scholarly journals ANÁLISE COMPARATIVA DE MÉTODOS DE CLASSIFICAÇÃO SUPERVISIONADA APLICADA AO MAPEAMENTO DA COBERTURA DO SOLO NO MUNICÍPIO DE MEDICILÂNDIA, PARÁ

2018 ◽  
Vol 4 (13) ◽  
pp. 26
Author(s):  
Jones Remo Barbosa Vale ◽  
Jamer Andrade da Costa ◽  
Jefferson Ferreira dos Santos ◽  
Elton Luis Silva da Silva ◽  
Artur Trindade Favacho
Keyword(s):  
Remote Sensing ◽  
Maximum Likelihood ◽  
Minimum Distance ◽  
Supervised Classification ◽  
Satellite Images ◽  
Soil Cover ◽  
Landsat 8 ◽  
Kappa Index ◽  
Global Accuracy

COMPARATIVE ANALYSIS THE METHODS OF SUPERVISED CLASSIFICATION APPLIED TO THE MAPPING OF SOIL COVER IN THE MUNICIPALITY OF MEDICILÂNDIA, PARÁANÁLISIS COMPARATIVO DE MÉTODOS DE CLASIFICACIÓN SUPERVISADA APLICADA AL MAPEO DE LA COBERTURA DEL SUELO EN EL MUNICIPIO DE MEDICILÂNDIA, PARÁAs imagens de satélite são produtos gerados por sensoriamento remoto e, estão associadas aos fenômenos e eventos que ocorrem na superfície a partir do registro e da análise das interações entre a radiação eletromagnética e os alvos. O objetivo do trabalho é comparar métodos de classificação supervisionada de imagens de satélite para o mapeamento da cobertura do solo. A área de estudo compreende o município de Medicilândia, localizado no sudoeste paraense. Para a realização do trabalho foram utilizados imagens do satélite Landsat 8, sensor OLI-TIRS, cenas 226/062 e 227/062. Foram realizados os testes de classificação, utilizando três classificadores: Distância Mínima, Distância Mahalanobis e Máxima Verossimilhança. Na etapa de classificação foram identificadas as seguintes classes: água, nuvem, sombra de nuvem, solo exposto, vegetação primária e vegetação secundária. Para fins de avaliação da fidedignidade da classificação de cada método foram calculados, o Índice Kappa e a Exatidão Global. A classificação pelo método Máxima Verossimilhança obteve maior exatidão apresentando Índice Kappa de 0,920 e Exatidão Global 96% quando comparada à classificação pelos métodos Distância Mínima e Distância Mahalanobis, que apresentaram Índice Kappa de 0,842 e 0,845 e Exatidão Global 92% respectivamente. As técnicas de classificação supervisionada são ferramentas essenciais no processo de mapeamento da cobertura do solo de grandes áreas, visto que dispondo-se de recursos limitados, imagens de baixo custo e de sistemas livres para processamento e integração das informações, é possível obter parâmetros com altos níveis de precisão, sendo fundamentais para subsidiar o planejamento territorial e ambiental.Palavras-chave: Sensoriamento Remoto; Classificação de Imagens de Satélite; Cobertura do Solo.ABSTRACTThe satellite images are products generated by remote sensing and are associated with phenomena and events that occur on the surface from the recording and analysis of interactions between electromagnetic radiation and targets. The objective of this work is to compare methods of supervised classification of satellite images for the mapping of the soil cover. The study area comprises the municipality of Medicilândia, located in southwest