECTRAL REFLECTANCE COEFFICIENTS FROM HYPERSPECTRAL IMAGES OBTAINED FROM LOW ALTITUDES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b7-107-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 107-110 ◽

Cited By ~ 1

Author(s):

P. Walczykowski ◽

A. Jenerowicz ◽

A. Orych ◽

K. Siok

Keyword(s):

Remote Sensing ◽

Spectral Reflectance ◽

Spectral Characteristics ◽

Research Work ◽

Object Identification ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Post Processing ◽

Ecosystem Studies ◽

Processing Techniques

Remote Sensing plays very important role in many different study fields, like hydrology, crop management, environmental and ecosystem studies. For all mentioned areas of interest different remote sensing and image processing techniques, such as: image classification (object and pixel- based), object identification, change detection, etc. can be applied. Most of this techniques use spectral reflectance coefficients as the basis for the identification and distinction of different objects and materials, e.g. monitoring of vegetation stress, identification of water pollutants, yield identification, etc. Spectral characteristics are usually acquired using discrete methods such as spectrometric measurements in both laboratory and field conditions. Such measurements however can be very time consuming, which has led many international researchers to investigate the reliability and accuracy of using image-based methods. According to published and ongoing studies, in order to acquire these spectral characteristics from images, it is necessary to have hyperspectral data. The presented article describes a series of experiments conducted using the push-broom Headwall MicroHyperspec A-series VNIR. This hyperspectral scanner allows for registration of images with more than 300 spectral channels with a 1.9 nm spectral bandwidth in the 380- 1000 nm range. The aim of these experiments was to establish a methodology for acquiring spectral reflectance characteristics of different forms of land cover using such sensor. All research work was conducted in controlled conditions from low altitudes. Hyperspectral images obtained with this specific type of sensor requires a unique approach in terms of post-processing, especially radiometric correction. Large amounts of acquired imagery data allowed the authors to establish a new post- processing approach. The developed methodology allowed the authors to obtain spectral reflectance coefficients from a hyperspectral sensor mounted on an unmanned aerial vehicle, ensuring a high accuracy of obtained data.

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Using curve fitting for spectral reflectance curves intervals in order to hyperspectral data compression

2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) ◽

10.1109/csndsp.2016.7573958 ◽

2016 ◽

Cited By ~ 2

Author(s):

Mersedeh Beitollahi ◽

S. Abolfazl Hosseini

Keyword(s):

Data Compression ◽

Curve Fitting ◽

Spectral Reflectance ◽

Hyperspectral Data

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An interactive tool for semi-automatic feature extraction of hyperspectral data

Open Geosciences ◽

10.1515/geo-2016-0040 ◽

2016 ◽

Vol 8 (1) ◽

Cited By ~ 1

Author(s):

Zoltán Kovács ◽

Szilárd Szabó

Keyword(s):

Spectral Data ◽

Spectral Reflectance ◽

Regression Models ◽

Hyperspectral Data ◽

Spectral Indices ◽

Automatic Feature Extraction ◽

Attribute Data ◽

Data Analyst ◽

Different Types ◽

Qualitative Variables

AbstractThe spectral reflectance of the surface provides valuable information about the environment, which can be used to identify objects (e.g. land cover classification) or to estimate quantities of substances (e.g. biomass). We aimed to develop an MS Excel add-in – Hyperspectral Data Analyst (HypDA) – for a multipurpose quantitative analysis of spectral data in VBA programming language. HypDA was designed to calculate spectral indices from spectral data with user defined formulas (in all possible combinations involving a maximum of 4 bands) and to find the best correlations between the quantitative attribute data of the same object. Different types of regression models reveal the relationships, and the best results are saved in a worksheet. Qualitative variables can also be involved in the analysis carried out with separability and hypothesis testing;

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