Academic literature on the topic 'Multi-temporal remote sensing'
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Journal articles on the topic "Multi-temporal remote sensing"
Franke, Jonas, and Gunter Menz. "Multi-temporal wheat disease detection by multi-spectral remote sensing." Precision Agriculture 8, no. 3 (June 24, 2007): 161–72. http://dx.doi.org/10.1007/s11119-007-9036-y.
Full textFu, N., L. Sun, H. Z. Yang, J. Ma, and B. Q. Liao. "RESEARCH ON MULTI-SOURCE SATELLITE IMAGE DATABASE MANAGEMENT SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 565–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-565-2020.
Full textZhu, Lilu, Xiaolu Su, Yanfeng Hu, Xianqing Tai, and Kun Fu. "A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data." Remote Sensing 13, no. 12 (June 14, 2021): 2333. http://dx.doi.org/10.3390/rs13122333.
Full textSmith, A. M., D. J. Major, C. W. Lindwall, and R. J. Brown. "Multi-Temporal, Multi-Sensor Remote Sensing for Monitoring Soil Conservation Farming." Canadian Journal of Remote Sensing 21, no. 2 (June 1995): 177–84. http://dx.doi.org/10.1080/07038992.1995.10874611.
Full textLi, Yinshuai, Chunyan Chang, Zhuoran Wang, Tao Li, Jianwei Li, and Gengxing Zhao. "Identification of Cultivated Land Quality Grade Using Fused Multi-Source Data and Multi-Temporal Crop Remote Sensing Information." Remote Sensing 14, no. 9 (April 27, 2022): 2109. http://dx.doi.org/10.3390/rs14092109.
Full textLiu, J., L. Liu, X. Xing, X. Zheng, Y. Gao, Q. Xu, and J. Du. "MULTI-TIER STORAGE MANAGEMENT AND APPLICATION OF REMOTE SENSING IMAGE DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 31, 2022): 1229–34. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1229-2022.
Full textChen, Peng Xiao, Shao Hong Shen, and Xiong Fei Wen. "Remote Sensing Dynamic Monitoring on Illegal Capacity Occupation of Reservoir." Advanced Materials Research 718-720 (July 2013): 1124–28. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1124.
Full textHuang, Fenghua, Zhengyuan Mao, and Wenzao Shi. "ICA-ASIFT-Based Multi-Temporal Matching of High-Resolution Remote Sensing Urban Images." Cybernetics and Information Technologies 16, no. 5 (October 1, 2016): 34–49. http://dx.doi.org/10.1515/cait-2016-0050.
Full textXia, Liheng, and Xueying Wu. "A review of hyperspectral remote sensing of crops." E3S Web of Conferences 338 (2022): 01029. http://dx.doi.org/10.1051/e3sconf/202233801029.
Full textLiu, C., X. Zhou, Y. Zhou, and A. Akbar. "MULTI-TEMPORAL MONITORING OF URBAN RIVER WATER QUALITY USING UAV-BORNE MULTI-SPECTRAL REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1469–75. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1469-2020.
Full textDissertations / Theses on the topic "Multi-temporal remote sensing"
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.
Books on the topic "Multi-temporal remote sensing"
International Workshop on the Analysis of Multi-temporal Remote Sensing Images (2007 Leuven, Belgium). 2007 International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Leuven, Belgium, 18 - 20 July 2007. Piscataway, NJ: IEEE, 2007.
Find full textInternational Workshop on the Analysis of Multi-temporal Remote Sensing Images (3rd 2005 Biloxi, Miss.). Proceedings of the Third International Workshop on the Analysis of Multi-temporal Remote Sensing Images: Multi Temp 2005, 16-18 May 2005, Beau Rivage Resort and Casino, Biloxi, Mississippi USA. Edited by King Roger L, Younan Nicolas H, and Institute of Electrical and Electronics Engineers. Piscataway, N.J: IEEE, 2005.
