Landsat near-infrared (NIR) band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

Abstract. Forest disturbance and regrowth are key processes in forest dynamics, but detailed information on these processes is difficult to obtain in remote forests such as the Amazon. We used chronosequences of Landsat satellite imagery (Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus) to determine the sensitivity of surface reflectance from all spectral bands to windthrow, clear-cut, and clear-cut and burned (cut + burn) and their successional pathways of forest regrowth in the Central Amazon. We also assessed whether the forest demography model Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM-FATES, accurately represents the changes for windthrow and clear-cut. The results show that all spectral bands from the Landsat satellites were sensitive to the disturbances but after 3 to 6 years only the near-infrared (NIR) band had significant changes associated with the successional pathways of forest regrowth for all the disturbances considered. In general, the NIR values decreased immediately after disturbance, increased to maximum values with the establishment of pioneers and early successional tree species, and then decreased slowly and almost linearly to pre-disturbance conditions with the dynamics of forest succession. Statistical methods predict that NIR values will return to pre-disturbance values in about 39, 36, and 56 years for windthrow, clear-cut, and cut + burn disturbances, respectively. The NIR band captured the observed, and different, successional pathways of forest regrowth after windthrow, clear-cut, and cut + burn. Consistent with inferences from the NIR observations, ELM-FATES predicted higher peaks of biomass and stem density after clear-cuts than after windthrows. ELM-FATES also predicted recovery of forest structure and canopy coverage back to pre-disturbance conditions in 38 years after windthrows and 41 years after clear-cut. The similarity of ELM-FATES predictions of regrowth patterns after windthrow and clear-cut to those of the NIR results suggests the NIR band can be used to benchmark forest regrowth in ecosystem models. Our results show the potential of Landsat imagery data for mapping forest regrowth from different types of disturbances, benchmarking, and the improvement of forest regrowth models.

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Landsat NIR band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

10.5194/bg-2019-451 ◽

2019 ◽

Author(s):

Robinson I. Negrón-Juárez ◽

Jennifer A. Holm ◽

Boris Faybishenko ◽

Daniel Magnabosco-Marra ◽

Rosie A. Fisher ◽

...

Keyword(s):

Near Infrared ◽

Forest Succession ◽

Forest Disturbance ◽

Terrestrial Ecosystem ◽

Stem Density ◽

Central Amazon ◽

Spectral Bands ◽

Forest Regrowth ◽

Landsat Satellite ◽

Successional Pathways

Abstract. Forest disturbance and regrowth are key processes in forest dynamics but detailed information of these processes is difficult to obtain in remote forests as the Amazon. We used chronosequences of Landsat satellite imagery to determine the sensitivity of surface reflectance from all spectral bands to windthrow, clearcutting, and burning and their successional pathways of forest regrowth in the Central Amazon. We also assess whether the forest demography model Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM-FATES, accurately represents the changes for windthrow and clearcut. The results show that all spectral bands from Landsat satellite were sensitive to the disturbances but after 3 to 6 years only the Near Infrared (NIR) band had significant changes associated with the successional pathways of forest regrowth for all the disturbances considered. In general, the NIR decreased immediately after disturbance, increased to maximum values with the establishment of pioneers and early-successional tree species, and then decreased slowly and almost linearly to pre-disturbance conditions with the dynamics of forest succession. Statistical methods predict that NIR will return to pre-disturbance values in about 39 years (consistent with observational data of biomass regrowth following windthrows), and 36 and 56 years for clearcut and burning. The NIR captured the observed successional pathways of forest regrowth after clearcut and burning that diverge through time. ELM-FATES predicted higher peaks of initial forest responses (e.g., biomass, stem density) after clearcuts than after windthrows, similar to the changes in NIR. However, ELM-FATES predicted a faster recovery of forest structure and canopy-coverage back to pre-disturbance conditions for windthrows compared to clearcuts. The similarity of ELM-FATES predictions of regrowth patterns after windthrow and clearcut to those of the NIR results suggest that the dynamics of forest regrowth for these disturbances are represented with appropriate fidelity within ELM-FATES and useful as a benchmarking tool.

