scholarly journals Comparison between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Assessment of Vegetation Indices

2017 ◽  
Vol 1 (2) ◽  
pp. 355-366
Author(s):  
E. O. Makinde ◽  
A. D. Obigha
Keyword(s):  
Statistical Analysis ◽  
Land Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Thematic Mapper ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Landsat 7 ◽  
Index 2

The Landsat system has contributed significantly to the understanding of the Earth observation for over forty years. Since May 2013, data from Landsat 8 has been available online for download, with substantial differences from its predecessors, having an extended number of spectral bands and narrower bandwidths. The objectives of this research were majorly to carry out a cross comparison analysis between vegetation indices derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and also performed statistical analysis on the results derived from the vegetation indices. Also, this research carried out a change detection on four land cover classes present within the study area, as well as projected the land cover for year 2030. The methods applied in this research include, carrying out image classification on the Landsat imageries acquired between 1984 – 2016 to ascertain the changes in the land cover types, calculated the mean values of differenced vegetation indices derived from the four land covers between Landsat 7 ETM+ and Landsat 8 OLI. Statistical analysis involving regression and correlation analysis were also carried out on the vegetation indices derived between the two sensors, as well as scatter plot diagrams with linear regression equation and coefficients of determination (R2). The results showed no noticeable differences between Landsat 7 and Landsat 8 sensors, which demonstrates high similarities. This was observed because Global Environmental Monitoring Index (GEMI), Improved Modified Triangular Vegetation Index 2 (MTVI2), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) and Land Surface Water Index (LSWI) had smaller standard deviations. However, Renormalized Difference Vegetation Index (RDVI), Anthocyanin Reflectance Index 1 (ARI1) and Anthocyanin Reflectance Index 2 (ARI2) performed relatively poorly because their standard deviations were high. the correlation analysis of the vegetation indices that both sensors had a very high linear correlation coefficient with R2 greater than 0.99. It was concluded from this research that Landsat 7 ETM+ and Landsat 8 OLI can be used as complimentary data.

scholarly journals Cross-Comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) Derived Vegetation Indices in a Mediterranean Environment

2020 ◽  
Vol 12 (2) ◽  
pp. 291 ◽  
Author(s):  
Giuseppe Mancino ◽  
Agostino Ferrara ◽  
Antonietta Padula ◽  
Angelo Nolè
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Landsat 8 ◽  
Mediterranean Environment ◽  
Water Index ◽  
Land Use Types ◽  
Landsat 7

Landsat 8 is the most recent generation of Landsat satellite missions that provides remote sensing imagery for earth observation. The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, together with Landsat-8 Operational Land Imager (OLI) and Thermal Infrared sensor (TIRS) represent fundamental tools for earth observation due to the optimal combination of the radiometric and geometric images resolution provided by these sensors. However, there are substantial differences between the information provided by Landsat 7 and Landsat 8. In order to perform a multi-temporal analysis, a cross-comparison between image from different Landsat satellites is required. The present study is based on the evaluation of specific intercalibration functions for the standardization of main vegetation indices calculated from the two Landsat generation images, with respect to main land use types. The NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), LSWI (Land Surface Water Index), NBR (Normalized Burn Ratio), VIgreen (Green Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and EVI (Enhanced Vegetation Index) have been derived from August 2017 ETM+ and OLI images (path: 188; row: 32) for the study area (Basilicata Region, located in the southern part of Italy) selected as a highly representative of Mediterranean environment. Main results show slight differences in the values of average reflectance for each band: OLI shows higher values in the near-infrared (NIR) wavelength for all the land use types, while in the short-wave infrared (SWIR) the ETM+ shows higher reflectance values. High correlation coefficients between different indices (in particular NDVI and NDWI) show that ETM+ and OLI can be used as complementary data. The best correlation in terms of cross-comparison was found for NDVI, NDWI, SAVI, and EVI indices; while according to land use classes, statistically significant differences were found for almost all the considered indices calculated with the two sensors.

scholarly journals Effect of Image Fusion on Vegetation Index Quality—A Comparative Study from Gaofen-1, Gaofen-2, Gaofen-4, Landsat-8 OLI and MODIS Imagery

2020 ◽  
Vol 12 (10) ◽  
pp. 1550 ◽  
Author(s):  
Prakash Ghimire ◽  
Deng Lei ◽  
Nie Juan
Keyword(s):  
Image Fusion ◽  
Vegetation Index ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Pass Filter ◽  
Modis Imagery