Find full textLorenzo, Bruzzone, and Smits Paul, eds. Proceedings of the First International Workshop on the Analysis of Multi-temporal Remote Sensing Images: University of Trento, Italy, 13-14 September 2001. River Edge, N.J: World Scientific, 2002.
Find full textInternational, Workshop on the Analysis of Multi-Temporal Remote Sensing Images (2nd 2003 Ispra Italy). Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003. [River Edge] N.J: World Scientific, 2004.
Find full textProceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003. Singapore: World Scientific, 2005.
Find full textUS GOVERNMENT. Proceedings of the Third International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Multi Temp 2005, 16-18 May 2005, Beau Rivage. Institute of Electrical & Electronics Enginee, 2005.
Find full text(Editor), Lorenzo Bruzzone, and Paul C. Smits (Editor), eds. Analysis of Multi-Temporal Remote Sensing Images: Proceedings of Multitemp 2001 University of Trento, Italy 13-14 September 2001 (Remote Sensing). World Scientific Publishing Company, 2002.
Find full text(Editor), Paul C. Smits, and Lorenzo Bruzzone (Editor), eds. Analysis Of Multi-Temporal Remote Sensing Images: Proceedings Of The Second International Workshop on the Joint Research Centre Ispra, Italy 16-18 July 2003. World Scientific Publishing Company, 2004.
Find full textBook chapters on the topic "Multi-temporal remote sensing"
Mercier, Grégoire, and Florence Tupin. "Analysis of Multi-Temporal Series and Change Detection." In Remote Sensing Imagery, 203–21. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118899106.ch8.
Full textMeghanadh, Devara, and Ramji Dwivedi. "Multi-Temporal SAR Interferometry." In Spaceborne Synthetic Aperture Radar Remote Sensing, 287–311. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003204466-13.
Full textRefice, Alberto, Annarita D’Addabbo, Francesco Paolo Lovergine, Khalid Tijani, Alberto Morea, Raffaele Nutricato, Fabio Bovenga, and Davide Oscar Nitti. "Monitoring Flood Extent and Area Through Multisensor, Multi-temporal Remote Sensing: The Strymonas (Greece) River Flood." In Flood Monitoring through Remote Sensing, 101–13. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63959-8_5.
Full textLiang, Hongyu, Wenbin Xu, Xiaoli Ding, Lei Zhang, and Songbo Wu. "Urban Sensing with Spaceborne Interferometric Synthetic Aperture Radar." In Urban Informatics, 345–65. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_21.
Full textMustafa, Yaseen T. "Multi-temporal Satellite Data for Land Use/Cover (LULC) Change Detection in Zakho, Kurdistan Region-Iraq." In Environmental Remote Sensing and GIS in Iraq, 161–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21344-2_7.
Full textPacifici, Fabio, Georgios K. Ouzounis, Lionel Gueguen, Giovanni Marchisio, and William J. Emery. "Very High Spatial Resolution Optical Imagery: Tree-Based Methods and Multi-temporal Models for Mining and Analysis." In Mathematical Models for Remote Sensing Image Processing, 81–135. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66330-2_3.
Full textGamon, John A., Ran Wang, Hamed Gholizadeh, Brian Zutta, Phil A. Townsend, and Jeannine Cavender-Bares. "Consideration of Scale in Remote Sensing of Biodiversity." In Remote Sensing of Plant Biodiversity, 425–47. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33157-3_16.
Full textAlkaradaghi, Karwan, Salahalddin S. Ali, Nadhir Al-Ansari, and Jan Laue. "Land Use Classification and Change Detection Using Multi-temporal Landsat Imagery in Sulaimaniyah Governorate, Iraq." In Advances in Remote Sensing and Geo Informatics Applications, 117–20. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01440-7_28.
Full textLin, Yi, Yuan Hu, and Jie Yu. "Analysis of Shanghai Urban Expansion Based on Multi-temporal Remote Sensing Images." In Sustainable Development of Water and Environment, 37–45. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16729-5_5.
