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

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.

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.

Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

2021 ◽  
Vol 66 (1) ◽  
pp. 175-187
Author(s):  
Duong Phung Thai ◽  
Son Ton
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Image Interpretation ◽  
Image Data ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Forest Change ◽  
Shrimp Ponds ◽  
Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

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 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.

scholarly journals Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing

Proceedings ◽  
2018 ◽  
Vol 2 (23) ◽  
pp. 1430
Author(s):  
V. M. Fernández-Pacheco ◽  
C. A. López-Sánchez ◽  
E. Álvarez-Álvarez ◽  
M. J. Suárez López ◽  
L. García-Expósito ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Pollutants ◽  
Satellite Images ◽  
Satellite Image ◽  
Remote Sensing Image ◽  
Regional Studies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm ◽  
Source Of Information

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

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

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.

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

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 

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

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%.

scholarly journals Structural interpretation of lineaments using satellite image processing: A case study in the vicinity of the Charvak reservoir

2020 ◽  
Vol 26 (2) ◽  
pp. 436-442
Author(s):  
Lola Sichugova ◽  
Dilbarkhon Fazilova
Keyword(s):  
Water Level ◽  
Satellite Images ◽  
Satellite Image ◽  
Water Reservoir ◽  
Water Level Fluctuations ◽  
Landsat 8 ◽  
Base Line ◽  
The North ◽  
Automated Method ◽  
Density Maps

This work presents the results of lineaments interpretation using the automated method of the satellite images in the territory of the Charvak water reservoir in Uzbekistan. Tectonic and local (water impoundment in Charvak reservoir) features of the region deformation were determined on base LINE algorithm in software PCI Geomatica. The thematic map with the geospatial arrangement of lineaments was constructed on base of satellite images LANDSAT-8 processing. We concluded that water level fluctuations have a greater influence on the appearance of the lineaments structure than periods of water filling and downstream in the reservoir. Lineament density maps showed dominantly increased density towards the north-southern direction is due to tectonic features of the region and the west-eastern direction is due to water level fluctuations in the reservoir. The lineaments density maps for summer-autumn periods showed the faults arising from water level fluctuations only. Winter-spring period affected with high influence of the seasonal (snow pack, rainfall) processes as well.

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