Tesis sobre el tema "GEE (Google Earth Engine)"
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BELCORE, ELENA. "Generation of a Land Cover Atlas of environmental critic zones using unconventional tools". Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2907028.
Texto completoVahidi, Mayamey Farzad. "Improving the water-extent monitoring of Swedish wetlands with open-source satellite data and Google Earth Engine". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
Buscar texto completoFATTORE, CARMEN. "Verso un approccio multi-scala, multi-temporale e multi-sensore per il monitoraggio del Capitale Naturale nell’era dei Big Earth Data. Metodi, tecniche e sviluppi futuri per la valutazione del danno post-evento". Doctoral thesis, Università degli studi della Basilicata, 2022. http://hdl.handle.net/11563/158586.
Texto completoThe increasing availability of new technologies, the improvement and miniaturization of current technologies, the greater accessibility of data with particular reference to free access to numerous (NASA, ESA) archives of remote sensing images, the growth in potential offered by Earth Observation (EO) technologies, can offer a great deal of information (EO) for the scientific and non-scientific community. However, in order to extract useful information from the immense amount of data available today, new analysis and interpretation approaches need to be defined, designed and implemented, especially with regard to long time series New challenges arise such as automatic, robust and efficient processing of this amount of data. The definition of new CD approaches is needed to replace techniques that were effective on the previous generation of images but not on the latest generation. Other challenges include defining techniques that are robust to input data with time-varying quality. Images from these long, dense time series have redundant information. Therefore, it is plausible that the change maps detected between pairs of images extracted from a time series are correlated with each other. Therefore, it becomes necessary, but not sufficient, to eliminate redundant notions in order to obtain 'pure' and valid results. This thesis explores mechanisms for exploiting the temporal correlation of images to improve the extraction of information from long time series. New techniques are proposed to improve CD results obtained for a pair of images or multiple remote sensing data using unsupervised classifications, logistic regressions to understand trends, and per-pixel value extrapolations to analyse vegetation changes within time series. In this context, cloud masking and restoration of cloud-covered pixels in long time series acquired by optical sensors is required. In the literature, solutions to this problem often do not take advantage of the availability of a large number of images available for the study area but rely only on monotemporal cloud discrimination. This requires the development of new paradigms and techniques capable of fully exploiting all data and efficiently processing long time series of images. The main objectives of this work are the development of automatic CD techniques and the study of unsupervised mechanisms for the evaluation and improvement of CD results exploiting long time series. The thesis studies multitemporal approaches capable of fully exploiting the information acquired on the scene. Specifically, the aims and new contributions of the thesis are: 1. the proposal of a new paradigm for extracting information from long time series of images. Approaches are proposed to exploit open-source tools such as Google Earth Engine and medium-high geometric resolution satellite data on different spatial scales, reducing image processing times and returning final outputs. 2. the definition of a model to monitor post-fire vegetation through non-invasive satellite techniques, being able to extrapolate trends on the annual maxima of vegetation indices, allowing the analysis of single pixel values in order to monitor possible anomalies such as: changes of use in protected natural areas, calamitous events triggered by the fire such as landslides, or inadequate land protection such as the cutting of uncontrolled forest areas for agricultural or breeding purposes. 3. the development of approaches for assessing and estimating ecosystem damage due to fires. The comparison of fire maps with current Italian urban plans, in order to delimit vulnerable areas for the safeguarding of Natural Capital.
Ільченко, Катерина Володимирівна. "Космічний моніторинг забудови території міста Києва". Thesis, Національний авіаційний університет, 2020. http://er.nau.edu.ua/handle/NAU/41605.
Texto completoGanem, Khalil Ali. "Classificação da cobertura do solo na Caatinga a partir de imagens do Landsat-8 e da ferramenta Google Earth Engine : uma comparação entre dados com e sem correção atmosférica". reponame:Repositório Institucional da UnB, 2017. http://repositorio.unb.br/handle/10482/23501.
