scholarly journals Investigation of Sediment-Rich Glacial Meltwater Plumes Using a High-Resolution Multispectral Sensor Mounted on an Unmanned Aerial Vehicle

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2405 ◽  
Author(s):  
Wójcik ◽  
Bialik ◽  
Osińska ◽  
Figielski
Keyword(s):  
Near Infrared ◽  
Spectral Band ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Spectral Bands ◽  
Aerial Vehicle ◽  
High Turbidity ◽  
Unfavourable Weather ◽  
Harsh Climate ◽  
Surface Ice

A Parrot Sequoia+ multispectral camera on a Parrot Bluegrass drone registered in four spectral bands (green, red, red edge (RE), and near-infrared (NIR)) to identify glacial outflow zones and determined the meltwater turbidity values in waters in front of the following Antarctic glaciers: Ecology, Dera Icefall, Zalewski, and Krak on King George Island, Southern Shetlands was used. This process was supported by a Red-Green-Blue (RGB) colour model from a Zenmuse X5 camera on an Inspire 2 quadcopter drone. Additional surface water turbidity measurements were carried out using a Yellow Springs Instruments (YSI) sonde EXO2. From this research, it was apparent that for mapping low-turbidity and medium-turbidity waters (<70 formazinenephelometricunits (FNU)), a red spectral band should be used, since it is insensitive to possible surface ice phenomena and registers the presence of both red and white sediments. High-turbidity plumes with elevated FNU values should be identified through the NIR band. Strong correlation coefficients between the reflectance at particular bands and FNU readings (RGreen = 0.85, RRed = 0.85, REdge = 0.84, and RNIR = 0.83) are shown that multispectral mapping using Unmanned Aerial Vehicles (UAVs) can be successfully usedeven in the unfavourable weather conditions and harsh climate of Antarctica. Lastly, the movement of water masses in Admiralty Bay is briefly discussed and supported by the results from EXO2 measurements.

scholarly journals TOPOGRAPHIC EFFECT ON SPECTRAL VEGETATION INDICES FROM LANDSAT TM DATA: IS TOPOGRAPHIC CORRECTION NECESSARY?

2016 ◽  
Vol 22 (1) ◽  
pp. 95-107 ◽  
Author(s):  
Eder Paulo Moreira* ◽  
Márcio de Morisson Valeriano ◽  
Ieda Del Arco Sanches ◽  
Antonio Roberto Formaggio
Keyword(s):  
Near Infrared ◽  
Incidence Angle ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Topographic Effect ◽  
Topographic Correction ◽  
Spectral Bands ◽  
Spectral Vegetation Indices ◽  
Current Availability ◽  
Elevation Model

The full potentiality of spectral vegetation indices (VIs) can only be evaluated after removing topographic, atmospheric and soil background effects from radiometric data. Concerning the former effect, the topographic effect was barely investigated in the context of VIs, despite the current availability correction methods and Digital elevation Model (DEM). In this study, we performed topographic correction on Landsat 5 TM spectral bands and evaluated the topographic effect on four VIs: NDVI, RVI, EVI and SAVI. The evaluation was based on analyses of mean and standard deviation of VIs and TM band 4 (near-infrared), and on linear regression analyses between these variables and the cosine of the solar incidence angle on terrain surface (cos i). The results indicated that VIs are less sensitive to topographic effect than the uncorrected spectral band. Among VIs, NDVI and RVI were less sensitive to topographic effect than EVI and SAVI. All VIs showed to be fully independent of topographic effect only after correction. It can be concluded that the topographic correction is required for a consistent reduction of the topographic effect on the VIs from rugged terrain.

scholarly journals Hypertemporal Imaging Capability of UAS Improves Photogrammetric Tree Canopy Models

2020 ◽  
Vol 12 (8) ◽  
pp. 1238 ◽  
Author(s):  
Andrew Fletcher ◽  
Richard Mather
Keyword(s):  
Forest Canopy ◽  
Near Infrared ◽  
Spectral Band ◽  
Point Clouds ◽  
Tree Canopy ◽  
Solar Elevation ◽  
Spectral Bands ◽  
Eucalyptus Forest ◽  
Dense Point ◽  
Solar Noon

