Dissertations / Theses on the topic 'Multi-temporal remote sensing'
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Saha, Sudipan. "Advanced deep learning based multi-temporal remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/263814.
Full textMahlayeye, Mbali. "Single and multi-temporal assessment approach of natural resources using remote sensing." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/65908.
Full textDissertation (MSc)--University of Pretoria, 2017.
Geography, Geoinformatics and Meteorology
MSc
Unrestricted
Ndegwa, Lucy W. "Monitoring the Status of Mt. Kenya Forest Using Multi-Temporal Landsat Data." Miami University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=miami1125426520.
Full textZheng, Baojuan. "Broad-scale Assessment of Crop Residue Management Using Multi-temporal Remote Sensing Imagery." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/19201.
Full textPh. D.
Yang, Bo. "Assimilation of multi-scale thermal remote sensing data using spatio-temporal cokriging method." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868463.
Full textWheeler, Brandon Myles. "Evaluating time-series smoothing algorithms for multi-temporal land cover classification." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/74313.
Full textMaster of Science
Zhang, Xiaohu, and 张啸虎. "Automatic detection of land cover changes using multi-temporal polarimetric SAR imagery." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/193496.
Full textpublished_or_final_version
Urban Planning and Design
Doctoral
Doctor of Philosophy
Shrestha, Bijay. "Parallel compositing of multi-temporal satellite imagery using temporal map algebra." Master's thesis, Mississippi State : Mississippi State University, 2005. http://sun.library.msstate.edu/ETD-db/ETD-browse/browse.
Full textRen, Jie. "Multi-temporal Remote Sensing of Changing Agricultural Land Uses within the Midwestern Corn Belt, 2001-2015." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/81559.
Full textPh. D.
Formigoni, Mileide de Holanda. "Análise multi-temporal da vegetação na região nordeste do Brasil através do EVI do sensor MODIS." Universidade Federal do Espírito Santo, 2008. http://repositorio.ufes.br/handle/10/6589.
Full textThe Brazilian Northeast (NEB) region presented different vegetation types that are essential component of its ecosystem. With remote sensing techniques it is possible, for example, to analyzed variations in vegetation community and alterations in vegetation phenological. Analysis the main objective of this work is to evaluate the temporal behavior of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), of different vegetation types in the NEB over period between February/2000 and July/2006. The study area was the NEB, where it was used to characterize the vegetations types a vegetation map of Brazil, in the 1:5,000,000 scale from Brazilian Institute of Geography and Statistics (IBGE). A total of 140 cloud-free EVI images with spatial resolution 250 m were acquired from National Aeronautics and Space Administration (NASA). Four CBERS-2/CCD images spatian resolution 20 m were also acquired from National Institute for Espace Research (INPE) to assist EVI data sample collection for each vegetation type. Precipitation data of the cities Petrolina and Pesqueira (Pernambuco), São Luiz and Carolina (Maranhão) located in regions of Caatinga, Atlantic Forest, Amazon and Savannah biome vegetation, respectively, were used to analyze its relationship with EVI from these vegetation. Also, EVI from irrigated area at Petrolina were used in these analysis. Results obtained showed that: i) multi-temporal EVI data from different vegetation types were sensitive to the vegetation phenological cycles, with minor and greater values of EVI in the periods of less and greater precipitation, respectively; ii) amazon biome vegetation presented lesser variation in the multitemporal EVI, however with greater values, justified by vegetation species the are always with green leaf; iii) Caatinga biome vegetation presented greater EVI values variation because the vegetation species on the dry periods occur total defoliation and on wet period the vegetation became green; iv) all EVI data from the vegetations studied presented significant relationship with precipitation (p-value< 0.05).
O Nordeste Brasileiro (NEB) apresenta diferentes tipos de vegetação, sendo importantes para o seu ecossistema. Com a utilização de técnicas de sensoriamento remoto é possível, por exemplo, analisar variações de comunidades de vegetação e suas alterações fenológicas. O objetivo principal deste trabalho é avaliar o comportamento temporal do Índice de Vegetação Melhorado (EVI) do sensor Spectroradiômetro de Resolução Espacial Moderada (MODIS), de diferentes tipos de vegetação do NEB no período entre fevereiro de 2000 a julho de 2006. A área de estudo foi a região do NEB, sendo utilizado para caracterização dos tipos de vegetação um mapa de vegetação na escala de 1:5.000.000 do Instituto Brasileiro de Geografia e Estatística (IBGE). Um total de 140 imagens EVI livres de nuvens com resolução espacial de 250 m foram adquiridas da Agência Nacional Aeroespacial Norteamericana (NASA). Quatro imagens CBERS-2/CCD com resolução espacial de 20 m foram também adquiridas do Instituto Nacional de Pesquisas Espaciais (INPE) para auxiliar na coleta das amostras de dados de EVI dos diferentes tipos de vegetação. Dados de precipitação das cidades de Petrolina e Pesqueira (Pernambuco), Barra do Corda e Carolina (Maranhão) localizadas nas regiões de vegetação do tipo Caatinga, Floresta Atlântica, Amazônia e Cerrado, respectivamente, foram utilizados para avaliar sua relação com os dados de EVI sob estas vegetações. Dados de EVI sobre área irrigada também foram utilizados para esta análise. Os resultados obtidos mostraram que: i) os dados multitemporais EVI de diferentes tipos de vegetação foram sensíveis às respectivas variações fenológicas, com os menores e maiores valores de EVI ocorrendo nos períodos de seca e chuva respectivamente; ii) a vegetação Amazônia apresentou a menor variação multitemporal dos valores de EVI, todavia apresentando os valores mais elevados, podendo-se justificar pela maior quantidade de folhas e por estarem sempre verdes; iii) a vegetação de caatinga analisada apresentou a maior variação dos valores de EVI, pois na época de seca, perde todas as folhas e na época de chuva, se torna verde devido a menor variabilidade da precipitação; iv) todos os dados de EVI das vegetações apresentaram relação significativa (valor-p<0,05) com a precipitação.
Li, Mao Li. "Spatial-temporal classification enhancement via 3-D iterative filtering for multi-temporal Very-High-Resolution satellite images." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1514939565470669.
