• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.83-89
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• 2012

Natural Vegetation cover, which is very important earth resource, has been significantly altered by humans in some manner. Since this has currently resulted in a significant effect on global climate, various studies on vegetation environment including forest have been performed and the results are utilized in policy decision making. Remotely sensed data can detect, identify and map vegetation cover change based on the analysis of spectral characteristics and thus are vigorously utilized for monitoring vegetation resources. Among various vegetation indices extracted from spectral reponses of remotely sensed data, NDVI is the most popular index which provides a measure of how much photosynthetically active vegetation is present in the scene. In this study, for change detection in vegetation cover, a Multi-layer Perceptron Network (MLPN) as a nonparametric approach has been designed and applied to MODIS/Aqua vegetation indices 16-day L3 global 250m SIN Grid(v005) (MYD13Q1) data. The feature vector for change detection is constructed with the direct NDVI diffenrence at a pixel as well as the differences in some subset of NDVI series data. The research covered 5 years (2006-20110) over Korean peninsular.

• Journal of Environmental Science International
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• v.15 no.4
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• pp.373-378
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• 2006

The western coast of South Korea is famous for its large and broad tidal lands. Nevertheless, land reclamation, which has been conducted on a large scale, such as Sihwa embankment construction project has accelerated coastal environmental changes in the embankment inland. For monitoring of environmental change, vegetation change detecting of the embankment inland were carried out and field survey data compared with Landsat TM, ETM+, IKONOS, and EOC satellite remotely sensed data. In order to utilize multi-temporal remotely sensed images effectively, all data set with pixel size were analyzed by same geometric correction method. To detect the tidal land vegetation change, the spectral characteristics and spatial resolution of Landsat TM and ETM+ images were analyzed by SMA(spectral mixture analysis). We obtained the 78.96% classification accuracy and Kappa index 0.2376 using March 2000 Landsat data. The SMA(spectral mixture analysis) results were considered with comparing of vegetation seasonal change detection method.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1133-1148
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• 2019

Tree growth and vitality in forest shows seasonal changes. So, in order to detect forest damage accurately, we have to use satellite images before and after damages taken at the same season. However, temporal resolution of high or medium resolution images is very low,so it is not easy to acquire satellite images of the same seasons. Therefore, in this study, we estimated spectral information of the same DOY using time-series Landsat images and used the estimates as reference values to assess forest damages. The study site is Hwasun, Jeollanam-do, where forest damage occurred due to hail and drought in 2017. Time-series vegetation index (NDVI, EVI, NDMI) maps were produced using all Landsat 8 images taken in the past 3 years. Daily normal vegetation index maps were produced through cloud removal and data interpolation processes. We analyzed the difference of daily normal vegetation index value before damage event and vegetation index value after event at the same DOY, and applied the criteria of forest damage. Finally, forest damage map based on daily normal vegetation index was produced. Forest damage map based on Landsat images could detect better subtle changes of vegetation vitality than the existing map based on UAV images. In the extreme damage areas, forest damage map based on NDMI using the SWIR band showed similar results to the existing forest damage map. The daily normal vegetation index map can used to detect forest damage more rapidly and accurately.

• Journal of Forest and Environmental Science
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• v.39 no.3
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• pp.167-179
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• 2023

The extent of change in the Land use/Land cover (LULC) of Okomu National Park (ONP) and fringe communities was evaluated. High resolution Landsat imagery was used to identify the major vegetation cover/land use systems and changes around the national park and fringe communities while field visits/ground truthing, involving the collection of coordinates of the locations was carried out to ascertain the various land cover/land use types identified on the images, and the extent of change over three-time series (2000, 2010 and 2020). The change detection was analyzed using area calculation, change detection by nature and normalized difference vegetation index (NDVI). The result of the classification and analysis of the LULC Change of ONP and fringe communities revealed an alarming rate of encroachment into the protected area. All the classification features analyzed had notable changes from 2000-2020. The forest, which was the dominant LULC feature in 2000, covering about 66.19% of the area reduced drastically to 36.12% in 2020. Agricultural land increased from 6.14% in 2000 to 34.06% in 2020 while vegetation (degraded land) increased from 27.18% in 2000 to 38.89% in 2020. The magnitude of the change in ONP and surroundings showed the forest lost -247.136 km² (50.01%) to other land cover classes with annual rate change of 10%, implying that 10% of forest land was lost annually in the area for 20 years. The NDVI classification values of 2020 indicate that the increase in medium (399.62 km² ) and secondary high (210.17 km² ) vegetation classes which drastically reduced the size of the high (38.07 km² ) vegetation class. Consequent disappearance of the high forests of Okomu is inevitable if this trend of exploitation is not checked. It is pertinent to explore other forest management strategies involving community participation.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.102-106
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• 2005

As long as the effective contributions of satellite images in the continuous monitoring of the wide area and long range of time period, Landsat TM and Landsat ETM+ satellite images are surveyed. After quantization and classification of the deviations between TM and ETM+ images based on approved thresholds such as gains and biases or offsets, a correlation analysis method for the compared calibration is suggested in this paper. Four time points of raster data for 15 years of the highest group of land surface temperature and the lowest group of vegetation of the Kunsan city Chollabuk_do Korea located beneath the Yellow sea coast, are observed and analyzed their correlations for the change detection of urban land cover. This experiment based on proposed algorithm detected strong and proportional correlation relationship between the highest group of land surface temperature and the lowest group of vegetation index which exceeded R=(+)0.9478, so the proposed Correlation Analysis Model between the highest group of land surface temperature and the lowest group of vegetation index will be able to give proof an effective suitability to the land cover change detection and monitoring.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.3
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• pp.16-22
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• 1999

