Design and Development of Calculation System for Normalized Difference in Vegetation Index (NDVI) Using Landsat 8 Satellite Image

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.

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Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)

HAYATI Journal of Biosciences ◽

10.4308/hjb.26.3.117 ◽

2019 ◽

Vol 26 (3) ◽

pp. 117

Author(s):

Tri Muji Susantoro ◽

Ketut Wikantika ◽

Agung Budi Harto ◽

Deni Suwardi

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Landsat 8 ◽

Gas Field ◽

Research Results ◽

The Third ◽

Third Phase ◽

The Impact

This study is intended to examine the growing phases and the harvest of sugarcane crops. The growing phases is analyzed with remote sensing approaches. The remote sensing data employed is Landsat 8. The vegetation indices of Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI) are employed to analyze the growing phases and the harvest of sugarcane crops. Field survey was conducted in March and August 2017. The research results shows that March is the peak of the third phase (Stem elonging phase or grand growth phase), the period from May to July is the fourth phase (maturing or ripening phase), and the period from August to October is the peak of harvest. In January, the sugarcane crops begin to grow and some sugarcane crops enter the third phase again. The research results also found the sugarcane plants that do not grow well near the oil and gas field. This condition is estimated due as the impact of hydrocarbon microseepage. The benefit of this research is to identify the sugarcane growth cycle and harvest. Having knowing this, it will be easier to plan the seed development and crops transport.

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NDVI Based Assessment of Land Cover Changes Using Remote Sensing and GIS (A case study of Srinagar district, Kashmir)

Sustainability Agri Food and Environmental Research ◽

10.7770/safer-v0n0-art2174 ◽

2020 ◽

Author(s):

Perminder Singh ◽

Ovais Javeed

Keyword(s):

Land Use ◽

Land Cover ◽

Urban Areas ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Spectral Signature ◽

Landsat 8 ◽

Thematic Mapping

Normalized Difference Vegetation Index (NDVI) is an index of greenness or photosynthetic activity in a plant. It is a technique of obtaining various features based upon their spectral signature such as vegetation index, land cover classification, urban areas and remaining areas presented in the image. The NDVI differencing method using Landsat thematic mapping images and Landsat oli was implemented to assess the chane in vegetation cover from 2001to 2017. In the present study, Landsat TM images of 2001 and landsat 8 of 2017 were used to extract NDVI values. The NDVI values calculated from the satellite image of the year 2001 ranges from 0.62 to -0.41 and that of the year 2017 shows a significant change across the whole region and its value ranges from 0.53 to -0.10 based upon their spectral signature .This technique is also used for the mapping of changes in land use and land cover. NDVI method is applied according to its characteristic like vegetation at different NDVI threshold values such as -0.1, -0.09, 0.14, 0.06, 0.28, 0.35, and 0.5. The NDVI values were initially computed using the Natural Breaks (Jenks) method to classify NDVI map. Results confirmed that the area without vegetation, such as water bodies, as well as built up areas and barren lands, increased from 35 % in 2001 to 39.67 % in 2017.Key words: Normalized Difference Vegetation Index,land use/landcover, spectral signature

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Possibility for Identifying/Extracting Rock Outcrop Using Landsat 8 OLI/TIRS - Case Study of Thua Thien Hue Province

VNU Journal of Science Earth and Environmental Sciences ◽

10.25073/2588-1094/vnuees.4661 ◽

2020 ◽

Vol 36 (3) ◽

Author(s):

Nguyen Quang Tuan ◽

Do Thi Viet Huong ◽

Doan Ngoc Nguyen Phong ◽

Nguyen Dinh Van

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Object Orientation ◽

Image Method ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Kappa Index ◽

Exposed Rock ◽

Ratio Image

This paper approaches the ratio image method to extract the exposed rock information from the Landsat 8 OLI/TIRS satellite image (2019) according to the object orientation classification. Combining automatic interpretation and interpretation through threshold of image index values according to interpretation key the object orientation classification to separate soil object containing exposed rock and no exposed rock in Thua Thien Hue province. Using the Topsoil Grain Size Index (TGSI), the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI) and other related analytical problems have identified 40 exposed rock storage areas in the study area. The results have been verified in the field and the Kappa index is 85.10%.

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Mapping and Area Estimation of Mango Orchards of Lucknow Region by Applying Knowledge Based Decision Tree to Landsat 8 OLI Satellite Images

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b8109.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 3627-3635

Keyword(s):

