scholarly journals Spektroskopi Reflektansi Sampel Tanah dan Batuan yang Mengandung Mineral Pembawa Unsur Tanah Jarang dan Radioaktif

EKSPLORIUM ◽  
2019 ◽  
Vol 40 (2) ◽  
pp. 89
Author(s):  
Arie Naftali Hawu Hede ◽  
Muhammad Anugrah Firdaus ◽  
Yogi La Ode Prianata ◽  
Mohamad Nur Heriawan ◽  
Syafrizal Syafrizal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rare Earth ◽  
Optical Sensors ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Infrared Range ◽  
Basic Principles ◽  
Spectral Signatures ◽  
Shortwave Infrared ◽  
Remote Sensing Methods

ABSTRAKSpektroskopi reflektansi merupakan salah satu metode nondestruktif untuk identifikasi mineral dan sebagai dasar dalam analisis pengindraan jauh (indraja) sensor optik. Penelitian ini bertujuan melakukan kajian penerapan spektroskopi reflektansi pada panjang gelombang 350–2.500 nm untuk sampel tanah dan batuan pembawa unsur tanah jarang (rare earth element-REE) dan radioaktif. Sampel diambil dari beberapa lokasi di Bangka Selatan dan Mamuju yang sebelumnya telah diidentifikasi memiliki potensi REE dan unsur radioaktif. Kurva reflektansi hasil analisis sampel dari Bangka Selatan menunjukan adanya kenampakan absorpsi yang menjadi karakteristik untuk kehadiran REE, dalam bentuk mineral monasit, zirkon, dan xenotime khususnya pada sampel yang berasal dari material tailing dan konsentrat bijih timah. Panjang gelombang yang menjadi kunci khususnya berada pada rentang visible-near infrared (VNIR; 400–1.300 nm). Sedangkan untuk sampel yang berasal dari Mamuju, yang merupakan daerah prospeksi mineral radioaktif, karakteristik spektral memperlihatkan beberapa panjang gelombang kunci terutama pada rentang shortwave infrared (1.300–2.500 nm). Hasil interpretasi menunjukkan mineral mayor berupa mineral lempung, sulfat, spesies NH4, dan mineral yang mengandung Al-OH lainnya, sedangkan untuk beberapa sampel pada panjang gelombang VNIR diidentifikasi mengandung mineral besi oksida/hidroksida. Hasil penelitian ini diharapkan dapat berguna untuk pemetaan eksplorasi REE dan radioaktif dengan menggunakan metode indraja.ABSTRACTReflectance spectroscopy is one of the nondestructive methods of mineral identification and is one of the basic principles in the remote sensing analysis using optical sensors. This research aimed at applying reflectance spectroscopy at 350–2,500 nm wavelength range for samples containing rare earth elements (REE) and radioactive minerals. Samples were taken from several locations in South Bangka and Mamuju that had previously been identified as potential location of REE and radioactive-bearing minerals. Reflectance data shows that there are absorption characteristics for REE-bearing minerals; monazite, zircon, and xenotime minerals especially from tailings and tin ore concentrate for the samples from South Bangka. The key wavelengths are specifically in the visible-near infrared range (VNIR; 400–1300 nm). For the samples from Mamuju, which is known as radioactive mineral prospecting areas, spectral characteristics provide information that there are spectral signatures in the shortwave infrared range (1,300–2,500 nm). The results of major mineral interpretations include clay minerals, sulfates, NH4 species, and other minerals containing Al-OH. However, some samples at the VNIR wavelength identified as iron oxide/hydroxide minerals. It is hoped that these results can be useful for REE and radioactive exploration mapping using remote sensing methods.

scholarly journals Normalized Dryness Built-up Index (NDBI) to Detect Settlement Change In Buleleng Sub-District

2021 ◽  
Vol 20 (1) ◽  
pp. 46
Author(s):  
Muhammad Rahman ◽  
A Sediyo Adi Nugraha
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Settlement Development ◽  
Landsat 7 ◽  
Shortwave Infrared ◽  
Remote Sensing Methods

This research aims to find out the development of settlements that occur over the next 20 years. Monitoring the development of settlements is carried out by remote sensing methods using Landsat 7 ETM+ imagery and Landsat 8 OLI imagery. Landsat 7 ETM+ used in 2000, and Landsat 8 OLI used in 2019. The algorithm is used to identify settlement development using the Normalized Dryness Built-up Index (NDBI). This algorithm uses two bands, such as Near-infrared and shortwave infrared, to calculate. The results showed that the growth of settlements occurred very significant because, in 2000, the number of settlements amounted to 628.2 hectares and in 2019 amounted to 1891.8 hectares. The increase in settlements occurred throughout the region in the Buleleng sub-district. Therefore, it can be concluded that NDBI can be used to monitor the development of settlements and the increase in settlements occurring as much as 28 % over 20 years.

