Spectral reflectance of vegetation in the Idaho Cobalt District

potential for exploration using remote sensing

Publisher: U.S. Dept. of the Interior, U.S. Geological Survey, Publisher: [Books and Open-File Reports Section, distributor] in [Denver, Colo.?]

Written in English
Published: Downloads: 240
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Subjects:

  • Vegetation mapping -- Idaho,
  • Botany -- Idaho,
  • Vegetation surveys -- Idaho

Edition Notes

Includes bibliographical references (leaves 4-5)

Empirical models that based on spectral features of water reflectance generally showed good correlations with water quality parameters. The re- trieval model that using spectral bands at red/NIR showed a high correlation with chlorophyll a concentration (R2 = ). The ratio of green to blue spectral bands is the best predictor for TSSFile Size: KB. Soil reflectance (Figure ) typically increases with wavelength in the visible portion of the spectrum and then stays relatively constant in the near-IR and shortwave IR, with some local dips due to water absorption at and. Devices FieldSpec spectroradiometer (Analytical Spectral Devices ). The reflectance data for the pertinent wavelength intervals were used to calculate NDVI from the formula: NDVI~ ðÞNIR{Red ðÞNIRzRed ð1Þ where NIR is the near-infrared reflectance (–nm), and Red is the red reflectance (–nm) of the vegetation. Summary of Spectral Curves (Vegetation)

First, vegetation indices expand the limited spectral features of vegetation in the QuickBird HMS image and hold potential for tree classification. Second, leaf greenness, crown density (inter-canopy gaps), and background pixels (intra-canopy gaps) often cause noise and Cited by: of the plant in the visible light area. But the reflectance of Glauca does not change proportional to the incident solar radiance. This situation could cause an unexpected change of the spectral characteristics of the vegetation and hence make it difficult to detect detail information of mount ainous vegetation by the techniques of remote. Spectral vegetation indices have been shown to be useful in discriminating between different vegetation types but are not effective for all crops. Spectral vegetation indices (SVIs) are usually ratios of reflectance values at different wavelengths but may be other functions of reflectance (e.g. average reflectance over a range of wavelengths). If. Reflectance spectroscopy (or hyperspectral remote sensing) proved to be a tool that offers a non-destructive investigative method to identify anomalous spectral features in vegetation. One of the major environmental problems related to pipelines is the leakage of hydrocarbons into the environment.

In contrast to live vegetation, dead, dry, or senescent vegetation scatters photons very efficiently throughout the spectrum, with the most scattering occurring in the SWIR-1 and SWIR-2 ranges. The change in canopy reflectance due to increasing amounts of NPV is shown in the following figure. 4 Factors affecting spectral reflectance measurements Introduction Spectral measurements need to be accurate and precise representations of the target material but there are a variety of factors that affect the quality of spectral measurements. Careful consideration must be. You need to take reflectance measurements in order to calibrate the processed results from your corrected imagery. Your supervised classification of raw imagery will be driven by the ground truth values you collect for various land cover types. Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon Todd A. Schroeder a,⁎, Warren B. Cohen b, Conghe Song c, Morton J. Canty d, Zhiqiang Yang a a Department of Forest Science, Oregon State University, Corvallis, OR , United States b Forestry Sciences Laboratory, Pacific Northwest Research Station, USDA Forest.

Spectral reflectance of vegetation in the Idaho Cobalt District Download PDF EPUB FB2

Get this from a library. Spectral reflectance of vegetation in the Idaho Cobalt District: potential for exploration using remote sensing. [Terri L Purdy; N M Milton; B A Eiswerth; Geological Survey (U.S.)]. Spectral reflectance of Vegetation •Beyond μmenergy incident upon vegetation is essentially absorbed or reflected with little to no transmittance of energy •Dips in reflectance occur atand μm because water in the leaf absorbs strongly at these wavelengths (water absorption bands).

Vegetation Spectral Reflectance Curves. Vegetation has a unique spectral signature, but different types of vegetation differ in their reflectance. Plants that are stressed or diseased can also be identified by their distinct spectral signatures.

