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Journal articles on the topic 'Multi-temporal Analysi'

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Author: Grafiati

Published: 11 March 2023

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1

Ronchi, Bruno, Andrea Amici, Carlo Maía Rossi, Riccardo Primi, Rita Biasi, and María Nicolina Ripa. "Multi-temporal analysis of urban and periurban land use changes in medieval towns of central Italy." Hábitat y Sociedad, no. 7 (2014): 77–92. http://dx.doi.org/10.12795/habitatysociedad.2013.i6.05.

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2

García Culqui, Cristina Lourdes, and Michael Xavier Hachi Pazmiño. "Análisis multitemporal de la dinámica de uso de suelo y cobertura vegetal en la microcuenca del Río Illangama." Revista de Investigación Talentos 9, no. 2 (July 1, 2022): 101–16. http://dx.doi.org/10.33789/talentos.9.2.173.

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Esta investigación tuvo como objetivo realizar un análisis multitemporal de la dinámica de uso de suelo y cobertura vegetal de la microcuenca del Río Illangama durante el período 1996 - 2021, su incidencia social y ambiental, para el desarrollo sostenible de la misma. La metodología utilizada fue no experimental, tuvo un enfoque de estudio cualitativo con alcance descriptivo. Se realizó la clasificación supervisada de imágenes satelitales, con seis clases. Se empleó el Índice Diferencial Normalizado de Vegetación que evidenciando que en el año 1996 el valor bajo fue de -0,41 y el valor alto a 0,48, mientras que, en el año 2021 el valor bajo fue de -0,7 y el valor alto de 0,54. El área poblada en el 2021 en la microcuenca del Río Illangama disminuyó. Se determinó que, del año 1996 al 2021 ha crecido la zona de páramo sobre la cobertura denominada área sin cobertura vegetal en la zona alta de la microcuenca. Durante el lapso de 25 años se ha perdido en la microcuenca el tipo de vegetación densa y poco densa, sin embargo, han aumentado los reservorios de agua. La población principalmente se dedicaba a la agricultura, expandiendo sus cultivos y viviendas hacia la cuenca alta, no obstante, debido a mayores estrategias de conservación tanto de páramos como de bosques, se han ido controlando estas actividades.

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3

Zhao, Ling, Hanhan Deng, Linyao Qiu, Sumin Li, Zhixiang Hou, Hai Sun, and Yun Chen. "Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding." Symmetry 12, no. 2 (February 1, 2020): 199. http://dx.doi.org/10.3390/sym12020199.

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Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical significance of results. To explore the network structure and semantic relationships, we propose a general framework for multi-source spatio-temporal data analysis via knowledge graph embedding. The framework extracts low-dimensional feature representation from multi-source spatio-temporal data in a high-dimensional space, and recognizes the network structure and semantic relationships about multi-source spatio-temporal data. Experiment results show that the framework can not only effectively utilize multi-source spatio-temporal data, but also explore the network structure and semantic relationship. Taking real Shanghai datasets as an example, we confirm the validity of the multi-source spatio-temporal data analytical framework based on knowledge graph embedding.

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4

Meixner, P., and M. Eckstein. "MULTI-TEMPORAL ANALYSIS OF WWII RECONNAISSANCE PHOTOS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 973–78. http://dx.doi.org/10.5194/isprs-archives-xli-b8-973-2016.

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There are millions of aerial photographs from the period of the Second Wold War available in the Allied archives, obtained by aerial photo reconnaissance, covering most of today’s European countries. They are spanning the time from 1938 until the end of the war and even beyond. Photo reconnaissance provided intelligence information for the Allied headquarters and accompanied the bombing offensive against the German homeland and the occupied territories. <br><br> One of the initial principal targets in Bohemia were the synthetized fuel works STW AG (Sudetenländische Treibstoffwerke AG) in Zaluzi (formerly Maltheuren) near Most (formerly Brück), Czech Republic. The STW AG synthetized fuel plant was not only subject to bombing raids, but a subject to quite intensive photo reconnaissance, too - long before the start of the bombing campaign. With a multi-temporal analysis of the available imagery from international archives we will demonstrate the factory build-up during 1942 and 1943, the effects of the bombing raids in 1944 and the struggle to keep the plant working in the last year of the war. Furthermore we would like to show the impact the bombings have today, in form of potential unexploded ordnance in the adjacent area of the open cast mines.

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5

Meixner, P., and M. Eckstein. "MULTI-TEMPORAL ANALYSIS OF WWII RECONNAISSANCE PHOTOS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 973–78. http://dx.doi.org/10.5194/isprsarchives-xli-b8-973-2016.

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There are millions of aerial photographs from the period of the Second Wold War available in the Allied archives, obtained by aerial photo reconnaissance, covering most of today’s European countries. They are spanning the time from 1938 until the end of the war and even beyond. Photo reconnaissance provided intelligence information for the Allied headquarters and accompanied the bombing offensive against the German homeland and the occupied territories. <br><br> One of the initial principal targets in Bohemia were the synthetized fuel works STW AG (Sudetenländische Treibstoffwerke AG) in Zaluzi (formerly Maltheuren) near Most (formerly Brück), Czech Republic. The STW AG synthetized fuel plant was not only subject to bombing raids, but a subject to quite intensive photo reconnaissance, too - long before the start of the bombing campaign. With a multi-temporal analysis of the available imagery from international archives we will demonstrate the factory build-up during 1942 and 1943, the effects of the bombing raids in 1944 and the struggle to keep the plant working in the last year of the war. Furthermore we would like to show the impact the bombings have today, in form of potential unexploded ordnance in the adjacent area of the open cast mines.

