scholarly journals Assessment of vegetal cover changes using Normalized Difference Vegetation Index (NDVI) and subtractive (NDVI) time-series, Karbala province, Iraq

2018 ◽  
Vol 15 (35) ◽  
pp. 133-141
Author(s):  
Israa J. Muhsin
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Gap Filling ◽  
Economic Resource ◽  
Detection Techniques ◽  
Similar Region ◽  
Ndvi Time Series ◽  
Vegetal Cover

Karbala province regarded one part significant zones in Iraq and considered an economic resource of vegetation such as trees of fruits, sieve and other vegetation. This research aimed to utilize Normalized Difference Vegetation index (NDVI) and Subtracted (NDVI) for investigating the current vegetation cover at last four decay. The Normalized Difference Vegetation Index (NDVI) is the most extensively used satellite index of vegetation health and density. The primary goals of this research are gather a gathering of studied area (Karbala province) satellite images in sequence time for a similar region, these image captured by Landsat (TM 1985, TM 1995, ETM+ 2005 and Landsat 8 OLI (Operational Land Imager) 2015. Preprocessing such gap filling consider being vital stride has been implied on the defected image which captured in Landsat 2005 and isolate the regions of studied region. The Assessment vegetal cover changes of the studied area in this paper has been implemented using Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI) and change detection techniques such as Subtracted (NDVI) method also have been used to detect the change in vegetal cover of the studied region. Many histogram and statistical properties were illustrated has been computed. From The results shows there are increasing in the vegetal cover from 1985 to 2015.

TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

2019 ◽  
Vol 21 (2) ◽  
pp. 1310-1320
Author(s):  
Cícera Celiane Januário da Silva ◽  
Vinicius Ferreira Luna ◽  
Joyce Ferreira Gomes ◽  
Juliana Maria Oliveira Silva
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Temperature Measurement ◽  
Rainy Season ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Low Density ◽  
Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia.                                                                                  ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

scholarly journals Remote Sensing of the Earth as a Method of Assessing Environmental Quality of Urban Areas

2020 ◽  
pp. 5-12
Author(s):  
S.A. Yeprintsev ◽  
O.V. Klepikov ◽  
S.V. Shekoyan
Keyword(s):  
Remote Sensing ◽  
Environmental Quality ◽  
Urban Areas ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Space Images ◽  
The Earth ◽  
Voronezh Region

Introduction: Spatial zoning of an urban area by the level of anthropogenic burden using land-based research methods is very time-consuming. Since the end of the 20th century, the usage of the Earth remote sensing (ERS) techniques has served as their more efficient alternative. The study objectives included geoinformation zoning and evaluation of the level of technogenic changes in the areas according to NDVI (Normalized Difference Vegetation Index) values. Materials and methods: The cities of the Voronezh Region and their suburban ten-kilometer territories were chosen as the study objects. For the spatial analysis of the area of anthropogenically modified territories based on the example of the cities of the Voronezh Region we created an archive of multichannel satellite images taken by the Landsat-7 and Landsat-8 satellites. The data were borrowed from the Website of the US Geological Survey. Space images were grouped by two periods (the years of 2001 and 2016). Depending on NDVI values, territories with high and low anthropogenic burden, natural framework zones, and water bodies were distinguished. Results: We established that the smallest percentage of areas of the natural framework and their poor location was observed in the city of Voronezh. The largest area occupied by the natural framework was identified within the town of Borisoglebsk. This fact is attributed to the sensible policy of ensuring environmental and hygienic safety of the population implemented by the regional and municipal authorities. Discussion: At present, it is still impossible to fully use space monitoring data to assess health risks of technogenic factors; they can only be used simultaneously with ground monitoring that includes instrumental and laboratory monitoring of environmental quality indicators within the framework of the socio-hygienic monitoring. Conclusions: The analysis of changes in the proportion of areas with a high anthropogenic burden relative to the natural framework performed using satellite images taken in 2001 and 2016 showed an increase in the technogenic burden on the urban environment.

scholarly journals SISTEM PENDETEKSI PERUBAHAN LAHAN HIJAU DI JABODETABEK

2018 ◽  
Vol 2 (1) ◽  
pp. 91
Author(s):  
Andree Phanderson ◽  
Dyah Erny Herwindiati ◽  
Bagus Mulyawan
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Correction Method ◽  
Google Earth ◽  
Linear Regression Method ◽  
Landsat 8 ◽  
Index Method ◽  
Radiometric Correction ◽  
Green Area