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A Landsat-based model for retrieving total suspended solids concentration of estuaries and coasts in China

Geoscientific Model Development ◽

10.5194/gmd-10-4347-2017 ◽

2017 ◽

Vol 10 (12) ◽

pp. 4347-4365 ◽

Cited By ~ 13

Author(s):

Chongyang Wang ◽

Shuisen Chen ◽

Dan Li ◽

Danni Wang ◽

Wei Liu ◽

...

Keyword(s):

Suspended Solids ◽

Near Infrared ◽

Landsat Imagery ◽

River Estuary ◽

Total Suspended Solids ◽

Pearl River Estuary ◽

Thematic Mapper ◽

Pearl River ◽

Retrieval Models

Abstract. Retrieving total suspended solids (TSS) concentration accurately is essential for sustainable management of estuaries and coasts, which plays a key role in the interaction between hydrosphere, pedosphere and atmosphere. Although many TSS retrieval models have been published, the general inversion method that is applicable to different field conditions is still under research. In order to obtain a TSS remote sensing model that is suitable for estimating TSS concentrations with wide range in estuaries and coasts by Landsat imagery, after reviewing a number of Landsat-based TSS retrieval models and improving a comparatively better one among them, this study developed a quadratic model using the ratio of logarithmic transformation of red band and near-infrared band and logarithmic transformation of TSS concentration (QRLTSS) based on 119 in situ samples collected in 2006–2013 from five regions of China. It was found that the QRLTSS model works well and shows a satisfactory performance. The QRLTSS model based on Landsat TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus) and OLI (Operational Land Imager) sensors explained about 72 % of the TSS concentration variation (TSS: 4.3–577.2 mg L−1, N = 84, P value  < 0.001) and had an acceptable validation accuracy (TSS: 4.5–474 mg L−1, root mean squared error (RMSE)  ≤ 25 mg L−1, N = 35). In addition, a threshold method of red-band reflectance (OLI: 0.032, ETM+ and TM: 0.031) was proposed to solve the two-valued issue of the QRLTSS model and to retrieve TSS concentration from Landsat imagery. After a 6S model-based atmospheric correction of Landsat OLI and ETM+ imagery, the TSS concentrations of three regions (Moyangjiang River estuary, Pearl River estuary and Hanjiang River estuary) in Guangdong Province in China were mapped by the QRLTSS model. The results indicated that TSS concentrations in the three estuaries showed large variation ranging from 0.295 to 370.4 mg L−1. Meanwhile we found that TSS concentrations retrieved from Landsat imagery showed good validation accuracies with the synchronous water samples (TSS: 7–160 mg L−1, RMSE: 11.06 mg L−1, N = 22). The further validation from EO-1 Hyperion imagery also showed good performance (in situ synchronous measurement of TSS: 106–220.7 mg L−1, RMSE: 26.66 mg L−1, N = 13) of the QRLTSS model for the area of high TSS concentrations in the Lingding Bay of the Pearl River estuary. Evidently, the QRLTSS model is potentially applied to simulate high-dynamic TSS concentrations of other estuaries and coasts by Landsat imagery, improving the understanding of the spatial and temporal variation of TSS concentrations on regional and global scales. Furthermore, the QRLTSS model can be optimized to establish a regional or unified TSS retrieval model of estuaries and coasts in the world for different satellite sensors with medium- and high-resolution similar to Landsat TM, ETM+ and OLI sensors or with similar red bands and near-infrared bands, such as ALI, HJ-1 A and B, LISS, CBERS, ASTER, ALOS, RapidEye, Kanopus-V, and GF.

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Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

Remote Sensing ◽

10.3390/rs13030536 ◽

2021 ◽

Vol 13 (3) ◽

pp. 536

Author(s):

Eve Laroche-Pinel ◽

Mohanad Albughdadi ◽

Sylvie Duthoit ◽

Véronique Chéret ◽

Jacques Rousseau ◽

...