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 Hazardous Noxious Substance Detection Based on Ground Experiment and Hyperspectral Remote Sensing

2021 ◽  
Vol 13 (2) ◽  
pp. 318
Author(s):  
Jae-Jin Park ◽  
Kyung-Ae Park ◽  
Pierre-Yves Foucher ◽  
Philippe Deliot ◽  
Stephane Le Floch ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spectral Characteristics ◽  
Chemical Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Noise Removal ◽  
Vertex Component Analysis ◽  
Shortwave Infrared ◽  
Spectral Mixture ◽  
Physical And Chemical

With an increase in the overseas maritime transport of hazardous and noxious substances (HNSs), HNS-related spill accidents are on the rise. Thus, there is a need to completely understand the physical and chemical properties of HNSs. This can be achieved through establishing a library of spectral characteristics with respect to wavelengths from visible and near-infrared (VNIR) bands to shortwave infrared (SWIR) wavelengths. In this study, a ground HNS measurement experiment was conducted for artificially spilled HNS by using two hyperspectral cameras at VNIR and SWIR wavelengths. Representative HNSs such as styrene and toluene were spilled into an outdoor pool and their spectral characteristics were obtained. The relative ratio of HNS to seawater decreased and increased at 550 nm and showed different constant ratios at the SWIR wavelength. Noise removal and dimensional compression procedures were conducted by applying principal component analysis on HNS hyperspectral images. Pure HNS and seawater endmember spectra were extracted using four spectral mixture techniques—N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA). The accuracy of detection values of styrene and toluene through the comparison of the abundance fraction were 99.42% and 99.56%, respectively. The results of this study are useful for spectrum-based HNS detection in marine HNS accidents.

scholarly journals Deriving seasonal dynamics in ecosystem properties of semi-arid savanna grasslands from in situ-based hyperspectral reflectance

2015 ◽  
Vol 12 (15) ◽  
pp. 4621-4635 ◽  
Author(s):  
T. Tagesson ◽  
R. Fensholt ◽  
S. Huber ◽  
S. Horion ◽  
I. Guiro ◽  
...  
Keyword(s):  
Seasonal Dynamics ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Arid Ecosystems ◽  
Hyperspectral Reflectance ◽  
Data Set ◽  
Ecosystem Properties ◽  
Shortwave Infrared ◽  
Semi Arid

Abstract. This paper investigates how hyperspectral reflectance (between 350 and 1800 nm) can be used to infer ecosystem properties for a semi-arid savanna grassland in West Africa using a unique in situ-based multi-angular data set of hemispherical conical reflectance factor (HCRF) measurements. Relationships between seasonal dynamics in hyperspectral HCRF and ecosystem properties (biomass, gross primary productivity (GPP), light use efficiency (LUE), and fraction of photosynthetically active radiation absorbed by vegetation (FAPAR)) were analysed. HCRF data (ρ) were used to study the relationship between normalised difference spectral indices (NDSIs) and the measured ecosystem properties. Finally, the effects of variable sun sensor viewing geometry on different NDSI wavelength combinations were analysed. The wavelengths with the strongest correlation to seasonal dynamics in ecosystem properties were shortwave infrared (biomass), the peak absorption band for chlorophyll a and b (at 682 nm) (GPP), the oxygen A band at 761 nm used for estimating chlorophyll fluorescence (GPP and LUE), and blue wavelengths (ρ412) (FAPAR). The NDSI with the strongest correlation to (i) biomass combined red-edge HCRF (ρ705) with green HCRF (ρ587), (ii) GPP combined wavelengths at the peak of green reflection (ρ518, ρ556), (iii) LUE combined red (ρ688) with blue HCRF (ρ436), and (iv) FAPAR combined blue (ρ399) and near-infrared (ρ1295) wavelengths. NDSIs combining near infrared and shortwave infrared were strongly affected by solar zenith angles and sensor viewing geometry, as were many combinations of visible wavelengths. This study provides analyses based upon novel multi-angular hyperspectral data for validation of Earth-observation-based properties of semi-arid ecosystems, as well as insights for designing spectral characteristics of future sensors for ecosystem monitoring.

