Статті в журналах: "Multi-temporal Data Analysi"

1

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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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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3

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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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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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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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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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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L.V., Sichugova. "Statistical Analysis Of Lineaments Using Landsat 8 Data: A Case Study Of The Fergana Valley (East Uzbekistan)." American Journal of Applied Sciences 03, no. 03 (March 31, 2021): 83–92. http://dx.doi.org/10.37547/tajas/volume03issue03-14.

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This paper describes a statistical analysis of lineaments based on multi-temporal Landsat 8 imageries in the Fergana Valley (East Uzbekistan). The results of the statistical analysis showed that determined that the count of lineament structures changes by months. It was also noted that the Namangan region is more prone to the manifestation of lineament structures. The maximum count of lineament structures was in July. And in November, we observe a sharp decrease in lineament structures. According to the results of the rose diagrams, various orientations are observed for these months. There is a coincidence of directions.

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Rodenacker, Karsten, Klaus Hahn, Gerhard Winkler, and Dorothea P. Auer. "SPATIO-TEMPORAL DATA ANALYSIS WITH NON-LINEAR FILTERS: BRAIN MAPPING WITH fMRI DATA." Image Analysis & Stereology 19, no. 3 (May 3, 2011): 189. http://dx.doi.org/10.5566/ias.v19.p189-194.

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Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D) is gathered during relatively long time ranges (3-5 min). From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters). Filters applied are compared by classifications of activations.

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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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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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12

Wu, C., Q. Zhu, Y. T. Zhang, Z. Q. Du, Y. Zhou, X. Xie, and F. He. "AN ADAPTIVE ORGANIZATION METHOD OF GEOVIDEO DATA FOR SPATIO-TEMPORAL ASSOCIATION ANALYSIS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (July 10, 2015): 29–34. http://dx.doi.org/10.5194/isprsannals-ii-4-w2-29-2015.

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Public security incidents have been increasingly challenging to address with their new features, including large-scale mobility, multi-stage dynamic evolution, spatio-temporal concurrency and uncertainty in the complex urban environment, which require spatio-temporal association analysis among multiple regional video data for global cognition. However, the existing video data organizational methods that view video as a property of the spatial object or position in space dissever the spatio-temporal relationship of scattered video shots captured from multiple video channels, limit the query functions on interactive retrieval between a camera and its video clips and hinder the comprehensive management of event-related scattered video shots. GeoVideo, which maps video frames onto a geographic space, is a new approach to represent the geographic world, promote security monitoring in a spatial perspective and provide a highly feasible solution to this problem. This paper analyzes the large-scale personnel mobility in public safety events and proposes a multi-level, event-related organization method with massive GeoVideo data by spatio-temporal trajectory. This paper designs a unified object identify(ID) structure to implicitly store the spatio-temporal relationship of scattered video clips and support the distributed storage management of massive cases. Finally, the validity and feasibility of this method are demonstrated through suspect tracking experiments.

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Chung, M., M. Jung, and Y. Kim. "WILDFIRE DAMAGE ASSESSMENT USING MULTI-TEMPORAL SENTINEL-2 DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (August 20, 2019): 97–102. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-97-2019.

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Abstract. Recently, the drastic climate changes have increased the importance of wildfire monitoring and damage assessment as well as the possibility of wildfire occurrence. Estimation of wildfire damage provides the information on wildfire-induced ecological changes and supports the decision-making process for post-fire treatment activities. For accurate wildfire damage assessment, the discrimination between disaster-induced and natural changes is crucial because they usually coupled together. In this study, Sentinel-2 images were employed to assess the damage from a wildfire, which occurred in the coniferous forest of Gangneung, Gangwon Province, South Korea on April 2019. The images were captured from both Sentinel-2A and -2B, shortening the temporal interval of available pre- and post-fire images. Multi-temporal image analysis was performed in both object and pixel-based with two commonly used spectral indices, NDVI and NBR. Additional image pair from the same period of 2018 was used to distinguish the fire-affected regions from the naturally changed area and compared with the results from using only one pair of images from 2019. The experimental results showed that the change detection performance could be affected by the number of image pairs and spectral indices used to discriminate burned region from unburned region. Thus it verified the significance of adequately employing annual multi-pair satellite images for wildfire damage assessment.

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14

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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Maktav, D., and F. S. Erbek. "Analysis of urban growth using multi‐temporal satellite data in Istanbul, Turkey." International Journal of Remote Sensing 26, no. 4 (February 2005): 797–810. http://dx.doi.org/10.1080/01431160512331316784.

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SILJESTROM RIBED, P., and A. MORENO LOPEZ. "Monitoring burnt areas by principal components analysis of multi-temporal TM data." International Journal of Remote Sensing 16, no. 9 (June 1995): 1577–87. http://dx.doi.org/10.1080/01431169508954497.