Keyword(s):

Near Infrared ◽

Water Status ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Short Wave ◽

Spatial And Temporal Scales ◽

Spectral Bands ◽

Main Challenge ◽

Spectral Scale ◽

Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

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Multiangular Polarized Characteristics of Optically Thin Cirrus in the Visible and Near-Infrared Spectral Region

Journal of the Atmospheric Sciences ◽

10.1175/2009jas2996.1 ◽

2010 ◽

Vol 67 (3) ◽

pp. 749-768 ◽

Cited By ~ 1

Author(s):

T. H. Cheng ◽

X. F. Gu ◽

L. F. Chen ◽

T. Yu ◽

G. L. Tian

Keyword(s):

Optical Thickness ◽

Near Infrared ◽

Global Climate ◽

Sensitivity Study ◽

Radiation Budget ◽

Sensitivity Index ◽

Transfer Program ◽

Conceptual Approach ◽

Spectral Bands ◽

Polarized Reflectance

Abstract Optically thin cirrus play a key role in the earth’s radiation budget and global climate change. Their radiative effects depend critically on the thin cirrus optical and microphysical properties. In this paper, inhomogeneous hexagonal monocrystals (IHMs), which consist of a pure hexagon with spherical air bubble or aerosol inclusions, are applied to calculate the single-scattering properties of individual ice crystals. The multiangular polarized characteristics of optically thin cirrus for the 0.865- and 1.38-μm spectral bands are simulated on the basis of an adding–doubling radiative transfer program. The sensitivity of total and polarized reflectance at the top of the atmosphere (TOA) to different aerosol, cirrus, and surface parameters is studied. A new sensitivity index is introduced to further quantify the sensitivity study. The TOA polarized reflectance measured by the Polarization and Directionality of the Earth’s Reflectance (POLDER) instruments is compared to simulated TOA total and polarized reflectance. The test results are reasonable, although small deviations caused by the change of aerosol properties and thin cirrus optical thickness do exist. Finally, on the basis of the sensitivity study, a conceptual approach is suggested to simultaneously retrieve thin cirrus clouds’ optical thickness, ice particle shape, and the underlying aerosol optical thickness using the TOA total and polarized reflectance of the 0.865- and 1.38-μm spectral bands measured at multiple viewing angles.

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Reflectance characteristics of cumulative defoliation of balsam fir

Canadian Journal of Forest Research ◽

10.1139/x88-154 ◽

1988 ◽

Vol 18 (8) ◽

pp. 1008-1016 ◽

Cited By ~ 18

Author(s):

D. G. Leckie ◽

P. M. Teillet ◽

G. Fedosejevs ◽

D. P. Ostaff

Keyword(s):

Near Infrared ◽

Spectral Characteristics ◽

Broad Band ◽

Integrating Sphere ◽

Simple Relationship ◽

Spectral Bands ◽

Infrared Wavelengths ◽

Remote Sensing Techniques ◽

Middle Infrared ◽

Airborne Sensors

Knowledge of the spectral characteristics of trees with varying degrees of needle loss is essential for developing remote sensing techniques for assessing defoliation. Spectra covering the range 400–2400 nm were acquired for single tree crowns suffering varying degrees of cumulative defoliation due to the spruce budworm (Choristoneurafumiferana (Clem.)), using a spectrometer mounted in the bucket of a boom truck. Spectra over the range 360–1100 nm were also obtained for the components of defoliated trees (i.e., needles, bare branches, and lichen), using a separate spectrometer and integrating sphere. Estimates of defoliation symptoms of each tree were made from the ground and above the tree. Changes in reflectance had a close and simple relationship with the defoliation symptoms measured. The spectral differences due to cumulative defoliation that were observed were broad-band features. The best spectral regions for differentiating levels of cumulative defoliation symptoms were the blue, red, shorter near-infrared wavelengths, and middle-infrared. Although currently available satellite and airborne sensors operate in these spectral regions, defoliation assessment may be improved by the use of optimized spectral bands.

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Sea Ice Albedo from MISR and MODIS: Production, Validation, and Trend Analysis

Remote Sensing ◽

10.3390/rs11010009 ◽

2018 ◽

Vol 11 (1) ◽

pp. 9 ◽

Cited By ~ 1

Author(s):

Said Kharbouche ◽

Jan-Peter Muller

Keyword(s):