Full textMetzler, Jacob W. "Use of Multi-temporal IKONOS and LANDSAT ETM+ Satellite Imagery to Determine Forest Stand Conditions in Northern Maine." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/MetzlerJW2004.pdf.
Full textDiGirolamo, Paul Alrik. "A Comparison of Change Detection Methods in an Urban Environment Using LANDSAT TM and ETM+ Satellite Imagery: A Multi-Temporal, Multi-Spectral Analysis of Gwinnett County, GA 1991-2000." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/anthro_theses/18.
Full textVijay, Saurabh [Verfasser], and Matthias Holger [Gutachter] Braun. "Changes of mountain glaciers on different time scales − a multi-temporal remote sensing data analysis / Saurabh Vijay ; Gutachter: Matthias Holger Braun." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2017. http://d-nb.info/1142002349/34.
Full textCord, Anna [Verfasser], and Stefan [Akademischer Betreuer] Dech. "Potential of multi-temporal remote sensing data for modeling tree species distributions and species richness in Mexico / Anna Cord. Betreuer: Stefan Dech." Würzburg : Universitätsbibliothek der Universität Würzburg, 2012. http://d-nb.info/1022790919/34.
Full textKeifer, Jarrett Alexander. "Agricultural Classification of Multi-Temporal MODIS Imagery in Northwest Argentina Using Kansas Crop Phenologies." PDXScholar, 2014. https://pdxscholar.library.pdx.edu/open_access_etds/2102.
Full textBurchfield, David Richard. "Mapping eastern redcedar (Juniperus Virginiana L.) and quantifying its biomass in Riley County, Kansas." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/18404.
Full textDepartment of Geography
Kevin P. Price
Due primarily to changes in land management practices, eastern redcedar (Juniperus virginiana L.), a native Kansas conifer, is rapidly invading onto valuable rangelands. The suppression of fire and increase of intensive grazing, combined with the rapid growth rate, high reproductive output, and dispersal ability of the species have allowed it to dramatically expand beyond its original range. There is a growing interest in harvesting this species for use as a biofuel. For economic planning purposes, density and biomass quantities for the trees are needed. Three methods are explored for mapping eastern redcedar and quantifying its biomass in Riley County, Kansas. First, a land cover classification of redcedar cover is performed using a method that utilizes a support vector machine classifier applied to a multi-temporal stack of Landsat TM satellite images. Second, a Small Unmanned Aircraft System (sUAS) is used to measure individual redcedar trees in an area where they are encroaching into a pasture. Finally, a hybrid approach is used to estimate redcedar biomass using high resolution multispectral and light detection and ranging (LiDAR) imagery. These methods showed promise in the forestry, range management, and bioenergy industries for better understanding of an invasive species that shows great potential for use as a biofuel resource.
Fritsch, Sebastian [Verfasser], and Christopher [Akademischer Betreuer] Conrad. "Spatial and temporal patterns of crop yield and marginal land in the Aral Sea Basin: derivation by combining multi-scale and multi-temporal remote sensing data with alight use efficiency model / Sebastian Fritsch. Betreuer: Christopher Conrad." Würzburg : Universität Würzburg, 2013. http://d-nb.info/1111815038/34.
Full textAouragh, M’bark. "Dynamique des paysages de l'arganeraie du Sud-Ouest marocain : apport des données de télédétection et perspectives de les intégrer dans un SIG." Thesis, Paris 4, 2012. http://www.theses.fr/2012PA040135.
Full textThe Argan [Argania spinosa (L.) Skeels] is a species of tree endemic to the calcareous semi-desert Sous valley of southwestern Morocco. It is the sole species in the genus Argania (family of Sapotaceae). It is a multi-purpose tree, and the main resource provider for the population of this semi-arid and arid area (source of forage, oil, timber and fuel). Argan is the keystone species of the traditional agro-ecosystem of the Berber society, ensuring a meta-stable equilibrium between resource availability and anthropic use; it plays a major role in preventing erosion and desertification damages.Currently, in spite of the Biosphere Reserve label attributed by UNESCO in 1998, the threat of degradation of the sparse Argan forest is a main concern for both local population and scientists. Since several decades, a decrease of extension area of the species and of tree density has been observed. According to this preoccupation, we have studied the multidimensional space of the Argan forest, in view of identifying its main features and the potential drivers of degradation processes. Then the originality of this area has been demonstrated through the assessment of its social and spatial organization, and of land-use and management practices.In the second part, we have shown the possible use of remotely sensed data and of Geographic Information Systems for surveying land-use/land-cover and for monitoring changes through a multi-temporal analysis of satellite images: SPOT, Landsat, Ikonos and Google Earth imagery. The evaluation of tree density has been performed through object-oriented classification of high spatial resolution satellite imagery (Ikonos, Google Earth). In conclusion, we recommend the effective use of a monitoring system to follow environmental changes in the Argan tree area, and to produce the detailed information needed for implementation of management and conservation strategies ensuring a sustainable development of the area
Wehmann, Adam. "A Spatial-Temporal Contextual Kernel Method for Generating High-Quality Land-Cover Time Series." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398866264.
Full textKleinpaul, Joel Juliano. "ANÁLISE MULTITEMPORAL DA COBERTURA FLORESTAL DA MICROBACIA DO ARROIO GRANDE, SANTA MARIA, RS." Universidade Federal de Santa Maria, 2005. http://repositorio.ufsm.br/handle/1/8615.
Full textThis study had as objective to do a multi-temporal analysis from Arroio Grande watershed area, located in Santa Maria, RS, to detect the forest covering changes, its localization and quantification, as well as, to monitor the deforestation and regeneration processes and its main determinant factors. Four satellite images were used: LANDSAT 5 (1987), LANDSAT 5 (1995), LANDSAT 7 (2002) and CBERS 2 (2005). The SPRING applicative was used to elaborate the cartography dada basis and to digitally process the images. The images were segmented with threshold 10 to similarity and 20 to area and classified with Bhattacharya algorithm in the following land uses: forest, field, exposed soil, agriculture, irrigated agriculture and water lamina. After the images classification, a thematic maps cross was done with LEGAL programming. As a result, maps with the following land uses were obtained: forest maintenance, regeneration and deforestation. For a period of 18 years, the forest covering increased 25,59% or 10,24% in the area, mainly in the hillside and in the plateaus, changing from 14.135,42 ha (40,01%) in 1987 to 17.752,20 ha (50,25%) in 2005. However, there is still a great deficit of riparian forest in plain (depression), mainly due to rice cultivation. The obtained results show the potential of Remote Sensing and Geoprocessing techniques in mapping the land use. They also can be used to support researches, territorial planning, economical development and environmental preservation in this region. With the data bank obtained, it will be possible to create models able to simulate the forest covering dynamic in the studied area.