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O projeto intitulado Mapeamento Anual da Cobertura e Uso do Solo no Brasil -MapBiomas - é um projeto atual voltado para a classificação da cobertura do solo a nível nacional. As diversas classes abordadas no âmbito do projeto estão sendo identificadas e mapeadas com base em imagens das séries Landsat. Após a seleção de duas cartas, a SC-24-V-C e a SD-23-X-D, com base em critérios específicos, foi feito um comparativo entre o dado com correção atmosférica obtido por meio dos algoritmos FLAASH e QUAC, e o produto em reflectância no topo da atmosfera (LC8_L1T_TOA), sem correção atmosférica. Este último é o dado atualmente utilizado pelo MapBiomas para o Landsat-8 em classificações resultantes de uma árvore de decisão definida empiricamente pela equipe do projeto. Tal investigação se torna necessária pois o MapBiomas necessita obter um panorama acerca de qual dado apresenta o melhor ajuste às classificações, demonstrando-se mais realista ao contexto da Caatinga, o qual se trata do bioma proporcionalmente menos estudado dentre as regiões naturais brasileiras. As classificações foram geradas pelo Code Editor do Google Earth Engine, uma plataforma capaz de processar imagens de satélite na nuvem de forma distribuída e rápida, permitindo que ferramentas de alto desempenho interpretem e analisem uma gama de informações, as quais são visualizadas em mapas. A partir de análises visuais e da aplicação de testes estatísticos de exatidão global e por classe identificou-se o dado que melhor se ajustou ao contexto da Caatinga, nas cartas selecionadas, mostrando-se mais adequado para proceder com o mapeamento da cobertura do solo no bioma. A carta SC-24-V-C apresentou valores do coeficiente Tau para as classificações oriundas do dado sem correção atmosférica e com dados corrigidos pelo FLAASH e QUAC, de, respectivamente, 54,13%, 39,13% e 58,10%. Já a carta SD-23-X-D apresentou resultados para o mesmo índice de, respectivamente, 55,45%, 68,90% e 64,90%. Isso mostrou que o dado com correção atmosférica, de modo geral, mostrou melhor ajuste ao contexto da Caatinga em comparação com o dado em reflectância no topo da atmosfera. Além disso, dentre os dados utilizados, o FLAASH apresentou maior inconsistência, mostrando-se ora o melhor, ora o pior para cada carta, sendo ainda bem complexo para executar em comparação ao QUAC, que por sua vez é mais rápido no tempo de processamento e apresentou melhor desempenho. O dado sem correção atmosférica não demonstrou diferenças significativas em comparação com os dados corrigidos. E, apesar de ter sido mais baixo, mostrou resultados praticamente idênticos em ambas as análises, o que faz com que o dado não deva ser dispensado, devendo apenas ser feitos ajustes nos parâmetros da árvore de decisão para que seu uso seja mais eficiente. Apesar da variabilidade dos resultados estatísticos em função dos tipos de dados utilizados, constatou-se que a plataforma Google Earth Engine se demonstrou prática, rápida e satisfatória para proceder com a classificação da cobertura do solo na Caatinga.