of Para. In order to perform the work, were used images from the Landsat 8 satellite, OLI-TIRS sensor, scenes 226/062 and 227/062. The classification tests were performed using three classifiers: Minimum Distance, Mahalanobis Distance and Maximum Likelihood. In the classification processe were identified the following classes: water, cloud, cloud shadow, exposed soil, primary vegetation and secondary vegetation. For the purposes of evaluating the reliability of the classification of each method were calculated, Kappa Index and Global Accuracy. The classification by the Maximum Likelihood method obtained a greater accuracy presenting Kappa Index of 0,920 and Global Accuracy 96% when compared to the classification by the Minimum Distance and Mahalanobis Distance, which presented Kappa Index of 0,842 and 0,845 and Global Accuracy 92% respectively. The supervised classification techniques are essential tools in the mapping process of large-area soil cover, since with limited resources, low-cost images and free systems for processing and integrating information, it is possible to obtain parameters with high levels of precision, being fundamental to subsidize territorial and environmental planning.Keywords: Remote Sensing; Classification of Satellite Images; Soil Cover.RESUMENLas imágenes de satélite son productos generados por la detección remota y están asociados a los fenómenos y eventos que ocurren en la superficie a partir del registro y del análisis de las interacciones entre la radiación electromagnética y los blancos. El objetivo del trabajo es comparar métodos de clasificación supervisada de imágenes de satélite para el mapeo de la cobertura del suelo. El área de estudio comprende el municipio de Medicilândia, ubicado en el suroeste paraense. Para la realización del trabajo se utilizaron imágenes del satélite Landsat 8, sensor OLI-TIRS, escenas 226/062 y 227/062. Se utilizaron tres clasificadores: Distancia Mínima, Distancia Mahalanobis y Máxima Verosimilitud. En la etapa de clasificación se identificaron las siguientes clases: agua, nube, sombra de nube, suelo expuesto, vegetación primaria y vegetación secundaria. Para evaluar la confianza de la clasificación de cada método se ha calculado, el Índice Kappa y la Exactitud Global. La clasificación por Máxima Verosimilitud obtuvo mayor exactitud presentando Índice Kappa de 0,920 y Exactitud Global 96% cuando comparada a la clasificación por Distancia Mínima y Distancia Mahalanobis, que presentaron Índice Kappa de 0,842 y 0,845 y Exactitud Global 92% respectivamente. Las técnicas de clasificación supervisada son herramientas esenciales en el proceso de mapeo de la cobertura del suelo de grandes áreas, ya que disponiendo de recursos limitados, imágenes de bajo costo y de sistemas libres para procesamiento e integración de la información, es posible obtener parámetros con altos niveles de precisión, siendo fundamentales para subsidiar la planificación territorial y ambiental.Palabras clave: Sensoramiento Remoto; Clasificación de Imágenes de Satélite; Cobertura del Suelo.