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17

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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Venkatesan, M., S. Pazhanivelan, and N. S. Sudarmanian. "MULTI-TEMPORAL FEATURE EXTRACTION FOR PRECISE MAIZE AREA MAPPING USING TIME-SERIES SENTINEL 1A SAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 169–73. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-169-2019.

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Abstract. A research study was conducted to map maize area in Ariyalur and Perambalur districts of Tamil Nadu, India using multi-temporal features extracted from time-series Sentinel 1A SAR data. Multi-temporal Sentinel 1A GRD data at VV and VH polarizations and SLC products were acquired for the study area at 12 days interval and processed using MAPscape-RICE software. Multi-temporal Sentinel 1A data was used to identify the backscattering dB curve of maize crop. Analysis of temporal signatures of the crop showed minimum values at sowing period and maximum during the tasseling stage, which decreased during maturity stage of the crop. The maximum increase in the signature was observed during seedling to vegetative growth period. The signature derived from dB values for maize crop expressed a significant temporal behavior with the range of &minus;21.26 to &minus;13.18 in VH polarization and &minus;14.05 to &minus;6.54 in VV polarization. Considering the accuracy of SAR data to phenological variations of maize growing period, Multi-Temporal Features were extracted from multi-temporal dB images of VV and VH polarization and coherence images. Multi-Temporal Features viz., max, min, mean, max date, min date and span ratio were extracted from VV and VH polarizations of Sentinel 1A GRD and SLC data to classify maize pixels in the study area using parameterized classification approach. The overall classification accuracy was 91 percent with the kappa score of 0.82.

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Zhu, Jixiang, Miao Gao, Anmin Zhang, Yingjun Hu, and Xi Zeng. "Multi-Ship Encounter Situation Identification and Analysis Based on AIS Data and Graph Complex Network Theory." Journal of Marine Science and Engineering 10, no. 10 (October 19, 2022): 1536. http://dx.doi.org/10.3390/jmse10101536.

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In order to detect multi-ship encounter situations and improve the safety of navigation, this paper proposed a model which was able to mine multi-ship encounter situations from Automatic identification system (AIS) data and analyze the encounter spatial-temporal process and make collision avoidance decisions. Pairwise encounters identification results and ship motion index were combined into a ship encounter graph network which can use the complex network theory to describe the encounter spatial-temporal process. Network average degree, network average distance and network average clustering coefficient were selected. Based on the recognition results of pairwise encounter identification results, a discrete multi-ship encounter network is constructed. The process of multi-ship encounters from simple to complex to simple is mined based on the process of average network degree from 0 to 0 to obtain a continuous spatial-temporal process. The results can be used for multi-ship encounter situation awareness, multi-ship collision avoidance decision-making and channel navigation evaluation, and also provide data for machine learning. Quaternary dynamic ship domain, fuzzy logic and the weighted PageRank algorithm were used to rank the whole network risk, which is critical to “key ship collision avoidance.” This method overcame the problem that the traditional collision risk evaluation method is only applicable to the difference between two ships and ship perception. The risk rank combined with the artificial potential field method was used. Compared with the traditional artificial potential field method, this method has fewer turns and a smoother trajectory.

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Giannopoulos, Michalis, Grigorios Tsagkatakis, and Panagiotis Tsakalides. "4D U-Nets for Multi-Temporal Remote Sensing Data Classification." Remote Sensing 14, no. 3 (January 28, 2022): 634. http://dx.doi.org/10.3390/rs14030634.

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Multispectral sensors constitute a core earth observation imaging technology generating massive high-dimensional observations acquired across multiple time instances. The collected multi-temporal remote sensed data contain rich information for Earth monitoring applications, from flood detection to crop classification. To easily classify such naturally multidimensional data, conventional low-order deep learning models unavoidably toss away valuable information residing across the available dimensions. In this work, we extend state-of-the-art convolutional network models based on the U-Net architecture to their high-dimensional analogs, which can naturally capture multi-dimensional dependencies and correlations. We introduce several model architectures, both of low as well as of high order, and we quantify the achieved classification performance vis-à-vis the latest state-of-the-art methods. The experimental analysis on observations from Landsat-8 reveals that approaches based on low-order U-Net models exhibit poor classification performance and are outperformed by our proposed high-dimensional U-Net scheme.

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Ge, Xingtong, Yi Yang, Ling Peng, Luanjie Chen, Weichao Li, Wenyue Zhang, and Jiahui Chen. "Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi Source Heterogeneous Data." Remote Sensing 14, no. 14 (July 21, 2022): 3496. http://dx.doi.org/10.3390/rs14143496.