Decision Tree ◽

Satellite Images ◽

Vegetation Index ◽

Accuracy Assessment ◽

Satellite Image ◽

Accurate Information ◽

Landsat 8 ◽

Atmospheric Conditions ◽

Area Estimation ◽

Knowledge Based

Mango is a very important fruit which is liked by majority of the population due to its nutritional value and excellent taste. India is the largest producer of mango in the world. Accurate information is required for policy decision making in terms of providing subsidy, area expansion, and crop insurance planning. Hence, this type of information may be retrieve through satellite images by using the image classification techniques, which are playing a crucial role in crop cover classification, yield prediction and crop monitoring etc. Classification of optical satellite images is still a challenging task due to effect of changing atmospheric conditions such as cloud, snow, haze, dust, fog, and rain etc. In this paper, knowledge based decision tree classification (DTC) has been proposed to classify the mango orchards of Lucknow district using multi-temporal Landsat 8 operational land imager (OLI) images from year 2015 to 2017 and further mango orchard area were also estimated. In order to develop the DTC, separability analysis for various land cover classes was carried out on different vegetation indices namely, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and soil adjusted vegetation index (SAVI). In order to analyze the performance of DTC, most commonly used satellite image classifiers such as unsupervised classifier (i.e. ISODATA) and supervised classifier (i.e. Maximum Likelihood) have been used and it is observed that the proposed DTC outperformed these traditional classifiers. Also, accuracy assessment has been carried out to measure the performance of proposed DTC and it is observed that all of the three images from 2015 to 2017 are classified with high overall accuracy, which is ranging from 70.66% to 86.69%. Kappa Coefficient (KC) for all the three images ranged from 0.65 to 0.83, which indicates that classified images are highly acceptable for area estimation.

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Satellite image processing of the Buxus hyrcana Pojark dieback in the Northern Forests of Iran

Journal of Forest Science ◽

10.17221/56/2020-jfs ◽

2021 ◽

Vol 67 (No. 2) ◽

pp. 71-79

Author(s):

Marzieh Ghavidel ◽

Peyman Bayat ◽

Mohammad Ebrahim Farashiani

Keyword(s):

Image Processing ◽

Disease Progression ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Leaf Size ◽

Disease Spread ◽

Landsat 8 ◽

Distribution Maps ◽

Satellite Image Processing

Pests and diseases can cause a variety of reactions in plants. In recent years, the boxwood dieback has become one of the essential concerns of practitioners and natural resources managers in Iran. To control the boxwood dieback spread, the early detection and disease distribution maps are required. The boxwood dieback causes a range of changes in colour, shape and leaf size with respect to photosynthesis and transpiration. Through remote sensing techniques, e.g. satellite image processing data, the variation of thermal and visual characteristics of the plant could be used to measure and illustrate the symptoms of the disease. In this study, five common vegetation indices like difference vegetation index (DVI), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), simple ratio (SR), and plant health index (PHI) were extracted and calculated from Landsat 8 satellite image data from six regions in the Gilan province, located in the northern part of Iran out of 150 maps over the time period 2014‒2018. It turned out that among the aforementioned indices, based upon the results of the models, SR and NDVI indices were more useful for the disease spread, respectively. Our disease progression model fitting criteria showed that this technique could probably be used to assess the extent of the affected areas and also the disease progression in the investigated regions in future.

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A Validation Experiment of the Reflectance Products of KOMPSAT-3A Based on RadCalNet Data and Its Applicability to Vegetation Indexing

Remote Sensing ◽

10.3390/rs12233971 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3971 ◽

Cited By ~ 1

Author(s):

Kwangseob Kim ◽

Kiwon Lee

Keyword(s):

High Resolution ◽

Field Measurement ◽

Vegetation Index ◽

Satellite Image ◽

Vegetation Indices ◽

Atmospheric Correction ◽

Measurement Data ◽

Google Earth ◽

Landsat 8 ◽

Validation Experiment

Surface reflectance products obtained through the absolute atmospheric correction of multispectral satellite images are useful for precise scientific applications. For broader applications, the reflectance products computed using high-resolution images need to be validated with field measurement data. This study dealt with 2.2-m resolution Korea Multi-Purpose Satellite (KOMPSAT)-3A images with four multispectral bands, which were used to obtain top-of-atmosphere (TOA) and top-of-canopy (TOC) reflectance products. The open-source Orfeo Toolbox (OTB) extension was used to generate these products. Next, these were subsequently validated by considering three sites (i.e., Railroad Valley Playa, NV, USA (RVUS), Baotou, China (BTCN), and La Crau, France (LCFR)) in RadCalNet, as well as a calibration and validation portal for remote sensing. We conducted the validations comparing satellite image-based reflectance products and field measurement reflectance based on data sets acquired at different times. The experimental results showed that the overall trend of validation accuracy of KOPSAT-3A was well fitted in all the RadCalNet sites and that the accuracy remained quite constant. Reflectance bands showing the minimum and maximum differences between the sets of experimental data are presented in this paper. The vegetation indices (i.e., the atmospherically resistant vegetation index (ARVI) and the structure insensitive pigment index (SIPI)) and three TOC reflectance bands obtained from KOMPSAT-3A were computed as a case study and used to achieve a detailed vegetation interpretation; finally, the correspondent results were compared with those obtained from Landsat-8 images (downloaded from the Google Earth Engine (GEE)). The validation and the application scheme presented in this study can be potentially applied to the generation of analysis ready data from high-resolution satellite sensor images.