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6

Rybakov, V. V. "Multi-Agent Temporal Nontransitive Linear Logics and the Admissibility Problem." Algebra and Logic 59, no. 1 (March 2020): 87–100. http://dx.doi.org/10.1007/s10469-020-09581-0.

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7

KIRIMOTO, Kensuke, and Shigeru NISHIO. "Multi-Dimensional Analysis of Spatio-Temporal Correlation Information." Transaction of the Visualization Society of Japan 29, no. 11 (2009): 67–75. http://dx.doi.org/10.3154/tvsj.29.67.

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8

Nocerino, E., F. Menna, and F. Menna. "MULTI-TEMPORAL ANALYSIS OF LANDSCAPES AND URBAN AREAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B4 (July 27, 2012): 85–90. http://dx.doi.org/10.5194/isprsarchives-xxxix-b4-85-2012.

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9

Alessandretti, Laura, Piotr Sapiezynski, Sune Lehmann, and Andrea Baronchelli. "Multi-scale spatio-temporal analysis of human mobility." PLOS ONE 12, no. 2 (February 15, 2017): e0171686. http://dx.doi.org/10.1371/journal.pone.0171686.

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10

Lin, Yi-Chun, Jinyuan Shao, Sang-Yeop Shin, Zainab Saka, Mina Joseph, Raja Manish, Songlin Fei, and Ayman Habib. "Comparative Analysis of Multi-Platform, Multi-Resolution, Multi-Temporal LiDAR Data for Forest Inventory." Remote Sensing 14, no. 3 (January 29, 2022): 649. http://dx.doi.org/10.3390/rs14030649.

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LiDAR technology is rapidly evolving as various new systems emerge, providing unprecedented data to characterize forest vertical structure. Data from different LiDAR systems present distinct characteristics owing to a combined effect of sensor specifications, data acquisition strategies, as well as forest conditions such as tree density and canopy cover. Comparative analysis of multi-platform, multi-resolution, and multi-temporal LiDAR data provides guidelines for selecting appropriate LiDAR systems and data processing tools for different research questions, and thus is of crucial importance. This study presents a comprehensive comparison of point clouds from four systems, linear and Geiger-mode LiDAR from manned aircraft and multi-beam LiDAR on unmanned aerial vehicle (UAV), and in-house developed Backpack, with the consideration of different forest canopy cover scenarios. The results suggest that the proximal Backpack LiDAR can provide the finest level of information, followed by UAV LiDAR, Geiger-mode LiDAR, and linear LiDAR. The emerging Geiger-mode LiDAR can capture a significantly higher level of detail while operating at a higher altitude as compared to the traditional linear LiDAR. The results also show: (1) canopy cover percentage has a critical impact on the ability of aerial and terrestrial systems to acquire information corresponding to the lower and upper portions of the tree canopy, respectively; (2) all the systems can obtain adequate ground points for digital terrain model generation irrespective of canopy cover conditions; and (3) point clouds from different systems are in agreement within a ±3 cm and ±7 cm range along the vertical and planimetric directions, respectively.

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11

Mohammadimanesh, Fariba, Bahram Salehi, Masoud Mahdianpari, Brian Brisco, and Mahdi Motagh. "Multi-temporal, multi-frequency, and multi-polarization coherence and SAR backscatter analysis of wetlands." ISPRS Journal of Photogrammetry and Remote Sensing 142 (August 2018): 78–93. http://dx.doi.org/10.1016/j.isprsjprs.2018.05.009.

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12

Nicolás Campos, Alfredo, and Carlos Marcelo Di Bella. "Multi-Temporal Analysis of Remotely Sensed Information Using Wavelets." Journal of Geographic Information System 04, no. 04 (2012): 383–91. http://dx.doi.org/10.4236/jgis.2012.44044.

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13

Chang, K. T., H. M. Fang, S. S. Hsiao, and C. S. Li. "Beach Topographic Change Analysis Using Multi-temporal UAV Data." IOP Conference Series: Earth and Environmental Science 799, no. 1 (June 1, 2021): 012022. http://dx.doi.org/10.1088/1755-1315/799/1/012022.

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14

De Angeli, Silvia, Bruce D. Malamud, Lauro Rossi, Faith E. Taylor, Eva Trasforini, and Roberto Rudari. "A multi-hazard framework for spatial-temporal impact analysis." International Journal of Disaster Risk Reduction 73 (April 2022): 102829. http://dx.doi.org/10.1016/j.ijdrr.2022.102829.

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15

Grignetti, A., R. Salvatori, R. Casacchia, and F. Manes. "Mediterranean vegetation analysis by multi-temporal satellite sensor data." International Journal of Remote Sensing 18, no. 6 (April 1997): 1307–18. http://dx.doi.org/10.1080/014311697218430.