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Forest fires have frequently occurred and caused great harm to people’s lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features. However, it is difficult to efficiently obtain the features from large-scale, multi-source, heterogeneous data. There is a lack of a method that can effectively extract features required by machine learning-based forest fire predictions from multi-source spatio-temporal data. This paper proposes a forest fire prediction method that integrates spatio-temporal knowledge graphs and machine learning models. This method can fuse multi-source heterogeneous spatio-temporal forest fire data by constructing a forest fire semantic ontology and a knowledge graph-based spatio-temporal framework. This paper defines the domain expertise of forest fire analysis as the semantic rules of the knowledge graph. This paper proposes a rule-based reasoning method to obtain the corresponding data for the specific machine learning-based forest fire prediction methods, which are dedicated to tackling the problem with real-time prediction scenarios. This paper performs experiments regarding forest fire predictions based on real-world data in the experimental areas Xichang and Yanyuan in Sichuan province. The results show that the proposed method is beneficial for the fusion of multi-source spatio-temporal data and highly improves the prediction performance in real forest fire prediction scenarios.

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22

Plaster, Benjamin, and Gautam Kumar. "Data-Driven Predictive Modeling of Neuronal Dynamics Using Long Short-Term Memory." Algorithms 12, no. 10 (September 24, 2019): 203. http://dx.doi.org/10.3390/a12100203.

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Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions have been of interest to engineers, mathematicians and physicists over the last several decades. With the motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

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23

Wakulińska, Martyna, and Adriana Marcinkowska-Ochtyra. "Multi-Temporal Sentinel-2 Data in Classification of Mountain Vegetation." Remote Sensing 12, no. 17 (August 20, 2020): 2696. http://dx.doi.org/10.3390/rs12172696.

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The electromagnetic spectrum registered via satellite remote sensing methods became a popular data source that can enrich traditional methods of vegetation monitoring. The European Space Agency Sentinel-2 mission, thanks to its spatial (10–20 m) and spectral resolution (12 spectral bands registered in visible-, near-, and mid-infrared spectrum) and primarily its short revisit time (5 days), helps to provide reliable and accurate material for the identification of mountain vegetation. Using the support vector machines (SVM) algorithm and reference data (botanical map of non-forest vegetation, field survey data, and high spatial resolution images) it was possible to classify eight vegetation types of Giant Mountains: bogs and fens, deciduous shrub vegetation, forests, grasslands, heathlands, subalpine tall forbs, subalpine dwarf pine scrubs, and rock and scree vegetation. Additional variables such as principal component analysis (PCA) bands and selected vegetation indices were included in the best classified dataset. The results of the iterative classification, repeated 100 times, were assessed as approximately 80% median overall accuracy (OA) based on multi-temporal datasets composed of images acquired through the vegetation growing season (from late spring to early autumn 2018), better than using a single-date scene (70%–72% OA). Additional variables did not significantly improve the results, showing the importance of spectral and temporal information themselves. Our study confirms the possibility of fully available data for the identification of mountain vegetation for management purposes and protection within national parks.

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Štych, Přemysl, Lucie Malíková, Jan Kříž, and Lukáš Holman. "Multi-temporal analysis of vegetation reflectance using MERIS data in the Czech Republic." Miscellanea Geographica 18, no. 2 (June 1, 2014): 30–34. http://dx.doi.org/10.2478/mgrsd-2014-0015.

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Abstract Accurate high temporal resolution data is a very important source of information for understanding processes in the landscape. High temporal and spectral resolution data enable the monitoring of dynamic landscape processes. For this reason, since 2008 a receiving station for Metosat, NOAA and Envisat data has been installed at the Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University in Prague. The aim of this study is to analyse the spectral characteristics of vegetation using MERIS data in the Czech Republic. Spectral characteristics of vegetation were examined both by analysing changes in reflectivity as well as by utilising vegetation indices. Vegetation in forests and agricultural land was evaluated. The results present the spectral characteristics of selected associations of vegetation based on MERIS data and a discussion of the methods of multitemporal classification of land cover.

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ANDRES, LUDOVIC, WILLIAM A. SALAS, and DAVID SKOLE. "Fourier analysis of multi-temporal AVHRR data applied to a land cover classification." International Journal of Remote Sensing 15, no. 5 (March 1994): 1115–21. http://dx.doi.org/10.1080/01431169408954145.

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Schlaffer, Stefan, Patrick Matgen, Markus Hollaus, and Wolfgang Wagner. "Flood detection from multi-temporal SAR data using harmonic analysis and change detection." International Journal of Applied Earth Observation and Geoinformation 38 (June 2015): 15–24. http://dx.doi.org/10.1016/j.jag.2014.12.001.

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Dou, S. Q., H. H. Zhang, Y. Q. Zhao, A. M. Wang, Y. T. Xiong, and J. M. Zuo. "RESEARCH ON CONSTRUCTION OF SPATIO-TEMPORAL DATA VISUALIZATION PLATFORM FOR GIS AND BIM FUSION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 555–63. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-555-2020.