In recent years, the use of image fusion method has received increasing attention in remote sensing, vegetation cover changes, vegetation indices (VIs) mapping, etc. For making high-resolution and good quality (with low-cost) VI mapping from a fused image, its quality and underlying factors need to be identified properly. For example, same-sensor image fusion generally has a higher spatial resolution ratio (SRR) (1:3 to 1:5) but multi-sensor fusion has a lower SRR (1:8 to 1:10). In addition to SRR, there might be other factors affecting the fused vegetation index (FVI) result which have not been investigated in detail before. In this research, we used a strategy on image fusion and quality assessment to find the effect of image fusion for VI quality using Gaofen-1 (GF1), Gaofen-2 (GF2), Gaofen-4 (GF4), Landsat-8 OLI, and MODIS imagery with their panchromatic (PAN) and multispectral (MS) bands in low SRR (1:6 to 1:15). For this research, we acquired a total of nine images (4 PAN+5 MS) on the same (almost) date (GF1, GF2, GF4 and MODIS images were acquired on 2017/07/13 and the Landsat-8 OLI image was acquired on 2017/07/17). The results show that image fusion has the least impact on Green Normalized Vegetation Index (GNDVI) and Atmospherically Resistant Vegetation Index (ARVI) compared to other VIs. The quality of VI is mostly insensitive with image fusion except for the high-pass filter (HPF) algorithm. The subjective and objective quality evaluation shows that Gram-Schmidt (GS) fusion has the least impact on FVI quality, and with decreasing SRR, the FVI quality is decreasing at a slow rate. FVI quality varies with types image fusion algorithms and SRR along with spectral response function (SRF) and signal-to-noise ratio (SNR). However, the FVI quality seems good even for small SRR (1:6 to 1:15 or lower) as long as they have good SNR and minimum SRF effect. The findings of this study could be cost-effective and highly applicable for high-quality VI mapping even in small SRR (1:15 or even lower).

scholarly journals Land Cover and Forest Type Classification by Values of Vegetation Indices and Forest Structure of Tropical Lowland Forests in Central Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Hung Nguyen Trong ◽  
The Dung Nguyen ◽  
Martin Kappas
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Forest Type ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Tropical Lowland ◽  
Central Vietnam ◽  
Lowland Forests ◽  
Type Classification ◽  
Sentinel 2

This paper aims to (i) optimize the application of multiple bands of satellite images for land cover classification by using random forest algorithms and (ii) assess correlations and regression of vegetation indices of a better-performed land cover classification image with vertical and horizontal structures of tropical lowland forests in Central Vietnam. In this study, we used Sentinel-2 and Landsat-8 to classify seven land cover classes of which three forest types were substratified as undisturbed, low disturbed, and disturbed forests where forest inventory of 90 plots, as ground-truth, was randomly sampled to measure forest tree parameters. A total of 3226 training points were sampled on seven land cover types. The performance of Landsat-8 showed out-of-bag error of 31.6%, overall accuracy of 68%, kappa of 67.5%, while Sentinel-2 showed out-of-bag error of 14.3% and overall accuracy of 85.7% and kappa of 83%. Ten vegetation indices of the better-performed image were extracted to find out (i) the correlation and regression of horizontal and vertical structures of trees and (ii) assess the variation values between ground-truthing plots and training sample plots in three forest types. The result of the t test on vegetation indices showed that six out of ten vegetation indices were significant at p<0.05. Seven vegetation indices had a correlation with the horizontal structure, but four vegetation indices, namely, Enhanced Vegetation Index, Perpendicular Vegetation Index, Difference Vegetation Index, and Transformed Normalized Difference Vegetation Index, had better correlations r = 0.66, 0.65, 0.65, 0.63 and regression results were of R2 = 0.44, 0.43, 0.43, and 0.40, respectively. The correlations of tree height were r = 0.46, 0.43, 0.43, and 0.49 and its regressions were of R2 = 0.21, 0.19, 0.18, and 0.24, respectively. The results show the possibility of using random forest algorithm with Sentinel-2 in forest type classification in line with vegetation indices application.

scholarly journals ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 821-828
Author(s):  
N. Aslan ◽  
D. Koc-San
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Irrigated Agriculture ◽  
Time Interval ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
Study Results ◽  
Landsat 7

