scholarly journals Application of an airborne hyper-spectral survey system CASI/SASI in the gold-silver-lead-zinc ore district of Huaniushan, Gansu, China

2021 ◽  
Vol 74 (1) ◽  
pp. 73-83
Author(s):  
Yuhai Fan ◽  
◽  
Yuiqing Wan ◽  
Hui Wang ◽  
Xingke Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spectral Data ◽  
Spectral Characteristics ◽  
Hyperspectral Remote Sensing ◽  
Alteration Zones ◽  
Hyper Spectral ◽  
Gold Silver ◽  
Survey System ◽  
Lead Zinc ◽  
Spectral Survey

The airborne hyper-spectral survey system CASI/SASI, which has an integrated system for gathering both image an spectral data, is at the cutting edge developments in the remote-sensing field. It can be used to directly identify surface objects based on diagnostic spectral characteristics. In this paper, the CASI/SASI were used in the Huaniushan gold-silver-lead-zinc ore district–Gansu to produce a lithologic map, identify altered minerals, and map the mineralized-alteration zones. Radiometric correction, radiometric calibration, atmospheric correction (spectral reconstruction), and geometric corrections were carried out in ENVI to pre-process the measured data. A FieldSpec ® Pro FR portable spectrometer was used to obtain the spectral signatures of all types of rock samples, ore deposits, and mineralized-alteration zones. We extracted and analyzed the spectral characteristics of typical alteration minerals. On the basis of hyper-spectral data, ground-spectral data processing, and comparative analysis of the measured image spectrum, we used the spectral-angle-mapping (SAM) and mixture-tuned matchedfiltering (MTMF) methods to perform hyperspectral-alteration mineral mapping of wall rock and mineralized-alteration-zone hyperspectral identification. Hyperspectral- remote- sensing geological- classification maps were produced as well as distribution maps of all kinds of alteration minerals and mineralized-alteration zones. Based on geological comprehensive analysis and field investigations, the range of mineral alteration was proven to be the same as shown by the remote-sensing imagery. Indications are that airborne hyperspectral- remote-sensing -image CASI/SASI offer good application results and show a promising potential as a tool in geological investigations. The results will provide the basis for hyperspectral remote-sensing prospecting in the same or similar unexplored areas.

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 Hyperspectral remote sensing for extraction of soil salinization in the northern region of Ningxia

2020 ◽  
Vol 6 (4) ◽  
pp. 2487-2493 ◽  
Author(s):  
Hazem T. Abd El-Hamid ◽  
Guan Hong
Keyword(s):  
Remote Sensing ◽  
Spectral Data ◽  
Vegetation Index ◽  
Spectral Characteristics ◽  
Salt Content ◽  
Soil Salinization ◽  
Northern Region ◽  
Semiarid Region ◽  
First Order ◽  
Remote Sensing Monitoring

Abstract Soil salinization affects negatively on agricultural productivity in the semiarid region of Ningxia. In this study, the performance of inversion model to determine soil salinization was assessed using some analysis and reflectance of wavelength. About 42 vegetation samples and 42 soil samples were collected for model extraction. Hyper-spectral data processing method was used to analyze spectral characteristics of different levels of salinization area vegetation. Spectral data were transformed in 16 different approaches, including root mean squares, logarithm, inversion logarithm, and first-order differentiation. After the transformation, the obtained soil and vegetation characteristics spectra correlate well with soil salt content, built soil index, and many vegetation indices. Nonlinear regression was employed to establish soil salinization remote sensing monitoring model. By comparing various spectral transformations, the first-order differential of soil spectral was the most sensitive to soil salinization degrees. The model of the current research was based on salinity index (SI) and improved soil-adjusted vegetation index (MSAVI). The correlation between simulated values and measured values was 0.758. Therefore, remote sensing monitoring derived from MSAVI–SI can greatly improve the dynamic and periodical monitoring of soil salinity in the study area.

Discussion on Development of Mineral Identification System Based on IDL

2014 ◽  
Vol 971-973 ◽  
pp. 1607-1610
Author(s):  
Yong Fei Che ◽  
Ying Jun Zhao ◽  
Wen Huan Wu
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Spectral Characteristics ◽  
Processing System ◽  
Mineral Resources ◽  
Hyperspectral Remote Sensing ◽  
Identification System ◽  
Mechanism Analysis ◽  
Remote Sensing Image Processing ◽  
Mineral Identification

The traditional data processing and analysis method of remote sensing image processing system cannot meet the hyperspectral remote sensing mass data processing and the need of practical application in mineral resources exploration. By studying the systematical analysis and key technology on the hyperspectral mineral information identification module, and analyzing and thinking about the relevant theoretical methods and technical process, carried out the development of hyperspectral mineral information identification module based on IDL and integrated with ENVI software, providing the basic support platform for hyperspectral remote sensing mineral resources exploration. Meanwhile, the existing problems were discussed from the spectral characteristics mechanism analysis of rock and the hyperspectral mineral identification optimization algorithms.

Study on the Intelligent Extraction Based on Hyper-Spectral Characteristics of Typical Vegetation

2014 ◽  
Vol 1073-1076 ◽  
pp. 1960-1964
Author(s):  
Jie Zhang ◽  
Hao Yan Zhao ◽  
Min Xia Zhang
Keyword(s):  
Spectral Data ◽  
Spectral Characteristics ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
Desert Vegetation ◽  
Hyper Spectral ◽  
Differential Transform ◽  
Green Vegetation ◽  
Low Coverage ◽  
Grassland Types

By using hyper-spectral remote sensing data of desert vegetation, the original spectral data was simply pretreated firstly, then first order differential transform and smoothing was the hyper-spectral data. The spectral characteristics of different grassland types were extracted. The results showed that: desert vegetation has some unique spectral features of common green vegetation. However, affected by the underlying surface of spared leaves, low coverage, the spectrum of desert vegetation does not have obvious green peak, and the red edge characteristics decreased with the decline of vegetation coverage.