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Abstract. The visualization model of GIS and BIM fusion can provide data bearing platform and main technical support for future urban operation centers, digital twin cities, and smart cities. Based on the analysis of the features and advantages of GIS and BIM Fusion, this paper proposes a construction method of the spatio-temporal data visualization platform for GIS and BIM Fusion. It expounds and analyzes the overall architecture design of platform, multi-dimensional and multi-spatial scales visualization, space analysis for GIS and BIM fusion, and platform applications and so on. The urban virtual simulation spatio-temporal data platform project of Teda New District in Tianjin has verified and demonstrated that the effect of application is good. This provides a feasible solution for the construction of spatio-temporal Data Visualization Platform.

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Albanwan, Hessah, Rongjun Qin, Xiaohu Lu, Mao Li, Desheng Liu, and Jean-Michel Guldmann. "3D Iterative Spatiotemporal Filtering for Classification of Multitemporal Satellite Data Sets." Photogrammetric Engineering & Remote Sensing 86, no. 1 (January 1, 2020): 23–31. http://dx.doi.org/10.14358/pers.86.1.23.

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The current practice in land cover/land use change analysis relies heavily on the individually classified maps of the multi-temporal data set. Due to varying acquisition conditions (e.g., illumination, sensors, seasonal differences), the classification maps yielded are often inconsistent through time for robust statistical analysis. 3D geometric features have been shown to be stable for assessing differences across the temporal data set. Therefore, in this article we investigate the use of a multi-temporal orthophoto and digital surface model derived from satellite data for spatiotemporal classification. Our approach consists of two major steps: generating per-class probability distribution maps using the random-forest classifier with limited training samples, and making spatiotemporal inferences using an iterative 3D spatiotemporal filter operating on per-class probability maps. Our experimental results demonstrate that the proposed methods can consistently improve the individual classification results by 2%–6% and thus can be an important postclassification refinement approach.

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Faveri, Joanne De, Arūnas P. Verbyla, Wayne S. Pitchford, Shoba Venkatanagappa, and Brian R. Cullis. "Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials." Crop and Pasture Science 66, no. 9 (2015): 947. http://dx.doi.org/10.1071/cp14312.

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Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.

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Upadhyay, Priti, Mikolaj Czerkawski, Christopher Davison, Javier Cardona, Malcolm Macdonald, Ivan Andonovic, Craig Michie, et al. "A Flexible Multi-Temporal and Multi-Modal Framework for Sentinel-1 and Sentinel-2 Analysis Ready Data." Remote Sensing 14, no. 5 (February 24, 2022): 1120. http://dx.doi.org/10.3390/rs14051120.

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The rich, complementary data provided by Sentinel-1 and Sentinel-2 satellite constellations host considerable potential to transform Earth observation (EO) applications. However, a substantial amount of effort and infrastructure is still required for the generation of analysis-ready data (ARD) from the low-level products provided by the European Space Agency (ESA). Here, a flexible Python framework able to generate a range of consistent ARD aligned with the ESA-recommended processing pipeline is detailed. Sentinel-1 Synthetic Aperture Radar (SAR) data are radiometrically calibrated, speckle-filtered and terrain-corrected, and Sentinel-2 multi-spectral data resampled in order to harmonise the spatial resolution between the two streams and to allow stacking with multiple scene classification masks. The global coverage and flexibility of the framework allows users to define a specific region of interest (ROI) and time window to create geo-referenced Sentinel-1 and Sentinel-2 images, or a combination of both with closest temporal alignment. The framework can be applied to any location and is user-centric and versatile in generating multi-modal and multi-temporal ARD. Finally, the framework handles automatically the inherent challenges in processing Sentinel data, such as boundary regions with missing values within Sentinel-1 and the filtering of Sentinel-2 scenes based on ROI cloud coverage.

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31

Sapena, M., and L. A. Ruiz. "Analysis of urban development by means of multi-temporal fragmentation metrics from LULC data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1411–18. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1411-2015.

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The monitoring and modelling of the evolution of urban areas is increasingly attracting the attention of land managers and administration. New data, tools and methods are being developed and made available for a better understanding of these dynamic areas. We study and analyse the concept of landscape fragmentation by means of GIS and remote sensing techniques, particularly focused on urban areas. Using LULC data obtained from the European Urban Atlas dataset developed by the local component of Copernicus Land Monitoring Services (scale 1:10,000), the urban fragmentation of the province of Rome is studied at 2006 and 2012. A selection of indices that are able to measure the land cover fragmentation level in the landscape are obtained employing a tool called IndiFrag, using as input data LULC data in vector format. In order to monitor the urban morphological changes and growth patterns, a new module with additional multi-temporal metrics has been developed for this purpose. These urban fragmentation and multi-temporal indices have been applied to the municipalities and districts of Rome, analysed and interpreted to characterise quantity, spatial distribution and structure of the urban change. This methodology is applicable to different regions, affording a dynamic quantification of urban spatial patterns and urban sprawl. The results show that urban form monitoring with multi-temporal data using these techniques highlights urbanization trends, having a great potential to quantify and model geographic development of metropolitan areas and to analyse its relationship with socioeconomic factors through the time.