The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88&thinsp;% for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6&thinsp;&deg;C for 2001 and 6.8&thinsp;&deg;C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r<sup>2</sup>&thinsp;=&thinsp;0.7 and r<sup>2</sup>&thinsp;=&thinsp;0.9 for 2001 and 2014, respectively).

scholarly journals Mapping Coastal Dune Landscape through Spectral Rao’s Q Temporal Diversity

2020 ◽  
Vol 12 (14) ◽  
pp. 2315
Author(s):  
Flavio Marzialetti ◽  
Mirko Di Febbraro ◽  
Marco Malavasi ◽  
Silvia Giulio ◽  
Alicia Teresa Rosario Acosta ◽  
...  
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Vegetation Index ◽  
Central Italy ◽  
Coastal Dunes ◽  
Water Index ◽  
Coastal Landscapes ◽  
Index 2 ◽  
Main Components ◽  
Q Index

Coastal dunes are found at the boundary between continents and seas representing unique transitional mosaics hosting highly dynamic habitats undergoing substantial seasonal changes. Here, we implemented a land cover classification approach specifically designed for coastal landscapes accounting for the within-year temporal variability of the main components of the coastal mosaic: vegetation, bare surfaces and water surfaces. Based on monthly Sentinel-2 satellite images of the year 2019, we used hierarchical clustering and a Random Forest model to produce an unsupervised land cover map of coastal dunes in a representative site of the Adriatic coast (central Italy). As classification variables, we used the within-year diversity computed through Rao’s Q index, along with three spectral indices describing the main components of the coastal mosaic (i.e., Modified Soil-adjusted Vegetation Index 2—MSAVI2, Normalized Difference Water Index 2—NDWI2 and Brightness Index 2—BI2). We identified seven land cover classes with high levels of accuracy, highlighting different covariates as the most important in differentiating them. The proposed framework proved effective in mapping a highly seasonal and heterogeneous landscape such as that of coastal dunes, highlighting Rao’s Q index as a sound base for natural cover monitoring and mapping. The applicability of the proposed framework on updated satellite images emphasizes the procedure as a reliable and replicable tool for coastal ecosystems monitoring.

PEMETAAN DAN ANALISIS PERUBAHAN GARIS PANTAI DI SEBAGIAN PESISIR BARAT LOMBOK BARAT MENGGUNAKAN NORMALIZED DIFFERENCE WATER INDEX PADA CITRA LANDSAT

2019 ◽  
Vol 3 ◽  
pp. 911
Author(s):  
Karunia Pasya Kusumawardani ◽  
Zulfian Isnaini Cahya ◽  
Wahyu Hendardi Giri Ananto ◽  
Galuh Hayun Mustika Asri
Keyword(s):  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Landsat 7

Pesisir Kabupaten Kabupaten Lombok Barat dan Kota Mataram merupakan wilayah rawan bencana dan perubahan garis pantai. Dalam 10 tahun terakhir telah terjadi abrasi sehingga pada tahun 2007 dibangun tanggul pemecah gelombang di sebagian pesisir Ampenan. Abrasi semakin parah terjadi pada dua tahun terkahir yaitu tahun 2017 dan 2018. Abrasi pantai terjadi di sepanjang Pantai Ampenan seperti di Kelurahan Bintaro sampai Mapak Indah (Radar Lombok, 2017). Penelitian bertujuan untuk memetakan garis pantai dan menganalisis perubahan garis pantai di sebagian pesisir Kabupaten Lombok Barat dan Kota Mataram. Data yang digunakan adalah data citra multitemporal yaitu citra Landsat 7 ETM+ tahun 2003 dan citra Landsat 8 OLI tahun 2018. Metode yang digunakan untuk memetakan garis pantai adalah transformasi indeks yaitu Normalized Difference Water Index (NDWI) dan filter highpass. Algoritma NDWI dapat digunakan untuk mengidentifikasi tubuh air. Transformasi NDWI pada penelitian digunakan untuk membedakan wilayah daratan dan perairan. Algoritma NDWI melibatkan band hijau dan band inframerah dekat yaitu dengan rumus NDWI = Green-NIR/Green+NIR. Pengujian model dilakukan dengan citra resolusi tinggi yaitu citra Planet dengan resolusi 3 meter. Output terdiri atas peta garis pantai tahun 2003 dan 2018 dengan skala 1: 125.000. Hasil pengujian peta garis pantai dengan citra resolusi tinggi menghasilkan nilai mean sebesar 14.972 m dengan standar deviasi sebesar 5.106 m. Perubahan garis pantai di sebagian pesisir Lombok Barat disebabkan karena adanya abrasi oleh kecepatan arus yang tinggi dan durasinya yang lama serta akresi yang disebabkan sedimentasi material dari 7 sungai di wilayah Ampenan Tengah, Ampenan Selatan, Loang Baloq, Labu Api, dan Gerung.

