Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging

Lim, Hwan-Hui; Cheon, Enok; Lee, Deukhwan; Jeon, Jun-Seo; Lee, Seung-Rae

doi:10.3390/s20061611

Cited by 9 publications

(3 citation statements)

References 32 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, the water content of soils at the Gokseong landslide site is estimated using UAV-acquired multi-spectral images. An artificial neural network (ANN) model developed by Lim and his co-workers 44 is employed for this purpose, which utilizes soil color and NIR reflectance characteristics as input parameters, extracted from the multi-spectral images, to predict the water content of soils.…”

Section: Discussionmentioning

confidence: 99%

Multi-source remote sensing-based landslide investigation: the case of the August 7, 2020, Gokseong landslide in South Korea

Choi,

Ramirez,

Lim

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

Landslides pose a growing concern worldwide, emphasizing the need for accurate prediction and assessment to mitigate their impact. Recent advancements in remote sensing technology offer unprecedented datasets at various scales, yet practical applications demand further case studies to fully integrate these technologies into landslide analysis. This study presents a case study approach to fully leverage variety of multi-source remote sensing technologies for analyzing the characteristics of a landslide. The selected case is a landslide with a long runout debris flow that occurred in Gokseong County, South Korea, on August 7, 2020. The chosen multi-source technologies encompass digital photogrammetry using RGB and multi-spectral imageries, 3D point clouds acquired by light detection and ranging (LiDAR) mounted on an unmanned aerial vehicle (UAV), and satellite interferometric synthetic aperture radar (InSAR). The satellite InSAR analysis identifies the initial displacement, triggered by rainfall and later transforming into a debris flow. The utilization of digital photogrammetry, employing UAV-RGB and multi-spectral image data, precisely delineates the extent affected by the landslide. The landslide encompassed a runout distance of 678 m, featuring an initiation zone characterized by an average slope of 35°. Notably, the eroded and deposited areas measured 2.55 × 104 m2 and 1.72 × 104 m2, respectively. The acquired UAV-LiDAR data further reveal the eroded and deposited landslide volumes approximately measuring 5.60 × 104 m3 and 1.58 × 104 m3, respectively. This study contributes a valuable dataset on a rainfall-induced landslide with a long runout debris flow, underscoring the effectiveness of multi-source remote sensing technology in monitoring and comprehending complex landslide events.

show abstract

Section: Discussionmentioning

confidence: 99%

Multi-source remote sensing-based landslide investigation: the case of the August 7, 2020, Gokseong landslide in South Korea

Choi,

Ramirez,

Lim

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hyperspectral imaging overcomes the problem of the local variability within the sample and the problem of the interpretation of a single measurement because it combines imagery and spectral behavior. Therefore, most hyperspectral applications and research result from this combination [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. The human eye can easily interpret the image and identify different areas, interface regions, and buried objects.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging Tera Hertz System for Soil Analysis: Initial Results

Dworak

Mahns

Selbeck

et al. 2020

Sensors

View full text Add to dashboard Cite

Analyzing soils using conventional methods is often time consuming and costly due to their complexity. These methods require soil sampling (e.g., by augering), pretreatment of samples (e.g., sieving, extraction), and wet chemical analysis in the laboratory. Researchers are seeking alternative sensor-based methods that can provide immediate results with little or no excavation and pretreatment of samples. Currently, visible and infrared spectroscopy, electrical resistivity, gamma ray spectroscopy, and X-ray spectroscopy have been investigated extensively for their potential utility in soil sensing. Little research has been conducted on the application of THz (Tera Hertz) spectroscopy in soil science. The Tera Hertz band covers the frequency range between 100 GHz and 10 THz of the electromagnetic spectrum. One important feature of THz radiation is its correspondence with the particle size of the fine fraction of soil minerals (clay < 2 µm to sand < 2 mm). The particle size distribution is a fundamental soil property that governs soil water and nutrient content, among other characteristics. The interaction of THz radiation with soil particles creates detectable Mie scattering, which is the elastic scattering of electromagnetic waves by particles whose diameter corresponds approximately to the wavelength of the radiation. However, single-spot Mie scattering spectra are difficult to analyze and the understanding of interaction between THz radiation and soil material requires basic research. To improve the interpretation of THz spectra, a hyperspectral imaging system was developed. The addition of the spatial dimension to THz spectra helps to detect relevant features. Additionally, multiple samples can be scanned in parallel and measured under identical conditions, and the high number of data points within an image can improve the statistical accuracy. Technical details of the newly designed hyperspectral imaging THz system working from 250 to 370 GHz are provided. Results from measurements of different soil samples and buried objects in soil demonstrated its performance. The system achieved an optical resolution of about 2 mm. The sensitivity of signal damping to the changes in particle size of 100 µm is about 10 dB. Therefore, particle size variations in the µm range should be detectable. In conclusion, automated hyperspectral imaging reduced experimental effort and time consumption, and provided reliable results because of the measurement of hundreds of sample positions in one run. At this stage, the proposed setup cannot replace the current standard laboratory methods, but the present study represents the initial step to develop a new automated method for soil analysis and imaging.

show abstract

“…Indirect methods measure the soil's physical or chemical property that depends on its water content. They include the radiation methods (neutron probe, gamma-ray attenuation, nuclear magnetic resonance) (Klenke and Flint, 1991;Manalo et al, 2003;Strati et al, 2018), dielectric methods (time-domain reflectometry, frequency domain reflectometry, amplitude domain reflectometry, capacitive technique) (Moroizumi and Sasaki, 2008;Noborio, 2001;Sheets and Hendrickx, 1995;Whalley et al, 1992;Xu et al, 2012), remote sensing methods (microwave remote sensing and ground-penetrating radar) (Huisman et al, 2003;Jackson, 2002;Liu et al, 2019;Njoku and Entekhabi, 1996), and optical methods (fiber optic sensor technique and nearinfrared optical technique) (Alessi and Prunty, 1986;Lekshmi et al, 2014;Lim et al, 2020;Robinson et al, 2008). Radiation methods measure soil water content by the property of radioactive substances concerning soil moisture.…”

mentioning

confidence: 99%

A calculation method of unsaturated soil water content based on thermodynamic equilibrium

Zhou

Yin

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. Accurate determination of soil water content is crucial for studying the local ecological water cycle, agriculture, forests and grasslands management, slope stability, environmental processes, and ecological problems. Current measurement methods of soil water content have challenges with the complicated operation, high costs, significant system errors, and soil type-specific accuracy. In this study, we proposed an alternative method to measure soil water content using the ratios of mass and density of water to vapor based on system science and thermodynamics. We derived the mass ratio and density ratio of water to vapor as a function of temperature from published data and validated the accuracy of the proposed method using observed temperature, relative humidity, and volumetric water content in two soil textures of medium and fine sand. We further demonstrated that the mass ratio function is independent of the nature of the water-containing media using theoretical and statistical analysis. The absolute error of soil water content between the calculated and measured ones is less than 1 % and is positively correlated with temperature. This study significantly improves the measurement accuracy of soil water content, eliminates the effect of water-containing medium on soil water content measuring, and has excellent potential for application in the field soil and even rock water content measurement.

show abstract

Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging

Cited by 9 publications

References 32 publications

Multi-source remote sensing-based landslide investigation: the case of the August 7, 2020, Gokseong landslide in South Korea

Multi-source remote sensing-based landslide investigation: the case of the August 7, 2020, Gokseong landslide in South Korea

Hyperspectral Imaging Tera Hertz System for Soil Analysis: Initial Results

A calculation method of unsaturated soil water content based on thermodynamic equilibrium

Contact Info

Product

Resources

About