Este trabalho teve como objetivo realizar uma análise multitemporal da área da microbacia do Arroio Grande, localizada em Santa Maria, RS, a fim de detectar mudanças na cobertura florestal, sua localização e quantificação, além de monitorar os processos de desmatamento e regeneração e seus principais fatores determinantes. Foram utilizadas quatro imagens de satélite: LANDSAT 5 (1987), LANDSAT 5 (1995), LANDSAT 7 (2002) e CBERS 2 (2005). Utilizou-se o aplicativo SPRING para a elaboração da base de dados cartográficos e do processamento digital das imagens. As imagens foram segmentadas com limiar de 10 para similaridade e 20 para área e classificadas com auxílio do algoritmo Bhattacharya nos seguintes usos da terra: floresta, campo, solo exposto, agricultura, agricultura irrigada e lâmina d água. Após a classificação das imagens, foi realizado o cruzamento dos mapas temáticos com ajuda da programação LEGAL. Como resultado, obtiveram-se mapas com os seguintes usos da terra: manutenção florestal, regeneração e desmatamento, ou seja, o que permaneceu inalterado de uma época para outra, o que regenerou e o que foi desmatado. Para um período de 18 anos, a cobertura florestal aumentou 25,59% ou 10,24% da área da microbacia, principalmente na encosta (rebordo) e no planalto, passando de 14.135,42 ha (40,01%) em 1987 para 17.752,20 ha (50,25%) em 2005. Porém, ainda há um déficit muito grande de mata ciliar na planície (depressão), principalmente devido ao cultivo de arroz. Os resultados obtidos mostram o potencial das técnicas de Sensoriamento Remoto e Geoprocessamento no mapeamento do uso da terra. Também servem para apoiar as mais diversas iniciativas de pesquisa, planejamento territorial, desenvolvimento econômico e preservação ambiental nesta região. Com o banco de dados gerado, será possível confeccionar modelos capazes de simular a dinâmica da cobertura florestal na área pesquisada.
Torres, Daniela Ricalde. "ANÁLISE MULTITEMPORAL DO USO DA TERRA E COBERTURA FLORESTAL COM DADOS DOS SATÉLITES LANDSAT E ALOS." Universidade Federal de Santa Maria, 2011. http://repositorio.ufsm.br/handle/1/8688.
Full textThe monitoring of the use and coverage is very important when studying determined regions, just because it helps knowing the environmental reality and contributes to solve problems that can probably appear. This research was done from the images of ALOS and LANDSAT satellites. Its main objective was to have a multi-temporal analysis of Arroio Grande micro watershed, central region of Rio Grande do Sul. The specific purposes were to identify and to quantify the different classes of land use found in this micro watershed along the 1987, 1998, 2002, 2005, 2007 and 2009 periods, as well as cross the land use information to show the forest coverage changes during the 22 years of analysis. The software SPRING 5.1.7 was employed to classify the supervised images through Bhattacharya, a sorter algorithm, and the map spatial analysis was done through the Spatial Language of Algebraic Geoprocessing program with the same computational application. The classes of land use as forest, field, agriculture, irrigated agriculture, exposed soil and water layer were observed in the images of each year in this analysis. These classes were utilized in the spatial analysis of the forest coverage in which forest monitoring parameters have been defined (forest maintenance and regeneration, deforestation). In this research the principal results that have been noticed were the increase of 17,98% on the distributed forest coverage, mainly in the areas of bigger declination, and the reduction of 16,32% on the field area. The analysis of the spatial forest coverage has presented stability with the landscape, in a gradual progression, because the area of forest maintenance, found in these 22 years, was 12.252,60ha, the forest regeneration was 4.389,12ha and only 1.853,82ha of deforested area.
O monitoramento do uso e cobertura da terra faz-se importante no estudo de determinadas regiões, pois auxilia no conhecimento da realidade ambiental e contribui na busca por soluções de problemas que possam se apresentar. A partir do uso de imagens dos satélites, ALOS e LANDSAT, foi realizada esta pesquisa com o objetivo principal de fazer uma análise multitemporal na microbacia do Arroio Grande, região central do Rio Grande do Sul, cujos objetivos específicos foram: Identificar e quantificar as diferentes classes de uso da terra encontradas na microbacia nos períodos de 1987, 1998, 2002, 2005, 2007 e 2009; além de cruzar as informações de uso da terra, evidenciando a cobertura florestal que sofreu alterações no decorrer dos 22 anos de análise. Para tanto, foram utilizados o software SPRING 5.1.7 para a classificação supervisionada das imagens, com a adoção do algoritmo classificador Bhattacharya, e a análise espacial dos mapas com a programação LEGAL do mesmo aplicativo computacional. Para esta análise, foram observadas as classes de uso do solo: floresta, campo, agricultura, agricultura irrigada, solo exposto e lâmina d água, nas imagens de cada ano. Estas classes foram empregadas na análise espacial da cobertura florestal em que foram definidos parâmetros para o monitoramento florestal (manutenção florestal, regeneração florestal e desmatamentos). Os principais resultados notados, nesta pesquisa, foram o aumento de 17,98% na cobertura florestal distribuída, principalmente, nas áreas de maiores declividade, e a redução de 16,32% sobre a área de campo. Quanto à análise espacial da cobertura florestal, esta mostrou-se em estabilidade com a paisagem, e em gradual progressão, pois a área de manutenção florestal encontrada, nestes 22 anos, foi de 12.252,60 ha, a regeneração florestal foi de 4.389,12 ha e apenas 1.853,82 ha de área desmatada.
