scholarly journals Analysis of Enhanced Built-up and Bare Land Index (EBBI) in the Urban Area of Yangon, Myanmar

2021 ◽  
pp. 83-94
Author(s):  
S.N. Tin
Keyword(s):  
Satellite Images ◽  
Land Development ◽  
Landsat Images ◽  
Land Distribution ◽  
Raster Calculator ◽  
Variable Approach ◽  
Gis Tools ◽  
Bare Land ◽  
Sentinel 2A ◽  
Population Urbanization

In planning and reviewing changes in the ground overview data, land distribution guidelines and identification of changes are critical. The availability of free global and historical satellite images offers a valuable resource for the built-up region to be continuously and accurately mapped and tracked year by year. For thirty years of data, this study uses Landsat images to obtain substantial and land spread data that is extremely useful for urban arrangement. This paper mainly focuses on the basic extraction of the built-up area for the urban planning area every five years from the satellite images of LANDSAT 5,7,8 and Sentinel 2A from USGS. The goal is to evaluate the year-by-year shift in the urban built-up area and to obtain the accuracy of the mapping of built-up and bare land areas in the study of the urban built-up trend from 1990 to 2020. In this research, GIS tools such as raster calculator and built-up region modeling are used to measure the indices that include the Enhanced Built-up and Bareness Index (EBBI), the Normalized Difference Built-up Index (NDBI) and the Urban Index (UI) or the Built-up Index (BUI). This study will therefore point out a variable approach to mapping traditional enhanced built-up and bare land changes (EBBI) automatically with simple indices and according to index outputs. The uncoordinated areas of land and population urbanization spread from areas and gradually the link between the expansion of urban land development and population growth has moved from weak positive to strong decoupling. The advantage of the method the enhanced built-up and bareness index (EBBI) can therefore be realized with the correlation of linear regression slightly expanded in 2020 over the last thirty years. The percentage of the outputs between the indexes and population rate was to use the entire spectral range of Landsat imageries which cause less spectral confusion between built-up area changes and higher accuracies compared to other indices. The modelling method was effective, quickly simple to implement, and can be used to find out the built-up area extraction.

The Use if GIS Tools in Tourism Research on the Example of Wodzislaw Poviat

2012 ◽  
Vol 1 (1) ◽  
pp. 51-56
Author(s):  
Katarzyna Pukowiec
Keyword(s):  
Land Development ◽  
Graphic Representation ◽  
Infrastructure Development ◽  
Tourism Research ◽  
Gis Tools ◽  
Large Databases ◽  
Tourist Infrastructure ◽  
Tourist Development ◽  
Gis Software

Abstract The activities in name of tourist development in Wodzislaw poviat are the reason to evaluate the tourist land development. The evaluation was prepared on the basis of selected indexes characterizing the level of tourist infrastructure development. It considered: the number of lodgings per km2, the number of restaurants per km2, the amount of additional attractions per km2 and the density of tourist tracks. This database was analyzed by the use of GIS tools. Using GIS software allowed working with large databases and provided the possibility to create a graphic representation of the results. The level of tourist land development is diversified and depends on it function. The cities with the best developed tourist infrastructure are Wodzislaw Slaski, Radlin, Pszow, Rydultowy and town in Odra Valley: Olza, Bukow and Nieboczowy. Pszow, Gorzyce and Godow commons have the biggest density of tourist tracks.

scholarly journals Non-Binary Snow Index for Multi-Component Surfaces

2021 ◽  
Vol 13 (14) ◽  
pp. 2777
Author(s):  
Mario Arreola-Esquivel ◽  
Carina Toxqui-Quitl ◽  
Maricela Delgadillo-Herrera ◽  
Alfonso Padilla-Vivanco ◽  
Gabriel Ortega-Mendoza ◽  
...  
Keyword(s):  
Spectral Characteristics ◽  
Ice Cover ◽  
Landsat 8 ◽  
Water Index ◽  
Bare Land ◽  
Snow Water ◽  
Precision Assessment ◽  
Normalized Difference Snow Index ◽  
Sentinel 2A ◽  
Water Vegetation

A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs), such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separability between NBSI-MS values corresponding to snow from other LCTs and accurately delineate the snow/ice cover in non-binary maps. To test the robustness of the NBSI-MS, regions in Greenland and France–Italy where snow interacts with highly diversified geographical ecosystems were examined. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites were used. The NBSI-MS performance was also compared against the well-known Normalized Difference Snow Index (NDSI), NDSII-1, S3, and Snow Water Index (SWI) methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieved an overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as the OA. The precision assessment confirmed the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices.