The project entitled Mapping Annual Coverage and Land Use in Brazil - MapBiomas - is a current project focused on the classification of soil cover at a national level. The various classes addressed in the scope of the project are being identified and mapped based on Landsat images. After the selection of the SC-24-VC and SD-23-XD letters based on specific criteria, a comparison was made between the atmospheric correction data obtained using the FLAASH and QUAC algorithms, and the product of reflectance on the top of atmosphere (LC8_L1T_TOA), without atmospheric correction. The latter data is currently used by MapBiomas for Landsat-8 in the soil cover classifications based on a decision tree empirically defined by the project team. This is a necessary research because the MapBiomas project needs to obtain a panorama about which data represents the best adjustment to the classifications, proving to be more realistic to the context of the Caatinga, which is the biome proportionally less studied among the Brazilian natural regions. The classifications were generated by the Code Editor of Google Earth Engine, a platform capable of processing satellite images in the cloud in a distributed and fast way, allowing high performance tools to interpret and analyze a range of information, which is visualized in the form of maps. Based on the visual analysis and the application of statistical tests of global accuracy and accuracy by class, the data that best fit the context of the Caatinga was identified as the more appropriate to proceed with the mapping of the soil cover in the biome. The letter SC-24-V-C presented Tau values for the classifications from the TOA data and with data corrected by FLAASH and QUAC, of 54.13%, 39.13% and 58.10%, respectively. SD-23-X-D presented results for the same index of, respectively, 55.45%, 68.90% and 64.90%. This showed that the atmospheric correction data, in general, presented a better fit to the context of the Caatinga compared to the top-of-atmosphere reflectance data. In addition, among the data used, FLAASH presented a greater inconsistency, showing to be sometimes the best and in other times the worst for each image. And it is still quite complex to be used if compared to QUAC, which is faster in processing time and had a better performance. The data without atmospheric correction did not show significant differences in comparison with the corrected data. And, even though it had lower values, it showed almost identical results in both analysis, which means that the data should not be dispensed, being necessary only adjustments in the decision tree parameters to achieve more efficient results. In the end, despite the variability of the data, it was found that Google Earth Engine is a highly effective tool to proceed with the classification of the ground cover.
Schirinzi, Mattia. "Action cam per la raccolta di verità a terra propedeutica all'identificazione di colture agricole attraverso immagini ottiche del satellite Sentinel-2". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22341/.
Texto completoMartino, Filippo. "Analisi multitemporale del dissesto da immagini satellitari Sentinel (provincia di Forlì-Cesena)". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Buscar texto completoSanchez, Luna Maria M. "MAPPING SMALL SCALE FARMING IN HETEROGENEOUS LANDSCAPES: A CASE STUDY OF SMALLHOLDER SHADE COFFEE AND PLASTIC AGRICULTURE FARMERS IN THE CHIAPAS HIGHLANDS". Miami University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=miami1564228778095931.
Texto completoStromann, Oliver. "Feature Extraction and FeatureSelection for Object-based LandCover Classification : Optimisation of Support Vector Machines in aCloud Computing Environment". Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-238727.
Texto completoKartläggning av jordens yta och dess snabba förändringar med fjärranalyserad data är ett viktigt verktyg för att förstå effekterna av en alltmer urban världsbefolkning har på miljön. Den imponerande mängden jordobservationsdata som är fritt och öppet tillgänglig idag utnyttjas dock endast marginellt i klassifikationer. Att hantera ett set av många variabler är inte lätt i standardprogram för bildklassificering. Detta leder ofta till manuellt val av få, antagligen lovande variabler. I det här arbetet använde jag Google Earth Engines och Google Cloud Platforms beräkningsstyrkan för att skapa ett överdimensionerat set av variabler i vilket jag undersöker variablernas betydelse och analyserar påverkan av dimensionsreducering. Jag använde stödvektormaskiner (SVM) för objektbaserad klassificering av segmenterade satellitbilder – en vanlig metod inom fjärranalys. Ett stort antal variabler utvärderas för att hitta de viktigaste och mest relevanta för att diskriminera klasserna och vilka därigenom mest bidrar till klassifikationens exakthet. Genom detta slipper man det känsliga kunskapsbaserade men ibland godtyckliga urvalet av variabler.Två typer av dimensionsreduceringsmetoder tillämpades. Å ena sidan är det extraktionsmetoder, Linjär diskriminantanalys (LDA) och oberoende komponentanalys (ICA), som omvandlar de ursprungliga variablers rum till ett projicerat rum med färre dimensioner. Å andra sidan är det filterbaserade selektionsmetoder, chi-två-test, ömsesidig information och Fisher-kriterium, som rangordnar och filtrerar variablerna enligt deras förmåga att diskriminera klasserna. Jag utvärderade dessa metoder mot standard SVM när det gäller exakthet och beräkningsmässiga prestanda.I en fallstudie av en marktäckeskarta över Stockholm, baserat på Sentinel-1 och Sentinel-2-bilder, demonstrerade jag integrationen av Google Earth Engine och Google Cloud Platform för en optimerad övervakad marktäckesklassifikation. Jag använde dimensionsreduceringsmetoder som tillhandahålls i open source scikit-learn-biblioteket och visade hur de kan förbättra klassificeringsexaktheten och minska databelastningen. Samtidigt gav detta projekt en indikation på hur utnyttjandet av stora jordobservationsdata kan nås i en molntjänstmiljö.Resultaten visar att dimensionsreducering är effektiv och nödvändig. Men resultaten stärker också behovet av ett jämförbart riktmärke för objektbaserad klassificering av marktäcket för att fullständigt och självständigt bedöma kvaliteten på de härledda produkterna. Som ett första steg för att möta detta behov och för att uppmuntra till ytterligare forskning publicerade jag dataseten och ger tillgång till källkoderna i Google Earth Engine och Python-skript som jag utvecklade i denna avhandling.