scholarly journals COMPARAÇÃO DA CLASSIFICAÇÃO DE OCUPAÇÃO DO SOLO DO MUNICÍPIO DE FREDERICO WESTPHALEN-RS, UTILIZANDO OS MÉTODOS ISODATA E DISTÂNCIA MÍNIMA

Nativa ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 727
Author(s):  
Ana Claudia Guedes Silva ◽  
Gabriel De Menezes Trevisan
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Minimum Distance ◽  
Spatial Information ◽  
Satellite Image ◽  
Kappa Index ◽  
Classification Techniques ◽  
Sentinel 2A ◽  
Global Accuracy

O uso dos Sistemas de Informações Geográficas (SIG), em produtos de sensoriamento remoto, tem sido cada vez mais utilizadas no mapeamento terrestre, facilitando na obtenção de informações espaciais. O objetivo deste trabalho foram avaliar duas diferentes técnicas de classificação digital, para o mapeamento de uso do solo do município de Frederico Westphalen - RS. Para este fim, utilizou-se o software Quantum Gis 2.18.13 (QGis) para primeiramente realizar a composição de bandas, realce de contraste e recorte da imagem de satélite Sentinel-2A (10m de resolução espacial) aos limites do município em estudo. Aplicaram-se diferentes técnicas de classificação digital: 1) Mínima Distância (supervisionada) e 2) ISODATA (não supervisionada); sendo o 1 realizado no QGis e o 2 no software ArcGIS 10.5. Foram obtidos mapas com diferentes informações, dos quais a acurácia foi avaliada pelos Índice Kappa, Exatidão Global, Erros de Omissão e Comissão. Constatou-se, pela análise dos valores das classes temáticas, em km², que os melhores resultados foram obtidos para a classificação supervisionada, a qual apresentou mais concordância com o mapa visual considerado a verdade de campo. Já para a validação a mesma classificação se destacou com maiores valores de Exatidão Global e Índice Kappa, (63,41% e 45%) diferente do encontrado para a classificação ISODATA (48,17% e 31%).Palavras-chave: geoprocessamento; sensoriamento remoto; classificação digital; mapeamento. COMPARISON OF THE CLASSIFICATION OF LAND USE OF THE MUNICIPALITY OF FREDERICO WESTPHALEN - RS, USING THE ISODATA AND MINIMUM DISTANCE ABSTRACT: The use of Geographic Information Systems (GIS) in remote sensing products has been increasingly used in terrestrial mapping, making it easier to obtain spatial information. The objective of this work was to evaluate different digital classification techniques for the land use mapping of the municipality of Frederico Westphalen - RS. For this purpose, the software Quantum Gis 2.18.13 (QGis) was used to first perform band composition, contrast enhancement and cut-off of the Sentinel-2A satellite image (10m spatial resolution) at the boundaries of the studied municipality. Different digital classification techniques were applied: 1) Minimum Distance (supervised) and 2) ISODATA (unsupervised); 1 being done in QGis and 2 in ArcGIS 10.5 software. We obtained maps with different information, of which the accuracy was evaluated by the Kappa Index, Global Accuracy, Errors of Omission and Commission. The best results were obtained for the supervised classification, which presented more agreement with the visual map considered the truth of the field, by analyzing the values of the thematic classes in km². For the validation, the same classification stood out with higher values of Global Accuracy and Kappa Index, (63.41% and 45%) than that found for the ISODATA classification (48.17% and 31%). However, the thematic classification should be adjusted as well as changing the RGB band composition to improve the statistical parameters.Keywords: geoprocessing; remote sensing; digital classification; mapping.

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 REMOTE SENSING IMAGE TIME SERIES METRICS FOR DISTINCTION BETWEEN PASTURE AND CROPLANDS USING THE RANDOM FOREST CLASSIFIER

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 557-562
Author(s):  
M. A. A. Rodrigues ◽  
H. N. Bendini ◽  
A. R. Soares ◽  
T. S. Körting ◽  
L. M. G. Fonseca
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Random Forest ◽  
Landsat 8 ◽  
Kappa Index ◽  
Object Based ◽  
One Year ◽  
Global Accuracy ◽  
Rural Production ◽  
Potential Use

Abstract. Pasture and croplands play an important role in Brazil’s economic and political scenarios, once its PIB (Raw Internal Product) is mainly based on what is exported from the rural production, such as meat and soybean, and government, with its regulations, is part-responsible for the establishment and maintaining of the conditions so that the trades can go well. In addition, these two types of land use correspond together to aprox. one third of the country extension. Moreover, frequently lands occupation is subject of discussion concerning its potential use for the reason of conflicts including Brazilian traditional communities, landless people and big farmers. Considering it, mapping pasture and croplands accurately is crucial for the country administration, in both economic and political spheres. Certainly, remote sensing is the very manner to tackle this issue, although this may not be an easy task due to the spectral similarity between these patterns. This work, hence, aims to distinct pasture from croplands in an experimental subset area of Brazilian Cerrado biome, using remote sensing metric images derived from one-year time series of the Landsat 8 products. In order to achieve this goal, we utilized six bands of the OLI sensor and calculated seven metrics, attaining a compiled dataset with 42 layers. We performed an object-based supervised classification with the Random Forest algorithm, considering both spectral and geometrical attributes. Results showed global accuracy of 80%, with Kappa index of 0.6, and the potential time series have in separating targets spectrally similar.