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 Snow Cover and Snow Persistence Changes in the Mocho-Choshuenco Volcano (Southern Chile) Derived From 35 Years of Landsat Satellite Images

2021 ◽  
Vol 9 ◽  
Author(s):  
Roberto O. Chávez ◽  
Verónica F. Briceño ◽  
José A. Lastra ◽  
Daniel Harris-Pascal ◽  
Sergio A. Estay
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Landsat Images ◽  
Microbial Composition ◽  
Mountain Areas ◽  
Pixel Resolution ◽  
Temperature Changes ◽  
Soil Microbial ◽  
Landsat Satellite

Mountain regions have experienced above-average warming in the 20th century and this trend is likely to continue. These accelerated temperature changes in alpine areas are causing reduced snowfall and changes in the timing of snowfall and melt. Snow is a critical component of alpine areas - it drives hibernation of animals, determines the length of the growing season for plants and the soil microbial composition. Thus, changes in snow patterns in mountain areas can have serious ecological consequences. Here we use 35 years of Landsat satellite images to study snow changes in the Mocho-Choshuenco Volcano in the Southern Andes of Chile. Landsat images have 30 m pixel resolution and a revisit period of 16 days. We calculated the total snow area in cloud-free Landsat scenes and the snow frequency per pixel, here called “snow persistence” for different periods and seasons. Permanent snow cover in summer was stable over a period of 30 years and decreased below 20 km2 from 2014 onward at middle elevations (1,530–2,000 m a.s.l.). This is confirmed by negative changes in snow persistence detected at the pixel level, concentrated in this altitudinal belt in summer and also in autumn. In winter and spring, negative changes in snow persistence are concentrated at lower elevations (1,200–1,530 m a.s.l.). Considering the snow persistence of the 1984–1990 period as a reference, the last period (2015–2019) is experiencing a −5.75 km2 reduction of permanent snow area (snow persistence > 95%) in summer, −8.75 km2 in autumn, −42.40 km2 in winter, and −18.23 km2 in spring. While permanent snow at the high elevational belt (>2,000 m a.s.l.) has not changed through the years, snow that used to be permanent in the middle elevational belt has become seasonal. In this study, we use a probabilistic snow persistence approach for identifying areas of snow reduction and potential changes in alpine vegetation. This approach permits a more efficient use of remote sensing data, increasing by three times the amount of usable scenes by including images with spatial gaps. Furthermore, we explore some ecological questions regarding alpine ecosystems that this method may help address in a global warming scenario.

scholarly journals The Use of the CORINE Land Cover (CLC) Database for Analyzing Urban Sprawl

2020 ◽  
Vol 12 (2) ◽  
pp. 282 ◽  
Author(s):  
Iwona Cieślak ◽  
Andrzej Biłozor ◽  
Karol Szuniewicz
Keyword(s):  
Land Cover ◽  
Urban Sprawl ◽  
Agricultural Land ◽  
Local Governance ◽  
Land Development ◽  
Buffer Zones ◽  
Corine Land Cover ◽  
Gis Tools ◽  
Reliable Source ◽  
Novel Method

Urban sprawl is generally defined as the urbanization of space adjacent to a city, which results from that city’s development. The discussed phenomenon involves land development, mainly agricultural land, in the proximity of cities, the development of infrastructure, and an increase in the number of residents who rely on urban services and commute to work in the city. Urban sprawl generates numerous problems which, in the broadest sense, result from the difficulty in identifying the boundaries of the central urban unit and the participation of local inhabitants, regardless of their actual place of residence, in that unit’s functional costs. These problems are associated not only with tax collection rights but with difficulties in measuring the extent of urban sprawl in research and local governance. The aim of this study was to analyze the applicability of the CORINE Land Cover (CLC) database for monitoring urbanization processes, including the dynamic process of urban sprawl. Polish cities with county rights, i.e., cities that implement independent spatial planning policies, were analyzed in the study to determine the pattern of urban sprawl in various types of cities. Buffer zones composed of municipalities that are directly adjacent to the central urban unit were mapped around the analyzed cities. The study proposes a novel method for measuring the extent of suburbanization with the use of the CLC database and Geographic Information System (GIS) tools. The developed method relies on the overgrowth of urbanization (OU) index calculated based on CLC data. The OU index revealed differences in the rate of urbanization in three groups of differently sized Polish cities. The analysis covered two periods: 2006–2012 and 2012–2018, and it revealed that urban sprawl in the examined cities proceeded in an unstable manner over time. The results of the present study indicate that the CLC database is a reliable source of information about urbanization processes.