Althén, Bergman Felix y Evelina Östblom. "GIS-based crisis communication : A platform for authorities to communicate with the public during wildfire". Thesis, KTH, Geoinformatik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-253072.
Texto completoI dagens samhälle är människan van vid teknik som ett ständigt hjälpmedel. Detta sätter också förväntningar på att information alltid ska vara tillgänglig och uppdaterad. Detta tillsammans med pågående klimatförändringar som lett till fler och svårare naturkatastrofer har lagt grunden till att det finns ett behov av att förändra hur man samlar in, sammanställer och visualiserar geografiska data som används för kommunikation i en krissituation. Denna studie utforskar hur myndigheter, i dagsläget, kommunicerar med allmänheten vid en krissituation och hur detta kan göras på ett enklare och mer givande sätt med hjälp av GIS. Målet är att visa ett nytt sätt att samla in, sammanställa och visualisera geografiska data för att, som myndighet, kommunicera med allmänheten under en kris. Detta har gjorts som i en fallstudie med fokus på skogs- och gräsbränder. Merparten av arbetet bestod därför av framtagande av en prototyp, CMAP – Crisis Management and Planning som visualiserar brandrelaterade data. Grundarbetet till prototypen bestod av att fastställa vilken data som finns och är nödvändig för att informationen skulle bli lättförstådd och komplett samt hur denna bäst implementeras. Den existerande data som implementerades hämtades online eller via ett schemalagt anrop av APIer. Händelserelaterade data skapas ofta i samband med själva händelsen och därför skapades en gemensam struktur och direktimplementation till prototypen för denna data med hjälp av Google Fusion Tables. I prototypen visualiserades data i två interaktiva kartbaserade sektioner. Dessa sektioner fokuserade kring att förse användaren med den information som kan behövas om man befarar att man befinner sig på en drabbad plats eller att förse användaren med allmän förberedande information inom olika län. Slutligen skapades även en icke kartbaserad sektion som möjliggjorde att allmänheten kan hjälpa myndigheter och varandra genom ”crowdsource” data. Denna samlades in i ett digitalt formulär som sedan direkt visualiserades i prototypens kartbaserade delar. Resultatet av detta visade bland annat att automatiska dataflöden är ett bra alternativ för att slippa manuell hantering av data och därmed möjliggöra en mer frekvent uppdatering. Vidare visade det även på vikten av att ha en gemensam struktur för vilken data som ska inkluderas och samlas in för att skapa en kommunikationsplattform. Slutligen är visualisering av dynamiska polygondata i en interaktiv miljö en utveckling av kriskommunikation som kan gynna förståelsen för situationen hos allmänheten. Studien är begränsad till att skapa en plattform baserad på den inbyggda funktionaliteten och designen som erbjuds i Googles plattform, detta inkluderat Google Earth Engine, Google Formulär, Google Fusion Tables etc.
Guo, Qi. "Bangladesh Shoreline Changes During the Last Four Decades Using Satellite Remote Sensing Data". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503258115717912.
Texto completoCosta, Susana Elisabete Pereira da. "Google Earth™ search engine: classificação de imagens aéreas". Master's thesis, 2013. http://hdl.handle.net/10400.6/3695.