Small uncrewed aerial systems (UASs) generate imagery that can provide detailed information regarding condition and change if the products are reproducible through time. Densified point clouds form the basic information for digital surface models and orthorectified mosaics, so variable dense point reconstruction will introduce uncertainty. Eucalyptus trees typically have sparse and discontinuous canopies with pendulous leaves that present a difficult target for photogrammetry software. We examine how spectral band, season, solar azimuth, elevation, and some processing settings impact completeness and reproducibility of dense point clouds for shrub swamp and Eucalyptus forest canopy. At the study site near solar noon, selecting near infrared camera increased projected tree canopy fourfold, and dense point features more than 2 m above ground were increased sixfold compared to red spectral bands. Near infrared (NIR) imagery improved projected and total dense features two- and threefold, respectively, compared to default green band imagery. The lowest solar elevation captured (25°) consistently improved canopy feature reconstruction in all spectral bands. Although low solar elevations are typically avoided for radiometric reasons, we demonstrate that these conditions improve the detection and reconstruction of complex tree canopy features in natural Eucalyptus forests. Combining imagery sets captured at different solar elevations improved the reproducibility of dense point clouds between seasons. Total dense point cloud features reconstructed were increased by almost 10 million points (20%) when imagery used was NIR combining solar noon and low solar elevation imagery. It is possible to use agricultural multispectral camera rigs to reconstruct Eucalyptus tree canopy and shrub swamp by combining imagery and selecting appropriate spectral bands for processing.

scholarly journals SHADOW DETECTION IN HYPERSPECTRAL IMAGES ACQUIRED BY UAV

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 371-377 ◽  
Author(s):  
N. N. Imai ◽  
A. M. G. Tommaselli ◽  
A. Berveglieri ◽  
E. A. S. Moriya
Keyword(s):  
Blue Light ◽  
Near Infrared ◽  
Shadow Detection ◽  
Hyperspectral Images ◽  
Agricultural Field ◽  
Additional Information ◽  
Spectral Bands ◽  
Agricultural Applications ◽  
Aerial Vehicle ◽  
Hyperspectral Camera

<p><strong>Abstract.</strong> Shadows are common in any kind of remote sensing images. Unmanned Aerial Vehicle &amp;ndash; UAV with a light camera attached can acquire images illuminated either by direct sunlight or by diffuse light under clouds. Indeed, areas with pixels shaded by clouds must be detected and labelled in order to use this additional information for image analysis. Classification of health and diseased plants in permanent culture as the orange plantation field can present some errors due to tree cast shadow. So, hyperspectral or multispectral image classification can be improved by previous shadow detection. Some FPI hyperspectral camera, designed for agricultural applications is limited in the spectral range between 500 to 900&amp;thinsp;nm. Wavelengths in the region of blue light and in the SWIR spectral region have physical properties that enable the enhancement of shaded regions in the images. In this work some combinations of different spectral bands were evaluated in order to specify those suitable to detect shadows in agricultural field images. In this sense, considering that vegetation and soil are the two main kind of coverage in an agricultural field, we hypothesized that wavelengths near blue light and the longest near infrared available in the camera range are good choices. In both spectral regions soil and vegetation targets have small spectral differences which contribute to enhance the differences between shaded and illuminated regions in the image. Hyperspectral images acquired with a FPI hyperspectral camera onboard a UAV over a plantation of oranges were used to evaluate these spectral bands. The results showed that the wavelengths of aproximatelly 510&amp;thinsp;nm and 840&amp;thinsp;nm available in the FPI camera are the best to detect any type of shadows in the agricultural fields.</p>

Variation in understory and canopy reflectance during stand development in Finnish coniferous forests

2015 ◽  
Vol 45 (8) ◽  
pp. 1077-1085 ◽  
Author(s):  
Nea Kuusinen ◽  
Pauline Stenberg ◽  
Erkki Tomppo ◽  
Pierre Bernier ◽  
Frank Berninger
Keyword(s):  
Norway Spruce ◽  
Scots Pine ◽  
Boreal Forests ◽  
Near Infrared ◽  
Spectral Band ◽  
Stand Development ◽  
Area Index ◽  
Spectral Bands ◽  
Site Fertility ◽  
Infrared Spectral