scholarly journals Protocolo Metodológico para Obtenção dos Valores de Reflectância e de NDVI de Imagens Landsat 8/OLI Utilizando LEGAL

2021 ◽  
Vol 14 (2) ◽  
pp. 869
Author(s):  
João Pedro Ocanha Krizek ◽  
Luciana Cavalcanti Maia Santos
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Environmental Parameters ◽  
Biomass Productivity ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Remote Sensing Images ◽  
De Se ◽  
Correct Estimation

A obtenção dos valores de reflectância se mostra imprescindível para se calcular índices de vegetação, como o NDVI (Normalized Difference Vegetation Index). Este índice é utilizado para classificar a distribuição global da vegetação e para inferir variáveis ecológicas e ambientais, como a produção de fitomassa.  Apesar disso, não é incomum encontrar trabalhos que utilizam os números digitais (ND) para a obtenção direta dos índices de vegetação; entretanto, tais números digitais não representam valores físicos reais e, portanto, não podem ser utilizados diretamente para o cálculo do NDVI. Assim, o objetivo deste artigo é demonstrar um protocolo metodológico para a conversão dos ND das imagens Landsat 8/OLI em valores de reflectância e a subsequente obtenção do NDVI, através da linguagem LEGAL (Linguagem Espacial para Geoprocessamento Algébrico), e, dessa forma, possibilitar a replicação e execução de outras pesquisas que visem obter esse índice de vegetação no software SPRING. Além disso, objetivou-se também demonstrar a importância da conversão dos ND em reflectância, a partir da comparação de uma imagem NDVI gerada através da reflectância com a mesma imagem NDVI gerada por meio dos dados brutos. Os resultados apontaram que a obtenção do NDVI através dos valores brutos de imagens de sensoriamento remoto, sem a necessária conversão dos números digitais em valores reais de reflectância, leva a resultados incorretos na estimativa de dados ecológicos da vegetação, subestimando a fitomassa. Dessa forma, esse trabalho ressalta a importância de se seguir um protocolo metodológico para a estimativa correta da fitomassa, produtividade e outros parâmetros da vegetação.   Methodological protocol for obtaining reflectance and NDVI values from Landsat 8/OLI images using LEGALA B S T R A C TObtaining reflectance values is essential for calculating vegetation indices, such as the NDVI (Normalized Difference Vegetation Index). This index is used to classify the global distribution of vegetation and to infer the ecological and environmental parameters such as phytomass production. Nevertheless, it is common to find works that use digital numbers (DN) to directly obtain vegetation indices; however, such digital numbers do not represent actual physical values and therefore cannot be used directly for NDVI calculation. Thus, this paper aims to demonstrate a methodological protocol for DN conversion of Landsat 8/OLI images into reflectance values and then for obtaining NDVI through the LEGAL (Spatial Language for Algebraic Geoprocessing). Therefore, this protocol enables the replication and execution of other studies aimed to obtain this vegetation index using SPRING. In addition, the objective was also to demonstrate the importance of converting DN to reflectance by comparing an NDVI image generated from reflectance with the same NDVI image generated through the raw data. The results showed that obtaining the NDVI through the raw values of remote sensing images, without the conversion of digital numbers to real reflectance values, leads to incorrect results in the estimation of ecological vegetation data, underestimating phytomass, thus emphasizing the importance of following a methodological protocol for the correct estimation of biomass, productivity and other phytological parameters.Keywords: protocol, NDVI, reflectance, Landsat 8, SPRING

scholarly journals ANÁLISE DO IMPACTO DA CORREÇÃO ATMOSFÉRICA NO CÁLCULO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA A PARTIR DE IMAGEM LANDSAT 8/OLI