Texto completoPattern recognition using neural networks is increasingly used in an attempt to provid to machines computational intelligence and learning ability. This work aims to recognize certain natural elements like ’water’, ’house’ or ’roads’ in aerial images from Google Earth TMand Google Maps TM, resorting to using neural networks for the purpose. Experiments were performed with four sets of images used for training the neural network, with varying number of neurons, and analyzed the classification errors by testing five new sets of images. Were also carried out several experiments on methods of feature extraction and application of morphological operators with different structural elements aimed at the post-processing of results.
Torres, Maria Leticia Cardozo y 馬莉亞. "Deforestation Assessment in the Paraguayan Chaco using Google Earth Engine". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/wh3fu4.
Texto completo國立中央大學
國際永續發展碩士在職專班
107
Forests act as a reservoir of biodiversity, shelter to wildlife, carbon sinks, and mitigates climate change. Despite their importance, they have been threatened by human activities, which is the main driver of deforestation. The objectives of this study are the assessment and analysis of deforestation in the Paraguayan Chaco, South America, by means of satellite image classification and analysis through the cloud-based Google Earth Engine (GEE) platform. Landsat images from the years of 2013 and 2017 were classified by a pixel-based supervised Random Forest Classifier. The classification results from different land-cover in the study area were then use to assess the deforestation, which expose a forest cover loss, in 2013 the forest cover was 172,862 km2 and 163,875.5 km2 in 2017; this revealed a cover loss of 8,986.3 km2 in 4 years. Furthermore, the classification results obtained in GEE platform were validated with validation points, the classification overall accuracy obtained was 0.86 for 2013 and 0.87 for 2017. The results indicate that GEE perform a rapid image processing and it is an effective reliable platform for deforestation assessment within the Chaco area. The results shown that the deforestation process leaves geometry rectangular features in the land surface and they are easily visualized, this suggests that there is a large scale clearing and heavy machinery is use. The main driver of deforestation in the Chaco is cattle ranching, and the results of this study indicate that the deforestation in continue advancing. During the years of study, Paraguay increase the amount of meat exportation, situating the country within the top 10 major countries worldwide exporter of meat, and this coincide with the increase of deforestation in the Chaco, this problem is likely to continue, because Paraguay set a country goal of reaching more cattle heads by 2020. Moreover, the results revealed that the deforestation process was spread within and in the buffer zones of the national forest protected areas, this suggest that there is lack of compliance with law were it stated that the protected areas are forbidden to use for economic purposes. Indigenous communities were also affected by the deforestation in the study period, threatening their home land and heritage.
Silva, Ana Luísa Fernandes. "Estudo de seca na Península Ibérica usando o Google Earth Engine". Master's thesis, 2018. https://hdl.handle.net/10216/118799.
Texto completoSilva, Ana Luísa Fernandes. "Estudo de seca na Península Ibérica usando o Google Earth Engine". Dissertação, 2018. https://hdl.handle.net/10216/118799.
Texto completoChih, Hung Peng y 洪鵬智. "A Study on the Critical Success Factors of Initiating the Google Earth Enterprise(GEE) of Soil and Water Conservation Bureau". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/7ns5bd.
Texto completo國立彰化師範大學
資訊管理學系所
102
The purpose of this project is to investigate Soil and Water Conservation Bureau to initiating Google Earth Enterprise's critical success factors by Delphi technique.The trends of Geographic Information Systems was being an early development of a unique style to the harmonious development of the recent, building unified related software from the previous stand-alone version evolved to a network version and cloud computing. In year 2012 has began to import Google Earth Enterprise Platform after a carefully evaluation by Soil and Water Conservation Bureau to execute global imaging (aerial photos, satellite photos, aerial unmanned vehicles photographs, site survey photographs), vector, terrain, 3D models, video, realtime monitoring screen, project management and graphic display of such information in order to reach the dataset, data sharing and data consistency purposes. SWCB has over 20 business units on-line in the GIS system currently, exchange of information between different systems, through GEE and gradually establish with service oriented architecture and modular platform for GIS integration services. In addition, in order to enrich GEE showcase in the future at home and abroad to provide interfacing multi collected the Open Data Information. The relevant information system which produced by SWCB (Such as rural information, mudslide disaster prevention information, etc.), it can also be collated and provided to government agencies or private institutions to utilize, other than purpose of sharing resources, it is also through various pipeline will promote the desired information about SWCB for promotion and marketing.