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PEMANFAATAN CITRA LANDSAT 8 UNTUK PENENTUAN KORELASI INDEKS VEGETASI DENGAN KADAR KLOROFIL TANAMAN PADI DI KECAMATAN KOTO TANGAH KOTA PADANG

Tunas Geografi ◽

10.24114/tgeo.v6i2.8573 ◽

2017 ◽

Vol 6 (2) ◽

pp. 151

Author(s):

Hanifa Fitri

Keyword(s):

Linear Regression ◽

Chlorophyll Content ◽

Vegetation Index ◽

Satellite Image ◽

Secondary Data ◽

Plant Health ◽

Primary Data ◽

Linear Regression Method ◽

Landsat 8 ◽

Rice Plants

Klorofil merupakan zat utama pada daun yang berfungsi sebagai alat fotosintesis dan secara tidak langsung akan mempengaruhi hasil produksi padi. Penggunaan Landsat 8 sangat banyak manfaatnya bagi pertanian, diantaranya untuk mengetahui kadar klorofil pada tanaman padi sehingga dapat ditentukan kesehatan tanaman, dan kesehatan tanaman juga bisa ditentukan dari kadar klorofil padi. Penelitian ini dilakukan di Kecamatan Koto Tangah Kota Padang yang bertujuan untuk mengetahui korelasi antara nilai Indeks Vegetasi dengan kadar klorofil pada tanaman padi. Metode yang digunakan dalam penelitian ini yaitu uji labolatorium dengan spektrofotometri dan metode regresi linier sederhana untuk mengetahui korelasi antara kedua variabel. Data yang digunakan dalam penelitian ini yaitu data primer dan data sekunder. Data primer berupa pengambilan sampel daun padi dan koordinat titik sampel, sedangkan data sekundernya adalah citra landsat 8 yang diperoleh dari website USGS. Daun padi yang diambil akan diukur kadar klorofilnya mengunakan metode Wintermans and De Mots denganalat Spektrofotometer. Hasil pengukuran tersebut selanjutnya dikorelasikan dengan indeks vegetasi dari citra satelit menggunakan SPSS. Berdasarkan hasil regresi maka didapatkan korelasi yang paling kuat yaitu NDGI dengan nilai R2=0.760 dan persamaan regresi linier Y=0.007+0.004x.Kata kunci : Spektrofotometer, Klorofil, Indeks Vegetasi, Landsat 8. AbstractChlorophyll is the main substance in the leaves that serves as a means of photosynthesis and will indirectly affect the yield of rice production. The use of Landsat 8 is very useful for agriculture, among others to know the chlorophyll content in rice plants so that can be determined plant health, and plant health can also be determined from rice chlorophyll content. This research was conducted in District Koto Tangah in Padang City which aims to know the correlation between Vegetation Index value with chlorophyll content of rice plants. The method used in this research using laboratory test with spectrophotometry and simple linear regression method to know the correlation between the two variables. The data used in this research are primary data and secondary data. Primary data are sampling of rice leaf and point coordinate, while secondary data is landsat 8 image obtained from USGS website. Rice leaves taken will be measured chlorophyll content using Wintermans and De Mots method with Spectrophotometer. The measurement results correlated with the vegetation index of the satellite image using SPSS. Based on the results of the regression obtained the strongest correlation is NDGI with value R2 = 0.760 and linear regression equation Y = 0.007 + 0.004x.Keywords: Spectrophotometer, Chlorophyll, Vegetation Index, Landsat 8.

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

Revista Cerrados ◽

10.22238/rc2448269220191701222239 ◽

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.

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Biophysical Parameters and Actual Evapotranspiration of Bean Culture by Means of Remote Sensing

Journal of Agricultural Science ◽

10.5539/jas.v11n13p156 ◽

2019 ◽

Vol 11 (13) ◽

pp. 156 ◽

Cited By ~ 3

Author(s):

Allisson Lucas Brandão Lima ◽

Roberto Filgueiras ◽

Everardo Chartuni Mantovani ◽

Daniel Althoff ◽

Robson Argolo dos Santos ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Simple Algorithm ◽

Actual Evapotranspiration ◽

Estimation Methods ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Biophysical Parameters

Agricultural irrigation is involved in an important chain that involves all sectors of the economy, either directly, by increasing food production, or indirectly, by withdrawing large amounts of fresh water. The relevance of this theme forces the search for alternatives to make water use as rational as possible. Evapotranspiration estimation methods based in remote sensing, such as the SAFER (Simple Algorithm for Evapotranspiration Retrieving) model, become extremely relevant in these scenarios, since it is possible to estimate this parameter in large scales. Therefore, the aim of this research was to apply the SAFER model in the estimation of bean crop actual evapotranspiration using Landsat-8 satellite image data. One of the parameters used as input in the SAFER model is the NDVI (Normalized Difference Vegetation Index), which presented a coefficient of determination (r²) equal to 0.80 when compared to the crop coefficient. The actual evapotranspiration (ETa) estimated by the SAFER model were compared to the FAO 56 model estimates for later correlation between the models. This information is expected to assist the producer in a better management of water resources used in irrigation. The correlation between the two models presented a relevant coefficient of determination (r2 = 0.73), representing the potential of the SAFER model in relation to the FAO model 56.

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