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 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.

Satellite Image Classes Categorization Schemes for United Nations Framework Convention on Climatic Change (UNFCCC)

2018 ◽  
pp. 101-113
Author(s):  
Joyce Gosata Maphanyane ◽  
Gofetamang Phunyuka
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Electromagnetic Waves ◽  
Satellite Image ◽  
National Representation ◽  
Thematic Resolution ◽  
The One

This chapter looks at the disparities between the UNFCCC – GHG – Land-Use and Land-Cover Change (LULCC) remote sensing images classification scheme with that of Botswana for the GHG inventory for the National Representation. This chapter has points out that the Botswana Scheme maximizes the LANDSAT System electromagnetic waves capabilities and maps produced give more classes and better thematic resolution for the classification of land cover classes. Suggestions are made for these two schemes to be reconciled and use the one which gives the best GHG calculated results for inventories for Inter-Governmental Panel on Climate Change (IPCC) Reporting

scholarly journals Study of land cover of Monte Forgoselo using Landsat Thematic Mapper 5 images (Galicia, NW Spain)

DYNA ◽  
2015 ◽  
Vol 82 (190) ◽  
pp. 173-181
Author(s):  
Xana Álvarez Bermúdez ◽  
Enrique Valero-Gutiérrez del Olmo ◽  
Juan Picos-Martín ◽  
Luis Ortiz-Torres
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Minimum Distance ◽  
Satellite Image ◽  
Landsat Thematic Mapper ◽  
Planning Tool ◽  
Nw Spain ◽  
Good Reliability ◽  
Forestry Management

The aim of this study was to carry out a land cover classification of Monte Forgoselo. Remote sensing as a planning tool has been used, specifically, a satellite image Landsat 5. We perform a supervised digital classification of the maximum likelihood, minimum distance and parallelepipeds. The result has been the division of Monte Forgoselo into categories of types of vegetation, particularly in pine forest, scrub and grassland, reaching a Kappa coefficient of 0.9762 and an overall accuracy of 98.3586%, which means good reliability. In addition, we found that the use of such techniques can get to make a planing of this small rural area, focusing on forestry management. Furthermore, the use of remote sensing is considered viable in the performance of work with more consideration.

More accurate less meaningful? A critical physical geographer’s reflection on interpreting remote sensing land-use analyses

2021 ◽  
pp. 030913332199181
Author(s):  
Andreas Christian Braun
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Geographic Information Systems ◽  
Image Classification ◽  
Satellite Image ◽  
Physical Geography ◽  
Data Availability ◽  
Scientific Results ◽  
Normative Considerations

Land-use and land-cover analyses based on satellite image classification are used in most, if not all, sub-disciplines of physical geography. Data availability and increasingly simple image classification techniques – nowadays, even implemented in simple geographic information systems – increase the use of such analyses. To assess the quality of such land-use analyses, accuracy metrics are applied. The results are considered to have sufficient quality, exceeding thresholds published in the literature. A typical practice in many studies is to confuse accuracy in remote sensing with quality, as required by physical geography. However, notions such as quality are subject to normative considerations and performative practices, which differ between scientific domains. Recent calls for critical physical geography have stressed that scientific results cannot be understood separately from the values and practices underlying them. This article critically discusses the specific understanding of quality in remote sensing, outlining norms and practices shaping it and their relation to physical geography. It points out that, as a seeming paradox, results considered more accurate in remote sensing terms can be less informative – or meaningful – in geographical terms. Finally, a roadmap of how to apply remote sensing land-use analyses more constructively in physical geography is proposed.

Based on IDRISI Remote Sensing Images Land-Use Types of Supervised Classification Techniques

2013 ◽  
Vol 415 ◽  
pp. 305-308
Author(s):  
Kun Zhang ◽  
Hai Feng Wang ◽  
Zhuang Li
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Classification Techniques ◽  
Analysis And Evaluation ◽  
Remote Sensing Classification ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Remote Sensing Image Classification ◽  
Land Use Types ◽  
Area Distribution

With remote sensing technology and computer technology, remote sensing classification technology has been rapid progress. In the traditional classification of remote sensing technology, based on the combination of today's technology in the field of remote sensing image classification, some new developments and applications for land cover classification techniques to make more comprehensive elaboration. Using the minimum distance classifier extracts of the study area land use types. Ultimately extracted land use study area distribution image and make its analysis and evaluation.

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.

Water Accounting Plus (WA+) through Remote Sensing in the Yarmouk Tributary Basin

2021 ◽  
Author(s):  
Chadi Abdallah ◽  
Gina Tarhini ◽  
Mariam Daher ◽  
Hussein Khatib ◽  
Mark Zeitoun
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Management Strategies ◽  
Initial Distribution ◽  
Yarmouk River ◽  
The Status ◽  
Irrigated Crops ◽  
Water Accounting ◽  
Derived Data

<p>Coping with the issue of water scarcity and growing competition for water among different sectors requires effective water management strategies and decision processes. ‘Getting it right’ becomes doubly important when dealing with intenational transboundary rivers. The Yarmouk tributary to the Jordan River is one highly exploited in the Middle East, and is enveloped by ambiguous treaties and decades of violent and non-violent conflict. Seeking to chart a more sustainable and equitable future, this work performs a 'water accounting plus' methodology employing readily available remotely sensed satellite-based data coupled with available measurements.  A variety of methods described herein were used to detect irrigated crops and produce maps showing the distribution throughout the basin. The framework also focuses on the classification of land use categories and the processes by which water is depleted over all land use classes that contributes to separate the beneficial from non-beneficial usage of water. The analysis was started prior to the 2011 start of the Syrian war in order to study the initial distribution of land use classes as well as the water depletion processes before any change in the basin. It shows that more than half of the exploitable water is not consumed within the basin and depleted outside. In contrast, most of the water consumed within the basin is wasted and depleted in a non-beneficial way. Roughly 35% of the cultivated area shown to be irrigated through withdrawals which exceed the capacity of the source. This result reflects the high abstraction rates from groundwater via a large number of unlicensed wells mostly located at the Syrian side. This study also detect a deficiency in the water balance of the Yarmouk River. The findings are relevant to sustainable management not only for water-dependent sectors but also for geopolitical stability among the riparian countries. In this way, open- access remote sensing derived data can provide useful information about the status of water resources especially when ground measurements are poor or absent.</p><p> </p><p>Keywords: Yarmouk, Water Accounting Plus, IWM, Irrigated crops, WAPOR.</p>

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