Alwahas, Areej. "Using Google Earth Engine for the Automated Mapping of Center Pivot Irrigation fields in Saudi Arabia". Thesis, 2021. http://hdl.handle.net/10754/668981.
Texto completoAlmeida, Sabrina Couto de. "ANÁLISE DO ABANDONO AGRÍCOLA NO BAIXO VOUGA LAGUNARRECORRENDO AO ÍNDICE DE NDVI E AO GOOGLE EARTH ENGINE". Master's thesis, 2020. https://hdl.handle.net/10216/128197.
Texto completoAlmeida, Sabrina Couto de. "ANÁLISE DO ABANDONO AGRÍCOLA NO BAIXO VOUGA LAGUNARRECORRENDO AO ÍNDICE DE NDVI E AO GOOGLE EARTH ENGINE". Dissertação, 2020. https://hdl.handle.net/10216/128197.
Texto completoKanee, Sarah. "A Multi-platform Comparison of Phenology for Semi-automated Classification of Crops". Thesis, 2021. http://hdl.handle.net/10754/670218.
Texto completoSantos, Maria João Gonçalves dos. "Classificação de culturas agrícolas de Inverno com recurso à plataforma Google Earth Engine e imagens dos satélites Sentinel-1 e Sentinel-2". Master's thesis, 2021. http://hdl.handle.net/10451/48443.
Texto completoA área de deteção remota é uma área em desenvolvimento devido a sua capacidade em adquirir remotamente dados da superfície e da atmosfera da Terra ou de qualquer outro planeta. A quantidade massiva de dados e de imagens de satélite existentes, tem contribuído para uma melhoria e para um aumento da qualidade de informação de observação da Terra. As metodologias científicas tais como a classificação supervisionada por imagens de satélite têm sido aplicadas na análise de dados de deteção remota. O presente estudo tem como objetivo avaliar os resultados obtidos dos processos de classificação por diferentes algoritmos, na utilização de diferentes tipos de dados e otimizá-los através da fusão dos mesmos (SAR e multiespectrais), com recurso a plataforma cloud Google Earth Engine. Recorreu-se à classificação supervisionada de imagens de satélite por cinco abordagens distintas, para a classificação de culturas de Inverno, com seis classificadores: Classification and regression trees (CART), Random Forest (RF), Support Vector Machine (SVM), Maximum Entropy (MAXE), Minimum Distance (MD) e Naive Bayes (NB); uma delas, a abordagem com recurso à fusão de dados SAR e dados multiespectrais. De todas as abordagens testadas, os melhores resultados foram obtidos com o classificador Random Forest (RF), na fusão de dados, com um valor de 76, 2% de exatidão global e de 68,3% de coeficiente kappa com 301 bandas, 102 pertencentes as imagens Sentinel-1 (SAR) com polarização VV e VH e 189 bandas de imagens do Sentinel-2 (multiespectrais) com nove bandas por imagem. Verificou-se que a fusão de dados multiespectrais e SAR beneficiam claramente a classificação efetuada, em parte pelo número de imagens utilizadas, fazendo com que as imagens SAR beneficiem os sistemas óticos principalmente na época de Inverno, onde as imagens óticas são mais limitadas, devido a nebulosidade presente; obtendo-se os valores de exatidão global de 76,2% comparativamente aos resultados individuais de 73% e 70,8%, um da coleção de imagens SAR com 112 bandas, e outro dos sistemas óticos com 189 bandas, respetivamente. O conjunto das imagens SAR em Sentinel-1 (com número de órbita 147 e 52) revelam resultados mais elevados do que as imagens individuais dos sistemas multiespectrais em Sentinel-2, tendo em conta a época de Inverno. A classificação de culturas de Inverno foi efetuada com dados fornecidos pelo Instituto de Financiamento de Agricultura e Pescas (IFAP) com informação geográfica das parcelas correspondente à área do Baixo Alentejo e com identificação da classe e área de cada cultura, num método de classificação supervisionado, por repartição em dados de treino e teste. Este projeto foi realizado com a utilização da plataforma Google Earth Engine (GEE) pelo processamento computacional demonstrado para a analise de um grande volume de dados como as coleções de imagens de satélite por series temporais prontas a usar existentes no catálogo de dados; e pela vasta oferta de funções presentes, entre elas os algoritmos de classificação. Revela-se uma plataforma excecional no processamento, análise e classificação, pela sua versatilidade e performance, tornando-se uma ferramenta imprescindível na área de deteção remota que potencializa a utilização de uma quantidade massiva e heterogénea de dados.