Full textSnehmani, Mritunjay Kumar Singh, Krishnanjan Pakrasi, Anshuman Bhardwaj, and A. Ganju. "Monitoring the Status of Siachen Glacier Using Multi Temporal Remote Sensing Approach." In Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment, 887–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-18663-4_137.
Full textConference papers on the topic "Multi-temporal remote sensing"
Roerink, G. J., M. H. G. I. Danes, O. Gomez Prieto, A. J. W. de Wit, and A. J. H. van Vliet. "Deriving plant phenology from remote sensing." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005098.
Full textResta, Salvatore, Nicola Acito, Marco Diani, and Giovanni Corsini. "Unsupervised mis-registration noise estimation in multi-temporal hyperspectral images." In SPIE Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2012. http://dx.doi.org/10.1117/12.974216.
Full textBovenga, Fabio, Alberto Refice, Guido Pasquariello, Davide O. Nitti, and Raffaele Nutricato. "Corner reflectors and multi-temporal SAR inteferometry for landslide monitoring." In SPIE Remote Sensing, edited by Claudia Notarnicola, Simonetta Paloscia, and Nazzareno Pierdicca. SPIE, 2014. http://dx.doi.org/10.1117/12.2066833.
Full textPeng, Chen, Juan Wang, and Donglin Li. "Oil platform investigation by multi-temporal SAR remote sensing image." In SPIE Remote Sensing. SPIE, 2011. http://dx.doi.org/10.1117/12.897937.
Full textSomers, Ben, and Gregory P. Asner. "Mapping tropical rainforest canopies using multi-temporal spaceborne imaging spectroscopy." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2013. http://dx.doi.org/10.1117/12.2028508.
Full textElsner, Paul. "Multi-temporal airborne remote sensing of intertidal sediment dynamics." In SPIE Europe Remote Sensing, edited by Ulrich Michel and Daniel L. Civco. SPIE, 2009. http://dx.doi.org/10.1117/12.830672.
Full textYule, Ian J., Reddy R. Pullanagari, and G. Kereszturi. "Detecting subtle environmental change: a multi-temporal airborne imaging spectroscopy approach." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2016. http://dx.doi.org/10.1117/12.2240418.
Full textBovenga, Fabio, Alberto Refice, Antonella Belmonte, and Guido Pasquariello. "Comparative analysis of recent satellite missions for multi-temporal SAR interferometry." In SPIE Remote Sensing, edited by Claudia Notarnicola, Simonetta Paloscia, Nazzareno Pierdicca, and Edward Mitchard. SPIE, 2016. http://dx.doi.org/10.1117/12.2240490.
Full textBetbeder, Julie, Sébastien Rapinel, Thomas Corpetti, Eric Pottier, Samuel Corgne, and Laurence Hubert Moy. "Multi-temporal classification of TerraSAR-X data for wetland vegetation mapping." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2013. http://dx.doi.org/10.1117/12.2029092.
Full textBovenga, Fabio, Davide Oscar Nitti, Alberto Refice, Raffaele Nutricato, and Maria Teresa Chiaradia. "Multi-temporal DInSAR analysis with X-band high resolution SAR data: examples and potential." In Remote Sensing, edited by Claudia Notarnicola. SPIE, 2010. http://dx.doi.org/10.1117/12.866459.
Full textReports on the topic "Multi-temporal remote sensing"
Lee Spangler, Lee A. Vierling, Eva K. Stand, Andrew T. Hudak, Jan U.H. Eitel, and Sebastian Martinuzzi. QUANTIFYING FOREST ABOVEGROUND CARBON POOLS AND FLUXES USING MULTI-TEMPORAL LIDAR A report on field monitoring, remote sensing MMV, GIS integration, and modeling results for forestry field validation test to quantify aboveground tree biomass and carbon. Office of Scientific and Technical Information (OSTI), April 2012. http://dx.doi.org/10.2172/1037874.
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