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16

Löw, Fabian, Patrick Knöfel, and Christopher Conrad. "Analysis of uncertainty in multi-temporal object-based classification." ISPRS Journal of Photogrammetry and Remote Sensing 105 (July 2015): 91–106. http://dx.doi.org/10.1016/j.isprsjprs.2015.03.004.

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17

Jimee, Ganesh Kumar, Kimiro Meguro, and Amod Mani Dixit. "Nepal, a multi-hazard risk country: Spatio-temporal analysis." Journal of Nepal Geological Society 58 (June 25, 2019): 145–52. http://dx.doi.org/10.3126/jngs.v58i0.24599.

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Nepal, though covers small area of the earth, exposes complex geology with active tectonic processes, high peaks, sloppy terrain and climatic variation. Combination of such geo-physical and climatic conditions with existing poor socio-economic conditions, unplanned settlements, rapidly increasing population and low level of awareness has put the country in highest risk to multi-hazard events. Fires, floods, landslides and epidemics are the most frequent hazard events, which have cumulatively caused a significant loss of lives and property every year. However, due to diversity in physiographic, climatic and socio-economic conditions within the country, the type, frequency and degree of the impact of such events differs in different places. During the period of 46 years (1971-2016), an average of 2 events have been occurred causing 3 deaths/missing every day. Disaster events occurred most frequently during the months of April, July and August, while relatively lesser number of events have been reported during January, November and December. However, earthquakes have been reported in different months, regardless with the season. This paper is an effort to analyse the spatial distribution and temporal variation of disaster events in Nepal. Further it has drawn a trend of disasters occurrence in Nepal, which will help the decision makers and other stakeholders for formulating Disaster Risk Management (DRM) plan and policies on one hand and heighten citizens’ awareness of against disasters on the other.

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18

Shi, Yan, and Zhenzhou Lu. "Dynamic reliability analysis for structure with temporal and spatial multi-parameter." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 6 (June 10, 2019): 1002–13. http://dx.doi.org/10.1177/1748006x19853413.

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For efficiently estimating the dynamic failure probability of the structure with random variables, stochastic processes and temporal and spatial multi-parameter, an estimation strategy is presented based on the random field transformation. The random field transformation focusing on the dynamic reliability with only one time parameter is further investigated, and it is extended to temporal and spatial multi-parameter issue, which simulates the output as multi-dimensional Gaussian random field. Also, the active learning Kriging method is used to construct the surrogate models for the mean function and auto-covariance function of performance function. After that, the temporal and spatial dynamic failure probability can be obtained by the simulation method. Although it doesn’t need to call the real performance function during the process of simulation method, it is time computationally expensive. To address this issue, the optimization algorithm procedure is established to estimate the dynamic failure probability. Several examples including an aero engine turbine disk and a cylindrical pressure vessel are introduced to illustrate the significance and effectiveness of the proposed methods for analyzing the temporal and spatial multi-parameter dynamic failure probability.

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19

Aydin Gol, Ebru, and Calin Belta. "Time-constrained temporal logic control of multi-affine systems." Nonlinear Analysis: Hybrid Systems 10 (November 2013): 21–33. http://dx.doi.org/10.1016/j.nahs.2013.03.002.

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20

Dyatmika, Haris Suka, and Liana Fibriawati. "ANALYSIS OF SCENE COMPATIBILITIES FOR MOSAIC OF LANDSAT 8 MULTI-TEMPORAL IMAGES BASED ON RADIOMETRIC PARAMETER." International Journal of Remote Sensing and Earth Sciences (IJReSES) 13, no. 1 (June 21, 2017): 9. http://dx.doi.org/10.30536/j.ijreses.2016.v13.a2713.

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Cloud free mosaic simplified the remote sensing imagery. Multi-temporal image mosaic needed to make a cloud free mosaic i.e. in the area covered by cloud throughout year like Indonesia. One of the satellite imagery that was widely used for various purposes was Landsat 8 image due to the temporal, spatial and spectral resolution which was suitable for many utilization themes. Landsat 8 could be used for multi-temporal image mosaic of the entire region in Indonesia. Landsat 8 had 16 days temporal resolution which allowed a region (scene image) acquired in a several times one year. However, not all the acquired Landsat 8 scene was proper when used for multi-temporal mosaic. The purpose of this work was observing radiometric parameters for scene selection method so a good multi-temporal mosaic image could be generated and more efficient processing. This study analyzed the relationship between radiometric parameters from image i.e. histogram and Scattergram with scene selection for multi-temporal mosaic purposes. Histogram and Scattergram representing radiometric imagery context such as mean, standard deviation, median and mode which was displayed visually. The data used were Landsat 8 imagery with the Area of Interest (AOI) in Kalimantan and Lombok. Then the histogram and Scattergram of the image AOI was analyzed. From the histogram and Scattergram analysis could be obtained that less shift between the data’s histogram and the more Scattergram forming 45 degree angle for distribution of the data then indicated more similar to radiometric of the image.

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21

Hu, Yong, Wen Luo, Zhaoyuan Yu, Linwang Yuan, and Guonian Lü. "Geometric Algebra-based Modeling and Analysis for Multi-layer, Multi-temporal Geographic Data." Advances in Applied Clifford Algebras 26, no. 1 (July 9, 2015): 151–68. http://dx.doi.org/10.1007/s00006-015-0574-5.