Sea Ice ◽

Near Infrared ◽

Time Window ◽

Solar Spectrum ◽

High Accuracy ◽

Dynamic Surface ◽

Spectral Bands ◽

Langley Research Center ◽

Terra Satellite ◽

Cloud Mask

The Multi-angle Imaging SpectroRadiometer (MISR) sensor onboard the Terra satellite provides high accuracy albedo products. MISR deploys nine cameras each at different view angles, which allow a near-simultaneous angular sampling of the surface anisotropy. This is particularly important to measure the near-instantaneous albedo of dynamic surface features such as clouds or sea ice. However, MISR’s cloud mask over snow or sea ice is not yet sufficiently robust because MISR’s spectral bands are only located in the visible and the near infrared. To overcome this obstacle, we performed data fusion using a specially processed MISR sea ice albedo product (that was generated at Langley Research Center using Rayleigh correction) combining this with a cloud mask of a sea ice mask product, MOD29, which is derived from the MODerate Resolution Imaging Spectroradiometer (MODIS), which is also, like MISR, onboard the Terra satellite. The accuracy of the MOD29 cloud mask has been assessed as >90% due to the fact that MODIS has a much larger number of spectral bands and covers a much wider range of the solar spectrum. Four daily sea ice products have been created, each with a different averaging time window (24 h, 7 days, 15 days, 31 days). For each time window, the number of samples, mean and standard deviation of MISR cloud-free sea ice albedo is calculated. These products are publicly available on a predefined polar stereographic grid at three spatial resolutions (1 km, 5 km, 25 km). The time span of the generated sea ice albedo covers the months between March and September of each year from 2000 to 2016 inclusive. In addition to data production, an evaluation of the accuracy of sea ice albedo was performed through a comparison with a dataset generated from a tower based albedometer from NOAA/ESRL/GMD/GRAD. This comparison confirms the high accuracy and stability of MISR’s sea ice albedo since its launch in February 2000. We also performed an evaluation of the day-of-year trend of sea ice albedo between 2000 and 2016, which confirm the reduction of sea ice shortwave albedo with an order of 0.4–1%, depending on the day of year and the length of observed time window.

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Replacing missing values in the standard Multi-angle Imaging SpectroRadiometer (MISR) radiometric camera-by-camera cloud mask (RCCM) data product

Earth System Science Data ◽

10.5194/essd-12-611-2020 ◽

2020 ◽

Vol 12 (1) ◽

pp. 611-628 ◽

Cited By ~ 1

Author(s):

Michel M. Verstraete ◽

Linda A. Hunt ◽

Hugo De Lemos ◽

Larry Di Girolamo

Keyword(s):

Missing Values ◽

Near Infrared ◽

Data Repository ◽

Observation Data ◽

Science Data ◽

Data Services ◽

Data Product ◽

Spectral Bands ◽

Earth Observation Data ◽

Cloud Mask

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) is one of the five instruments hosted on board the NASA Terra platform, launched on 18 December 1999. This instrument has been operational since 24 February 2000 and is still acquiring Earth observation data as of this writing. The primary mission of the MISR is to document the state and properties of the atmosphere, in particular the clouds and aerosols it contains, as well as the planetary surface, on the basis of 36 data channels collectively gathered by its nine cameras (pointing in different directions along the orbital track) in four spectral bands (blue, green, red and near-infrared). The radiometric camera-by-camera cloud mask (RCCM) is derived from the calibrated measurements at the nominal top of the atmosphere and is provided separately for each of the nine cameras. This RCCM data product is permanently archived at the NASA Atmospheric Science Data Center (ASDC) in Hampton, VA, USA, and is openly accessible (Diner et al., 1999b, and https://doi.org/10.5067/Terra/MISR/MIRCCM_L2.004). For various technical reasons described in this paper, this RCCM product exhibits missing data, even though an estimate of the clear or cloudy status of the environment at each individual observed location can be deduced from the available measurements. The aims of this paper are (1) to describe how to replace over 99 % of the missing values by estimates and (2) to briefly describe the software to replace missing RCCM values, which is openly available to the community from the GitHub website, https://github.com/mmverstraete/MISR\\ RCCM/ (last access: 12 March 2020), or https://doi.org/10.5281/ZENODO.3240017 (Verstraete, 2019e). Two additional sets of resources are also made available on the research data repository of GFZ Data Services in conjunction with this paper. The first set (A; Verstraete et al., 2020; https://doi.org/10.5880/fidgeo.2020.004) includes three items: (A1) a compressed archive, RCCM_Out.zip, containing all intermediary, final and ancillary outputs created while generating the figures of this paper; (A2) a user manual, RCCM_Out.pdf, describing how to install, uncompress and explore those files; and (A3) a separate input MISR data archive, RCCM_input_68050.zip, for Path 168, Orbit 68050. This latter archive is usable with (B), the second set (Verstraete and Vogt, 2020; https://doi.org/10.5880/fidgeo.2020.008), which includes (B1), a stand-alone, self-contained, executable version of the RCCM correction codes, RCCM_Soft_Win.zip, using the IDL Virtual Machine technology that does not require a paid IDL license, as well as (B2), a user manual, RCCM_Soft_Win.pdf, to explain how to install, uncompress and use this software.

