scholarly journals Mapeamento do Uso e Ocupação do Solo no Município de Belém de São Francisco-PE nos Anos de 1985 e 2010 (Mapping of Use and Occupancy of Soil in Town of Belém de São Francisco-PE in Years of 1985 and 2010)

2015 ◽  
Vol 7 (5) ◽  
pp. 859 ◽  
Author(s):  
Janaina Maria Oliveira de Assis ◽  
Ludmilla Oliveira Calado ◽  
Werônica Meira Souza ◽  
Maria do Carmo Sobral
Keyword(s):  
Remote Sensing ◽  
San Francisco ◽  
Supervised Classification ◽  
Bare Soil ◽  
Geographic Information ◽  
Gis And Remote Sensing ◽  
Landsat 5 Tm ◽  
Spatio Temporal ◽  
The City

R E S U M O Este artigo tem como objetivo mapear o uso e ocupação do solo no município de Belém de São Francisco, localizado na mesorregião do São Francisco, Pernambuco, na porção semiárida do nordeste brasileiro. Foram utilizadas ferramentas de Sistemas de Informações Geográficas (SIGs) e técnicas de sensoriamento remoto. Foi realizada uma classificação não-supervisionada do uso e ocupação do solo, onde foi feita a identificação de quatro temas: corpos d’água, vegetação densa, vegetação rasteira e solo exposto/área urbana, nos diferentes anos de 1985 e 2010. As imagens utilizadas foram do sensor Landsat 5 TM, coletadas no acervo de imagens do INPE. Os mapas foram elaborados no software ArcGIS 10.1, utilizando o sistema de coordenadas Sirgas2000, no fuso 24S. Os resultados mostraram diferentes fases de uso e ocupação do solo, apresentando diferentes causas de sua variação espaço-temporal, incluindo mudanças nos recursos hídricos, na vegetação e consequentemente na ocupação urbana do município.    A B S T R A C T This article aims to map the use and occupation of land in the city of Bethlehem in San Francisco , located in the middle region of the San Francisco PE in semiarid northeastern part of Brazil . Geographic Information Systems ( GIS ) and remote sensing tools were used . Water bodies , dense vegetation , low vegetation and bare soil / urban area in different years 1985 and 2010 : methodology as a non - supervised classification of the use and occupation of land , where the identification of four themes was done was done. The images used were from Landsat 5 TM , collected in the collection of images from INPE . The maps were drawn with ArcGIS 10.1 software , using SIRGAS2000 coordinate system , the spindle 24S . The results showed different phases of use and occupation of land , with different causes of their spatio-temporal variation , including changes in water resources , vegetation and consequently the urban occupation of the city .Keywords: Use and land cover, remote sensing, geographic information system.  

scholarly journals MAPEAMENTO DO USO E OCUPAÇÃO DO SOLO NO MUNICÍPIO DE BELÉM DE SÃO FRANCISCO – PE NOS ANOS DE 1985 E 2010 (MAPPING THE USE AND OCCUPANCY IN TOWN OF BELÉM FROM SAN FRANCISCO - PE IN YEARS FROM 1985 AND 2010)

2015 ◽  
Vol 7 (5) ◽  
pp. 949
Author(s):  
Janaina Maria Oliveira de Assis
Keyword(s):  
Remote Sensing ◽  
San Francisco ◽  
Supervised Classification ◽  
Bare Soil ◽  
Geographic Information ◽  
Gis And Remote Sensing ◽  
Landsat 5 Tm ◽  
Spatio Temporal ◽  
The City