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22

Liu, Wu Ping, and Fu Wei. "Using Local Transition Probability Models in Markov Random Field for Multi-Temporal Image Classification." Applied Mechanics and Materials 687-691 (November 2014): 3963–67. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3963.

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Making use full of multi-source and multi-temporal information to extract richer and interesting information is a tendency in analysis of remote sensing images. In this paper, spatial and temporal contextual classification based on Markov Random Field (MRF) is used to classify ecological function vegetation in Poyang Lake. The results show that spatial and temporal neighborhood complementary information from different images can be used to remove the spectral confusion of different kinds of vegetation on single image and improve classification accuracy compared to MLC method. Building effective spatial and temporal neighborhood model for information extraction in special application is the key of multi-source and multi-temporal image analysis. Although spatial and temporal contextual classification method is computation demanding, it’s promising in the application emphasizing classification accuracy.

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23

Bhavani, M., V. Hanifar Sangeetha, K. Kalaivani, K. Ulagapriya, and A. Saritha. "Change detection algorithm for multi-temporal satellite images: a review." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 206. http://dx.doi.org/10.14419/ijet.v7i2.21.12173.

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Change detection (CD) is the process of detecting changes from multitemporal satellite images that have undergone spatial changes due to natural and man-made disaster. The objective is to analyse different change detection techniques, in order to use appropriately in various applications with the help of image processing. Techniques that are used in current researches are Image Differencing, Image Regression, Change Vector Analysis (CVA),Principal Component Analysis(PCA), Tasselled Cap, Gramm-Schmidt(GS), Post Classification Comparison, EM Detection, Unsupervised Change Detection, Li-Strahler Reflectance Model, Spectral Mixture Model, Biophysical Parameter Method, Integrated GIS and Remote Sensing Method, GIS Approach, Visual Interpretation and so on. Effective change detection is required for various applications such as rate of deforestation, costal changes, urban developments, damage evaluation, resource monitoring and land disposition.

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24

Yao, Guangle, Tao Lei, Xianyuan Liu, and Ping Jiang. "Temporal Modeling on Multi-Temporal-Scale Spatiotemporal Atoms for Action Recognition." Applied Sciences 8, no. 10 (October 6, 2018): 1835. http://dx.doi.org/10.3390/app8101835.

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As an important branch of video analysis, human action recognition has attracted extensive research attention in computer vision and artificial intelligence communities. In this paper, we propose to model the temporal evolution of multi-temporal-scale atoms for action recognition. An action can be considered as a temporal sequence of action units. These action units which we referred to as action atoms, can capture the key semantic and characteristic spatiotemporal features of actions in different temporal scales. We first investigate Res3D, a powerful 3D CNN architecture and create the variants of Res3D for different temporal scale. In each temporal scale, we design some practices to transfer the knowledge learned from RGB to optical flow (OF) and build RGB and OF streams to extract deep spatiotemporal information using Res3D. Then we propose an unsupervised method to mine action atoms in the deep spatiotemporal space. Finally, we use long short-term memory (LSTM) to model the temporal evolution of atoms for action recognition. The experimental results show that our proposed multi-temporal-scale spatiotemporal atoms modeling method achieves recognition performance comparable to that of state-of-the-art methods on two challenging action recognition datasets: UCF101 and HMDB51.

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25

Schmitt, Andreas, Anna Wendleder, Rüdiger Kleynmans, Maximilian Hell, Achim Roth, and Stefan Hinz. "Multi-Source and Multi-Temporal Image Fusion on Hypercomplex Bases." Remote Sensing 12, no. 6 (March 14, 2020): 943. http://dx.doi.org/10.3390/rs12060943.

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This article spanned a new, consistent framework for production, archiving, and provision of analysis ready data (ARD) from multi-source and multi-temporal satellite acquisitions and an subsequent image fusion. The core of the image fusion was an orthogonal transform of the reflectance channels from optical sensors on hypercomplex bases delivered in Kennaugh-like elements, which are well-known from polarimetric radar. In this way, SAR and Optics could be fused to one image data set sharing the characteristics of both: the sharpness of Optics and the texture of SAR. The special properties of Kennaugh elements regarding their scaling—linear, logarithmic, normalized—applied likewise to the new elements and guaranteed their robustness towards noise, radiometric sub-sampling, and therewith data compression. This study combined Sentinel-1 and Sentinel-2 on an Octonion basis as well as Sentinel-2 and ALOS-PALSAR-2 on a Sedenion basis. The validation using signatures of typical land cover classes showed that the efficient archiving in 4 bit images still guaranteed an accuracy over 90% in the class assignment. Due to the stability of the resulting class signatures, the fuzziness to be caught by Machine Learning Algorithms was minimized at the same time. Thus, this methodology was predestined to act as new standard for ARD remote sensing data with an subsequent image fusion processed in so-called data cubes.

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26

Wah, Win, Nathan papa, Melanie Evans, Susannah Ahern, and Arul Earnest. "A multi-level spatio-temporal analysis on prostate cancer outcomes." Cancer Epidemiology 72 (June 2021): 101939. http://dx.doi.org/10.1016/j.canep.2021.101939.

