• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.381-392
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• 2003

Multi-temporal approaches using sequential data acquired over multiple years are essential for satisfactory discrimination between many land-cover classes whose signatures exhibit seasonal trends. At any particular time, the response of several classes may be indistinguishable. A harmonic model that can represent seasonal variability is characterized by four components: mean level, frequency, phase and amplitude. The trigonometric components of the harmonic function inherently contain temporal information about changes in land-cover characteristics. Using the estimates which are obtained from sequential images through spectral analysis, seasonal periodicity can be incorporates into multi-temporal classification. The Normalized Difference Vegetation Index (NDVI) was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula for 1996 ～ 2000 using a dynamic technique. Land-cover types were then classified both with the estimated harmonic components using an unsupervised classification approach based on a hierarchical clustering algorithm. The results of the classification using the harmonic components show that the new approach is potentially very effective for identifying land-cover types by the analysis of its multi-temporal behavior.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.573-587
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• 2019

A multi-temporal approach using remotely sensed time series data obtained over multiple years is a very useful method for monitoring land covers and land-cover changes. While spectral-based methods at any particular time limits the application utility due to instability of the quality of data obtained at that time, the approach based on the temporal profile can produce more accurate results since data is analyzed from a long-term perspective rather than on one point in time. In this study, a multi-temporal approach applying a multi-periodic harmonic model is proposed for classification of remotely sensed data. A harmonic model characterizes the seasonal variation of a time series by four parameters: average level, frequency, phase, and amplitude. The availability of high-quality data is very important for multi-temporal analysis.An satellite image usually have many unobserved data and bad-quality data due to the influence of observation environment and sensing system, which impede the analysis and might possibly produce inaccurate results. Harmonic analysis is also very useful for real-time data reconstruction. Multi-periodic harmonic model is applied to the reconstructed data to classify land covers and monitor land-cover change by tracking the temporal profiles. The proposed method is tested with the MODIS and GOCI NDVI time series over the Korean Peninsula for 5 years from 2012 to 2016. The results show that the multi-periodic harmonic model has a great potential for classification of land-cover types and monitoring of land-cover changes through characterizing annual temporal dynamics.

• Journal of the Korean Institute of Landscape Architecture
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• v.30 no.1
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• pp.1-10
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• 2002

The purpose of this article is to examine land use change in the fringe area of a metropolitan city through multi-temporal data analysis. Change detection has been regarded as one of the most important applications for utilization of remotely sensed imageries. Conventionally, two images were used for change detection, and Arithmetic calculators were generally used on the process. Meanwhile, multi-temporal change detection for a large number of images has been carried out. In this paper, a digital land-use map and three Landsat TM data were utilized for the multi-temporal change detection Each urban area map was extracted as a base map on the process of multi-temporal change detection. Each urban area map was converted to bit image by using boolean logic. Various urban change types could be obtained by stacking the urban area maps derived from the multi-temporal data using Geographic Information System(GIS). Urban change type map was created by using the process of piling up the bit images. Then the urban change type map was compared with each land cover map for the change detection. Dalseo-gu of Daegu city and Hwawon-eup of Dalsung-gun, the fringe area of Daegu Metropolitan city, were selected for the test area of this multi-temporal change detection method. The districts are adjacent to each other. Dalseo-gu has been developed for 30 yeais and so a large area of paddy land has been changed into a built-up area. Hwawon-eup, near by Dalseo-gu, has been influenced by the urbanization of Dalseo-gu. From 1972 to 1999, 3,507.9ha of agricultural area has been changed into other land uses, while 72.7ha of forest area has been altered. This agricultural area was designated as a 'Semi-agricultural area'by the National landuse Management Law. And it was easy for the preserved area to be changed into a built-up area once it would be included as urban area. Finally, the method of treatment and management of the preserved area needs to be changed to prevent the destruction of paddy land by urban sprawl on the urban fringe.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.2
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• pp.81-86
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• 2022

Given that time series are used in various fields, such as finance, IoT, and manufacturing, data analytical methods for accurate time-series forecasting can serve to increase operational efficiency. Among time-series analysis methods, multi-horizon forecasting provides a better understanding of data because it can extract meaningful statistics and other characteristics of the entire time-series. Furthermore, time-series data with exogenous information can be accurately predicted by using multi-horizon forecasting methods. However, traditional deep learning-based models for time-series do not account for the heterogeneity of inputs. We proposed an improved time-series predicting method, called the temporal fusion transformer method, which combines multi-horizon forecasting with interpretable insights into temporal dynamics. Various real-world data such as stock prices, fine dust concentrates and electricity consumption were considered in experiments. Experimental results showed that our temporal fusion transformer method has better time-series forecasting performance than existing models.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.515-517
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• 2004

Although sustainable development of a city should in theory be based on updated spatial information like land cover/use changes, in practice there are no effective tools to get such information. However the development of satellite and sensor technologies has increased the supply of high resolution satellite data, allowing cost-effective, multi-temporal monitoring. Especially KOMPSAT-1(KOrea Multi-Purpose SATellite) acquired a large number of images of the whole Korean peninsula and covering some large cities a number of times. In this study land-use patterns and trends of Daejeon from the year 2000 to the year 2003 will be considered using land use maps which are generated by manual interpretation of multi-temporal KOMPSAT EOC imagery and to show the possibility of using high resolution satellite remote sensing data for urban analysis.