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Guo, Jiao, Henghui Li, Jifeng Ning, Wenting Han, Weitao Zhang, and Zheng-Shu Zhou. "Feature Dimension Reduction Using Stacked Sparse Auto-Encoders for Crop Classification with Multi-Temporal, Quad-Pol SAR Data." Remote Sensing 12, no. 2 (January 18, 2020): 321. http://dx.doi.org/10.3390/rs12020321.

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Crop classification in agriculture is one of important applications for polarimetric synthetic aperture radar (PolSAR) data. For agricultural crop discrimination, compared with single-temporal data, multi-temporal data can dramatically increase crop classification accuracies since the same crop shows different external phenomena as it grows up. In practice, the utilization of multi-temporal data encounters a serious problem known as a “dimension disaster”. Aiming to solve this problem and raise the classification accuracy, this study developed a feature dimension reduction method using stacked sparse auto-encoders (S-SAEs) for crop classification. First, various incoherent scattering decomposition algorithms were employed to extract a variety of detailed and quantitative parameters from multi-temporal PolSAR data. Second, based on analyzing the configuration and main parameters for constructing an S-SAE, a three-hidden-layer S-SAE network was built to reduce the dimensionality and extract effective features to manage the “dimension disaster” caused by excessive scattering parameters, especially for multi-temporal, quad-pol SAR images. Third, a convolutional neural network (CNN) was constructed and employed to further enhance the crop classification performance. Finally, the performances of the proposed strategy were assessed with the simulated multi-temporal Sentinel-1 data for two experimental sites established by the European Space Agency (ESA). The experimental results showed that the overall accuracy with the proposed method was raised by at least 17% compared with the long short-term memory (LSTM) method in the case of a 1% training ratio. Meanwhile, for a CNN classifier, the overall accuracy was almost 4% higher than those of the principle component analysis (PCA) and locally linear embedded (LLE) methods. The comparison studies clearly demonstrated the advantage of the proposed multi-temporal crop classification methodology in terms of classification accuracy, even with small training ratios.

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Zhu, 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.

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It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

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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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Abdikan, S., A. Sekertekin, M. Ustunern, F. Balik Sanli, and R. Nasirzadehdizaji. "BACKSCATTER ANALYSIS USING MULTI-TEMPORAL SENTINEL-1 SAR DATA FOR CROP GROWTH OF MAIZE IN KONYA BASIN, TURKEY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 9–13. http://dx.doi.org/10.5194/isprs-archives-xlii-3-9-2018.

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Temporal monitoring of crop types is essential for the sustainable management of agricultural activities on both national and global levels. As a practical and efficient tool, remote sensing is widely used in such applications. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) imagery was utilized to investigate the performance of the sensor backscatter image on crop monitoring. Multi-temporal C-band VV and VH polarized SAR images were acquired simultaneously by in-situ measurements which was conducted at Konya basin, central Anatolia Turkey. During the measurements, plant height of maize plant was collected and relationship between backscatter values and plant height was analysed. The maize growth development was described under Biologische Bundesanstalt, bundessortenamt und CHemische industrie (BBCH). Under BBCH stages, the test site was classified as leaf development, stem elongation, heading and flowering in general. The correlation coefficient values indicated high correlation for both polarimetry during the early stages of the plant, while late stages indicated lower values in both polarimetry. As a last step, multi-temporal coverage of crop fields was analysed to map seasonal land use. To this aim, object based image classification was applied following image segmentation. About 80&thinsp;% accuracies of land use maps were created in this experiment. As preliminary results, it is concluded that Sentinel-1 data provides beneficial information about plant growth. Dual-polarized Sentinel-1 data has high potential for multi-temporal analyses for agriculture monitoring and reliable mapping.

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Frantz, David. "FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond." Remote Sensing 11, no. 9 (May 10, 2019): 1124. http://dx.doi.org/10.3390/rs11091124.

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Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper describes the scientific software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), an ‘all-in-one’ solution for the mass-processing and analysis of Landsat and Sentinel-2 image archives. FORCE is increasingly used to support a wide range of scientific to operational applications that are in need of both large area, as well as deep and dense temporal information. FORCE is capable of generating Level 2 ARD, and higher-level products. Level 2 processing is comprised of state-of-the-art cloud masking and radiometric correction (including corrections that go beyond ARD specification, e.g., topographic or bidirectional reflectance distribution function correction). It further includes data cubing, i.e., spatial reorganization of the data into a non-overlapping grid system for enhanced efficiency and simplicity of ARD usage. However, the usage barrier of Level 2 ARD is still high due to the considerable data volume and spatial incompleteness of valid observations (e.g., clouds). Thus, the higher-level modules temporally condense multi-temporal ARD into manageable amounts of spatially seamless data. For data mining purposes, per-pixel statistics of clear sky data availability can be generated. FORCE provides functionality for compiling best-available-pixel composites and spectral temporal metrics, which both utilize all available observations within a defined temporal window using selection and statistical aggregation techniques, respectively. These products are immediately fit for common Earth observation analysis workflows, such as machine learning-based image classification, and are thus referred to as highly analysis ready data (hARD). FORCE provides data fusion functionality to improve the spatial resolution of (i) coarse continuous fields like land surface phenology and (ii) Landsat ARD using Sentinel-2 ARD as prediction targets. Quality controlled time series preparation and analysis functionality with a number of aggregation and interpolation techniques, land surface phenology retrieval, and change and trend analyses are provided. Outputs of this module can be directly ingested into a geographic information system (GIS) to fuel research questions without any further processing, i.e., hARD+. FORCE is open source software under the terms of the GNU General Public License v. >= 3, and can be downloaded from http://force.feut.de.