Change of green area in Jakarta, Bogor, Depok, Tangerang and Bekasi (Jabodetabek) has been something very important. Classification of green area aims to do classification in Jabodetabek using Landsat 8 satellite images, band 1, 2, 3, 4 and 5. Before classification was done, the satellite images will be corrected using Radiometric Correction method called Mini-max algorithm. After doing radiometric correction, the classification will use the NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) method. The selected area will be classified as green when NDVI values similar or has more than 0.3. After perform two categories, Y1 and Y2 are selected by NDVI values using dummy dependent variable. Linear regression method use that dummy dependent variable to classify the selected area in Jabodetabek. To see how can we trust the result, the classified area will be compared with the appearance of selected area in Google Earth. The highest degradation of green area is in Bogor, May 2015, 325.7368 Km2.

scholarly journals Estimation of Wheat Yields at the Intra-Plot Scale by Combining Multi-Temporal Landsat-8 and Sentinel-2 Images

Proceedings ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 14
Author(s):  
Remy Fieuzal ◽  
Vincent Bustillo ◽  
David Collado ◽  
Gerard Dedieu
Keyword(s):  
Relative Error ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Yield Variability ◽  
Landsat 8 ◽  
Optical Images ◽  
Multi Temporal ◽  
Input Variables ◽  
Sentinel 2

The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat over a study site located in southwestern France. The methodology is based on the use of Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the NDVI (Normalized Difference Vegetation Index) is combined with the previous yield maps, the agricultural season 2014 showing the lower level of performances with a R² of 0.44 and a RMSE (Root Mean Square Error) of 8.13 q.h−1 (corresponding to a relative error of 12.9%), the three other years being associated with values of R² close or upper of 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%).

scholarly journals VARIAÇÃO TEMPORAL DO ÍNDICE DE VEGETAÇÃO NORMALIZADA COMO FERRAMENTA DE IDENTIFICAÇÃO DOS AÇUDES NA BACIA HIDROGRÁFICA DO RIBEIRÃO DAS CABRAS

2020 ◽  
Vol 17 (01) ◽  
pp. 222-239
Author(s):  
Denivaldo Ferreira de Souza ◽  
German Dario Duarte Gonzalez ◽  
José Teixeira Filho
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Hydrographic Basin ◽  
Different Types ◽  
Landsat 5 Tm ◽  
Remote Sensing Techniques ◽  
Vegetal Cover