scholarly journals CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE

Irriga ◽  
2020 ◽  
Vol 25 (1) ◽  
pp. 160-169
Author(s):  
Cesar De Oliveira Ferreira Silva
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Supervised Classification ◽  
Source Code ◽  
Regression Trees ◽  
Google Earth ◽  
Landsat 8 ◽  
Classification And Regression ◽  
Google Earth Engine

CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE   CÉSAR DE OLIVEIRA FERREIRA SILVA1   1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista (UNESP) Campus de Botucatu. Avenida Universitária, n° 3780, Altos do Paraíso, CEP: 18610-034, Botucatu – SP, Brasil, e-mail: [email protected].     1 RESUMO   Identificar áreas de irrigação usando imagens de satélite é um desafio que encontra em soluções de computação em nuvem um grande potencial, como na ferramenta Google Earth Engine (GEE), que facilita o processo de busca, filtragem e manipulação de grandes volumes de dados de sensoriamento remoto sem a necessidade de softwares pagos ou de download de imagens. O presente trabalho apresenta uma implementação de classificação supervisionada de áreas irrigadas e não-irrigadas na região de Sorriso e Lucas do Rio Verde/MT com o algoritmo Classification and Regression Trees (CART) em ambiente GEE utilizando as bandas 2-7 do satélite Landsat-8 e os índices NDVI, NDWI e SAVI. A acurácia da classificação supervisionada foi de 99,4% ao utilizar os índices NDWI, NDVI e SAVI e de 98,7% sem utilizar esses índices, todas consideradas excelentes. O tempo de processamento médio, refeito 10 vezes, foi de 52 segundos, considerando todo o código-fonte desenvolvido desde a filtragem das imagens até a conclusão da classificação. O código-fonte desenvolvido é apresentado em anexo de modo a difundir e incentivar o uso do GEE para estudos de inteligência espacial em irrigação e drenagem por sua usabilidade e fácil manipulação.   Keywords: computação em nuvem, sensoriamento remoto, hidrologia, modelagem.     SILVA, C. O .F SUPERVISED CLASSIFICATION OF IRRIGATED AREA USING SPECTRAL INDEXES FROM LANDSAT-8 IMAGES WITH GOOGLE EARTH ENGINE     2 ABSTRACT   Identifying irrigation areas using satellite images is a challenge that finds great potential in cloud computing solutions as the Google Earth Engine (GEE) tool, which facilitates the process of searching, filtering and manipulating large volumes of remote sensing data without the need for paid software or image downloading. The present work presents an implementation of the supervised classification of irrigated and rain-fed areas in the region of Sorriso and Lucas do Rio Verde/MT with the Classification and Regression Trees (CART) algorithm in GEE environment using bands 2-7 of the Landsat- 8 and the NDVI, NDWI and SAVI indices. The accuracy of the supervised classification was 99.4% when using NDWI, NDVI and SAVI indices and 98.7% without using these indices, which were considered excellent. The average processing time, redone 10 times, was 52 seconds, considering all the source code developed from the filtering of the images to the conclusion of the classification. The developed source code is available in the appendix in order to disseminate and encourage the use of GEE for studies of spatial intelligence in irrigation and drainage due to its usability and easy manipulation.   Keywords: cloud computing, remote sensing, hydrology, modeling.

scholarly journals Selection of an algorithm for automatic classification of satellite images for the study of agricultural crops on the territory of Vietnam

2021 ◽  
Vol 937 (3) ◽  
pp. 032082
Author(s):  
B N Olzoev ◽  
H Z Huang ◽  
L A Plastinin ◽  
V E Gagin ◽  
O V Danchenko
Keyword(s):  
Maximum Likelihood ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Classification Algorithms ◽  
Agricultural Activity ◽  
Landsat 8 ◽  
Agricultural Crop ◽  
Agricultural Crops ◽  
Selection Of

