scholarly journals INFLUÊNCIA DO USO E OCUPAÇÃO DO SOLO NA DISPONIBILIDADE HÍDRICA DA BACIA HIDROGRÁFICA DO RIO URUÇUÍ-PRETO, PIAUÍ

Nativa ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 567
Author(s):  
Kaíse Barbosa Souza ◽  
João Batista Lopes Silva ◽  
Rafael Felippe Ratke ◽  
Gerson Santos Lisboa ◽  
Karla Nayara Santos Almeida
Keyword(s):  
Land Use ◽  
River Basin ◽  
Supervised Classification ◽  
Thematic Mapper ◽  
Large Area ◽  
Simple Correlation ◽  
Burned Areas ◽  
Crop Field ◽  
Annual Duration

Objetivou-se verificar a influência das mudanças no uso e ocupação do solo na disponibilidade hídrica da bacia hidrográfica do rio Uruçuí-Preto, Piauí, no período de 1984 a 2007. Para a avaliação do uso do solo foram utilizadas imagens do Satélite Landsat 5, sensor TM (Thematic Mapper) e realizou-se a classificação automática supervisionada auxiliada pelo algoritmo de Máxima Verossimilhança. Para a análise do comportamento hidrológico foram utilizados dados da vazão média, máxima e mínima anual; vazão mínima com sete dias de duração anual (Q7); e as vazões associadas às permanências de 90% (Q90) e 95% (Q95). Para a associação entre as vazões e o uso do solo, fez-se a correlação simples entre as variáveis, testando seu nível de significância a 5% (p < 0,05) de probabilidade. Os resultados demonstraram que ao comparar os anos de 1984 e 2007, as classes Cerrado e Mata Ciliar reduziram 20,9% e 2,4% respectivamente, a classe Solo Exposto/Talhão Agrícola aumentou 13,48% e a classe Queimada aumentou 9,83%. Não ocorreram correlações significativas entre as variáveis classes de mudanças no uso e ocupação do solo e as vazões, devido a extensa área da bacia que amenizou consequentemente os efeitos hidrológicos.Palavras-chave: desmatamento; sensoriamento remoto; vazões. INFLUENCE OF THE LAND USE AND OCCUPATION IN HIDROLOGIC AVAILABILITY OF RIVER BASIN URUÇUÍ-PRETO, PIAUÍ ABSTRACT: The aim’s was to verify the influence of changes in land use and occupation in water availability in the river basin Uruçuí-Preto, Brazil, in the period 1984 to 2007. For the assessment of land use were used images from satellite Landsat 5 TM sensor (Thematic Mapper) and the classification of imagens was made by automatic supervised classification with the maximum likelihood algorithm. For the analysis of the hydrological behavior were used average flow data, maximum and minimum annual, minimum flow with seven days of annual duration (Q7) and flow rates associated with stays of 90% (Q90) and 95% (Q95) of the year. For the association between flow and land use was made the simple correlation between the variables, testing its 5% significance (p < 0.05). The results showed that when comparing the years 1984 and 2007, the Cerrado and Mata Riparian classes decreased 20.9% and 2.4% respectively, Solo Exposed / Crop Field class increased 13.48% and Burned Areas class increased by 9.83%. There were no significant correlations between changes of class variables in the use and occupation of land and the flows, due to the large area of the basin that has consequently reduced the hydrological effects.Keywords: deforestation; remote sensing; flow.

scholarly journals Comparação entre diferentes algoritmos de classificação supervisionada em imagens Landsat-8 no mapeamento temático da Bacia Hidrográfica do rio Caldas, Goiás

2019 ◽  
Vol 12 (3) ◽  
pp. 961
Author(s):  
Leovigildo Aparecido Costa Santos ◽  
Paulo Eliardo Morais de Lima
Keyword(s):  
Land Use ◽  
Maximum Likelihood ◽  
River Basin ◽  
Supervised Classification ◽  
Likelihood Method ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Entire Area ◽  
Area Of Interest

