Revisiting hyperspectral remote sensing: origin, processing, applications and way forward

Srivastava, Prashant K.; Malhi, Ramandeep Kaur M.; Pandey, Prem C.; Asundi, Anand; Singh, Prachi; Pandey, Manish; Gupta, Ayushi

doi:10.1016/b978-0-08-102894-0.00001-2

Cited by 26 publications

(8 citation statements)

References 76 publications

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“…The enriched spectral information captured by hyperspectral data can obscure valuable information regarding some target variables, which is why many scholars note that preprocessing hyperspectral data is obligatory [31,35,36]. Fractional order discretization (FOD) is one of the frequently applied and more efficient methods for preprocessing hyperspectral data [37].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Liu

Ding

et al. 2021

Remote Sensing

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Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R2 values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R2 = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Liu

Ding

et al. 2021

Remote Sensing

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“…Global positioning system (GPS), Inertial measurement unit (IMU). The figure is inspired by the following sources: Omasa et al ( 2007 ), Admin ( 2017 ), Bhatta and Priya ( 2017 ), Jahncke et al ( 2018 ), Martone et al ( 2018 ), Srivastava et al ( 2020 ). The components of the figure are modifications of Portree ( 2006 ), Organikos ( 2012 ); Smithsonian’s National Zoo and Conservation Biology Institute Smithsonian’s National Zoo ( 2016 ), and Freepik ( 2021 ).…”

Section: Techniques For Monitoring Biodiversitymentioning

confidence: 99%

“…Passive remote sensing is often understood as a system that operates by passive sensors which can only be used for detection in presence of the natural source of energy, i.e., sunlight (visible to shortwave spectrum and infrared thermal radiation) (Srivastava et al 2020 ). These sensors have a specialty to detect natural energy (radiation) that is either emitted or reflected from the source of energy or object (Earthdata 2021 ).…”

Section: Techniques For Monitoring Biodiversitymentioning

confidence: 99%

An overview of remote monitoring methods in biodiversity conservation

Kerry

Montalbo

Das

et al. 2022

Environ Sci Pollut Res

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Conservation of biodiversity is critical for the coexistence of humans and the sustenance of other living organisms within the ecosystem. Identification and prioritization of specific regions to be conserved are impossible without proper information about the sites. Advanced monitoring agencies like the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) had accredited that the sum total of species that are now threatened with extinction is higher than ever before in the past and are progressing toward extinct at an alarming rate. Besides this, the conceptualized global responses to these crises are still inadequate and entail drastic changes. Therefore, more sophisticated monitoring and conservation techniques are required which can simultaneously cover a larger surface area within a stipulated time frame and gather a large pool of data. Hence, this study is an overview of remote monitoring methods in biodiversity conservation via a survey of evidence-based reviews and related studies, wherein the description of the application of some technology for biodiversity conservation and monitoring is highlighted. Finally, the paper also describes various transformative smart technologies like artificial intelligence (AI) and/or machine learning algorithms for enhanced working efficiency of currently available techniques that will aid remote monitoring methods in biodiversity conservation.

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“…Spectral reflectance is the part or a fraction of incident electromagnetic radiation that is reflected from any interface. The reflectance is plotted with a wavelength known as the reflectance spectrum or spectral reflectance curve, which is the end product of the device [29,30]. A Spectralon reference panel was used to optimize and adjust the sensitivity of the instrument.…”

Section: Sample and Radiometer Data Collectionmentioning

confidence: 99%

Statistical Unfolding Approach to Understand Influencing Factors for Taxol Content Variation in High Altitude Himalayan Region

Gupta

Srivastava

Petropoulos

et al. 2021

Forests

Self Cite

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Taxol drugs can be extracted from various species of the taxaceae family. It is an alkaloid (metabolic product) used for the treatment of various types of cancer. Since taxol is a metabolic product, multiple aspects such as edaphic, biochemical, topographic factors need to be assessed in determining the variation in Taxol Content (TC). In this study, both sensor-based hyperspectral reflectance data and absorption-based indices were tested together for the development of an advanced statistical unfolding approach to understand the influencing factors for TC in high altitude Himalayan region. Seriation analysis based on permutation matrix was applied with complete linkage and a multi-fragment heuristic scaling rule along with the common techniques such as Principal Component Analysis (PCA) and correlation to understand the relationship of TC with various factors. This study also tested the newly developed taxol indices to rule out the possibility of overlapping of TC determining bands with the foliar pigment’s wavelengths in the visible region. The result implies that T. wallichiana with a high TC is found more in its natural habitat of deep forest, relating it indirectly to elevation in the case of the montane ecosystem. Taxol is the most varying parameter among the measured variables, followed by hyperspectral Taxol content (TC) indices such as TC 2, TC 5, and carotenoids, which suggests that the indices are well versed to capture variations in TC with elevation.

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Revisiting hyperspectral remote sensing: origin, processing, applications and way forward

Cited by 26 publications

References 76 publications

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

An overview of remote monitoring methods in biodiversity conservation

Statistical Unfolding Approach to Understand Influencing Factors for Taxol Content Variation in High Altitude Himalayan Region

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