Oune, Omar. "Monitoring desertification in south west Tripoli using multi-temporal remotely sensing data and GIS." Thesis, University of Dundee, 2006. https://discovery.dundee.ac.uk/en/studentTheses/f3249ca1-bcc8-42fb-a88b-fc6382a8f4fd.
Full textFaivre, Robin. "Multi-sensor remote sensing parameterization of heat fluxes over heterogeneous land surfaces." Thesis, Strasbourg, 2014. http://www.theses.fr/2014STRAD017/document.
Full textThe parameterization of heat transfer by remote sensing, and based on SEBS scheme for turbulent heat fluxes retrieval, already proved to be very convenient for estimating evapotranspiration (ET) over homogeneous land surfaces. However, the use of such a method over heterogeneous landscapes (e.g. semi-arid regions or agricultural land) becomes more difficult, since the principle of similarity theory is compromised by the presence of different heat sources with various heights. This thesis aims at first to propose and evaluate some models based on vegetation geometry for retrieving the surface roughness length for momentum transfer (z0m), which is a key parameter in the characterization of heat transfer. Such an investigation can only be led at a small scale with very-high resolution remote sensing data, for a precise description of the land surface. Therefore, the second aspect of this work is to determine how to address the characterization of heat transfer for regional studies. Then, the reliability of SEBS for estimating turbulent heat fluxes at large spatial and temporal scales has been evaluated. To do so, the Multi-Scale SEBS approach (MSSEBS) has been implemented for a 2.4 million km2 area including the Tibetan Plateau and the headwaters of the major rivers of East and South Asia. The addition of gap-filled hourly FY-2 LST data to advanced daily averaged net radiation and land surface parameters, allows to compute time-series of land surface ET over the Tibetan Plateau during the period 2008-2010, and on a daily basis
Garges, David Casimir. "Early Forest Fire Detection via Principal Component Analysis of Spectral and Temporal Smoke Signature." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1456.
Full textDeng, Ming-Sung, and 鄧敏松. "Integrating Multi-Temporal Remote Sensing Imagery with." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/82300815613252860045.
Full text國立成功大學
測量工程學系
85
Rice-field inventory is the principal mission of the Food Bureau of Taiwan. In the past years, the inventory is based on aerial photos interpreted by human operators. Due to the large number of rice fields, this task requires a large amount of photos and processing time. In this research, we suggest an approach that incorporate existing cultivating-field maps, multi -temporal SPOTXS images, and the spectral knowledge of rice growth to improve the automationof interpretation of rice fields. Spectral data are the key factors for satellite image interpretation. Because the land cover of rice fields changes as the rice grows, their spectral reflectance characteristics will change respectively. Selecting images at appropriate rice growing stages will help theinterpretation of rice-field. To achieve this goal, a thorough understanding about the knowledge of rice is the first requirement. In this research, the domain knowledge about rice interpretation includes knowledge of rice growth, the land-cover types of varying periods, the spectral characteristic of features, and their locations. We adopted region-based classification in which the regions are provided by using cultivating-field maps. Our experiment consists of three steps. First, the rice transplanting date is determined based on its location. Such information is served as the basis for deciding which rice growing stages of the images are related to. Second, we analyze spectral statistics of rice fields to determine the appropriate combinations of bands for interpreting images in different periods of time. Images in different rice growing stages are further processed to distinguish rice fields and non-rice fields. Finally, ISODATA algorithm is introduced for automatic interpretation experiment based on the region provided by cultivating-field maps. SPOT images in 1993 and 1995 were tested in this research. Three approaches, the NDVI method, the NDVI differences method, the disparate band differences method, were suggested and tested respectively. The results are compared against with the result of region-based maximum likelyhood classification. Except NDVI method, the suggested approaches provide competitive accuracy as the maximum likelyhood classification. We can therefore conclude that the "automatic" rice-field interpretation method suggested in this research is feasible and can provide competitive accuracy as the current methods do.
(8789954), Ali Masjedi. "MULTI-TEMPORAL MULTI-MODAL PREDICTIVE MODELLING OF PLANT PHENOTYPES." Thesis, 2020.
Find full textHigh-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this study, the potential of accurate and reliable sorghum biomass prediction using hyperspectral and LiDAR data acquired by sensors mounted on UAV platforms is investigated. Experiments comprised multiple varieties of grain and forage sorghum, including some photoperiod sensitive varieties, providing an opportunity to evaluate a wide range of genotypes and phenotypes.
Feature extraction is investigated, where various novel features, as well as traditional features, are extracted directly from the hyperspectral imagery and LiDAR point cloud data and input to classical machine learning (ML) regression based models. Predictive models are developed for multiple experiments conducted during the 2017, 2018, and 2019 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Using geometric based features derived from the LiDAR point cloud and the chemistry-based features extracted from hyperspectral data provided the most accurate predictions. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method. The characteristics of the experiments, including the number of samples and the type of sorghum genotypes in the experiment also impacted prediction accuracy.
Including the genomic information and weather data in the “multi-year” predictive models is also investigated for prediction of the end of season biomass. Models based on one and two years of data are used to predict the biomass yield for the future years. The results show the high potential of the models for biomass and biomass rank predictions. While models developed using one year of data are able to predict biomass rank, using two years of data resulted in more accurate models, especially when RS data, which encode the environmental variation, are included. Also, the possibility of developing predictive models using the RS data collected until mid-season, rather than the full season, is investigated. The results show that using the RS data until 60 days after sowing (DAS) in the models can predict the rank of biomass with R2 values of around 0.65-0.70. This not only reduces the time required for phenotyping by avoiding the manual sampling process, but also decreases the time and the cost of the RS data collections and the associated challenges of time-consuming processing and analysis of large data sets, and particularly for hyperspectral imaging data.
In addition to extracting features from the hyperspectral and LiDAR data and developing classical ML based predictive models, supervised and unsupervised feature learning based on fully connected, convolutional, and recurrent neural networks is also investigated. For hyperspectral data, supervised feature extraction provides more accurate predictions, while the features extracted from LiDAR data in an unsupervised training yield more accurate prediction.