scholarly journals Remote Sensing Landslide Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Wang ◽  
Xiaofei Wang ◽  
Junfan Jian
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Vegetation Index ◽  
Near Infrared ◽  
Spatial Information ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Landslide Event

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.

scholarly journals Development of an Optical System Based on Spectral Imaging Used for a Slug Control Robot

Horticulturae ◽  
2022 ◽  
Vol 8 (1) ◽  
pp. 77
Author(s):  
Christian Höing ◽  
Sharvari Raut ◽  
Abozar Nasirahmadi ◽  
Barbara Sturm ◽  
Oliver Hensel
Keyword(s):  
Optical System ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Optical Filter ◽  
Weather Conditions ◽  
Infrared Range ◽  
Target Area ◽  
Reflection Spectra ◽  
Threshold Method ◽  
Promising Alternative

The state-of-the-art technique to control slug pests in agriculture is the spreading of slug pellets. This method has some downsides, because slug pellets also harm beneficials and often fail because their efficiency depends on the prevailing weather conditions. This study is part of a research project which is developing a pest control robot to monitor the field, detect slugs, and eliminate them. Robots represent a promising alternative to slug pellets. They work independent of weather conditions and can distinguish between pests and beneficials. As a prerequisite, a robot must be able to reliably identify slugs irrespective of the characteristics of the surrounding conditions. In this context, the utilization of computer vision and image analysis methods are challenging, because slugs look very similar to the soil, particularly in color images. Therefore, the goal of this study was to develop an optical filter-based system that distinguishes between slugs and soil. In this context, the spectral characteristics of both slugs and soil in the visible and visible near-infrared (VNIR) wavebands were measured. Conspicuous maxima followed by conspicuous local minima were found for the reflection spectra of slugs in the near infrared range from 850 nm to 990 nm]. Thus, this enabled differentiation between slugs and soils; soils showed a monotonic increase in the intensity of the relative reflection for this wavelength. The extrema determined in the reflection spectra of slugs were used to develop and set up a slug detector device consisting of a monochromatic camera, a filter changer and two narrow bandpass filters with nominal wavelengths of 925 nm and 975 nm. The developed optical system takes two photographs of the target area at night. By subtracting the pixel values of the images, the slugs are highlighted, and the soil is removed in the image due to the properties of the reflection spectra of soils and slugs. In the resulting image, the pixels of slugs were, on average, 12.4 times brighter than pixels of soil. This enabled the detection of slugs by a threshold method.

Remote characterization of photosynthetic communities in the Fryxell basin of Taylor Valley, Antarctica

2020 ◽  
Vol 32 (4) ◽  
pp. 255-270 ◽  
Author(s):  
Mark R. Salvatore ◽  
Schuyler R. Borges ◽  
John E. Barrett ◽  
Eric R. Sokol ◽  
Lee F. Stanish ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Microbial Mats ◽  
Remote Sensing Data ◽  
Spectral Unmixing ◽  
Ecological Communities ◽  
Dry Valleys ◽  
Spectral Signatures ◽  
Taylor Valley ◽  
Infrared Spectral

AbstractWe investigate the spatial distribution, spectral properties and temporal variability of primary producers (e.g. communities of microbial mats and mosses) throughout the Fryxell basin of Taylor Valley, Antarctica, using high-resolution multispectral remote-sensing data. Our results suggest that photosynthetic communities can be readily detected throughout the Fryxell basin based on their unique near-infrared spectral signatures. Observed intra- and inter-annual variability in spectral signatures are consistent with short-term variations in mat distribution, hydration and photosynthetic activity. Spectral unmixing is also implemented in order to estimate mat abundance, with the most densely vegetated regions observed from orbit correlating spatially with some of the most productive regions of the Fryxell basin. Our work establishes remote sensing as a valuable tool in the study of these ecological communities in the McMurdo Dry Valleys and demonstrates how future scientific investigations and the management of specially protected areas could benefit from these tools and techniques.