The remote sensing area is an area under development, due to the amount of existing geospatial data. The massive amount of data and existing satellite images has contributed to an improvement and an increase in the quality of Earth observation information. Scientific methodologies such as the classification supervised by satellite images have been applied in the analysis of remote sensing data. This project aimed to evaluate the results obtained from the classification processes by different algorithms, in the use of different types of data and to optimize them through the fusion of them (SAR and multispectral), using the Google Earth Engine cloud platform. Supervised classification of satellite images is done by five different approaches, using the GEE cloud platform for the classification of winter crops, with six classifiers: Classification and regression trees (CART), Random Forest (RF), Support Vector Machine (SVM), Maximum Entropy (MAXE), Minimum Distance (MD) and Naive Bayes (NB); in one of them, approach A5 using fusion of SAR and multispectral data. Of all the approaches, RF demonstrated to have the best results in data fusion with a value of 76, 2% of global accuracy and 68.3% of kappa coefficient, reaching peaks in the A5 approach, in the use of 301 bands, 102 belonging to Sentinel-1 (SAR) images with VV and VH polarization and 189 Sentinel-2 image bands (multispectral) with nine bands per image; it was found that the fusion of optical and SAR data clearly benefits the classification made, in part by the number of images used, making SAR images benefit optical systems mainly in the winter season, where optical images are more limited, due the present cloudiness; obtaining the global accuracy values of 76.2% compared to the individual results of 73% and 70.8%, one from the collection of SAR images with 112 bands, and another from the optical systems with 189 bands, respectively. The set of SAR images in Sentinel-1 (with orbit number 147 and 52) show higher results than the individual images of the multispectral systems in Sentinel-2, taking into account the winter season. The classification of winter crops was carried out using data provided by IFAP with geographic information of the plots corresponding to the area of the lower Alentejo and with identification of the class and area of each crop, in a supervised classification method, by distribution in training and test data. This project was carried out using the Google earth Engine (GEE) platform for the computational processing demonstrated for the analysis of a large volume of data such as collections of satellite images by ready-to-use time series existing in the data catalog, and for the wide range of functions present, among them the classification algorithms. It proves to be an exceptional platform in the processing and analysis of the classification of satellite images, due to its versatility and performance, making it an essential tool in the area of remote sensing, leveraging the use of a massive and heterogeneous amount of data.
Machado, Daniel Carlos dos Santos. "Analyzing Geospatial patterns of syrian refugee flows in southeastern Turkey by use of remote sensing and complementary data". Master's thesis, 2015. http://hdl.handle.net/10362/14556.
Texto completoTraganos, Dimosthenis. "Development of seagrass monitoring techniques using remote sensing data". Doctoral thesis, 2020. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-202011243787.
Texto completoPaluba, Daniel. "Korekce lokálního dopadového úhlu SAR dat pro analýzu časových řad: metoda specifická pro krajinný pokryv". Master's thesis, 2020. http://www.nusl.cz/ntk/nusl-435973.
Texto completoBaker, Jack. "Caring for lhuq'us (pyropia spp.): mapping and remote sensing of Hul'qumi'num culturally important seaweeds in the Salish Sea". Thesis, 2020. http://hdl.handle.net/1828/12149.
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