2020 ◽  
Vol 13 (1) ◽  
pp. 076
Author(s):  
Cristiane Nunes Francisco ◽  
Paulo Roberto da Silva Ruiz ◽  
Cláudia Maria de Almeida ◽  
Nina Cardoso Gruber ◽  
Camila Souza dos Anjos
Keyword(s):  
Rio De Janeiro ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Topographic Effects ◽  
Landsat 8 Oli ◽  
Arithmetic Operations

As operações aritméticas efetuadas entre bandas espectrais de imagens de sensoriamento remoto necessitam de correção atmosférica para eliminar os efeitos atmosféricos na resposta espectral dos alvos, pois os números digitais não apresentam escala equivalente em todas as bandas. Índices de vegetação, calculados com base em operações aritméticas, além de caracterizarem a vegetação, minimizam os efeitos da iluminação da cena causados pela topografia. Com o objetivo de analisar a eficácia da correção atmosférica no cálculo de índices de vegetação, este trabalho comparou os Índices de Vegetação por Diferença Normalizada (Normalized Difference Vegetation Index - NDVI), calculados com base em imagens corrigidas e não corrigidas de um recorte de uma cena Landsat 8/OLI situado na cidade do Rio de Janeiro, Brasil. Os resultados mostraram que o NDVI calculado pela reflectância, ou seja, imagem corrigida, apresentou o melhor resultado, devido ao maior discriminação das classes de vegetação e de corpos d'água na imagem, bem como à minimização do efeito topográfico nos valores dos índices de vegetação.  Analysis of the atmospheric correction impact on the assessment of the Normalized Difference Vegetation Index for a Landsat 8 oli image A B S T R A C TThe image arithmetic operations must be executed on previously atmospherically corrected bands, since the digital numbers do not present equivalent scales in all bands. Vegetation indices, calculated by means of arithmetic operations, are meant for both targets characterization and the minimization of illumination effects caused by the topography. With the purpose to analyze the efficacy of atmospheric correction in the calculation of vegetation indices with respect to the mitigation of atmospheric and topographic effects on the targets spectral response, this paper compared the NDVI (Normalized Difference Vegetation Index) calculated using corrected and uncorrected images related to an inset of a Landsat 8 OLI scene from Rio de Janeiro, Brazil. The result showed that NDVI calculated from reflectance values, i.e, corrected images, presented the best results due to a greater number of vegetation patches and water bodies classes that could be discriminated in the image, as well the mitigation of the topographic effect in the vegetation indices values.Keywords: remote sensing, urban forest, atmospheric correction.

scholarly journals ACCURACY ANALYSIS OF SENTINEL 2A AND LANDSAT 8 OLI+ SATELLITE DATASETS OVER KANO STATE (NIGERIA) USING VEGETATION SPECTRAL INDICES

2020 ◽  
Vol XLIV-3/W1-2020 ◽  
pp. 65-72
Author(s):  
O. A. Isioye ◽  
E. A. Akomolafe ◽  
U. H. Ikwueze
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Stress Index ◽  
Landsat 8 ◽  
Agricultural Practice ◽  
Landsat 8 Oli ◽  
Area Index ◽  
Spatial Consistency ◽  
The Mean ◽  
Sentinel 2A

Abstract. This study explores the capabilities of Sentinel-2 over Landsat-8 Operational Land Imager (OLI) imageries for vegetation monitoring in the vegetated region of Minjibir LGA in Kano State. Accurate vegetation mapping is essential for monitoring crop and sustainable agricultural practice. Vegetation indices, comprising the Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Index (GCI), Leaf Area Index (LAI) and Moisture Stress Index (MSI) were determined for each year. The findings showed an increase in Sentinel 2A value of the vegetation indices with respect to Landsat 8 throughout the time of the study (2015–2019). The best average performance over the supervised classification was obtained using Sentinel-2A bands, which are dependent on the training sample and resolution. While the spectral consistency of the data was inferred by cross-calibration analysis using regression analysis. The spatial consistency was assessed by descriptive statistical analysis of examined variables. Regarding the spatial consistency, the mean and standard deviation values of all variables were steady for all seasons excluding for the mean value of the LAI and MSI. Based on this finding, it is recommended that Sentinel-2A data could be used as a complementary data source with Landsat 8 OLI in vegetation assessment.

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