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Snow grain-size determination from Landsat imagery over Terre Adélie, Antarctica

Annals of Glaciology ◽

10.3189/s0260305500012659 ◽

1993 ◽

Vol 17 ◽

pp. 86-92 ◽

Cited By ~ 12

Author(s):

Barbara Bourdelles ◽

Michel Fily

Keyword(s):

Grain Size ◽

Sea Water ◽

Landsat Imagery ◽

Thematic Mapper ◽

Atmospheric Effects ◽

Topographic Effects ◽

Landsat Thematic Mapper ◽

Grain Sizes ◽

Terre Adélie ◽

Calibration Coefficients

A Landsat Thematic Mapper scene over Terre Adélie, Antarctica, is used to estimate the snow grain-size. New calibration coefficients are computed for the TM scene, using sea-water reflectance. Topographic effects are corrected with the visible band TM2. Atmospheric effects are taken into account. The snow theoretical reflectance is calculated with the Wiscombe and Warren (1980) model. Estimated snow grain-sizes are similar to those given in the literature.

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

Boletim de Ciências Geodésicas ◽

10.1590/s1982-21702016000100006 ◽

2016 ◽

Vol 22 (1) ◽

pp. 95-107 ◽

Cited By ~ 9

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.

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Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves

Applied Sciences ◽

10.3390/app10072259 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2259 ◽

Cited By ~ 2

Author(s):

Haixia Qi ◽

Bingyu Zhu ◽

Lingxi Kong ◽

Weiguang Yang ◽

Jun Zou ◽

...

Keyword(s):

Chlorophyll Content ◽

Spectral Index ◽

Spectral Reflectance ◽

Near Infrared ◽

Plant Density ◽

Cell Structure ◽

Spectral Bands ◽

Study Results ◽

Leaf Spectral Reflectance ◽

Estimation Models

The purpose of this study is to determine a method for quickly and accurately estimating the chlorophyll content of peanut plants at different plant densities. This was explored using leaf spectral reflectance to monitor peanut chlorophyll content to detect sensitive spectral bands and the optimum spectral indicators to establish a quantitative model. Peanut plants under different plant density conditions were monitored during three consecutive growth periods; single-photon avalanche diode (SPAD) and hyperspectral data derived from the leaves under the different plant density conditions were recorded. By combining arbitrary bands, indices were constructed across the full spectral range (350–2500 nm) based on blade spectra: the normalized difference spectral index (NDSI), ratio spectral index (RSI), difference spectral index (DSI) and soil-adjusted spectral index (SASI). This enabled the best vegetation index reflecting peanut-leaf SPAD values to be screened out by quantifying correlations with chlorophyll content, and the peanut leaf SPAD estimation models established by regression analysis to be compared and analyzed. The results showed that the chlorophyll content of peanut leaves decreased when plant density was either too high or too low, and that it reached its maximum at the appropriate plant density. In addition, differences in the spectral reflectance of peanut leaves under different chlorophyll content levels were highly obvious. Without considering the influence of cell structure as chlorophyll content increased, leaf spectral reflectance in the visible (350–700 nm): near-infrared (700–1300 nm) ranges also increased. The spectral bands sensitive to chlorophyll content were mainly observed in the visible and near-infrared ranges. The study results showed that the best spectral indicators for determining peanut chlorophyll content were NDSI (R520, R528), RSI (R748, R561), DSI (R758, R602) and SASI (R753, R624). Testing of these regression models showed that coefficient of determination values based on the NDSI, RSI, DSI and SASI estimation models were all greater than 0.65, while root mean square error values were all lower than 2.04. Therefore, the regression model established according to the above spectral indicators was a valid predictor of the chlorophyll content of peanut leaves.

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