scholarly journals APPLICATION OF LANDSAT-8 SATELLITE IMAGES FOR MONITORING LANDSCAPES WITHIN THE BAKHCHISARAI DISTRICT OF THE REPUBLIC OF CRIMEA (USING THE EXAMPLE OF CALCULATING NDVI AND LST)

2021 ◽  
pp. 43-50
Author(s):  
R.V. Gorbunov ◽  
V.A. Tabunschik ◽  
Ya.O. Andronchik
Keyword(s):  
Satellite Images ◽  
Landsat 8 ◽  
The Republic

scholarly journals ECOSISTEMS PATTERN OF СENTRAL TUVINIAN BASIN

2021 ◽  
Vol 1 (6) ◽  
pp. 75-78
Author(s):  
L.L. Golubyatnikov ◽  
◽  
E.A. Zarov ◽  
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Satellite Images ◽  
Landsat 8 ◽  
Ecosystem Structure ◽  
Summer Period ◽  
The Past ◽  
Desert Steppes ◽  
The Republic ◽  
Continental Climate

To analyze the pattern of ecosystems on the territory of the Central Tuvinian Basin the images from the Landsat-8 satellite for the summer period of 2020 were used. Satellite images were processed in the GRASS geographic information system using the SMAP classification method. The basin under study is located in the center of the Republic of Tuva. The region is characterized by a sharply continental climate with low snowy and very cold winters, hot and dry summers. Based on satellite information and the cartographic information, 13 land units were identified in the studied territory of the Central Tuvinian Basin. The total area of the Central Tuvinian Basin is 18.6 thousand km2 . A mosaic ecosystem structure of typical, dry and desert steppes is presented on the flat territory of the basin. Based on the satellite images, a map-scheme for ecosystems pattern on the territory of the Central Tuvinian Basin was made. Steppe vegetation is gradually occupying the abandoned arable lands. The area of cultivated fields in the Central Tuvinian Basin has decreased by 90% over the past 30 years. There was an expansion of the territories of dry and desert steppes with the participation of shrubs of Caragana Bunge. Desertification processes are ongoing in the Central Tuvinian Basin.

scholarly journals ANALYSIS OF CHANGES IN VEGETATION COVER FOR NORTHERN PART OF UVS NUUR BASIN

2020 ◽  
Vol 1 (5) ◽  
pp. 111-114
Author(s):  
L.L. Golubyatnikov ◽  
◽  
E.A. Zarov ◽  
Keyword(s):  
Information System ◽  
Vegetation Cover ◽  
Satellite Images ◽  
Landsat 8 ◽  
Mosaic Landscape ◽  
The Past ◽  
Russian Part ◽  
Desert Steppes ◽  
The Republic ◽  
Continental Climate

To analyze changes in the vegetation cover on the territory of the Russian part of the Uvs Nuur Basin the images from the Landsat-5 and Landsat-8 satellites for the summer periods of 1988 and 2019 were used. Satellite images were processed in the GRASS geographic information system using the SMAP classification method. The Uvs Nuur Basin is located on the border of the Republic of Tuva and Mongolian People’s Republic. The region is characterized by a sharply continental climate with low snowy winters, hot and dry summers. A mosaic landscape structure of typical, dry and desert steppes is presented on the flat territory of the northern part of the Uvs Nuur Basin. Based on satellite information and the cartographic information, 9 landunits were identified in the studied territory of the Uvs Nuur Basin. According to the satellite images, the total area of the Russian part of the Uvs Nuur Basin is 19.5 thousand km2 . Changes in the areas of the selected landunits are estimated as a result of the satellite images classification. From the obtained results it was found that over the past 30 years, the area of cultivated fields in the northern part of the Uvs Nuur Basin has decreased by 80 %. Steppe vegetation is gradually occupying the abandoned arable lands. Desertification processes are ongoing in the Uvs Nuur Basin territory. There was a reduction in the area of meadows and an expansion of the territories of dry and desert steppes with the participation of shrubs of Caragana Bunge.

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 Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

2021 ◽  
Vol 13 (4) ◽  
pp. 606
Author(s):  
Tee-Ann Teo ◽  
Yu-Ju Fu
Keyword(s):  
Satellite Images ◽  
Traditional Approach ◽  
Spectral Band ◽  
Super Resolution ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fusion Model ◽  
Image Blending ◽  
Fine Resolution

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.

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 Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Water Bodies ◽  
Water Extraction ◽  
Landsat 8 ◽  
Sensing Data ◽  
Optimal Classifier ◽  
Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

scholarly journals Coupling threshold theory and satellite-derived channel width to estimate the formative discharge of Himalayan foreland rivers

2021 ◽  
Vol 9 (1) ◽  
pp. 47-70
Author(s):  
Kumar Gaurav ◽  
François Métivier ◽  
Rajiv Sinha ◽  
Amit Kumar ◽  
Sampat Kumar Tandon ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Force Balance ◽  
Channel Width ◽  
Landsat 8 ◽  
Remote Sensing Images ◽  
Physical Force ◽  
Regulated Rivers ◽  
Threshold Theory ◽  
Discharge Regime

Abstract. We propose an innovative methodology to estimate the formative discharge of alluvial rivers from remote sensing images. This procedure involves automatic extraction of the width of a channel from Landsat Thematic Mapper, Landsat 8, and Sentinel-1 satellite images. We translate the channel width extracted from satellite images to discharge using a width–discharge regime curve established previously by us for the Himalayan rivers. This regime curve is based on the threshold theory, a simple physical force balance that explains the first-order geometry of alluvial channels. Using this procedure, we estimate the formative discharge of six major rivers of the Himalayan foreland: the Brahmaputra, Chenab, Ganga, Indus, Kosi, and Teesta rivers. Except highly regulated rivers (Indus and Chenab), our estimates of the discharge from satellite images can be compared with the mean annual discharge obtained from historical records of gauging stations. We have shown that this procedure applies both to braided and single-thread rivers over a large territory. Furthermore, our methodology to estimate discharge from remote sensing images does not rely on continuous ground calibration.

scholarly journals DETERMINING THE SUITABILITY OF DIFFERENT DIGITAL ELEVATION MODELS AND SATELLITE IMAGES FOR FANCY MAPS. AN EXAMPLE OF CYPRUS

2016 ◽  
Vol XLI-B4 ◽  
pp. 591-597
Author(s):  
J. Drachal ◽  
A. K. Kawel
Keyword(s):  
Small Area ◽  
Satellite Images ◽  
Digital Elevation Models ◽  
Test Area ◽  
Landsat 8 ◽  
Digital Terrain Models ◽  
Digital Terrain ◽  
Terrain Models ◽  
Digital Elevation ◽  
Shaded Relief

The article describes the possibility of developing an overall map of the selected area on the basis of publicly available data. Such a map would take the form designed by the author with the colors that meets his expectations and a content, which he considers to be appropriate. Among the data available it was considered the use of satellite images of the terrain in real colors and, in the form of shaded relief, digital terrain models with different resolutions of the terrain mesh. Specifically the considered data were: MODIS, Landsat 8, GTOPO-30, SRTM-30, SRTM-1, SRTM-3, ASTER. For the test area the island of Cyprus was chosen because of the importance in tourism, a relatively small area and a clearly defined boundary. In the paper there are shown and discussed various options of the Cyprus terrain image obtained synthetically from variants of Modis, Landsat and digital elevation models of different resolutions.

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.

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