Inherent variability in the spectral properties of boreal forests complicates the retrieval of canopy properties such as canopy leaf area index from satellite images. Understanding the drivers of this variability could help provide better estimates of desired canopy cover properties. Field plot data from the Finnish National Forest Inventory and Landsat thematic mapper (TM) images were used to investigate the variation in canopy and understory reflectance during stand development in coniferous boreal forests. Spectral data for each plot were obtained from the Landsat pixel within which the plot center coordinates fell. Nonlinear unmixing was used to estimate the bidirectional reflectance factors (BRFs) of the “sunlit understory” and “canopy and shaded ground” components by site fertility and stand development classes. A forest albedo model was used to estimate the contribution of diffuse radiation reflected downwards from the canopy to the sunlit understory component. The sunlit understory BRF in the near-infrared spectral band decreased as the site fertility decreased and the forest matured, whereas the sunlit understory BRFs in the red and shortwave-infrared spectral bands concurrently increased. The BRFs of the canopy and shaded ground component decreased slightly during stand development, mostly in the near-infrared spectral band. Adding the diffuse contribution to the sunlit understory component changed the estimated component BRFs only a little (0.1%–1.7%) compared with those obtained using a linear mixing assumption. This effect was largest in the near-infrared spectral band and smallest in the red spectral band. For Norway spruce plots, the measured and estimated forest variables were well correlated with the BRFs in all of the studied spectral bands, but for the Scots pine plots, the correlations were notably weaker. Results show a greater importance of the fraction of visible sunlit understory on forest reflectance in Scots pine than in Norway spruce forests.

scholarly journals Modeling radiative transfer in tropical rainforest canopies: sensitivity of simulated albedo to canopy architectural and optical parameters

2011 ◽  
Vol 83 (4) ◽  
pp. 1231-1242 ◽  
Author(s):  
Sílvia N. M. Yanagi ◽  
Marcos H. Costa
Keyword(s):  
Radiative Transfer ◽  
Near Infrared ◽  
Tropical Rainforest ◽  
Spectral Band ◽  
Canopy Structure ◽  
Optical Parameters ◽  
Strong Response ◽  
Spectral Bands ◽  
Infrared Spectral ◽  
Integrated Biosphere Simulator

This study evaluates the sensitivity of the surface albedo simulated by the Integrated Biosphere Simulator (IBIS) to a set of Amazonian tropical rainforest canopy architectural and optical parameters. The parameters tested in this study are the orientation and reflectance of the leaves of upper and lower canopies in the visible (VIS) and near-infrared (NIR) spectral bands. The results are evaluated against albedo measurements taken above the K34 site at the INPA (Instituto Nacional de Pesquisas da Amazônia) Cuieiras Biological Reserve. The sensitivity analysis indicates a strong response to the upper canopy leaves orientation (x up) and to the reflectivity in the near-infrared spectral band (rNIR,up), a smaller sensitivity to the reflectivity in the visible spectral band (rVIS,up) and no sensitivity at all to the lower canopy parameters, which is consistent with the canopy structure. The combination of parameters that minimized the Root Mean Square Error and mean relative error are Xup = 0.86, rVIS,up = 0.062 and rNIR,up = 0.275. The parameterizations performed resulted in successful simulations of tropical rainforest albedo by IBIS, indicating its potential to simulate the canopy radiative transfer for narrow spectral bands and permitting close comparison with remote sensing products.

scholarly journals Spectral Feature Selection Optimization for Water Quality Estimation

2019 ◽  
Vol 17 (1) ◽  
pp. 272 ◽  
Author(s):  
Manh Van Nguyen ◽  
Chao-Hung Lin ◽  
Hone-Jay Chu ◽  
Lalu Muhamad Jaelani ◽  
Muhammad Aldila Syariz
Keyword(s):  
Near Infrared ◽  
Spectral Feature ◽  
Correlation Coefficients ◽  
Estimation Model ◽  
Chl A ◽  
Spectral Bands ◽  
Spectral Models ◽  
Multispectral Satellite Images ◽  
Estimation Models