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27

Zelaya Wziątek, Terefenko, and Kurylczyk. "Multi-Temporal Cliff Erosion Analysis Using Airborne Laser Scanning Surveys." Remote Sensing 11, no. 22 (November 14, 2019): 2666. http://dx.doi.org/10.3390/rs11222666.

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Rock cliffs are a significant component of world coastal zones. However, rocky coasts and factors contributing to their erosion have not received as much attention as soft cliffs. In this study, two rocky-cliff systems in the southern Baltic Sea were analyzed with Airborne Laser Scanners (ALS) to track changes in cliff morphology. The present contribution aimed to study the volumetric changes in cliff profiles, spatial distribution of erosion, and rate of cliff retreat corresponding to the cliff exposure and rock resistance of the Jasmund National Park chalk cliffs in Rugen, Germany. The study combined multi-temporal Light Detection and Ranging (LiDAR) data analyses, rock sampling, laboratory analyses of chemical and mechanical resistance, and along-shore wave power flux estimation. The spatial distribution of the active erosion areas appear to follow the cliff exposure variations; however, that trend is weaker for the sections of the coastline in which structural changes occurred. The rate of retreat for each cliff–beach profile, including the cliff crest, vertical cliff base, and cliff base with talus material, indicates that wave action is the dominant erosive force in areas in which the cliff was eroded quickly at equal rates along the cliff profile. However, the erosion proceeded with different rates in favor of cliff toe erosion. The effects of chemical and mechanical rock resistance are shown to be less prominent than the wave action owing to very small differences in the measured values, which proves the homogeneous structure of the cliff. The rock resistance did not follow the trends of cliff erosion revealed by volume changes during the period of analysis.

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Yosuke, Yamaguchi, Matsui Kai, Ohya Jun, Hasegawa Katsuya, and Nagahashi Hiroshi. "Efficient landslide detection by UAV-based multi-temporal visual analysis." Electronic Imaging 34, no. 6 (January 16, 2022): 307–1. http://dx.doi.org/10.2352/ei.2022.34.6.iriacv-307.

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KANG, Jungwon, and Myung Jin CHUNG. "Fast Online Motion Segmentation through Multi-Temporal Interval Motion Analysis." IEICE Transactions on Information and Systems E98.D, no. 2 (2015): 479–84. http://dx.doi.org/10.1587/transinf.2014edl8123.

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30

Bovenga, Fabio, Antonella Belmonte, Alberto Refice, Guido Pasquariello, Raffaele Nutricato, Davide Nitti, and Maria Chiaradia. "Performance Analysis of Satellite Missions for Multi-Temporal SAR Interferometry." Sensors 18, no. 5 (April 27, 2018): 1359. http://dx.doi.org/10.3390/s18051359.

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Xavier, Alexandre Cândido, Bernardo F. T. Rudorff, Yosio Edemir Shimabukuro, Luciana Miura Sugawara Berka, and Mauricio Alves Moreira. "Multi‐temporal analysis of MODIS data to classify sugarcane crop." International Journal of Remote Sensing 27, no. 4 (February 20, 2006): 755–68. http://dx.doi.org/10.1080/01431160500296735.

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32

Pádua, Luís, Jonáš Hruška, José Bessa, Telmo Adão, Luís Martins, José Gonçalves, Emanuel Peres, António Sousa, João Castro, and Joaquim Sousa. "Multi-Temporal Analysis of Forestry and Coastal Environments Using UASs." Remote Sensing 10, no. 2 (December 24, 2017): 24. http://dx.doi.org/10.3390/rs10010024.

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33

Landsberg, Florence, Sabine Vanhuysse, and Eleonore Wolff. "Fuzzy Multi-temporal Land-use Analysis and Mine Clearance Application." Photogrammetric Engineering & Remote Sensing 72, no. 11 (November 1, 2006): 1245–53. http://dx.doi.org/10.14358/pers.72.11.1245.

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Liu, Tongtong, Zheng Yang, Yi Zhao, Chenshu Wu, Zimu Zhou, and Yunhao Liu. "Temporal understanding of human mobility: A multi-time scale analysis." PLOS ONE 13, no. 11 (November 27, 2018): e0207697. http://dx.doi.org/10.1371/journal.pone.0207697.

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35

CORR, D. G., A. M. TAILOR, A. CROSS, D. C. HOGG, D. H. LAWRENCE, D. C. MASON, and M. PETROU. "Progress in automatic analysis of multi-temporal remotely-sensed data." International Journal of Remote Sensing 10, no. 7 (July 1989): 1175–95. http://dx.doi.org/10.1080/01431168908903957.

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36

Mauri, Luca, Roberta Masin, and Paolo Tarolli. "Wildlife impact on cultivated lands: A multi-temporal spatial analysis." Agricultural Systems 184 (September 2020): 102890. http://dx.doi.org/10.1016/j.agsy.2020.102890.

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37

Pádua, Luís, Telmo Adão, António Sousa, Emanuel Peres, and Joaquim J. Sousa. "Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery." Remote Sensing 12, no. 1 (January 1, 2020): 139. http://dx.doi.org/10.3390/rs12010139.