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Lieskovský, Juraj, and Dana Lieskovská. "Cropland Abandonment in Slovakia: Analysis and Comparison of Different Data Sources." Land 10, no. 4 (March 25, 2021): 334. http://dx.doi.org/10.3390/land10040334.

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This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower.

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Tomppo, Erkki, Oleg Antropov, and Jaan Praks. "Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data." Remote Sensing 11, no. 4 (February 13, 2019): 384. http://dx.doi.org/10.3390/rs11040384.

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Natural disturbances significantly influence forest ecosystem services and biodiversity. Accurate delineation and early detection of areas affected by disturbances are critical for estimating extent of damage, assessing economical influence and guiding forest management activities. In this study we focus on snow load damage detection from C-Band SAR images. Snow damage is one of the least studied forest damages, which is getting more common due to current climate trends. The study site was located in the southern part of Northern Finland and the SAR data were represented by the time series of C-band SAR scenes acquired by the Sentinel-1 sensor. Methods used in the study included improved k nearest neighbour method, logistic regression analysis and support vector machine classification. Snow damage recordings from a large snow damage event that took place in Finland during late 2018 were used as reference data. Our results showed an overall detection accuracy of 90%, indicating potential of C-band SAR for operational use in snow damage mapping. Additionally, potential of multitemporal Sentinel-1 data in estimating growing stock volume in damaged forest areas were carried out, with obtained results indicating strong potential for estimating the overall volume of timber within the affected areas. The results and research questions for further studies are discussed.

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Yen, Barbara T. H., and Yu-Chiun Chiou. "Dynamic fuzzy data envelopment analysis models: case of bus transport performance assessment." RAIRO - Operations Research 53, no. 3 (July 2019): 991–1005. http://dx.doi.org/10.1051/ro/2017064.

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In the transport field, two characteristics–inter-temporal dependency and fuzziness–need to be considered when assessing transport performance. First, input and output levels are inter-temporal dependent due to heavy capital investment and because quasi-fixed input can influence output levels over multiple periods. Second, conventional Data Envelopment Analysis (DEA) models are, in nature, formulated with quantitative variables. However, qualitative measurements that are characterized with “vagueness” or “fuzziness” are as important as quantitative variables for multi-period transport performance assessment. To rectify these problems, the present study extends previous research by proposing a Dynamic Fuzzy Data Envelopment Analysis (DFDEA) method for assessing the comparative efficiency where inter-temporal dependence exists in operating production processes with some “fuzzy” variables. An case study was conducted to evaluate the performance of city bus transport companies in Taipei, Taiwan. Results showed the superiority of the proposed DFDEA model by comparing the results with static models.

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Nolz, R., and W. Loiskandl. "Evaluating soil water content data monitored at different locations in a vineyard with regard to irrigation control." Soil and Water Research 12, No. 3 (June 28, 2017): 152–60. http://dx.doi.org/10.17221/9/2016-swr.

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Knowledge on the water content of a certain soil profile and its temporal changes due to rainfall and plant water uptake is a key issue for irrigation management. In this regard, sensors can be utilized to monitor soil water content (SWC). Due to the characteristic spatial variability of SWC, a key question is whether the measurements are representative and reliable. This study focused on the assessment of SWC and its variability in a vineyard with subsurface drip irrigation. SWC was measured in profiles down to a 50 cm depth by means of multi-sensor capacitance probes. The probes were installed at six locations along vine rows. A temporal stability analysis was performed to evaluate the representativeness and reliability of each monitoring profile with regard to irrigation control. Mean SWC was within a plausible range compared to unsaturated hydraulic parameters determined in a laboratory. The measurements revealed a considerable variability, but standard deviations were comparable to values from literature. The main finding was that some monitoring profiles (probes) proved to be more suitable to monitor SWC with respect to irrigation control than the others. Considering temporal stability provided helpful insights into the spatio-temporal variability of SWC measurements. However, not all questions that are related to the concept of temporal stability could be answered based on the given dataset.

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Cao, Rui, Wei Tu, Jinzhou Cao, and Qingquan Li. "COMPARISON OF URBAN HUMAN MOVEMENTS INFERRING FROM MULTI-SOURCE SPATIAL-TEMPORAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 471–76. http://dx.doi.org/10.5194/isprs-archives-xli-b2-471-2016.