The spatial heterogeneity and nonlinearity exhibited by bio-optical relationships in turbid inland waters complicate the retrieval of chlorophyll-a (Chl-a) concentration from multispectral satellite images. Most studies achieved satisfactory Chl-a estimation and focused solely on the spectral regions from near-infrared (NIR) to red spectral bands. However, the optical complexity of turbid waters may vary with locations and seasons, which renders the selection of spectral bands challenging. Accordingly, this study proposes an optimization process utilizing available spectral models to achieve optimal Chl-a retrieval. The method begins with the generation of a set of feature candidates, followed by candidate selection and optimization. Each candidate links to a Chl-a estimation model, including two-band, three-band, and normalized different chlorophyll index models. Moreover, a set of selected candidates using available spectral bands implies an optimal composition of estimation models, which results in an optimal Chl-a estimation. Remote sensing images and in situ Chl-a measurements in Lake Kasumigaura, Japan, are analyzed quantitatively and qualitatively to evaluate the proposed method. Results indicate that the model outperforms related Chl-a estimation models. The root-mean-squared errors of the Chl-a concentration obtained by the resulting model (OptiM-3) improve from 11.95 mg · m − 3 to 6.37 mg · m − 3 , and the Pearson’s correlation coefficients between the predicted and in situ Chl- a improve from 0.56 to 0.89.

scholarly journals EXTRACTION OF CLOUD HEIGHTS FROM SENTINEL-2 MULTISPECTRAL IMAGES

2021 ◽  
Vol V-1-2021 ◽  
pp. 17-23
Author(s):  
T. Krauß
Keyword(s):  
Near Infrared ◽  
Moving Objects ◽  
Spectral Band ◽  
Acquisition Time ◽  
Effect Analysis ◽  
Spectral Bands ◽  
High Resolution Imagery ◽  
Digital Elevation ◽  
Stereo Imagery ◽  
Sentinel 2

Abstract. Investigation of the focal plane assembly of the Sentinel-2 satellites show slight delays in the acquisition time of different bands on different CCD lines of about 0.5 to 1 second. This effect was already exploited in the detection of moving objects in very high resolution imagery as from WorldView-2 or -3 and also already for Sentinel-2 imagery. In our study we use the four 10-m-bands 2, 3, 4 and 8 (blue, green, red and near infrared) of Sentinel-2. In the level 1C processing each spectral band gets orthorectified separately on the same digital elevation model. So on the one hand moving objects on the ground experience a shift between the spectral bands. On the other hand objects not on the ground also show a slight shift between the spectral bands depending on the height of the object above ground. In this work we use this second effect. Analysis of cloudy Sentinel-2 scenes show small shifts of only one to two pixels depending on the height of the clouds above ground. So a new method based on algorithms for deriving dense digital elevation models from stereo imagery was developed to derive the cloud heights in Sentinel-2 images from the parallax from the 10-m-bands. After detailed description of the developed method it is applied to different cloudy Sentinel-2 images and the results are cross-checked using the shadows of the clouds together with the position of the sun at acquisition time.

scholarly journals Reflectance of Antarctica from 3 to 5 μm: discrimination of surface snow and cloud properties

2002 ◽  
Vol 34 ◽  
pp. 121-126 ◽  
Author(s):  
Wendy M. Calvin ◽  
Margaret Milman ◽  
Hugh H. Kieffer
Keyword(s):  
Near Infrared ◽  
Wavelength Region ◽  
Polar Regions ◽  
Water Ice ◽  
Cloud Properties ◽  
Spectral Bands ◽  
Surface Snow ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Surface Temperature ◽  
Surface Ice

AbstractCurrent techniques of cloud discrimination in polar regions, ice surface temperature measurement, sea-ice and snowfield extent mapping often rely on data acquired in the region from 3 to 5 mm. The Advanced Very High Resolution Radiometer (AVHRR) and the recently launched Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on Terra have spectral bands in this region used for these purposes. Approaches often consider the radiance value in this spectral range in terms of a single equivalent brightness temperature. However, this spectral region contains contributions from both solar-reflected and thermal-emitted radiance, and a water ice reflectance peak at 3.7 μm can be highly variable and a sensitive indicator of grain-size in icy particles either in clouds or as surface snow. In December 1992 the Galileo spacecraft, on its way to Jupiter, flew by and acquired images of Antarctica that included spectral coverage in 408 channels in the wavelength range 1–5 μm with the Near Infrared Mapping Spectrometer (NIMS). The NIMS spectra provide a basis for the separation of the reflected and emitted components in this wavelength region. This separation then allows the examination of the observed variation of the reflected component with respect to cloud and surface ice properties. This analysis may help refine current algorithms for cloud discrimination in AVHRR and MODIS using channels from 3 to 5 μm.

scholarly journals Subpixel Analysis of Primary and Secondary Infrared Emitters with Nighttime VIIRS Data