The various spatial structure of biological habitat has tighten relationship with biodiversity. Due to increasing of population, development of agriculture and urban structure, various change of landscape has became these days. These change of landscape has raised the decrease of habitat and landscape fragmentation. This paper summarizes research to analysis vegetation index according to P/A ratio, Shape Index, and Fractal dimension using Landsat Thematic Mapper(TM). The analysis of landscape fragmentation using NDVI(Normalized Difference Vegetation Index) 0.5~1 has the most profitable for detection of vegetation fragmentation. The analysis of vegetation index of Seoul and Kyunggi province has also showed that Fractal dimension has the most fragmentation index. In near future, time series analysis is needed for fragmentation of vegetation on the same area, and for various landuse of fragmentation analysis. These researches were carried out for preservation strategy of vegetation and biodiversity.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.975-988
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• 2020

Change detection is the process of identifying changes by observing the multi-temporal images at different times, and it is an important technique in remote sensing using satellite images. Among the change detection methods, the unsupervised change detection technique has the advantage of extracting rapidly the change area as a binary image. However, it is difficult to understand the changing pattern of land cover in binary images. This study used grid points generated from seamless digital map to evaluate the satellite image change detection results. The land cover change results were extracted using multi-temporal KOMPSAT-3A (K3A) data taken by Gimje Free Trade Zone and change detection algorithm used Spectral Angle Mapper (SAM). Change detection results were presented as binary images using the methods Otsu, Kittler, Kapur, and Tsai among the automated threshold selection algorithms. To consider the seasonal change of vegetation in the change detection process, we used the threshold of Differenced Normalized Difference Vegetation Index (dNDVI) through the probability density function. The experimental results showed the accuracy of the Otsu and Kapur was the highest at 58.16%, and the accuracy improved to 85.47% when the seasonal effects were removed through dNDVI. The algorithm generated based on this research is considered to be an effective method for accuracy assessment and identifying changes pattern when applied to unsupervised change detection.

• Spatial Information Research
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• v.4 no.1
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• pp.13-20
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• 1996

This study has been on detection of land-cover change on Korean peninsula (including the area of north Korean territory) between May of 1990 year and that of 1995 year using NOAA AVHRR data. It was necessary that imagery data should be registered to each other and should not be deviated much in seasonal variation in order to recognize land - cover change. Atmosphic effect such as clould and dirt was erased by maximum NDVI(Normalized Difference Vegetation Index) method the equation of which was as following $$NDVI(i,j,d)=\frac{ch2(j,j,d)-ch1(i,j,d)}{ch2(i,j,d)+ch1(i.j,d)}$$ Each image of maximum NDVI of '90 year and '95 year was c1assifed onto 8 categories ,using iso-clustering method each of which was water, wet barren and urban, crop field, field, mixed vegetation, shrub, forest and evergreen.

• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.351-360
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• 2013

Harmonic analysis enables to characterize patterns of variation in MODIS NDVI time series data and track changes in ground vegetation cover. In harmonic analysis, a periodic phenomenon of time series data is decomposed into the sum of a series of sinusoidal waves and an additive term. Each wave is defined by an amplitude and a phase angle and accounts for the portion of variance of complex curve. In this study, harmonic analysis was explored to tract ground vegetation variation through time for land-cover vegetation change detection. The process also enables to reconstruct observed time series data including various noise components. Harmonic model was tested with simulation data to validate its performance. Then, the suggested change detection method was applied to MODIS NDVI time series data over the study period (2006-2012) for a selected test area located in the northern plateau of Korean peninsula. The results show that the proposed approach is potentially an effective way to understand the pattern of NDVI variation and detect the change for long-term monitoring of land cover.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.665-675
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• 2014

In this paper spectral characteristics and spectral patterns of pine wilt disease at different development stage were analyzed in Geoje-do where the disease has already spread. Ground-based hyperspectral imaging containing hundreds of wavelength band is feasible with continuous screening and monitoring of disease symptoms during pathogenesis. The research is based on an hyperspectral imaging of trees from infection phase to witherer phase using a ground based hyperspectral camera within the area of pine wilt disease outbreaks in Geojedo for the analysis of pine wilt disease. Hyperspectral imaging through hundreds of wavelength band is feasible with a ground based hyperspectral camera. In this research, we carried out wavelength band change analysis on trees from infection phase to witherer phase using ground based hyperspectral camera and comparative analysis with major vegetation indices such as Normalized Difference Vegetation Index (NDVI), Red Edge Normalized Difference Vegetation Index (reNDVI), Photochemical Reflectance Index (PRI) and Anthocyanin Reflectance Index 2 (ARI2). As a result, NDVI and reNDVI were analyzed to be effective for infection tree detection. The 688 nm section, in which withered trees and healthy trees reflected the most distinctions, was applied to reNDVI to judge the applicability of the section. According to the analysis result, the vegetation index applied including 688 nm showed the biggest change range by infection progress.