Abstract The paper is devoted to the choice of an algorithm for automatic controlled classification of multi-zone satellite images of Landsat 8 OLI for the purposes of agricultural crop research based on the analysis of various mathematical classification algorithms and comparison of the practical results of these algorithms when using the ENVI 5.4 software package. In the period from June to August 2020, a field survey was conducted by coordinating and ground-based object recognition for the purpose of compiling decryption standards based on images. The paper analyzes four frequently used popular algorithms for automatic controlled classification – maximum likelihood, minimum distance, Mahalanobis distance, parallelepiped. As a result, it is concluded that when classifying objects with very close brightness values, the maximum likelihood algorithm gives optimal and objective results. This conclusion was confirmed by the cameral method by evaluating the reliability of the classification results. The result of the study can be used for mapping agricultural crops and solving other problems of agricultural activity in Vietnam. The methodology presented in the paper can be applied when choosing controlled classification algorithms for other groups of plant complexes and objects based on remote sensing data from space.

scholarly journals Indices Extraction from Multitemporal Remote Sensing Data for Mapping Urban Built-Up

2021 ◽  
Vol 887 (1) ◽  
pp. 012004
Author(s):  
A. K. Hayati ◽  
Y.F. Hestrio ◽  
N. Cendiana ◽  
K. Kustiyo
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Sensing Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Multitemporal Remote Sensing ◽  
Multitemporal Data

Abstract Remote sensing data analysis in the cloudy area is still a challenging process. Fortunately, remote sensing technology is fast growing. As a result, multitemporal data could be used to overcome the problem of the cloudy area. Using multitemporal data is a common approach to address the cloud problem. However, most methods only use two data, one as the main data and the other as complementary of the cloudy area. In this paper, a method to harness multitemporal remote sensing data for automatically extracting some indices is proposed. In this method, the process of extracting the indices is done without having to mask the cloud. Those indices could be further used for many applications such as the classification of urban built-up. Landsat-8 data that is acquired during 2019 are stacked, therefore each pixel at the same position creates a list. From each list, indices are extracted. In this study, NDVI, NDBI, and NDWI are used to mapping built-up areas. Furthermore, extracted indices are divided into four categories by their value (maximum, quantile 75, median, and mean). Those indices are then combined into a simple formula to mapping built-up to see which produces better accuracy. The Pleiades as high-resolution remote sensing data is used to assist supervised classification for assessment. In this study, the combination of mean NDBI, maximum NDVI, and mean NDWI result highest Kappa coefficient of 0.771.

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 Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Sensing Data ◽  
Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

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 Detection of soybean planted areas through orbital images based on culture spectral dynamics

2012 ◽  
Vol 32 (5) ◽  
pp. 920-931 ◽  
Author(s):  
Erivelto Mercante ◽  
Luiz E. P. de Lima ◽  
Diego D. D. Justina ◽  
Miguel A. Uribe-Opazo ◽  
Rubens A. C. Lamparelli
Keyword(s):  
Satellite Images ◽  
Lower Cost ◽  
Reference Area ◽  
Kappa Index ◽  
Specific System ◽  
The West ◽  
Spectral Dynamics ◽  
Training Samples ◽  
Landsat 5 Tm ◽  
Global Accuracy

The soybean is important to the economy of Brazil, so the estimation of the planted area and the production with higher antecedence and reliability becomes essential. Techniques related to Remote Sensing may help to obtain this information at lower cost and less subjectivity in relation to traditional surveys. The aim of this study is to estimate the planted area with soybean culture in the crop of 2008/2009 in cities in the west of the state of Paraná, in Brazil, based on the spectral dynamics of the culture and through the use of the specific system of analysis for images of Landsat 5/TM satellite. The obtained results were satisfactory, because the classification supervised by Maximum Verisimilitude - MaxVer along with the techniques of the specific system of analysis for satellite images has allowed an estimate of soybean planted area (soybean mask), obtaining values of the metrics of Global Accuracy with an average of 79.05% and Kappa Index over 63.50% in all cities. The monitoring of a reference area was of great importance for determining the vegetative phase in which the culture is more different from the other targets, facilitating the choice of training samples (ROIs) and avoiding misclassifications.

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