O avanço da tecnologia por meio do uso de imagens de satélites vem impulsionando os vários tipos de monitoramento da superfície terrestre. Embasado nesse avanço, este artigo tem como objetivo analisar a cobertura vegetal na bacia hidrográfica do Ribeirão das Cabras, localizada no município de Campinas/SP, utilizando técnicas de sensoriamento remoto para a determinação do Índice de Vegetação por Diferença Normalizada - IVDN. O trabalho utilizou imagens dos satélites Landsat 5 TM e Landsat 8 OLI no período da estação chuvosa da região nos anos de 1986, 1992, 1999, 2004, 2011 e 2018. Para cada imagem foi calculado os valores de IVDN e agrupados em seis classes. O resultado das imagens mostrou que as áreas com cobertura vegetal mais intensa sofreram pequenas alterações no período. O destaque principal foi observado na classe que caracterizam os corpos hídricos, demonstrando um aumento da capacidade de reserva por meio de construção de açudes na região. Essas estruturas foram implantadas, em grande parte, a partir de projetos e construções inadequadas. Esses elementos potencializam os eventos de inundações na região por rompimento destas estruturas de barragens. Sendo assim, considerou a classificação das imagens utilizando o IVDN uma ferramenta que propicia um entendimento e análise da dinâmica da cobertura vegetal em diferentes tipos de escala e sazonalidades, determinando condições de aumento do potencial de risco de desastres ao meio. Palavras-chave: Risco de enchentes. Reservatórios. Imagem de satélite. IVDN. Campinas.   TEMPORAL VARIATION OF THE NORMALIZED DIFFERENCE VEGETATION INDEX AS A TOOL IDENTIFICATION TOOL IN THE RIBEIRÃO DAS CABRAS HYDROGRAPHIC BASIN ABSTRACT Technology’s advance through of satellite imagery us have driven the different types of terrestrial surface monitoring. Based on this advance, this article aims to analyze the vegetal cover in Ribeirão das Cabras hydrographic basin, localized at Campinas/SP, using remote sensing techniques for Normalized difference vegetation index – NDVI. The work used images from Landsat 5 TM and Landsat 8 OLI satellites in the period of rainy season in the region from years 1986, 1992, 1999, 2004, 2011 and 2018. For each image were calculated the NDVI values and grouped in six classes. The result of the images showed that the intense vegetal cover areas suffered small alterations in the study period. The main highlight was observed in the class that characterize water bodies, demonstrating an increase in the reserve capacity through the construction of dams in the region. These structures were implanted, in large part, from inadequate projects and constructions. These elements potentiate flood events in the region by breaking the dams. Thus, it was considered the classification of the images using the NDVI, a tool that promotes an understanding and analysis of the dynamics of vegetation cover in different types of scale and seasonality, determining conditions for increasing the potential of disaster risks to the environment. Keywords: Flood risk. Reservoir. Satellite image. NDVI. Campinas.   VARIACIÓN TEMPORAL DEL ÍNDICE DE VEGETACIÓN POR DIFERENCIA NORMALIZADA COMO HERRAMIENTA DE IDENTIFICACIÓN DE LOS ACCESOS EN LA BACIA HIDROGRAFICA DEL RIBEIRÃO DAS CABRAS RESUMEN  El avance de la tecnología por medio del uso de imágenes de satélites viene siendo impulsado los diferentes tipos de monitoramiento de la superficie terrestre. Basado en ese avance, este artículo tiene como objetivo analizar la cobertura vegetal en la cuenca hidrográfica de Riberão das Cabras, localizada en el municipio de Campinas/SP, utilizando técnicas de percepción remota para la determinación del índice de vegetación por diferencia normalizada – IVDN. El trabajo utilizó imágenes de los satélites Landsat 5 TM y Landsat 8 OLI en el periodo de la estación lluviosa de la región los años 1986, 1992, 1999, 2004, 2011 y 2018. Para cada imagen fueron calculados los valores de IVDN y agrupados en seis clases. El resultado de las imágenes mostró que las áreas con cobertura vegetal más intensa sufrieron pequeñas alteraciones en el periodo. El principal destaque fue observado en la clase que caracterizan los cuerpos hídricos, demostrando un aumento de la capacidad de reserva por medio de construcción de presas en la región. Estas estructuras fueron implantadas, en grande parte, a partir de proyectos y construcciones inadecuadas. Estos elementos potencializan los eventos de inundaciones en la región por rompimiento de las presas. Siendo así, se consideró la clasificación de las imágenes utilizando el IVDN una herramienta que propicia un entendimiento e análisis de la dinámica de la cobertura vegetal en diferentes tipos de escala y estacionalidad, determinando condiciones de aumento del potencial de riesgos de desastres al medio ambiente. Palabras clave: Riesgo de inundación. Embalse Imagen de satélite. IVDN. Campinas.

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

ANALISIS PERBANDINGAN METODE NORMALIZED VEGETATION INDEX DAN MAXIMUM LIKELIHOOD CLASSIFICATION UNTUK ANALISIS RUANG TERBUKA HIJAU (Studi Kasus di Kota Pekalongan, Jawa Tengah)

2021 ◽  
pp. 513
Author(s):  
Mohammad Slamet Sigit Prakoso ◽  
Rizki Dwi Safitri
Keyword(s):  
Maximum Likelihood ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Maximum Likelihood Classification

Ruang Terbuka Hijau (RTH) adalah suatu tempat yang luas dan terbuka yang dimaksudkan untuk penghijauan suatu kota, di mana di dalamnya ditumbuhi pepohonan. Dalam analisis ruang terbuka hijau dapat menggunakan beberapa metode, di antaranya yaitu metode Normalized Difference Vegetation Index (NDVI) dan metode Maximum Likelihood Classification. Tujuan penelitian ini untuk mengetahui perbedaan hasil dari analisis metode NDVI dan Maximum Likelihood Classification yang digunakan untuk mengetahui ruang terbuka hijau di Kota Pekalongan. Metode yang digunakan pada penelitian ini yaitu dengan menggunakan metode NDVI dan metode Maximum Likelihood Classification. Data yang digunakan yaitu Citra Landsat 8 OLI. Pengolahan data menggunakan software Arcgis 10.3. Hasil dari pengolahan berupa peta ruang terbuka hijau dari masing - masing metode. Secara kuantitatif dari hasil perhitungan luas metode NDVI, luas permukiman sebesar 3.016,53 ha, persawahan 609,39 ha, hutan kota 573,3 ha, dan badan air seluas 482,04 ha. Sedangkan untuk metode Maximum Likelihood Classification didapatkan hasil luas permukiman 2.278,26 ha, persawahan 1.141,83 ha, hutan kota 738,18 ha, dan badan air seluas 522,99 ha. Berdasarkan luasan RTH terhadap luas Kota Pekalongan, pada metode NDVI sebesar 25,2%, sedangkan untuk metode Maximum Likelihood Classification sebesar 40,1%. Dari hasil analisis diperoleh perbedaan luasan yang cukup signifikan yaitu pada luasan persawahan dan permukiman. Perbedaan hasil analisis terjadi akibat perbedaan klasifikasi warna citra pada saat pengolahan data.