Predictive models based on Recurrent Neural Networks (RNNs) are designed and implemented to accommodate high dimensional, multi-modal, multi-temporal data. RS data and weather data are incorporated in the RNN models. Results from multiple experiments focused on high throughput phenotyping of sorghum for biomass predictions are provided and evaluated. Using proposed RNNs for training on one experiment and predicting biomass for other experiments with different types of sorghum varieties illustrates the potential of the network for biomass prediction, and the challenges relative to small sample sizes, including weather and sensitivity to the associated ground reference information.
Touray, Ansumana, and 陶安時. "Multi-Temporal Land-Cover Change Monitoring of the Western Gambia Using Remote Sensing." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/09587137354718540621.
Full text國立中央大學
國際永續發展碩士在職專班
102
The main objective of this research is aimed at producing multi-temporal land cover maps of the Western Gambia in order to monitor land-cover changes (LCC) using Remote Sensing techniques with a focus on urban growth during the period 1985 – 2013. The study area is the Western Gambia covering West Coast Region and part of North Bank Region lying between Latitudes 13o 36’ N and 13o 03’ N and Longitude 16o 54’ W and 16o 06’ W. About 63.49% of the country’s population resides here with a population density of 478 people per km2. For this purpose, multi-temporal Landsat images for 1985, 1999, and 2013 were acquired. The existing land cover reference maps collected and modified for accuracy assessment. These multi-temporal data were processed using spatial analysis tools of geo-referencing, Support Vector Machine (SVM) classification, and post- classification processes, to map the patterns and extent of land cover in the study area as well as determine the magnitude of changes between the years of interest. The result of the study showed that the built-up areas have been on a constant positive and mostly uncontrolled expansion from 46.42 km2 of the study area in 1985 to 97.35 km2 in 1999 and to 193.02 km2 in 2013. On the other hand, terrestrial vegetation has been on a steady decline, from 1,184.22 km2 in 1985 to 929.69 km2 in 2013, while the cultivation land experienced a slight increase in area. The mangrove forest is fairly stable in the past three decades. Population pressure is the major driving forces of LCC in the Western Gambia; therefore the government and other stakeholders should develop policies and strategies to achieve a balanced, coordinated and sustainable natural resources management.
Lin, Yu-Zhi, and 林郁智. "Change Detection on Multi-temporal Remote Sensing Images Based on Textural and Spectral Features." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/58838680464124195179.
Full text國立成功大學
資訊工程學系碩博士班
92
Change detection in remote sensing has become an interesting topic of image processing. Conventionally, change detection in remote sensing depends on expert operators to visually analyze a series of multitemporal images. It usually takes a lot of human labor and time in order to achieve good performance. With development of computer technology, the work by human labor has gradually been accomplished by image processing. We still find it helpful to incorporate some operators’ domain knowledge into the early learning processing for successful change detection. For example, it depends on human operators for the selection of representative training samples in a supervised approach and the identification of change types resulting from an unsupervised approach. Nevertheless, an automatic change identification procedure is still in developing. In this study, a framework is proposed in order to fulfill the requirement of automation for change identification of remotely sensed images. First of all, a change detection process is performed to roughly discriminate between change and low-change areas based on textural and spectral features. Given a condition that class information in the first image is sufficient, those low-change samples are used for radiometric calibration and further provide class statistics for the second and subsequent images. By a post classification means, a series of multi-temporal images thus produce the change types with time. Our experimental results have demonstrated the feasibility of the proposed method.
Araya, Sofanit Girma. "Multi-temporal remote sensing for estimation of plant available water-holding capacity of soil." Thesis, 2017. http://hdl.handle.net/2440/114500.
Full textThesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Biological Sciences, 2017.
Kekana, Thabiso. "Mapping the spatiotemporal distribution of the exotic Tamarix species in riparian ecosystem using Multi-temporal remote sensing data." Thesis, 2019. https://hdl.handle.net/10539/29092.
Full textTamarix spp, commonly known as tamarisk or salt cedar, belong to the family of Tamaricaceae. It is a phreaphytic halophyte with 55 species in the genus Tamarix. South Africa has one indigenous (Tamarix usneoides) and two exotic (T. ramosissima and T.chinensis). Not only are the exotic Tamarix species becoming infamous invaders, but their hybridisation with the indigenous T. usneoides is also complicating morphological discrimination between the different species, and the prospect of potential use of bio-control agents to curb invasion. Thus, lack of spatial information about the current and the past distribution of tamarisk have hampered the effort to control its invasion. This study aimed at investigating the use of multi-temporal remotely sensed data to map the exotic Tamarix invasion in the riparian ecosystem of the Western Cape Province of South Africa, where it predominantly occurs. Random Forest (RF) and Support Vector Machine (SVM) were tested to classify Tamarix and other land-cover types. Sentinel 2 data and Landsat OLI earth observation data were used to map the current and the temporal exotic Tamarix distribution between 2007 and 2018, respectively. This included mapping the current and the multi-temporal Tamarix extent of invasion using the multi-spectral sensors Sentinel 2 and Landsat 5 and 8, respectively. Sentinel 2 was able to detect and discriminate the exotic Tamarix spp invasion using RF and SVM algorithms. The Random Forest classification achieved an overall accuracy of 87.83% and kappa of 0.85, while SVM achieved an overall accuracy of 86.31% and kappa of 0.83. Multi-temporal Landsat data was able to map the current and previous extent of exotic Tamarix invasion for the period between 2007 and 2018. Six land-cover types were classified using SVM. The overall accuracies achieved for 2007, 2014 and 2018 were 87.66%, 91.10%, and 90.62% respectively, and the kappa were 0.85, 0.89, and 0.88, respectively. It was found that the exotic Tamarix invasion increased from 284.67 ha to 647.10 ha in De Rust area, 74.70 ha to 97.29 ha in Leeu Gamka and 215.01 ha to 544.41 ha in Prince Albert region in a period of 11 years. Sentinel 2 and Landsat data have shown the potential to be used in Tamarix mapping. The results obtained in this study would help in implementation of conservation and rehabilitation plans.
GR 2020
Cord, Anna. "Potential of multi-temporal remote sensing data for modeling tree species distributions and species richness in Mexico." Doctoral thesis, 2012. https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-71021.