scholarly journals Sugarcane spatial-temporal monitoring and crop coefficient estimation through NDVI

2019 ◽  
Vol 23 (5) ◽  
pp. 330-335 ◽  
Author(s):  
Angelo B. Alface ◽  
Silvio B. Pereira ◽  
Roberto Filgueiras ◽  
Fernando F. Cunha
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Irrigation Management ◽  
Crop Coefficient ◽  
Irrigated Agriculture ◽  
Sugarcane Crop ◽  
Coefficient Estimation ◽  
Sentinel 2A ◽  
The Relationship

ABSTRACT The use of satellite images as a complement in irrigation management constitutes a primordial basis in the decision-making process for irrigated agriculture. In this context, the present study aimed to monitor through Normalized Difference Vegetation Index (NDVI) an irrigated sugarcane field belonging to the Mafambisse company, located at the District of Nhamatanda/Sofala, Republic of Mozambique, and establish its relationship with the crop coefficient established by FAO (kcFAO) and fit a regression model to estimate crop coefficient (kc) from the relationship between NDVI and kcFAO. The study was conducted using a series of Sentinel-2A/MSI images, relative to the period from October 2016 to October 2017. Based on the NDVI images generated, it was possible to monitor the sugarcane crop in the field and analyse the sensitivity of the index to its vegetative vigor. A similar pattern was observed between kcFAO profiles and NDVI values, which allowed the adjustment to be performed, demonstrating that this index is an alternative to obtain the crop coefficient.

scholarly journals Snow and Ice Studies By Thematic Mapper and Multispectral Scanner Landsat Images

1987 ◽  
Vol 9 ◽  
pp. 109-118 ◽  
Author(s):  
Olav Orheim ◽  
Baerbel K. Lucchitta
Keyword(s):  
Satellite Images ◽  
Thematic Mapper ◽  
Landsat Images ◽  
Snow Properties ◽  
Spectral Bands ◽  
Dronning Maud Land ◽  
Visible Wavelengths ◽  
Transverse Strains ◽  
Thermal Band ◽  
Snow And Ice

Digitally enhanced Landsat Thematic Mapper (TM) images of Antarctica reveal snow and ice features to a detail never seen before in satellite images. The six TM reflective spectral bands have a nominal spatial resolution of 30 m, compared to 80 m for the Multispectral Scanner (MSS). TM bands 2–4 are similar to the MSS bands. TM infra-red bands 5 and 7 discriminate better between clouds and snow than MSS or the lower TM bands. They also reveal snow features related to grain-size and possibly other snow properties. These features are not observed in the visible wavelengths. Large features such as flow lines show best in the MSS and lower TM bands. Their visibility is due to photometric effects on slopes. TM thermal band 6 has a resolution of 120 m. It shows ground radiation temperatures and may serve to detect liquid water and to discriminate between features having similar reflectivities in the other bands, such as blue ice.Repeated Landsat images can be used for sophisticated glaciological studies. By comparing images from 1975 and 1985, flow rates averaging 0.72 km a−1, and mean longitudinal and transverse strains of respectively 1.3 × 10 −4 a −1 and 130 × 10−4 a−1 have been measured for Jutulstraumen, Dronning Maud Land.

scholarly journals SenDiT: The Sentinel-2 Displacement Toolbox with Application to Glacier Surface Velocities

2019 ◽  
Vol 11 (10) ◽  
pp. 1151
Author(s):  
Teodor Nagy ◽  
Liss M. Andreassen ◽  
Robert A. Duller ◽  
Pablo J. Gonzalez
Keyword(s):  
Feature Tracking ◽  
World Wide ◽  
Satellite Images ◽  
Glacier Surface ◽  
Displacement Maps ◽  
Spatial And Temporal Changes ◽  
Glacier Velocity ◽  
Glacier Flow ◽  
Sentinel 2A ◽  
Sentinel 2