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The use of unmanned aerial vehicles (UAVs) for remote sensing applications in precision viticulture significantly increased in the last years. UAVs’ capability to acquire high spatiotemporal resolution and georeferenced imagery from different sensors make them a powerful tool for a better understanding of vineyard spatial and multitemporal heterogeneity, allowing the estimation of parameters directly impacting plants’ health status. In this way, the decision support process in precision viticulture can be greatly improved. However, despite the proliferation of these innovative technologies in viticulture, most of the published studies rely only on data from a single sensor in order to achieve a specific goal and/or in a single/small period of the vineyard development. In order to address these limitations and fully exploit the advantages offered by the use of UAVs, this study explores the multi-temporal analysis of vineyard plots at a grapevine scale using different imagery sensors. Individual grapevine detection enables the estimation of biophysical and geometrical parameters, as well as missing grapevine plants. A validation procedure was carried out in six vineyard plots focusing on the detected number of grapevines and missing grapevines. A high overall agreement was obtained concerning the number of grapevines present in each row (99.8%), as well as in the individual grapevine identification (mean overall accuracy of 97.5%). Aerial surveys were conducted in two vineyard plots at different growth stages, being acquired for RGB, multispectral and thermal imagery. Moreover, the extracted individual grapevine parameters enabled us to assess the vineyard variability in a given epoch and to monitor its multi-temporal evolution. This type of analysis is critical for precision viticulture, constituting as a tool to significantly support the decision-making process.

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Parry, Simon, Jamie Hannaford, Ben Lloyd-Hughes, and Christel Prudhomme. "Multi-year droughts in Europe: analysis of development and causes." Hydrology Research 43, no. 5 (April 12, 2012): 689–706. http://dx.doi.org/10.2166/nh.2012.024.

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Whilst hydrological systems can show resilience to short-term streamflow deficiencies during within-year droughts, prolonged deficits during multi-year droughts are a significant threat to water resources security in Europe. This study uses a threshold-based objective classification of regional hydrological drought to qualitatively examine the characteristics, spatio-temporal evolution and synoptic climatic drivers of multi-year drought events in 1962–64, 1975–76 and 1995–97, on a European scale but with particular focus on the UK. Whilst all three events are multi-year, pan-European phenomena, their development and causes can be contrasted. The critical factor in explaining the unprecedented severity of the 1975–76 event is the consecutive occurrence of winter and summer drought. In contrast, 1962–64 was a succession of dry winters, mitigated by quiescent summers, whilst 1995–97 lacked spatial coherence and was interrupted by wet interludes. Synoptic climatic conditions vary within and between multi-year droughts, suggesting that regional factors modulate the climate signal in streamflow drought occurrence. Despite being underpinned by qualitatively similar climatic conditions and commonalities in evolution and characteristics, each of the three droughts has a unique spatio-temporal signature. An improved understanding of the spatio-temporal evolution and characteristics of multi-year droughts has much to contribute to monitoring and forecasting capability, and to improved mitigation strategies.

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Wang, Caiqiong, Lei Zhao, Wangfei Zhang, Xiyun Mu, and Shitao Li. "Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning." PeerJ 10 (January 19, 2022): e12805. http://dx.doi.org/10.7717/peerj.12805.

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Abstract Polarimetric SAR (PolSAR) image segmentation is a key step in its interpretation. For the targets with time series changes, the single-temporal PolSAR image segmentation algorithm is difficult to provide correct segmentation results for its target recognition, time series analysis and other applications. For this, a new algorithm for multi-temporal PolSAR image segmentation is proposed in this paper. Firstly, the over-segmentation of single-temporal PolSAR images is carried out by the mean-shift algorithm, and the over-segmentation results of single-temporal PolSAR are combined to get the over-segmentation results of multi-temporal PolSAR images. Secondly, the edge detectors are constructed to extract the edge information of single-temporal PolSAR images and fuse them to get the edge fusion results of multi-temporal PolSAR images. Then, the similarity measurement matrix is constructed based on the over-segmentation results and edge fusion results of multi-temporal PolSAR images. Finally, the normalized cut criterion is used to complete the segmentation of multi-temporal PolSAR images. The performance of the proposed algorithm is verified based on three temporal PolSAR images of Radarsat-2, and compared with the segmentation algorithm of single-temporal PolSAR image. Experimental results revealed the following findings: (1) The proposed algorithm effectively realizes the segmentation of multi-temporal PolSAR images, and achieves ideal segmentation results. Moreover, the segmentation details are excellent, and the region consistency is good. The objects which can’t be distinguished by the single-temporal PolSAR image segmentation algorithm can be segmented. (2) The segmentation accuracy of the proposed multi-temporal algorithm is up to 86.5%, which is significantly higher than that of the single-temporal PolSAR image segmentation algorithm. In general, the segmentation result of proposed algorithm is closer to the optimal segmentation. The optimal segmentation of farmland parcel objects to meet the needs of agricultural production is realized. This lays a good foundation for the further interpretation of multi-temporal PolSAR image.

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Ezimand, Keyvan, Manouchehr Chahardoli, Mohsen Azadbakht, and Ali Akbar Matkan. "Spatiotemporal analysis of land surface temperature using multi-temporal and multi-sensor image fusion techniques." Sustainable Cities and Society 64 (January 2021): 102508. http://dx.doi.org/10.1016/j.scs.2020.102508.