Full textSämtliche aktuell zu beobachtenden Veränderungen in der Biodiversität lassen sich fast ausschließlich auf menschliche Aktivitäten zurückführen. In den letzten Jahrzehnten hat insbesondere die anthropogene Umwandlung bisher unberührter, natürlicher Ökosysteme zur sogenannten ‚Biodiversitätskrise‘ geführt. Diese umfasst nicht nur das Aussterben von Arten, sondern auch räumliche Verschiebungen in deren Verbreitungsgebieten. Global gesehen ist der Artenreichtum ungleich verteilt. Nur insgesamt 17 sogenannte ‚megadiverse‘ Länder, welche 10% der globalen Landoberfläche umfassen, beherbergen fast 70% der weltweiten Artenvielfalt. Mexiko, das Studiengebiet dieser Arbeit, ist eine dieser außerordentlich artenreichen Nationen. Aufgrund seiner großen Ausdehnung und geographischen Komplexität kann eine verlässliche und detaillierte räumliche Erfassung von Artverbreitungsgebieten in Mexiko jedoch nicht nur auf Basis dieser Datenbanken sowie von Feldarbeiten erfolgen. In den letzten beiden Jahrzehnten haben sich Artverbreitungsmodelle (Species distribution models, SDMs) als wichtige Werkzeuge für die räumliche Interpolation solcher in situ Beobachtungen in der Ökologie etabliert. Artverbreitungsmodelle umfassen die Analyse empirischer Zusammenhänge zwischen georeferenzierten Fundpunkten einer Art und Umweltvariablen mit dem Ziel, räumlich kontinuierliche Vorhersagen zur Wahrscheinlichkeit des Vorkommens der jeweiligen Art zu treffen. Mittels Fernerkundung können Umweltvariablen mit Bezug zu den biophysikalischen Eigenschaften der Landoberfläche in hohen effektiven räumlichen Auflösungen bereitgestellt werden. Insbesondere in den letzten drei bis fünf Jahren ist daher die Verwendung von Fernerkundungsdaten in der Artverbreitungsmodellierung sprunghaft angestiegen. Da es sich hierbei jedoch immer noch um ein sehr neues Forschungsfeld handelt, stellen diese meist nur Einzelstudien mit Beispielcharakter dar. Eine systematische Untersuchung zur Modellierung von Artverbreitungsgebieten mit Hilfe von Fernerkundungsdaten fehlt bisher. Diese Forschungslücke wurde in der vorliegenden Arbeit aufgegriffen. Hierzu wurden spezifische Untersuchungen durchgeführt, welche insbesondere folgende Aspekte betrachteten: die sinnvolle Verknüpfung von Klima- und Fernerkundungsdaten im Rahmen von Artverbreitungsmodellen, den quantitativen Vergleich von kontinuierlichen Fernerkundungsdaten und einer bestehenden kategorialen Landbedeckungsklassifikation, die Identifizierung von Kriterien zur Auswahl geeigneter Fernerkundungsprodukte, welche die Eigenschaften der Studienarten berücksichtigen, sowie der Einfluss inter-annueller Variabilität in fernerkundlichen Zeitreihen auf die Ergebnisse und Leistungsfähigkeit von Artverbreitungsmodellen. Die entsprechenden neuen Analysen wurden mit Hilfe des von Phillips et al. (2004) entwickelten Maximum Entropy Algorithmus zur Artverbreitungsmodellierung durchgeführt. Im Rahmen dieser Arbeit wurde ein umfangreicherer Datensatz an Fernerkundungsvariablen als in der bisherigen Literatur verwendet. Die entsprechenden Fernerkundungsprodukte wurden spezifisch aufgrund ihrer Eignung für die Beschreibung ökologisch relevanter Parameter, die sich auf die Verbreitungsgebiete von Arten auswirken, ausgewählt. Für den Zeitraum von 2001 bis 2009 wurden zwei Terra-MODIS Standardprodukte mit 1 km räumlicher und 16-tägiger zeitlicher Auflösung zu geglätteten, kontinuierlichen Zeitreihen zusammengefügt. Diese Produkte beinhalten den verbesserten Vegetationsindex (Enhanced Vegetation Index, EVI), Reflexionsgrade und die Landoberflächentemperatur (Land Surface Temperature, LST). Diese hochdimensionalen Zeitreihendaten wurden in insgesamt 18 phänologische sowie 35 statistische Maßzahlen überführt, welche auf der Basis einer umfassenden Sichtung der vorhandenen Literatur zusammengestellt wurden. Die Verbreitungsgebiete von zwölf Baumarten wurden mit Hilfe eines hierarchisch aufgebauten Ansatzes, welcher sowohl Klimadaten (WorldClim) als auch Fernerkundungsdaten des MODIS-Sensors berücksichtigt, modelliert. Die Studienarten sind repräsentativ für die in Mexiko vorkommenden Waldtypen und decken eine breite Spannweite ökologischer Eigenschaften wie Größe des Verbreitungsgebietes und Breite der ökologischen Nische ab. Als Studienobjekte wurden Bäume ausgewählt, weil sie im Feld mit hoher Wahrscheinlichkeit richtig erfasst werden und außerdem die fernerkundungsbasierte Kartierung von Vegetation bereits auf eine Vielzahl an Studien zurückgreifen kann. Durch die im Rahmen dieser Dissertation durchgeführten Untersuchungen konnte gezeigt werden, dass die Integration von Fernerkundungsdaten in Artverbreitungsmodelle ein signifikantes Potential zur Verbesserung der räumlichen Detailgenauigkeit und der Güte der Modellvorhersagen bietet
Quinn, Geoffrey. "Derivation of forest productivity and structure attributes from remote sensing imaging technology." Thesis, 2018. https://dspace.library.uvic.ca//handle/1828/10471.
Full textGraduate
Els, Anja. "Tracking sand dune movements using multi-temporal remote sensing imagery: a case study of central Sahara (Libyan Fazzan / Ubari Sand Sea)." Thesis, 2017. http://hdl.handle.net/10539/22732.