Advantages using combined VNIR-SWIR and LWIR hyperspectral remote sensing for estimation of soil properties in the Amyntaio agricultural region, Northern Greece

2021 ◽  
Author(s):  
Robert Milewski ◽  
Sabine Chabrillat ◽  
Christopher Loy ◽  
Maximilian Brell ◽  
Nikos Tziolas ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Land Degradation ◽  
Spectral Characteristics ◽  
Hyperspectral Remote Sensing ◽  
Quantitative Prediction ◽  
Soil Conditions ◽  
Agricultural Activity ◽  
Northern Greece ◽  
Sampling Campaign

<p>A deeper understanding of the agricultural sector is needed to provide the informed and transparent framework required to meet increasing resource demands and pressures, without compromising sustainability. In this regard, an integrated management of the ecosystems is critical to address the priorities laid out by global policies and, achieve land degradation neutrality and resource efficient regions. Soils are an essential component of the ecosystem, they function as an important carbon storage, and provide the basis of agricultural activity. For the sustainable management of soil resources, and to prevent land degradation the regular assessments of spatially referenced soil conditions is essential. Critical soil properties, such as texture and organic and inorganic carbon content, provides farmers with the information to detect soil vulnerable to soil erosion and land degradation in its early stages in order to locally intervene and to assess soil fertility. Hyperspectral remote sensing been proven to be an effective method for the quantitative prediction of topsoil properties. However, remote sensing observations of the traditionally used visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelength regions (0.4-2.5 µm) can be limited for the estimation of coarse texture soils due to the lack of distinct spectral characteristics of these properties in the VNIR-SWIR (e.g., sand content, quartz and feldspar mineralogy). Spectral information from the longwave infrared region (LWIR, 8-12 μm) has the potential to improve the determination of these properties, due to the presence of fundamental vibration modes of silicate and carbonate minerals, as well carbon-hydrogen bonds in this spectral range.</p><p>The main objective of this study is to evaluate the increased analytical potential of combined VNIR-SWIR and LWIR hyperspectral remote sensing for the estimation of soil properties with the focus on soil organic matter, texture and mineralogical composition. In the frame of EnMAP GFZ/FU airborne campaign in Northern Greece in September 2019, an airborne survey with the HySpex VNIR-SWIR and Hyper-Cam LWIR cameras mounted on a Cessna airplane. A simultaneous ground sampling campaign took place at the agricultural landscape of the Amyntaio region including fields spectroscopy for calibration and validation porpoise, as well as soil sampling of bare soil fields. Fields in the study area have highly variable topsoil composition ranging from silicate to carbonate rich mineralogy, loamy to clay texture and to organic carbon rich fields around a lignite mine in the south-east of the area. Different statistical and machine learning methods such as Partial Least Squares (PLS) and Random Forest (RF) regression are applied to derive soil properties and the variable importance of the spectral dataset is discussed. A further goal of this study is the simulation and validation of the soil products with recent relevant satellite sensors (e.g., EnMAP, PRISMA, ECOSTRESS), as well as upcoming next generation of hyperspectral optical and thermal multispectral satellite missions (ESA CHIME and LSTM, NASA/JPL SBG) to evaluate their potential for quantitative soil properties mapping.</p>

scholarly journals Lead-Induced Changes in Fluorescence and Spectral Characteristics of Pea Leaves

2019 ◽  
Vol 11 (16) ◽  
pp. 1885 ◽  
Author(s):  
Marlena Kycko ◽  
Elżbieta Romanowska ◽  
Bogdan Zagajewski
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Spectral Characteristics ◽  
Hyperspectral Remote Sensing ◽  
Lead Ions ◽  
Chlorophyll Fluorescence Parameters ◽  
Fluorescence Parameters ◽  
Fluorescence Measurements ◽  
Pb Ions ◽  
Remote Sensing Indices

Chlorophyll fluorescence parameters can provide useful indications of photosynthetic performance in vivo. Coupling appropriate fluorescence measurements with other noninvasive techniques, such as absorption spectroscopy or gas exchange, can provide insights into the limitations to photosynthesis under given conditions. Chlorophyll content is one of the dominant factors influencing the conditions of a vegetation growing season, and can be tested using both fluorescence and remote sensing methods. Hyperspectral remote sensing and recording the narrow range of the spectrum can be used to accurately analyze the parameters and properties of plants. The aim of this study was to analyze the influence of lead ions (Pb, 5 mM Pb(NO3)2) on the growth of pea plants using spectral properties. Hyperspectral remote sensing and chlorophyll fluorescence measurements were used to assess the physiological state of plants seedlings treated by lead ions during the experiment. The plants were growing in hydroponic cultures supplemented with Pb ions under various conditions (control, complete Knop + phosphorus (+P); complete Knop + phosphorus (+P) + Pb; Knop (-P) + Pb, distilled water + Pb) affecting lead uptake via the root system. Spectrometric measurements allowed us to calculate the remote sensing indices of vegetation, which were compared with chlorophyll and carotenoids content and fluorescence parameters. The lead contents in the leaves, roots, and stems were also analyzed. Spectral characteristics and vegetation properties were analyzed using statistical tests. We conclude that: (1) pea seedlings grown in complete Knop (with P) and in the presence of Pb ions were spectrally similar to the control plants because lead was not transported to the shoots of plants; (2) lead most influenced plants that were grown in water, according to the highest lead content in the leaves; and (3) the effects of lead on plant growth were confirmed by remote sensing indices, whereas fluorescence parameters identified physiological changes induced by Pb ions in the plants.

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