Diferentes métodos são empregados para a classificação digital de imagens, porém, podem apresentar desempenhos diferentes, sendo importante testá-los para verificar suas eficácias no mapeamento de uso e cobertura da terra com intuito de se selecionar o classificador que apresente os melhores resultados e maior veracidade em relação à verdade de campo. O objetivo deste estudo foi avaliar e comparar os desempenhos de quatro algoritmos de classificação supervisionada para o mapeamento do uso e cobertura da terra da bacia hidrográfica do Rio Caldas – GO, utilizando imagens Landsat-8. Para tanto, foram utilizadas as cenas de órbita/ponto 222/71 e 222/72, com datas de passagem em 24/10/2017 e 22/10/2017, mosaicadas para formar uma única imagem de dimensões que abrangesse toda a área de interesse. A composição RGB utilizada foi das bandas 6, 5 e 4 (R=6, G=5, B=4). Para a realização do processamento digital da imagem foi empregado o software ENVI versão 5.0 e à elaboração de mapas temáticos o QGIS 2.18. Os algoritmos testados foram: Paralelepípedo, Distância de Mahalanobis, Distância Mínima e Máxima-verossimilhança. Como parâmetros de comparação foram utilizados os coeficientes de Kappa, acurácias global e matrizes de confusão. Os melhores resultados para a classificação de uso e cobertura foram obtidos pelo método da Máxima-verossimilhança (MaxVer), os piores pelo método do Paralelepípedo, os outros classificadores apresentaram resultados intermediários entre o melhor e o pior. Com os resultados obtidos pela classificação por MaxVer, constatou-se que atualmente a maior parte do solo da bacia é ocupada pelas classes Pastagem (63,14%) e Vegetação nativa (22,07%). Comparison between different supervised classification algorithms in Landsat-8 images in the thematic mapping of the caldas river basin, GoiásA B S T R A C TDifferent methods are used for a digital classification of images, however, they can present different performances, being important to test them to verify their efficiencies in the mapping of land use and coverage in order to select the classifier that presents the best results and greater truthfulness In relation to the truth of the field. The objective of this study was to evaluate and compare the performance of four supervised classification algorithms for the mapping of the land use and land cover of the Caldas river basin - GO, using Landsat-8 images. To do so, they were like the orbit / dot scenes 222/71 and 222/72, with passing date on 10/24/2017 and 10/22/2017, mosaicked to form a single image of dimensions covering an entire area of interest . An RGB composition used for bands 6, 5 and 4 (R = 6, G = 5, B = 4). For the realization of digital image processing and the use of ENVI version 5.0 software and the development of thematic maps, QGIS 2.18. The algorithms tested were: Parallelepiped, Mahalanobis Distance, Minimum Distance and Maximum Likelihood. As the comparison parameter is used by Kappa coefficients, global accuracy and matrices of confusion. The best results for a classification of use and coverage are obtained by the Maximum-likelihood method (MaxVer), the most common methods, the other classifiers presented the intermediates between the best and the worst. With the results obtained by classification by MaxVer, it was verified that at the moment it is part of the soil of the basin is occupied by classes Pasture (63.14%) and native vegetation (22.07%).Keywords: Use and coverage; remote sensing; geoprocessing; Landsat.

scholarly journals Supervised Classification of Satellite Images to Analyze Multi-Temporal Land Use and Coverage : A Case Study for the Town of MARABA, State of PARA, Brazil

2015 ◽  
Author(s):  
Priscila Siqueira Aranha ◽  
Flavia Pessoa Monteiro ◽  
Paulo Andre Ignacio Pontes ◽  
Jorge Antonio Moraes de Souza ◽  
Nandamudi Lankalapalli Vijaykumar ◽  
...  
Keyword(s):  
Land Use ◽  
Supervised Classification ◽  
Satellite Images ◽  
Multi Temporal ◽  
The Town

scholarly journals Assessing the Accuracy of Different Supervised Classification Methods of Satellite Image

2020 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Ibochi Andrew Abah ◽  
Richard jeremiah Uriah
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Satellite Image ◽  
Image Data ◽  
Image Process ◽  
Land Use Land Cover ◽  
Error Matrix

Assessing the accuracy of the classification map is an essential area in remote sensing digital image process. This is because a poorly classified map will result in inestimable errors of spatial analysis and modeling arising from the use of such data. This study was designed to evaluate different supervised classification algorithms in terms of accuracy assessment with a view of recommending an appropriate algorithm for image processing. The analysis was carried out using Andoni L.G.A. Rivers State, Nigeria as the study area. Supervised classification of ETM+ 2014 Landsat image of the study area was carried out using ENVI 5.0 software. Seven land use/land cover categories were identified on the image data and appropriate information classes were also assigned using region of interest. The classifiers adopted for the study include SAM, SVM, and MDC and each classifier was set using appropriate thresholds and parameters. The output error matrix of the classified map produced overall accuracy and kappa coefficient for MDC as 94.00% and 0.91, SAM as 64.45% and 0.53, and SVM as 98.92% and 0.98 respectively. The overall accuracy obtained from SVM indicates that a perfect classification map will be produced from the algorithm. The advanced supervised classification should be utilized for classification of land use/ land cover for both high and medium resolution images for improved classification accuracy.

scholarly journals Land use/ Land cover Change Detection in Auranga River Basin, Jharkhand