Full textSand dune movements can be effectively monitored through the comparison of multitemporal satellite images. However, not all remote sensing platforms are suitable to study sand dunes. This study compares coarse (Landsat 7 and 8) and fine (Worldview 2) resolution platforms, specifically focussing on sand dunes within the Ubārī Sand Sea (Libya), and identified the average migration rate and direction for the linear dunes within a section of the Ubārī sand sea for the time period from 2002-2015 with the use of Landsat imagery. Two band combinations were compared with the use of two supervised classifications. The best combination was found to be red, green, blue and near-infrared band combination and the maximum likelihood classifier. The dune features, namely the crest, slope and interdunal areas were successfully classified based on both the coarse and fine resolution imagery, but the accuracy with which it can be classified are different between the two resolutions. The classifications based on the Worldview 2 imagery had overall accuracies ranging from 55.43 - 60.83% with kappa values of 0.3486 – 0.4225 compared to the overall accuracies and kappa values of the classifications based on the Landsat 8 imagery ranging from 52.11 – 64.67% and 0.3878 – 0.4927 respectively. An average migration rate of 8.64 (± 4.65) m/yr in a generally north western direction was calculated based on the analysis of remote sensing data with some variations in this rate and the size and shape of the dunes. It was found that although Worldview 2 imagery provides more accurate and precise mensuration data, and smaller dunes identified from Worldview data were not delineated clearly on the Landsat imagery. Landsat imagery is sufficient for the studying of dunes at a regional scale. This means that for studies concerned with the dune patterns and movements within sand seas, Landsat is sufficient. In studies where the specific dynamics of specific dunes are to be selected, a finer resolution is required; platforms such as Worldview are needed in order to gain more detailed insight and to link the past and present day climate and environmental change.
MT2017
Fritsch, Sebastian. "Spatial and temporal patterns of crop yield and marginal land in the Aral Sea Basin: derivation by combining multi-scale and multi-temporal remote sensing data with alight use efficiency model." Doctoral thesis, 2013. https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-87939.
Full textDie vorliegende Arbeit beschäftigt sich mit der Modellierung regionaler Erntemengen von Baumwolle und Reis in der usbekischen Region Khorezm, einem Bewässerungsgebiet das geprägt ist von langjähriger Baumwoll-Monokultur und nicht-nachhaltiger Land- und Wassernutzung. Basis für die Methodik waren Satellitendaten, die durch ihre großflächige Abdeckung und Objektivität einen enormen Vorteil in solch datenarmen und schwer zugänglichen Regionen darstellen. Bei dem verwendeten Modell handelt es sich um ein sog. Lichtnutzungseffizienz-Modell (im Englischen Light Use Efficiency [LUE] Model), das auf dem Anteil der photosynthetisch aktiven Strahlung basiert, welcher von Pflanzen für das Wachstum aufgenommen wird (Fraction of Photosynthetic Active Radiation, FPAR). Dieser Parameter kann aus Satellitendaten abgeleitet werden. Das allgemeine Ziel der vorliegenden Arbeit war die Nutzung von Satellitendaten für die Ableitung der Erntemengen von Baumwolle und Reis. Dazu wurde ein Modell entwickelt, das sowohl auf der Feldebene (Auflösung von 6,5 m) als auch auf der regionalen Ebene (Auflösung von 250 m) operieren kann. Während die Ableitung der Erntemengen auf der Feldebene nur für ein Jahr erfolgte (2009), wurden sie auf der regionalen Ebene für den Zeitraum 2003 bis 2009 modelliert. Intensive Feldmessungen von FPAR wurden im Studiengebiet während der Wachstumssaison 2009 durchgeführt. Parallel dazu wurden RapidEye-Daten in ca. zweiwöchentlichem Abstand aufgezeichnet. Aus den RapidEye-Daten wurde der Normalized Difference Vegetation Index (NDVI) berechnet, der anschließend mit den im Feld gemessenen FPAR-Werten korreliert wurde. Die entstandenen Regressionsgleichungen wurden benutzt um Zeitserien von FPAR auf RapidEye-Niveau zu erstellen. Anschließend wurden diese Zeitserien auf die MODIS-Skala aggregiert um damit das MODIS FPAR-Produkt zu validieren (1 km), bzw. eine Kalibrierung des 8-tägigen 250 m NDVI-Datensatzes vorzunehmen. Der erste Schritt zeigte dass das MODIS-Produkt im Allgemeinen die RapidEye-basierten FPAR-Werte um 6 bis 15 % überschätzt. Aufgrund der besseren Auflösung wurde das kalibrierte 250 m FPAR-Produkt für die weitere Modellierung verwendet. Für die eigentliche Modellierung wurden neben den FPAR-Eingangsdaten noch weitere Daten und Parameter benötigt. Dazu gehörte z.B. die tatsächliche Lichtnutzungseffizienz (LUEact), welche von Temperatur- und Wasserstress beeinflusst wird. Wasserstress wurde berechnet aus dem Verhältnis von tatsächlicher (ETact) zu potentieller, feldfruchtspezifischer Evapotranspiration (ETc), die beide aus einer Kombination von Satelliten- und Wetterdaten abgeleitet wurden. Der durchschnittliche Wasserstress schwankte nur geringfügig von Jahr zu Jahr, mit Ausnahmen in den Jahren 2008 und 2009. Die Modellschätzungen wurden durch feldbasierte Ernteinformationen (RapidEye-Ebene) sowie regionale statistische Daten (MODIS-Ebene) evaluiert. Die Ergebnisse zeigten, dass beide Modellskalen regionale Ernteerträge mit guter Genauigkeit nachbilden können. Der Fehler für das RapidEye-basierte Modell betrug 29,1 % für Baumwolle und 30,4 % für Reis. Die Genauigkeiten für das MODIS-basierte Modell variierten, in Abhängigkeit des betrachteten Jahres, zwischen 10,5 % und 23,8 % für Baumwolle und zwischen -0,4 % und -19,4 % für Reis. Insgesamt gab es eine leichte Unterschätzung der Baumwoll- (Bias = 0,22) und Reisernte (Bias = 0,11) seitens des RapidEye-Modells. Das MODIS-Modell hingegen unterschätzte zwar auch die (offizielle) Reisernte (mit einem Bias zwischen 0,01 und 0,87), überschätzte jedoch die offiziellen Erntemengen für die Baumwolle (Bias zwischen -0,28 und -0,6). Die Evaluierung der MODIS-Skala zeigte dass die Genauigkeiten extrem zwischen den verschiedenen Distrikten schwankten. Die erstellten Erntekarten zeigten dass Erntemengen grundsätzlich mit der Distanz zum Fluss abnehmen. Die niedrigsten Erntemengen traten in den südlichsten Distrikten auf, in der Nähe der Wüste. Betrachtet man die Ergebnisse schließlich über die Zeit hinweg, gab es Gebiete die über den gesamten Zeitraum von sieben Jahren stets von niedrigen Erntemengen gekennzeichnet waren. Die vorliegende Studie zeigt, dass satellitenbasierte Lichtnutzungseffizienzmodelle ein geeignetes Werkzeug für die Ableitung und die Analyse regionaler Erntemengen in zentralasiatischen Bewässerungsregionen darstellen. Verglichen mit verwandten Studien stellten sich die ermittelten Genauigkeiten sowohl auf der RapidEye- als auch auf der MODIS-Skala als gut dar. Vom methodischen Standpunkt aus gesehen ergänzte diese Arbeit vorhanden LUE-Modelle um einige Neuerungen und Verbesserungen, wie z.B. die Validierung und Kalibrierung von FPAR für die Studienregion mittels Feld- und hochaufgelösten RapidEye-Daten und dem Einbezug von feldfrucht-spezifischem Wasserstress in die Modellierung
Lourenço, Miguel do Espírito Santo. "Geo-rectification and cloud-cover correction of multi-temporal Earth observation imagery." Master's thesis, 2020. http://hdl.handle.net/10362/104482.