Este artigo tem como objetivo mapear o uso e ocupação do solo no município de Belém de São Francisco, localizado na mesorregião do São Francisco, Pernambuco, na porção semiárida do nordeste brasileiro. Foram utilizadas ferramentas de Sistemas de Informações Geográficas (SIGs) e técnicas de sensoriamento remoto. Foi realizada uma classificação não-supervisionada do uso e ocupação do solo, onde foi feita a identificação de quatro temas: corpos d’água, vegetação densa, vegetação rasteira e solo exposto/área urbana, nos diferentes anos de 1985 e 2010. As imagens utilizadas foram do sensor Landsat 5 TM, coletadas no acervo de imagens do INPE. Os mapas foram elaborados no software ArcGIS 10.1, utilizando o sistema de coordenadas Sirgas2000, no fuso 24S. Os resultados mostraram diferentes fases de uso e ocupação do solo, apresentando diferentes causas de sua variação espaço-temporal, incluindo mudanças nos recursos hídricos, na vegetação e consequentemente na ocupação urbana do município.    A B S T R A C T This article aims to map the use and occupation of land in the city of Bethlehem in San Francisco , located in the middle region of the San Francisco PE in semiarid northeastern part of Brazil . Geographic Information Systems ( GIS ) and remote sensing tools were used . Water bodies , dense vegetation , low vegetation and bare soil / urban area in different years 1985 and 2010 : methodology as a non - supervised classification of the use and occupation of land , where the identification of four themes was done was done. The images used were from Landsat 5 TM , collected in the collection of images from INPE . The maps were drawn with ArcGIS 10.1 software , using SIRGAS2000 coordinate system , the spindle 24S . The results showed different phases of use and occupation of land , with different causes of their spatio-temporal variation , including changes in water resources , vegetation and consequently the urban occupation of the city .Keywords: Use and land cover, remote sensing, geographic information system.   

scholarly journals Critical Land Detection using Remote Sensing Device and Geographic Information (Case Study of Critical Land Basic of Catchments Area, Alang, Wonogiri)

2017 ◽  
Vol 19 (1) ◽  
pp. 1
Author(s):  
Beny Harjadi
Keyword(s):  
Remote Sensing ◽  
Field Survey ◽  
Critical Level ◽  
Satellite Image ◽  
Geographic Information ◽  
Level Data ◽  
The Difference

Work criteria and indicator of Catchments Area need to be determined because the success and the failure of cultivating Catchments Area can be monitored and evaluated through the determined criteria. Criteria Indicators in utilizing land, one of them is determined based on the erosion index and the ability of utilizing land, for analyzing the land critical level. However, the determination of identification and classification of land critical level has not been determined; as a result the measurement of how wide the real critical land is always changed all the year. In this study, it will be tried a formula to determine the land critical/eve/ with various criteria such as: Class KPL (Ability of Utilizing Land) and the difference of the erosion tolerance value with the great of the erosion compared with land critical level analysis using remote sensing devices. The aim of studying land critical level detection using remote sensing tool and Geographic Information System (SIG) are:1. The backwards and the advantages of critical and analysis method2. Remote Sensing Method for critical and classification3. Critical/and surveyed method in the field (SIG) Collecting and analyzing data can be found from the field survey and interpretation of satellite image visually and using computer. The collected data are analyzed as:a. Comparing the efficiency level and affectivity of collecting biophysical data through field survey, sky photo interpretation, and satellite image analysis.b. Comparing the efficiency level and affectivity of land critical level data that are found from the result of KPL with the result of the measurement of the erosion difference and erosion tolerance.

scholarly journals Heat Islands in the city of San Francisco de Campeche: Detection and solution

2020 ◽  
pp. 16-20
Author(s):  
Román Alejandro Canul-Turriza ◽  
Francisco Javier Barrera-Lao ◽  
Gabriela Patricia Aldana Narváez
Keyword(s):  
Soil Temperature ◽  
San Francisco ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heat Islands ◽  
Mathematical Algorithm ◽  
Green Areas ◽  
Landsat 5 Tm ◽  
The City

This paper presents the identification of heat islands in the city of San Francisco de Campeche, period 1990 - 2020 and their relationship with changes in the vegetation cover areas. To identify the heat islands in the city, 6 Landsat 5 (TM), 7 (TM) and 8 (OIL) images were obtained from the USGS database (http://earthexplorer.usgs.gov/). In geographic information software, soil temperature was calculated from a mathematical algorithm applied to thermal infrared bands 6 and 10, in addition, the Normalized Difference Vegetation Index (NDVI) was calculated, in order to find a relationship between changes in temperature and vegetation cover. It was found that the green areas have reduced their surface by more than 50% and the soil temperature has increased up to 7 ° C

scholarly journals Remote sensing appraisal of Lake Chad shrinkage connotes severe impacts on green economics and socio-economics of the catchment area