Satellite imagery represents a unique opportunity to quantify the spatial and temporal changes of glaciers world-wide. Glacier velocity has been measured from repeat satellite scenes for decades now, yet a range of satellite missions, feature tracking programs, and user approaches have made it a laborious task. To date, there has been no tool developed that would allow a user to obtain displacement maps of any specified glacier simply by establishing the key temporal, spatial and feature tracking parameters. This work presents the application and development of a unique, semi-automatic, open-source, flexible processing toolbox for the retrieval of displacement maps with a focus on obtaining glacier surface velocities. SenDiT combines the download, pre-processing, feature tracking, and postprocessing of the highest resolution Sentinel-2A and Sentinel-2B satellite images into a semi-automatic toolbox, leaving a user with a set of rasterized and georeferenced glacier flow magnitude and direction maps for their further analyses. The solution is freely available and is tailored so that non-glaciologists and people with limited geographic information system (GIS) knowledge can also benefit from it. The system can be used to provide both regional and global sets of ice velocities. The system was tested and applied on a range of glaciers in mainland Norway, Iceland, Greenland and New Zealand. It was also tested on areas of stable terrain in Libya and Australia, where sources of error involved in the feature tracking using Sentinel-2 imagery are thoroughly described and quantified.

scholarly journals Sustainable Development: Evaluating Optimal Technique for Spatial Data Forecast

Proceedings ◽  
2018 ◽  
Vol 2 (22) ◽  
pp. 1371
Author(s):  
Gaurav Kumar ◽  
Rajiv Gupta
Keyword(s):  
Information System ◽  
Spatial Data ◽  
Satellite Images ◽  
Moving Average ◽  
Vegetation Indices ◽  
Geographical Information ◽  
One Dimensional ◽  
Regression Methods ◽  
Gis Tools ◽  
Normalized Difference Vegetation Indices

This paper is an approach to forecast the spatial data in time series domain. Normally in GIS (Geographical Information System), we need raster forecasting. Moving average, exponential smoothing, and linear regression methods of forecasting are used over one-dimensional data. Present work concentrates on using these methods on satellite images applying them from pixel to pixel of historical temporal satellite data. An example set of satellite images from years 2011 to 2015 has been used to forecast the image in the year 2016. GIS tools have been developed in ArcGIS 10.1 using python to implement the methods of forecasting. Forecasted and actual images of the year 2016 have been compared by calculating the Normalized Difference Vegetation Indices (NDVI) and change detection to identify the best method.

The effect of forest fire on mass movement in Lebanese mountainous areas

2014 ◽  
Vol 23 (6) ◽  
pp. 845
Author(s):  
Rouba Ziadé ◽  
Chadi Abdallah ◽  
Nicolas Baghdadi
Keyword(s):  
Forest Fire ◽  
Satellite Images ◽  
Mass Movement ◽  
Potential Effect ◽  
Burn Severity ◽  
Susceptibility Map ◽  
Mass Movements ◽  
Mountainous Areas ◽  
Effect Factors ◽  
Landsat Images

Mass movements are major hazards that threaten natural and human environments. In Lebanon, the occurrence of mass movements increased by almost 60% between 1956 and 2008. Forest fire has emerged as an additional hazard: it destroyed over 25% of Lebanon’s forests in a period less than 40 years. This paper investigates the potential effect of forest fire on the occurrence of mass movements in the Damour and Nahr Ibrahim watersheds of Lebanon. Mass movement and forest fire inventory maps were produced through remote sensing using aerial and satellite images. Forest fire was included as an additional factor in mass movement induction, and its effect was quantified from Landsat images through the normalised burn ratio (NBR) index. A field study was conducted to substantiate the mass movement inventory and NBR maps. Following the standardisation of the effect factors into layers using geographic information systems, the weight factor of each layer for inducing mass movements was evaluated using the modified InfoVal method, and a mass movement susceptibility map was generated. Exceeded only by changes in land cover, the NBR produced the highest weights, making forest fire burn severity the second highest factor influencing mass movement occurrence in the study areas.

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