• Korean Journal of Remote Sensing
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• v.31 no.4
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• pp.321-330
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• 2015

Atmospheric correction is an essential part in time-series analysis on biophysical parameters of surface features. In this study, we tried to examine possible problems in atmospheric correction of multitemporal High Spatial Resolution (HSR) images obtained from two different sensor systems. Three KOMPSAT-2 and two IKONOS-2 multispectral images were used. Three atmospheric correction methods were applied to derive surface reflectance: (1) Radiative Transfer (RT) - based absolute atmospheric correction method, (2) the Dark Object Subtraction (DOS) method, and (3) the Cosine Of the Uun zeniTh angle (COST) method. Atmospheric correction results were evaluated by comparing spectral reflectance values extracted from invariant targets and vegetation cover types. In overall, multi-temporal reflectance from five images obtained from January to December did not show consistent pattern in invariant targets and did not follow a typical profile of vegetation growth in forests and rice field. The multi-temporal reflectance values were different by sensor type and atmospheric correction methods. The inconsistent atmospheric correction results from these multi-temporal HSR images may be explained by several factors including unstable radiometric calibration coefficients for each sensor and wide range of sun and sensor geometry with the off-nadir viewing HSR images.

• Korean Journal of Remote Sensing
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• v.16 no.1
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• pp.1-11
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• 2000

Multi-temporal data have been used frequently for analyzing dynamic characteristics of ecological environment. Little research, however, shows the characteristics and problems of the analysis of continental- or global-scale, multi-temporal satellite data. This research investigated the characteristics of large-area, multi-temporal data analysis and the problems of phenological difference of ground vegetation and scarcity of training data for a long term period. This research suggested a latitudinal image segmentation method and an invariant pixel method. As an application, the image segmentation and invariant pixel methods were applied to a set of AVHRR data covering most part of Asia from 1982 to 1993. Fuzzy classification results showed the decrease of forests and the increase of croplands at densely populated areas, however an opposite trend was detected at sparsely populated or depopulated areas.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.545-560
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• 2019

Unlike other critical forest diseases, pine pitch canker in Korea has shown rather mild symptoms of partial loss of crown foliage and leaf discoloration. This study used high-resolution satellite images to detect and monitor canopy decline by pine pitch canker. To enhance the subtle change of canopy reflectance in pitch canker damaged tree crowns, multi-temporal analysis was applied to two KOMPSAT multispectral images obtained in 2011 and 2015. To assure the spectral consistency between the two images, radiometric corrections of atmospheric and shadow effects were applied prior to multi-temporal analysis. The normalized difference vegetation index (NDVI) of each image and the NDVI difference (${\Delta}NDVI=NDVI_{2015}-NDVI_{2011}$) between two images were derived. All negative ΔNDVI values were initially considered any pine stands, including both pitch canker damaged trees and other trees, that showed the decrease of crown foliage from 2011 to 2015. Next, $NDVI_{2015}$ was used to exclude the canopy decline unrelated to the pitch canker damage. Field survey data were used to find the spectral characteristics of the damaged canopy and to evaluate the detection accuracy from further analysis.Although the detection accuracy as assessed by limited number of field survey on 21 sites was 71%, there were also many false alarms that were spectrally very similar to the damaged canopy. The false alarms were mostly found at the mixed stands of pine and young deciduous trees, which might invade these sites after the pine canopy had already opened by any crown damages. Using both ${\Delta}NDVI$ and $NDVI_{2015}$ could be an effective way to narrow down the potential area of the pitch canker damage in Korea.

• Aerospace Engineering and Technology
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• v.8 no.1
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• pp.132-137
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• 2009

This study has regard to classification by using multi-temporal SAR data. Multi-temporal JERS-1 SAR images are used for extract the land cover information and possibility. So far, land cover information extracted by high resolution aerial photo, satellite images, and field survey. This study developed on multi-temporal land cover status monitoring and coherence information mapping can be processing by L band SAR image. From July, 1997 to October, 1998 JERS SAR images (9 scenes) coherence values are analyzed and then extracted land cover information factors, so on. This technique which forms the basis of what is called SAR Interferometry or InSAR for short has also been employed in spaceborne systems. In such systems the separation of the antennas, called the baseline is obtained by utilizing a single antenna in a repeat pass.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3037-3054
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• 2019

Public bicycle system (PBS) is a new emerging and popular mode of public transportation. PBS data can be adopted to analyze human movement patterns. Previous work usually focused on specific scales, and the relationships between different levels of hierarchies are ignored. In this paper, we introduce a multi-scale and interactive visual analytics system to investigate human cycling movement and PBS usage condition. The system supports level-of-detail explorative analysis of spatio-temporal characteristics in PBS. Visual views are designed from global, regional and microcosmic scales. For the regional scale, a bicycle network is constructed to model PBS data, and an flow-based community detection algorithm is applied on the bicycle network to determine station clusters. In contrast to the previous used Louvain algorithm, our method avoids producing super-communities and generates better results. We provide two cases to demonstrate how our system can help analysts explore the overall cycling condition in the city and spatio-temporal aggregation of stations.