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

2021 ◽  
Vol 67 (No. 2) ◽  
pp. 71-79
Author(s):  
Marzieh Ghavidel ◽  
Peyman Bayat ◽  
Mohammad Ebrahim Farashiani

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.

Author(s):  
Made Arya Bhaskara Putra ◽  
I Wayan Nuarsa ◽  
I Wayan Sandi Adnyana

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.


2019 ◽  
Vol 9 (2) ◽  
pp. 16-22
Author(s):  
Nadya Fiqi Nurcahyani

Mangrove forests have high ecological, economic and social values ??which function to maintain shoreline stability, protect beaches and riverbanks, filter and remediate waste, and to withstand floods and waves. The facts show that mangrove damage is everywhere, even the intensity of damage and its area tends to increase significantly. Many roles of mangroves require proper management to maintain the existence of mangroves. One way to determine the area of ??mangroves is by processing Landsat 8 satellite imagery. The stages of mangrove identification are carried out by using 564 RGB band merger, then separating the mangrove and non-mangrove objects. Next step is to analyze the density of mangroves using NDVI formula. To maximize monitoring of mangrove area, an android application was created that provides information on the area and density of mangroves at several locations, namely Clungup, Bangsong Teluk Asmara and Cengkrong from 2015 to 2018.The results showed that Landsat 8 satellite imagery can be used to identify changes in the area of ??mangrove forests with good accuracy, namely in the Clungup area of ??90% and Cengkrong of 86.67%. From processing results, the mangrove area in the Clungup area has also decreased from 2015 to 2017 but has increased in 2018 so that the application provides recommendations for embroidering mangroves in 2016 to 2017 and mangrove recommendations are maintained in 2018. As for Bangsong Teluk area Asmara and Cengkrong have increased the area of ??mangroves every year so that the application provides recommendations to be maintained from 2016 to 2018.


2019 ◽  
Vol 26 (3) ◽  
pp. 117
Author(s):  
Tri Muji Susantoro ◽  
Ketut Wikantika ◽  
Agung Budi Harto ◽  
Deni Suwardi

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.


Author(s):  
Perminder Singh ◽  
Ovais Javeed

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 


Author(s):  
Nguyen Quang Tuan ◽  
Do Thi Viet Huong ◽  
Doan Ngoc Nguyen Phong ◽  
Nguyen Dinh Van

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%.


2020 ◽  
Vol 12 (16) ◽  
pp. 2648 ◽  
Author(s):  
Kyriacos Themistocleous ◽  
Christiana Papoutsa ◽  
Silas Michaelides ◽  
Diofantos Hadjimitsis

Plastic litter floating in the ocean is a significant problem on a global scale. This study examines whether Sentinel-2 satellite images can be used to identify plastic litter on the sea surface for monitoring, collection and disposal. A pilot study was conducted to determine if plastic targets on the sea surface can be detected using remote sensing techniques with Sentinel-2 data. A target made up of plastic water bottles with a surface measuring 3 m × 10 m was created, which was subsequently placed in the sea near the Old Port in Limassol, Cyprus. An unmanned aerial vehicle (UAV) was used to acquire multispectral aerial images of the area of interest during the same time as the Sentinel-2 satellite overpass. Spectral signatures of the water and the plastic litter after it was placed in the water were taken with an SVC HR1024 spectroradiometer. The study found that the plastic litter target was easiest to detect in the NIR wavelengths. Seven established indices for satellite image processing were examined to determine whether they can identify plastic litter in the water. Further, the authors examined two new indices, the Plastics Index (PI) and the Reversed Normalized Difference Vegetation Index (RNDVI) to be used in the processing of the satellite image. The newly developed Plastic Index (PI) was able to identify plastic objects floating on the water surface and was the most effective index in identifying the plastic litter target in the sea.


2020 ◽  
Vol 17 (01) ◽  
pp. 222-239
Author(s):  
Denivaldo Ferreira de Souza ◽  
German Dario Duarte Gonzalez ◽  
José Teixeira Filho

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.


2019 ◽  
Vol 11 (13) ◽  
pp. 156 ◽  
Author(s):  
Allisson Lucas Brandão Lima ◽  
Roberto Filgueiras ◽  
Everardo Chartuni Mantovani ◽  
Daniel Althoff ◽  
Robson Argolo dos Santos ◽  
...  

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.


2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.


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