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The quantification of human movements is very hard because of the sparsity of traditional data and the labour intensive of the data collecting process. Recently, much spatial-temporal data give us an opportunity to observe human movement. This research investigates the relationship of city-wide human movements inferring from two types of spatial-temporal data at traffic analysis zone (TAZ) level. The first type of human movement is inferred from long-time smart card transaction data recording the boarding actions. The second type of human movement is extracted from citywide time sequenced mobile phone data with 30 minutes interval. Travel volume, travel distance and travel time are used to measure aggregated human movements in the city. To further examine the relationship between the two types of inferred movements, the linear correlation analysis is conducted on the hourly travel volume. The obtained results show that human movements inferred from smart card data and mobile phone data have a correlation of 0.635. However, there are still some non-ignorable differences in some special areas. This research not only reveals the citywide spatial-temporal human dynamic but also benefits the understanding of the reliability of the inference of human movements with big spatial-temporal data.

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Cao, Rui, Wei Tu, Jinzhou Cao, and Qingquan Li. "COMPARISON OF URBAN HUMAN MOVEMENTS INFERRING FROM MULTI-SOURCE SPATIAL-TEMPORAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 471–76. http://dx.doi.org/10.5194/isprsarchives-xli-b2-471-2016.

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Анотація:

The quantification of human movements is very hard because of the sparsity of traditional data and the labour intensive of the data collecting process. Recently, much spatial-temporal data give us an opportunity to observe human movement. This research investigates the relationship of city-wide human movements inferring from two types of spatial-temporal data at traffic analysis zone (TAZ) level. The first type of human movement is inferred from long-time smart card transaction data recording the boarding actions. The second type of human movement is extracted from citywide time sequenced mobile phone data with 30 minutes interval. Travel volume, travel distance and travel time are used to measure aggregated human movements in the city. To further examine the relationship between the two types of inferred movements, the linear correlation analysis is conducted on the hourly travel volume. The obtained results show that human movements inferred from smart card data and mobile phone data have a correlation of 0.635. However, there are still some non-ignorable differences in some special areas. This research not only reveals the citywide spatial-temporal human dynamic but also benefits the understanding of the reliability of the inference of human movements with big spatial-temporal data.

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Bayik, C., S. Abdikan, G. Ozbulak, T. Alasag, S. Aydemir, and F. Balik Sanli. "EXPLOITING MULTI-TEMPORAL SENTINEL-1 SAR DATA FOR FLOOD EXTEND MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W4 (March 6, 2018): 109–13. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w4-109-2018.

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Abstract. Recently, global climate change is one of the biggest challenges in the world. Dense downfall and following catastrophic floods are one of the most destructive natural hazards among all. Consequences do not only risk human life but also cause economical damage. It is critical rapid mapping of flooding for decision making and emergency services in river management. In this study, we apply a multi-temporal change detection analysis to investigate the flooded areas occurred in Edirne province of Turkey. The study area is located at the lower course of Meric River (Evros in Greece or Maritsa in Bulgarian) which is the border between Turkey and Greece. The river basin is dominated by cropland which suffers from strong catastrophic precipitation. This situation cause overflow of capacity of the dams located along the river and serious flooding occur. Due to its dynamic structure the region exposed to heavy flooding in the past. One of the biggest inundations was occurred at 2nd February 2015 which resulted severe devastation in both urban and rural areas. For the analyses of the temporal and spatial dynamics of the disaster we use Sentinel-1 Synthetic Aperture Radar (SAR) data due to its systematic frequent acquisition. A dataset of pre-event and post-event Sentinel-1 images within the January and February of 2015 period was acquired. Flooded areas were extracted with threshold, random forest and deep learning approaches.

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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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Quintanilha, José Alberto, Linda Lee Ho, Cláudia Aparecida Soares Machado, and Denizar Blitzkow. "Use of control charts for multi-temporal analysis of geodetic auscultation data from dams." Boletim de Ciências Geodésicas 19, no. 4 (December 2013): 653–66. http://dx.doi.org/10.1590/s1982-21702013000400009.

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Geodesic auscultation can be used to monitor the movement of dam structures by measuring the distance, at different epochs from fixed positions (pillars) to other positions (targets). It is important to identify the targets that present atypical measurements to permit managers to take corrective actions. After fitting a model using the Least Squares Method (LSM), the residuals normally display random behavior. Multivariate control charts are then applied to the residuals of the fitted model from data taken of geodesic survey campaigns conducted at different epochs. Control charts have been widely applied in other fields of research than production processes such as public health, marketing, services. The results show that it is possible for monitoring the multi-temporal stability by the multivariate control charts. The method provides complementary information than the classical univariate statistical analysis.

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46

Sang, Yan-Fang, Dong Wang, Ji-Chun Wu, Qing-Ping Zhu, and Ling Wang. "Wavelet-Based Analysis on the Complexity of Hydrologic Series Data under Multi-Temporal Scales." Entropy 13, no. 1 (January 17, 2011): 195–210. http://dx.doi.org/10.3390/e13010195.