The leaf pigments, cell structure and water content all impact the spectral reflectance of vegetation. 1. Spectral Reflectance Curve of Dead/Stressed Vegetation 2. Spectral Reflectance *The reflectance characteristics of Earth’s surface features may be quantified by measuring the portion of incident energy that is reflected.

*This is measured as a function of. In other words, although the moderate or even low sampling density of the used mobile laser scanner cannot help solve the puzzle of spectral mixing in the pixel level (partly indicated by the large Spectral reflectance of vegetation in the Idaho Cobalt District book of the feature-pair points with NDVI value less than ), it is still useful for improving the investigation of tree spectral reflectance Cited by: 7.

11/16/13 Spectral reflectance of land covers 3/9 Fig 2: Partitioning of Vegetation Spectral Reflectance in the VIS, NIR and MIR File Size: 1MB.

Spectral properties of plants have been utilized in the context of their usefulness in studying vegetation from remote sensing platforms. A synthesis of data on spectral properties, vegetation types, growth and energy conditions provides valuable information about biomass and productivity.

A comparison of capabilities and methodologies to quantify vegetation from remote sensing platforms is Cited by: 9. Influence of pigments on leaf reflectance 5. Leaf absorptions by water and other biochemicals 6.

Spectral indexes: bands sensitive to biochemical of interest 7. Spectral Reflectance Curves for Natural Grass and Artificial Turf Visible versus Infrared Photos Visible Portion (Panchromatic) Near Infrared Football field has Artificial Turf Typical Spectral Reflectance curves for Vegetation, Soil and Water Lines in the figure represent average reflectance curves VEGETATION (Healthy Green Vegetation)File Size: KB.

Vegetation interacts with solar radiation in a different way than other natural materials. Vegetation typically absorbs in the red and blue wavelengths, reflects in the green wavelength, strongly reflects in the near infrared (NIR) wavelength, and displays strong absorption features in wavelengths where atmospheric water is present.

Different plant materials, water content, pigment, carbon. Spectral Reflectivity Curve of Vegetation Typical spectral response curve of vegetation from to µm. Wavelength (µm) Reflectance. Relatively high green response due to chlorophyll pigmentation.

High near infrared response due to healthy plant cell structure. Relatively low responses in the mid-infrared due to water File Size: KB. Knowledge of wetland vegetation spectral reflectance signatures can assist in spectral classification of remotely sensed images for monitoring of wetland hydroperiod.

This study aimed at assessing the differences between wetland vegetation communities of varying species composition and density in terms of spectral reflectance. The investigation was carried out in floodplains at Nxaraga Cited by: 4.

Spectral reflectance data are tabulated and displayed in graphical form for a wide variety of surface vegetation - soil texture, moisture and composition - and for various types of rock weathering.

A complete bibliography is included along with an index of geomorphological, pedological and botanical terms with Russian and Latin equivalents. Reflectance of green vegetation (Figure ) is low in the visible portion of the spectrum owing to chlorophyll absorp- tion, high in the near IR due to the cell structure of the plant, and lower again in the.

Spectral reflectance characteristics of arctic vegetation. Paul Budkewitsch, This figure is intended to show the reflectance profile of three varieties of plants (arctic willow, green moss and sedge) in the portion of the electromagnetic spectrum ranging from nanometers to nanometers, with gaps between and nanometers, and.

Reflected near-infrared radiation can be sensed by satellites, allowing scientists to study vegetation from space. Healthy vegetation absorbs blue- and red-light energy to fuel photosynthesis and create chlorophyll. A plant with more chlorophyll will reflect more near-infrared energy than an unhealthy plant.

Spectral reflectance of floodplain vegetation communities of the Okavango Delta Article (PDF Available) in Wetlands Ecology and Management 23(4) August with Reads. Overview of the radiometric and biophysical performance of the MODIS vegetation indices A.

Huetea,*, K. Didana, T. Miuraa, E.P. Rodrigueza, X. Gaoa, L.G. Ferreirab aDepartment of Soil, Water, and Environmental Science, University of Arizona, Tucson, AZUSA bUniversidade Federal de Goia´s, Goiaˆnia, GOBrazil Received 1 May ; received in revised form 4 February The canopy spectral characteristics of typical plants in the overburden of the Fuxin coal mine dump were measured and analyzed.