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 83
Author(s):  
Christopher D. Elvidge ◽  
Mikhail Zhizhin ◽  
Feng Chi Hsu ◽  
Tamara Sparks ◽  
Tilottama Ghosh
Keyword(s):  
Biomass Burning ◽  
Near Infrared ◽  
Spectral Band ◽  
Source Area ◽  
Short Wave ◽  
Test Case ◽  
Source Areas ◽  
Spectral Bands ◽  
Wide Range ◽  
Spectral Ranges

Biomass burning is a coupled exothermic/endothermic system that transfers carbon in several forms to the atmosphere, ultimately leaving mineral ash. The exothermic phases include flaming and smoldering, which produce the heat that drives the endothermic processes. The endothermic components include pre-heating and pyrolysis, which produce the fuel consumed by flaming and smoldering. These components can be broadly distinguished from each other based on temperature. For several years, we have researched the subpixel analysis of two temperature phases present in fire pixels detected in nighttime VIIRS data. Here, we present the flaming subtractive method, with which we have successfully derived temperatures and source areas for two infrared (IR) emitters and a cooler background. This is developed as an add-on to the existing VIIRS nightfire algorithm version 3 (VNF v.3) which uses Planck curve fitting to calculate temperatures and source areas for a single IR emitter and background. The flaming subtractive method works with data collected in four spectral ranges: near-infrared (NIR), short-wave infrared (SWIR), mid-wave infrared (MWIR) and long-wave infrared (LWIR). With sunlight eliminated, the NIR and SWIR radiances can be fully attributed to the primary IR emitter. The analysis begins with Planck curve modeling for the primary emitter based on the NIR and SWIR radiances, yielding temperature, source area and primary emitter radiances in all spectral bands. The primary emitter radiances are subtracted from each spectral band and then the residual radiance is analyzed for a secondary IR emitter and the background. Spurious results are obtained in pixels lacking a discernable secondary emitter. These misfit pixels revert back to the single IR emitter analysis of VNF v.3. In tests run for two California megafires, we found that the primary emitters straddle the temperature ranges for flaming and smoldering, the exothermic portions of biomass burning, which are apparently commingled on the ground. The secondary emitter temperatures span 350–750 K, corresponding to pre-heating and slow pyrolysis. The natural gas flare test case had few numbers of successful secondary emitter retrievals and a wide range of secondary emitter temperatures. The flaming subtractive analysis is the key addition to VNF version 4, which will commence production later in 2021. In 2022, we will seek validation of the VNF v.4 from nighttime Landsat and other data sources.

scholarly journals SPECTRAL CURVES FOR IDENTIFICATION OF WEEDS IN WHEAT CROP

2020 ◽  
Vol 28 ◽  
pp. 51-57
Author(s):  
Luan Pierre Pott ◽  
Telmo Jorge Carneiro Amado ◽  
Elodio Sebem ◽  
Raí Augusto Schwalbert
Keyword(s):  
Near Infrared ◽  
Spectral Band ◽  
Wheat Crop ◽  
Weed Species ◽  
Full Bloom ◽  
Reflectance Measurement ◽  
Spectral Curves ◽  
Spectral Bands ◽  
Randomized Design ◽  
Yellow Orange

The principal weeds in wheat cultivation are black oats and ryegrass and their control is generally performed without considering the spatial variability of the density of weed infestation. One way to identify weed species is by analyzing spectral curves of the targets. The objective of this work was to evaluate the spectral curves of wheat, black oats and ryegrass to identify which wavelengths are able to distinguish these species. The experiment was set using the species: black oats, ryegrass and wheat. Each species was sown in individual experimental plots in a completely randomized design with nine replications. HandHeld 2, ASD® spectroradiometer with 325-1075 nm spectral range was used to perform readings at full bloom stage. Then, the reflectance spectral data were grouped into eight spectral bands: violet, blue, green, yellow, orange, red, red edge and near infrared. Descriptive statistics of reflectance of the targets as well as analysis of variance (p<0.05) and test of Tukey for comparison of the means (p<0.01) were performed using the reflectance measurement of each spectral band. The results showed that the yellow and orange spectral bands obtained higher capacities of differentiation of the species under study. It can be concluded that the analysis of spectral curves of target of black oat and ryegrass weeds and wheat crop makes it possible to differentiate species in full bloom stage.

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