Full textOnyango, Otunga Charles. "Multi-temporal mapping and projection of urban land-use-land-cover change : implication on urban green spaces." Thesis, 2013. http://hdl.handle.net/10413/10559.
Full textThesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.
Deng, Min-Song, and 鄧敏松. "Integrating Multi-Temporal Remote Sensing Imagery with Cultivating Field Data and Doamin Knowledge for a Region-Based Image Interpretation on the Application of Rice-Field Inventory." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/08329431450252182111.
Full textOliveira, Pedro André de. "Caracterização da ocupação do solo com recurso à aplicação de modelos de misturas espectrais em séries multi-temporais de imagens MODIS." Master's thesis, 2005. http://hdl.handle.net/10362/3643.
Full textRealizou-se uma investigação sobre a aplicação do Modelo de Mistura Linear (MML) numa série multi-temporal. Depois de aplicado a uma sequência anual completa de imagens MODIS com 500 metros de resolução especial, o MML gerou um conjunto de imagens fracção com uma variação intra-anual da abundância de componentes puros vegetação, solo e sombra na mistura do pixel. No pré-processamento das imagens de MOD09A1 utilizou-se uma abordagem do Compósito do Valor Máximo, para obter os dados de base para input do modelo de MML. Baseado em 36 imagens fracção, caracterizaram-se 304 amostras, correspondendo cada amostra a uma de onze classes de ocupação do solo definidas neste estudo. Estabeleceu-se uma relação entre estas classes e o CLC2000. Para a classificação das unidades de ocupação do solo, baseadas na variação do perfil temporal dos componentes puros no pixel das amostras, recorreu-se à utilização de uma rede neuronal de mapas autoorganizados (SOM). A análise da qualidade dos resultados resultou da construção da matriz de convergência, sendo calculados os índices de exatidão global, do produtor e do utilizador para avaliação de resultados. O trabalho reporta uma análise descritiva dos resultados obtidos segundo a metodologia proposta e apresenta-se como um estudo preliminar para análise de mistura espectrais, numa vertente multi-temporal, para caracterização da ocupação do solo.
Bourbonnais, Mathieu Louis. "A multi-scale assessment of spatial-temporal change in the movement ecology and habitat of a threatened Grizzly Bear (Ursus arctos) population in Alberta, Canada." Thesis, 2018. https://dspace.library.uvic.ca//handle/1828/10012.
Full textGraduate
Senthilnath, J. "Nature Inspired Optimization Techniques For Flood Assesment And Land Cover Mapping Using Satellite Images." Thesis, 2014. http://etd.iisc.ernet.in/handle/2005/2606.
Full textAhmed, Bayes. "Urban land cover change detection analysis and modeling spatio-temporal Growth dynamics using Remote Sensing and GIS Techniques: A case study of Dhaka, Bangladesh." Master's thesis, 2011. http://hdl.handle.net/10362/8298.
Full textDhaka, the capital of Bangladesh, has undergone radical changes in its physical form, not only in its vast territorial expansion, but also through internal physical transformations over the last decades. In the process of urbanization, the physical characteristic of Dhaka is gradually changing as open spaces have been transformed into building areas, low land and water bodies into reclaimed builtup lands etc. This new urban fabric should be analyzed to understand the changes that have led to its creation. The primary objective of this research is to predict and analyze the future urban growth of Dhaka City. Another objective is to quantify and investigate the characteristics of urban land cover changes (1989-2009) using the Landsat satellite images of 1989, 1999 and 2009. Dhaka City Corporation (DCC) and its surrounding impact areas have been selected as the study area. A fisher supervised classification method has been applied to prepare the base maps with five land cover classes. To observe the change detection, different spatial metrics have been used for quantitative analysis. Moreover, some postclassification change detection techniques have also been implemented. Then it is found that the ‘builtup area’ land cover type is increasing in high rate over the years. The major contributors to this change are ‘fallow land’ and ‘water body’ land cover types. In the next stage, three different models have been implemented to simulate the land cover map of Dhaka city of 2009. These are named as ‘Stochastic Markov (St_Markov)’ Model, ‘Cellular Automata Markov (CA_Markov)’ Model and ‘Multi Layer Perceptron Markov (MLP_Markov)’ Model. Then the best-fitted model has been selected based on various Kappa statistics values and also by implementing other model validation techniques. This is how the ‘Multi Layer Perceptron Markov (MLP_Markov)’ Model has been qualified as the most suitable model for this research. Later, using the MLP_Markov model, the land cover map of 2019 has been predicted. The MLP_Markov model shows that 58% of the total study area will be converted into builtup area cover type in 2019. The interpretation of depicting the future scenario in quantitative accounts, as demonstrated in this research, will be of great value to the urban planners and decision makers, for the future planning of modern Dhaka City.