The reflectance of Leymus chinensis was affected by the soil, with a slight shift from green ( nm) to the near infrared (NIR) region. Changes in chlorophyll and water absorption were not significant in the red ( nm) and NIR bands, by: This band combination offers similar results to the traditional color infrared aerial photography.

The spectral reflectance is based on water and chlorophyll absorption in the leaf. Needles have a dark response comparing to the leaves. There are various shades of vegetation due to type, health, leaf structure, and moisture content of the plant.

produce Burned Area Reflectance Classification (BARC) maps for use by Burned Area Emergency Rehabilitation (BAER) teams in rapid response to wildfires. BAER teams desire maps indicative of soil burn severity, but photosynthetic and non-photosynthetic vegetation also influences the spectral properties of post-fire imagery.

OurCited by: the most reliable indicator of plant stress. Visible reflectance responses to stress were spectrally similar among agents of stress and species. Within the ,nm wavelength range, which in-cludes most of the incident solar spectrum (Gates, ), the spectral.

Spectral reflectance properties of mangrove species of the Muthupettai mangrove environment, Tamil Nadu Article (PDF Available) in Journal of Environmental Biology 29(5) October with. Adv. Space Res. Vol.1, pp /81/$oo/0 COSPAR, Printed in Great Britain.

SPECTRAL REFLECTANCE CHARACTERISTICS OF AGRICULTURAL CROPS AND APPLICATION TO CROP GROWTH MONITORING N.J.J. Bunnik National Aerospace Laboratory NLR, Amsterdam, The Netherlands ABSTRACT Information provided by analysis of remotely sensed multispectral Cited by: 1. reflectance and the results of Gausman et al.

() predict that the influence of litter on the canopy reflectance in the NIR region of the spectrum is a function of live, green, biomass rather than dead biomass.

The purpose of this study was to evaluate the extent to which patterns of seasonal change in reflectance of blue, green, red, and Cited by: EMS (Purkis and Klemas, ). First, in the visible spectrum, vegetation reflectance and transmittance are small due to a plants ability to greatly absorb chlorophyll (Jensen, ).

Second, vegetation in the high near infrared (NIR) portion is a strong reflectance that enables great detection of healthy foliage. Assessment of Vegetation Stress Using Reflectance or Fluorescence Measurements P. Campbell,* E. Middleton, J. McMurtrey, L. Corp, and E. Chappelle ABSTRACT Current methods for large-scale vegetation monitoring rely on mul-tispectral remote sensing, which has seriouslimitation for the detection by: REMOTE SENSING OF VEGETATION Due Febru In class, we have discussed the characteristics and causes of the spectral reflectance of vegetation: 0 10 20 30 40 50 60 1 2 Deciduous Trees Reflectance Wavelength (microns).

The spectral response of vegetation is due to chlorophyll in the visible, leaf structure in the NIR and. A pixel associated with low reflectance values in the visible band and high reflectance in the near-infrared band would produce an NDVI score nearindicating the presence of healthy vegetation.

Conversely, the NDVI scores of pixels associated with high reflectance in the visible band and low reflectance in the near-infrared band approach. of Texas. Reflectance and transmittance measurements were taken on single leaves, and were reduced to optical constants at each of 41 wave-lengths over the spectral region.- pm.

Reflectance corresponding to an infinitely thick stack of such leaves was used in a correlation analysis. Figure 9 is an abstracted plot of the. Barley spectral reflectance throughout the growing season for unpolluted (a) and Ni-polluted plots (b). Thus spectral reflectance variations are a function of plant state which in return depends on growth conditions.

The relation ‘growth conditionsplant state reflectance ability’ determines the informational potential of vegetation spectral.Figure The spectral response patterns of conifer trees and deciduous trees (California Institute of Technology, ) Spectral response patterns are sometimes called spectral signatures.

This term is misleading, however, because the reflectance of an entity varies with its condition, the time of year, and even the time of day.IMAGING SPECTROMETRY AND VEGETATION SCIENCE 3 characteristic signatures, data processing techniques and applications of hyperspectral remote sensing in vegetation studies.

2 Spectroscopy versus spectrometry Spectroscopy is the branch of physics concerned with the production, transmission,File Size: KB.