2017 ◽  
Vol 4 (11) ◽  
pp. 171120 ◽  
Author(s):  
Olapeju Y. Onamuti ◽  
Emmanuel C. Okogbue ◽  
Israel R. Orimoloye
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Catchment Area ◽  
Supervised Classification ◽  
Economic Status ◽  
Land Use Land Cover ◽  
Economic Activities ◽  
Lake Chad ◽  
Border Communities

Lake Chad commonly serves as a major hub of fertile economic activities for the border communities and contributes immensely to the national growth of all the countries that form its boundaries. However, incessant and multi-decadal drying via climate change pose greater threats to this transnational water resource, and adverse effects on ecological sustainability and socio-economic status of the catchment area. Therefore, this study assessed the extent of shrinkage of Lake Chad using remote sensing. Landsat imageries of the lake and its surroundings between 1987 and 2005 were retrieved from Global Land Cover Facility website and analysed using Integrated Land and Water Information System version 3.3 (ILWIS 3.3). Supervised classification of area around the lake was performed into various land use/land cover classes, and the shrunk part of its environs was assessed based on the land cover changes. The shrinkage trend within the study period was also analysed. The lake water size reduced from 1339.018 to 130.686 km 2 (4.08–3.39%) in 1987–2005. The supervised classification of the Landsat imageries revealed an increase in portion of the lake covered by bare ground and sandy soil within the reference years (13 490.8–17 503.10 km 2 ) with 4.98% total range of increase. The lake portion intersected with vegetated ground and soil also reduced within the period (11 046.44–10 078.82 km 2 ) with 5.40% (967.62 km 2 ) total decrease. The shrunk part of the lake covered singly with vegetation increased by 2.74% from 1987 to 2005. The shrunk part of the lake reduced to sand and turbid water showed 5.62% total decrease from 1987 to 2005 and a total decrease of 1805.942 km 2 in area. The study disclosed an appalling rate of shrinkage and damaging influences on the hydrologic potential, eco-sustainability and socio-economics of the drainage area as revealed using ILWIS 3.3.

CLASSIFICATION OF MULTITEMPORAL REMOTE-SENSING IMAGES BY A FUZZY FUSION OF SPECTRAL AND SPATIO-TEMPORAL CONTEXTUAL INFORMATION

2004 ◽  
Vol 18 (02) ◽  
pp. 143-156 ◽  
Author(s):  
FARID MELGANI
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Multilayer Perceptron ◽  
Contextual Information ◽  
Fuzzy Reasoning ◽  
Remote Sensing Images ◽  
Single Time ◽  
Spatio Temporal ◽  
Fuzzy Fusion

A fuzzy-logic approach to the classification of multitemporal, multisensor remote-sensing images is proposed. The approach is based on a fuzzy fusion of three basic sources of information: spectral, spatial and temporal contextual information sources. It aims at improving the accuracy over that of single-time noncontextual classification. Single-time class posterior probabilities, which are used to represent spectral information, are estimated by Multilayer Perceptron neural networks trained for each single-time image, thus making the approach applicable to multisensor data. Both the spatial and temporal kinds of contextual information are derived from the single-time classification maps obtained by the neural networks. The expert's knowledge of possible transitions between classes at two different times is exploited to extract temporal contextual information. The three kinds of information are then fuzzified in order to apply a fuzzy reasoning rule for their fusion. Fuzzy reasoning is based on the "MAX" fuzzy operator and on information about class prior probabilities. Finally, the class with the largest fuzzy output value is selected for each pixel in order to provide the final classification map. Experimental results on a multitemporal data set consisting of two multisensor (Landsat TM and ERS-1 SAR) images are reported. The accuracy of the proposed fuzzy spatio-temporal contextual classifier is compared with those obtained by the Multilayer Perceptron neural networks and a reference classification approach based on Markov Random Fields (MRFs). Results show the benefit of adding spatio-temporal contextual information to the classification scheme, and suggest that the proposed approach represents an interesting alternative to the MRF-based approach, in particular, in terms of simplicity.

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