scholarly journals ESTIMASI PRODUKSI PADI DENGAN ANALISIS CITRA SATELIT LANDSAT 8 DI KABUPATEN KLUNGKUNG PROVINSI BALI

2018 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Made Arya Bhaskara Putra ◽  
I Wayan Nuarsa ◽  
I Wayan Sandi Adnyana
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Image Data ◽  
Research Area ◽  
Rice Production ◽  
Landsat 8 ◽  
Estimation Model ◽  
Accuracy Test ◽  
Accuracy Level

Rice crop is one of the important commodities that must always be available, so estimation of rice production becomes very important to do before harvesting time to know the food availability. The technology that can be used is remote sensing technology using Landsat 8 Satellite. The aims of this study were (1) to obtain the model of estimation of rice production with Landsat 8 image analysis, and (2) to know the accuracy of the model that obtained by Landsat 8. The research area is located in three sub-districts in Klungkung regency. Analysis in this research was conducted by single band analysis and analysis of vegetation index of satellite image of Landsat 8. Estimation model of rice production was developed by finding the relationship between satellite image data and rice production data. The final stage is the accuracy test of the rice production estimation model, with t test and regression analysis. The results showed: (1) estimation of rice production can be calculated between 67 to 77 days after planting; (2) there was a positive correlation between NDVI (Normalized Difference Vegetation Index) vegetation index value with rice yield; (3) the model of rice production estimation is y = 2.0442e1.8787x (x is NDVI value of Landsat 8 and y is rice production); (4) The results of the model accuracy test showed that the obtained model is suitable to predict rice production with accuracy level is 89.29% and standard error of production estimation is + 0.443 ton/ha. Based on research results, it can be concluded that Landsat 8 Satellite image can be used to estimate rice production and the accuracy level is 89.29%. The results are expected to be a reference in estimating rice production in Klungkung Regency.

scholarly journals Consideration of NDVI thematic changes in density analysis and floristic composition of Wadi Yalamlam, Saudi Arabia

2018 ◽  
Vol 7 (4) ◽  
pp. 297-306 ◽  
Author(s):  
Amal Y. Aldhebiani ◽  
Mohamed Elhag ◽  
Ahmad K. Hegazy ◽  
Hanaa K. Galal ◽  
Norah S. Mufareh
Keyword(s):  
Saudi Arabia ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Floristic Composition ◽  
Water Source ◽  
Shannon Index ◽  
Landsat 8 ◽  
Main Stream

Abstract. Wadi Yalamlam is known as one of the significant wadis in the west of Saudi Arabia. It is a very important water source for the western region of the country. Thus, it supplies the holy places in Mecca and the surrounding areas with drinking water. The floristic composition of Wadi Yalamlam has not been comprehensively studied. For that reason, this work aimed to assess the wadi vegetation cover, life-form presence, chorotype, diversity, and community structure using temporal remote sensing data. Temporal datasets spanning 4 years were acquired from the Landsat 8 sensor in 2013 as an early acquisition and in 2017 as a late acquisition to estimate normalized difference vegetation index (NDVI) changes. The wadi was divided into seven stands. Stands 7, 1, and 3 were the richest with the highest Shannon index values of 2.98, 2.69, and 2.64, respectively. On the other hand, stand 6 has the least plant biodiversity with a Shannon index of 1.8. The study also revealed the presence of 48 different plant species belonging to 24 families. Fabaceae (17 %) and Poaceae (13 %) were the main families that form most of the vegetation in the study area, while many families were represented by only 2 % of the vegetation of the wadi. NDVI analysis showed that the wadi suffers from various types of degradation of the vegetation cover along with the wadi main stream.

Sign in / Sign up

Export Citation Format

Share Document