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47

Tanaka, S., T. Sugimura, and K. Kameda. "Spatial resolution and frequency of satellite data acquisition for multi-temporal analysis of environment." Advances in Space Research 12, no. 7 (July 1992): 333–42. http://dx.doi.org/10.1016/0273-1177(92)90237-r.

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Agapiou, Athos, and Vasiliki Lysandrou. "Observing Thermal Conditions of Historic Buildings through Earth Observation Data and Big Data Engine." Sensors 21, no. 13 (July 2, 2021): 4557. http://dx.doi.org/10.3390/s21134557.

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This study combines satellite observation, cloud platforms, and geographical information systems (GIS) to investigate at a macro-scale level of observation the thermal conditions of two historic clusters in Cyprus, namely in Limassol and Strovolos municipalities. The two case studies share different environmental and climatic conditions. The former site is coastal, the last a hinterland, and they both contain historic buildings with similar building materials and techniques. For the needs of the study, more than 140 Landsat 7 ETM+ and 8 LDCM images were processed at the Google Earth Engine big data cloud platform to investigate the thermal conditions of the two historic clusters over the period 2013–2020. The multi-temporal thermal analysis included the calibration of all images to provide land surface temperature (LST) products at a 100 m spatial resolution. Moreover, to investigate anomalies related to possible land cover changes of the area, two indices were extracted from the satellite images, the normalised difference vegetation index (NDVI) and the normalised difference build index (NDBI). Anticipated results include the macro-scale identification of multi-temporal changes, diachronic changes, the establishment of change patterns based on seasonality and location, occurring in large clusters of historic buildings.

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Azmoon, Behnam, Aynaz Biniyaz, and Zhen Liu. "Use of High-Resolution Multi-Temporal DEM Data for Landslide Detection." Geosciences 12, no. 10 (October 11, 2022): 378. http://dx.doi.org/10.3390/geosciences12100378.

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Landslides in urban areas have been relatively well-documented in landslide inventories despite issues in accuracy and completeness, e.g., the absence of small landslides. By contrast, less attention has been paid to landslides in sparsely populated areas in terms of their occurrences and locations. This study utilizes high-resolution and LiDAR-derived digital elevation models (DEMs) at two different times for landslide detection to (1) improve the localization and detection accuracies in landslide inventories, (2) minimize human intervention in the landslide detection process, and (3) identify landslides that cannot be easily documented in the current state of the practice. To achieve this goal, multiple preprocessing steps were used to ensure the spatial alignment of the multi-temporal DEMs. Map algebra was then used to calculate the vertical displacement for each cell and create a DEM of Difference (DoD) to obtain a quantitative estimation of ground deformations. Next, the elevation changes were filtered via an appropriate Level of Detection (LoD) threshold to mark potential landslide candidates. The landslide candidates were further assessed with the aid of customized topographic maps as auxiliary data and pattern recognition to distinguish landslides (true positive changes) from construction, erosion, and deposition (false positives). The results from the proposed method were compared with existing landslide inventories and reports to evaluate its performance. The new method was also validated with temporal high-resolution Google Earth images. The results showed the successful application of the method in landslide detection and mapping. Compared with traditional methods, the proposed method provides a semi-automatic way to obtain landslide inventories with publicly available yet lowly utilized DEM data, which can be valuable in preliminary analysis for landslide detection.

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Campalani, P., A. Beccati, S. Mantovani, and P. Baumann. "TEMPORAL ANALYSIS OF ATMOSPHERIC DATA USING OPEN STANDARDS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4 (April 23, 2014): 21–27. http://dx.doi.org/10.5194/isprsannals-ii-4-21-2014.

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The continuous growth of remotely sensed data raises the need for efficient ways of accessing data archives. The classical model of accessing remote sensing (satellite) archives via distribution of large files is increasingly making way for a more dynamic and interactive data service. A challenge, though, is interoperability of such services, in particular when multi-dimensional data and advanced processing are involved. Individually crafted service interfaces typically do not allow substitution and combination of services. Open standards can provide a way forward if they are powerful enough to address both data and processing model. The OGC Web Coverage Service (WCS) is a modular service suite which provides high-level interface definitions for data access, subsetting, filtering, and processing of spatio-temporal raster data. WCS based service interfaces to data archives deliver data in their original semantics useful for further client-side processing, as opposed to the Web Map Service (WMS) (de la Beaujardière, 2006) which performs a pre-rendering into images only useful for display to humans. In this paper we present a case study where the OGC coverage data and service model defines the client/server interface for a climate data service. In particular, we show how flexible temporal analysis can be performed efficiently on massive spatio-temporal coverage objects. This service, which is operational on a several Terabyte data holding, has been established as part of the EarthServer initiative focusing on Big Data in the Earth and Planetary sciences.

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