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Capodici, Fulvio, Antonino Maltese, Giuseppe Ciraolo, Guido D’Urso, and Goffredo La Loggia. "Power Sensitivity Analysis of Multi-Frequency, Multi-Polarized, Multi-Temporal SAR Data for Soil-Vegetation System Variables Characterization." Remote Sensing 9, no. 7 (July 4, 2017): 677. http://dx.doi.org/10.3390/rs9070677.

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Van de Weghe, N., B. de Roo, Y. Qiang, M. Versichele, T. Neutens, and P. de Maeyer. "The continuous spatio-temporal model (CSTM) as an exhaustive framework for multi-scale spatio-temporal analysis." International Journal of Geographical Information Science 28, no. 5 (March 14, 2014): 1047–60. http://dx.doi.org/10.1080/13658816.2014.886329.

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Yang, Fan, Bunkei Matsushita, Takehiko Fukushima, and Wei Yang. "Temporal mixture analysis for estimating impervious surface area from multi-temporal MODIS NDVI data in Japan." ISPRS Journal of Photogrammetry and Remote Sensing 72 (August 2012): 90–98. http://dx.doi.org/10.1016/j.isprsjprs.2012.05.016.

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Blasch, Gerald, Zhenhai Li, and James A. Taylor. "Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems." Precision Agriculture 21, no. 6 (May 6, 2020): 1263–90. http://dx.doi.org/10.1007/s11119-020-09719-1.

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Abstract Easy-to-use tools using modern data analysis techniques are needed to handle spatio-temporal agri-data. This research proposes a novel pattern recognition-based method, Multi-temporal Yield Pattern Analysis (MYPA), to reveal long-term (> 10 years) spatio-temporal variations in multi-temporal yield data. The specific objectives are: i) synthesis of information within multiple yield maps into a single understandable and interpretable layer that is indicative of the variability and stability in yield over a 10 + years period, and ii) evaluation of the hypothesis that the MYPA enhances multi-temporal yield interpretation compared to commonly-used statistical approaches. The MYPA method automatically identifies potential erroneous yield maps; detects yield patterns using principal component analysis; evaluates temporal yield pattern stability using a per-pixel analysis; and generates productivity-stability units based on k-means clustering and zonal statistics. The MYPA method was applied to two commercial cereal fields in Australian dryland systems and two commercial fields in a UK cool-climate system. To evaluate the MYPA, its output was compared to results from a classic, statistical yield analysis on the same data sets. The MYPA explained more of the variance in the yield data and generated larger and more coherent yield zones that are more amenable to site-specific management. Detected yield patterns were associated with varying production conditions, such as soil properties, precipitation patterns and management decisions. The MYPA was demonstrated as a robust approach that can be encoded into an easy-to-use tool to produce information layers from a time-series of yield data to support management.

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Hu, Yan. "Reliability Analysis of Multi-Objective Spatio-Temporal Segmentation of Human Motion in Video Sequences." International Journal of Distributed Systems and Technologies 12, no. 1 (January 2021): 16–29. http://dx.doi.org/10.4018/ijdst.2021010102.

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In view of the problem of uneven distribution of edge contour of multi-target human motion image in video sequence, which leads to the decline of target detection ability, an algorithm of multi-target spatial-temporal segmentation of human motion in video sequence based on edge contour feature detection and block fusion is proposed. Firstly, a multi-target spatial-temporal detection model of human motion in video sequence was constructed, extracting video image frame sequence, using discrete frame fusion method to segment and fuse moving target image, matching moving multi-target in video sequence, secondly segmenting motion features in moving target image, combining with SURF algorithm (speeded up robust features, accelerated robust features) to detect and extract human motion objects in video sequence. The experimental results show that the gray histogram of human motion multi-target space-time segmentation is close to the original image histogram, and the detection and recognition ability of human motion target is improved.

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Tosiani, Anna. "ANALISIS MULTI TEMPORAL CITRA SATELIT LANDSAT UNTUKPEMANTAUAN CADANGAN KARBON NASIONAL." Seminar Nasional Geomatika 2 (February 9, 2018): 65. http://dx.doi.org/10.24895/sng.2017.2-0.398.

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<p>Indonesia adalah salah satu negara tropis yang mempunyai luas kawasan hutan terbesar ketiga di dunia setelah Brazil dan Congo. Namun berdasarkan data dari FAO (2015), Indonesia merupakan negara dengan kehilangan hutan yang besar. Berdasarkan <em>Second National Communication</em> (2010) dan<em>First Biennial Update Report</em> (2016), perubahan penutupan hutan dan lahan merupakan sektor yang menyumbangkan emisi gas rumah kaca terbesar, termasuk di dalamnya adalah kebakaran di lahan gambut. Melalui <em>Nationally Determined Contribution </em>(NDC), Indonesia menyampaikan komitmen untuk menurunkan emisi gas rumah kaca sebesar 29% dengan usaha sendiri dan 41% dengan dukungan Internasional di tahun 2030. Pemetaan dan pemantauan cadangan karbon di sektor kehutanan adalah bagian dari proses untuk menentukan kebijakan dalam rangka memenuhi komitmen tersebut. Tujuan dari penelitian ini adalah untuk menghitung dan memetakan cadangan, emisi dan serapan karbon di atas permukaan (<em>Aboveground Carbon</em>) berdasarkan series data citra resolusi sedang. Penelitian ini menggunakan metode penghitungan perubahan cadangan karbon (<em>Stock Difference Approach</em>) dengan melakukan analisis spasial data penutupan lahan nasional hasil penafsiran citra Landsat tahun 2009-2014 dan data biomassa hasil Inventarisasi Hutan Nasional dengan menggunakan Sistem Informasi Geografis (SIG). Hasil dari penelitian ini adalah cadangan karbon, emisi dan serapan karbon untuk skala nasional dan sub nasional (7 pulau besar) yang disajikan dalam bentuk tabel, grafik dan peta cadangan karbon. Penelitian ini menunjukkan bahwa data citra satelit resolusi sedang (Landsat) dapat secara efektif memetakan sebaran cadangan karbon serta menghitung serapan dan emisi gas rumah kaca pada skala nasional.</p><p><strong>Kata kunci:</strong><strong> </strong>citra penginderaan jauh<strong>, </strong>cadangan karbon, faktor emisi, penutupan lahan, analisis spasial</p>