scholarly journals Commercial polycarbonate track-etched membranes as substrates for low-cost optical sensors

2019 ◽  
Vol 10 ◽  
pp. 677-683 ◽  
Author(s):  
Paula Martínez-Pérez ◽  
Jaime García-Rupérez
Keyword(s):  
Optical Sensors ◽  
Mesoporous Material ◽  
Near Infrared ◽  
Low Cost ◽  
Ethanol Solution ◽  
Infrared Range ◽  
Refractive Index Sensing ◽  
Fabry Perot ◽  
Pattern Characteristic ◽  
Near Infrared Range

Porous materials have become one of the best options for the development of optical sensors, since they maximize the interaction between the optical field and the target substances, which boosts the sensitivity. In this work, we propose the use of a readily available mesoporous material for the development of such sensors: commercial polycarbonate track-etched membranes. In order to demonstrate their utility for this purpose, we firstly characterized their optical response in the near-infrared range. This response is an interference fringe pattern, characteristic of a Fabry–Pérot interferometer, which is an optical device typically used for sensing purposes. Afterwards, several refractive index sensing experiments were performed by placing different concentrations of ethanol solution on the polycarbonate track-etched membranes. As a result, a sensitivity value of around 56 nm/RIU was obtained and the reusability of the substrate was demonstrated. These results pave the way for the development of optical porous sensors with such easily available mesoporous material.

scholarly journals Noninvasive Monitoring of Blood Glucose Using Color-Coded Photoplethysmographic Images of the Illuminated Fingertip Within the Visible and Near-Infrared Range: Opportunities and Questions

2018 ◽  
Vol 12 (6) ◽  
pp. 1169-1177 ◽  
Author(s):  
Thorsten Vahlsing ◽  
Sven Delbeck ◽  
Steffen Leonhardt ◽  
H. Michael Heise
Keyword(s):  
Blood Glucose ◽  
Glucose Concentration ◽  
Near Infrared ◽  
Molecular Spectroscopy ◽  
Blood Glucose Concentration ◽  
Noninvasive Monitoring ◽  
Infrared Range ◽  
Self Monitoring ◽  
Spectral Signatures ◽  
Near Infrared Range

Noninvasive blood glucose assays have been promised for many years and various molecular spectroscopy-based methods of skin are candidates for achieving this goal. Due to the small spectral signatures of the glucose used for direct physical detection, moreover hidden among a largely variable background, broad spectral intervals are usually required to provide the mandatory analytical selectivity, but no such device has so far reached the accuracy that is required for self-monitoring of blood glucose (SMBG). A recently presented device as described in this journal, based on photoplethysmographic fingertip images for measuring glucose in a nonspecific indirect manner, is especially evaluated for providing reliable blood glucose concentration predictions.

scholarly journals Spectral Characteristics of the Antarctic Vegetation: A Case Study of Barton Peninsula

2021 ◽  
Vol 13 (13) ◽  
pp. 2470
Author(s):  
Junhwa Chi ◽  
Hyoungseok Lee ◽  
Soon Gyu Hong ◽  
Hyun-Cheol Kim
Keyword(s):  
Remote Sensing ◽  
Spectral Characteristics ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Spectral Unmixing ◽  
Polar Regions ◽  
Spectral Library ◽  
Sensing Data ◽  
Barton Peninsula ◽  
Shortwave Infrared

Spectral information is a proxy for understanding the characteristics of ground targets without a potentially disruptive contact. A spectral library is a collection of this information and serves as reference data in remote sensing analyses. Although widely used, data of this type for most ground objects in polar regions are notably absent. Remote sensing data are widely used in polar research because they can provide helpful information for difficult-to-access or extensive areas. However, a lack of ground truth hinders remote sensing efforts. Accordingly, a spectral library was developed for 16 common vegetation species and decayed moss in the ice-free areas of Antarctica using a field spectrometer. In particular, the relative importance of shortwave infrared wavelengths in identifying Antarctic vegetation using spectral similarity comparisons was demonstrated. Due to the lack of available remote sensing images of the study area, simulated images were generated using the developed spectral library. Then, these images were used to evaluate the potential performance of the classification and spectral unmixing according to spectral resolution. We believe that the developed library will enhance our understanding of Antarctic vegetation and will assist in the analysis of various remote sensing data.

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