2020 ◽  
Vol 66 (1) ◽  
pp. 51-58
Author(s):  
Chnadrakesh Maurya ◽  
◽  
V. N. Sharma ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Supervised Classification ◽  
Land Resource ◽  
Land Use Land Cover ◽  
Ancillary Data ◽  
Classification Technique ◽  
Spatio Temporal ◽  
Cover Pattern

Land use is a man-made dynamic process in which human uses land resource to fulfil their various economic, social and cultural needs and at the same time it also provides a base for development. The proper management needed for sustainable development of land can improve the eco-system and its productivity in a particular geographical region. The present study focuses on spatio-temporal changes in land use and land cover pattern in Auranga river basin of Jharkhand using geospatial approach. Supervised classification technique was applied in this study to detect land use/ land cover changes. The main objective of the study is to analyse temporal change of land use/ land cover pattern during 1996, 2007 and 2018 using various dataset as well as other ancillary data. The result reveales both increase and decrease of the different land use/ land cover classes from 1996 to 2018.

scholarly journals Landscape analysis and land use evolution in the hydrographic basin of Barra Seca river, ES

2018 ◽  
Vol 40 ◽  
pp. 50
Author(s):  
Letícia Guarnier ◽  
Fabricia Benda de Oliveira ◽  
Vicente Sombra da Fonseca ◽  
Carlos Henrique Rodrigues de Oliveira
Keyword(s):  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Urban Areas ◽  
Supervised Classification ◽  
Evolutionary Dynamics ◽  
Tree Cover ◽  
Time Interval ◽  
Hydrographic Basin ◽  
Dense Tree

Multitemporal analysis for monitoring land cover and use is an important tool for understanding the evolutionary dynamics of a region, assisting the knowledge on the environmental reality. This study aimed at mapping the land cover classes of the Barra Seca River basin, in northern Espírito Santo, obtained using the Bhattacharya algorithm supervised classification in 1985, 1996, 2006 and 2016. The land use and occupation map allowed characterizing quantitatively the areas identified in the basin map in 10 classes as follows water bodies, agriculture and grasses, dense tree cover, sparse tree cover, exposed soil, wetlands, urban areas, rocky outcrops, shade, and clouds. The landscape maps were obtained using the Patch Analyst extension. In the studied time interval, the land use and occupation in the basin changed little, with areas dominated mostly by agriculture and grasslands, followed by forests while the basin vegetation area also remained mostly unchanged. However, the quantitative analysis using landscape metrics indicates an increasing fragmentation and edge effect in the Barra Seca River basin.

scholarly journals Simulation of the Hydrological Components of Dez River Basin by the classification of Land Use Categories Using SUFI-2 Algorithm

2022 ◽  
Vol 21 (63) ◽  
pp. 81-98
Author(s):  
Elaheh Asgari ◽  
Mohammad Baaghideh ◽  
Majid Hosseini ◽  
Alireza Entezari ◽  
Asghar Kamyar ◽  
...  
Keyword(s):  
Land Use ◽  
River Basin ◽  
Use Categories

scholarly journals Selection of Suitable Supervised Classification Techniques for the Geographic Analysis of Land Using GIS Techniques

2018 ◽  
Vol 7 (3.27) ◽  
pp. 82
Author(s):  
S L. Senthil Lekha ◽  
S S.Kumar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Kappa Coefficient ◽  
Landsat Image ◽  
Training Data ◽  
Spectral Angle Mapper ◽  
Gis Techniques ◽  
Spectral Angle

Nation has realised the changes in the land surface and the influence of this in the whole ecosystem. The activities of human on land is directly deteriorating the environment quality. This paper mainly focuses on the analysis of the destruction of land cover with the development of land use. The performance of five different Supervised Classification algorithms, which are Parallelepiped, Mahalanobis, Neurel Net, Adaptive Coherence and Spectral Angle Mapper  have been analysed in classifying the Landsat Image of kanyakumari district. Automatic classification of five classes using training data have been performed and the best suitable algorithm for the classification of each class have been analysed. Being a tourism centre with coastal areas on all three sides, the development and the deterioration of kanyakumari district have to be monitored constantly. The proposed system is an automatic approach which helps in the analysis of the patterns of land use and land cover which constantly changes and to map each class clearly and distinct from each other using GIS techniques. The system was evaluated using the performance measures like accuracy and  kappa coefficient using the tools Envi, ArcGIS and QGIS. From the performance analysis, the Spectral Angle Mapper with an overall accuracy  of 97% and kappa coefficient of 0.54 has been selected as the best suitable algorithm for the classification of landsat image of kanyakumari district. 

Sign in / Sign up

Export Citation Format

Share Document