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Yu, Tianyu, Cuiwei Liu, Zhuo Yan, and Xiangbin Shi. "A Multi-Task Framework for Action Prediction." Information 11, no. 3 (March 16, 2020): 158. http://dx.doi.org/10.3390/info11030158.

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Predicting the categories of actions in partially observed videos is a challenging task in the computer vision field. The temporal progress of an ongoing action is of great importance for action prediction, since actions can present different characteristics at different temporal stages. To this end, we propose a novel multi-task deep forest framework, which treats temporal progress analysis as a relevant task to action prediction and takes advantage of observation ratio labels of incomplete videos during training. The proposed multi-task deep forest is a cascade structure of random forests and multi-task random forests. Unlike the traditional single-task random forests, multi-task random forests are built upon incomplete training videos annotated with action labels as well as temporal progress labels. Meanwhile, incorporating both random forests and multi-task random forests can increase the diversity of classifiers and improve the discriminative power of the multi-task deep forest. Experiments on the UT-Interaction and the BIT-Interaction datasets demonstrate the effectiveness of the proposed multi-task deep forest.

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Han, Yakun, Jingui Zou, Zhong Lu, Feifei Qu, Ya Kang, and Jiangwei Li. "Ground Deformation of Wuhan, China, Revealed by Multi-Temporal InSAR Analysis." Remote Sensing 12, no. 22 (November 18, 2020): 3788. http://dx.doi.org/10.3390/rs12223788.

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Wuhan, the largest city in central China, has experienced rapid urban development leading to land subsidence as well as environmental concerns in recent years. Although a few studies have analyzed the land subsidence of Wuhan based on ALOS-1, Envisat, and Sentinel-1 datasets, the research on long-term land subsidence is still lacking. In this study, we employed multi-temporal InSAR to investigate and reveal the spatiotemporal evolution of land subsidence over Wuhan with ALOS-1, Envisat, and Sentinel-1 images from 2007–2010, 2008–2010, 2015–2019, respectively. The results detected by InSAR were cross-validated by two independent SAR datasets, and leveling observations were applied to the calibration of InSAR-derived measurements. The correlation coefficient between the leveling and InSAR has reached 0.89. The study detected six main land subsidence zones during the monitoring period, with the maximum land subsidence velocity of −46 mm/a during the 2015–2019 analysis. Both the magnitude and the extent of the land subsidence have reduced since 2017. The causes of land subsidence are discussed in terms of urban construction, Yangtze river water level changes, and subsurface water level changes. Our results provide insight for understanding the causes of land subsidence in Wuhan and serve as reference for city management for reducing the land subsidence in Wuhan and mitigating the potential hazards.

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López-Amoedo, Alberto, Xana Álvarez, Henrique Lorenzo, and Juan Luis Rodríguez. "Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control." Remote Sensing 13, no. 15 (July 29, 2021): 2983. http://dx.doi.org/10.3390/rs13152983.

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Land fragmentation and small plots are the main features of the rural environment of Galicia (NW Spain). Smallholding limits land use management, representing a drawback in local forest planning. This study analyzes the potential use of multitemporal Sentinel-2 images to detect and control forest cuts in very small pine and eucalyptus plots located in southern Galicia. The proposed approach is based on the analysis of Sentinel-2 NDVI time series in 4231 plots smaller than 3 ha (average 0.46 ha). The methodology allowed us to detect cuts, allocate cut dates and quantify plot areas due to different cutting cycles in an uneven-aged stand. An accuracy of approximately 95% was achieved when the whole plot was cut, with an 81% accuracy for partial cuts. The main difficulty in detecting and dating cuts was related to cloud cover, which affected the multitemporal analysis. In conclusion, the proposed methodology provides an accurate estimation of cutting date and area, helping to improve the monitoring system in sustainable forest certifications to ensure compliance with forest management plans.

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Luo, Qingli, Daniele Perissin, Yuanzhi Zhang, and Youliang Jia. "L- and X-Band Multi-Temporal InSAR Analysis of Tianjin Subsidence." Remote Sensing 6, no. 9 (August 26, 2014): 7933–51. http://dx.doi.org/10.3390/rs6097933.

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