• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.329-338
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• 2011

The time-series normalized difference vegetation index (NDVI) product has proven to be a powerful tool to investigate the phenological information because it can monitor the change of the forests with very high time-resolution, This study described the application of the DFT analysis over the 9 year MODIS data for the identification of the two types of vegetation cover, Pinus densiflora(Pd) and Querqus mongolica(Qm) which are dominant species of evergreen and broadleaved deciduous forest, respectively, The total number of samples was 5148 reference cycles which consist of 2160 Pd and 2988 Qm. They were extracted from the pixel-based MODIS scenes over the 9 years from 2000 to 2008 of South Korea. The DFT analysis was mainly focused on the 0th and $1^{st}$ harmonic components, each of which represents the mean value and the variation amplitude of the NDVI over the years, respectively. The $0^{th}$ harmonic values of the vegetation Pd and Qm averaged over the 9 years were 0.74 and 0.65, respectively. This implies that Pd has a higher NDVI than Qm. Similarly obtained $1^{st}$ harmonic values of Pd and Qm were 0.19 and 0.27, respectively. This can be intuitively understood considering that the seasonal variation of Qm is much larger than Pd. This distinctive difference of the $1^{st}$ harmonic value has been used to identify evergreen and deciduous forests. Overall agreement between the Fourier analysis-based map and the actal vegetation map has been estimated to be as high as 75%. This study found that the DFT analysis can be a concise and repeatable method to separate and trace the changes of evergreen and deciduous forest using the annual NDVI cycles.

• 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.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.235-244
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• 2016

In large-area crop classification with MODIS data, a mixed pixel problem caused by the low resolution of MODIS data has been one of main issues. To mitigate this problem, this paper proposes a hierarchical classification algorithm that selectively classifies the specific crop class of interest by using their spectral characteristics. This selective classification algorithm can reduce mixed pixel effects between crops and improve classification performance. The methodological developments are illustrated via a case study in Jilin city, China with MODIS Normalized Difference Vegetation Index (NDVI) and Near InfRared (NIR) reflectance datasets. First, paddy fields were extracted from unsupervised classification of NIR reflectance. Non-paddy areas were then classified into corn and bean using time-series NDVI datasets. In the case study result, the proposed classification algorithm showed the best classification performance by selectively classifying crops having similar spectral characteristics, compared with traditional direct supervised classification of time-series NDVI and NIR datasets. Thus, it is expected that the proposed selective hierarchical classification algorithm would be effectively used for producing reliable crop maps.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.199-209
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• 2021

By removing the increasing trend that long-term time series average of rice yield due to technological advancement of rice variety and cultivation management, we tried to improve the rice yield estimation model which developed earlier using MODIS NDVI and meteorological data. A multiple linear regression analysis was carried out by using the NDVI derived from MYD13Q1 and weather data from 2002 to 2019. The model was improved by analyzing the increasing trend of rime-series rice yield and removing it. After detrending, the accuracy of the model was evaluated through the correlation analysis between the estimated rice yield and the yield statistics using the improved model. It was found that the rice yield predicted by the improved model from which the trend was removed showed good agreement with the annual change of yield statistics. Compared with the model before the trend removal, the correlation coefficient and the coefficient of determination were also higher. It was indicated that the trend removal method effectively corrects the rice yield estimation model.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.477-480
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• 2009

최근 북한지역에 발생한 홍수와 가뭄 등 심각한 자연재해의 원인이 악화된 식량난으로 인한 과도한 산림의 훼손으로 추정되고 있어 북한 지역의 산림 생태계에 대한 연구의 필요성을 더욱 확인하는 계기가 되고 있다. 위성자료로부터 얻을 수 있는 식생지수(NDVI)는 농작물, 초목, 산림등과 같은 중요한 식생자원을 모니터링 하기 위해 설계된 도구로 식생의 활력도를 정량적으로 표현할 수 있다. 또한, 식생지수는 개략적인 식생을 파악하는데 주로 사용되기 때문에 초기조사에 많이 이용되고 있다. 따라서, 본 연구에서는 개마고원지대를 포함하는 표고 1000m 이상의 한대림지대의 산림식생에 대해 9년(2000~2008)간의 시계열 MODIS영상에서 나타나는 식생지수의 생물계절 특징을 분석하였다. 식생이 성장하기 시작하는 시기(the beginning of the growing season)는 5월 9일 이후부터 5월 25일 사이의 기간으로 나타났고, 식생의 휴면 시기(the end of growing season)는 9월 14일부터 9월 30일 사이 기간으로 관측되었다. 이는 4월 초에 시작하고 10월에 사라지는 한반도의 일반적이 식생패턴보다 성장은 한달 늦게, 휴면은 한달 이르게 한다는 것이 확인되었다. 또한, 8월 초순의 NDVI 값이 9년 동안 계속적으로 가장 높은 값을 보여주고 있다. 이것은 개마고원지역 및 주변 한대림 지대는 7월 말에서 8월 초순에 식생이 가장 활발한 것을 의미한다. 9년 동안 전반적인 식생지수는 0.2~0.9 값을 보였고, 특이할 점은 9년 동안의 평균적인 NDVI 값에 비해 2002년의 식생의 활동이 봄에 빨리 개화하고 가을에 일찍 지는 패턴을 보였다.

• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.17-24
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• 2011

Global warming and climatic changes due to human activities impact on marine and terrestrial ecosystems, which feedbacks to climate system. These negative feedbacks amplify or accelerate again global climate change. In particular, it is important to analyze vegetation change. This study attempts to analyze quantitatively vegetation change in Korea peninsula by using harmonic analysis. Harmonic-Analysis based on Fourier Transform is the method to effectively demonstrate for time series data. Especially, Harmonic-Analysis is very suitable method to analyze vegetation change because the vegetation repeats the cycle growth and extinction every year. The result of harmonic-analysis shows vegetation change as time passes. In this study, SPOTNEGETATION S10 MVC NDVI data was used during last 10 years (1999-2008) in Korea Peninsula. Also, land type classification used MODIS Land Cover Map data. The study estimated that phase values moved up approximately 0.5 day per year in cropland and 0.8 day per year in forest.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1087-1091
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• 2008

Evapotranspiration (ET) is an important factor while simulating daily streamflow in hydrological models. The LAI (Leaf Area Index) value reflecting the conditions of vegetation generally affects considerably in the estimation of ET, for example, when using FAO Penman Monteith equation. Recently in evaluating the vegetation condition as a fixed quantity, the remotely sensed LAIs from MODIS satellite data are avaliable, and the time series values of spatial LAI coupled with land use classes are utilized for ET evaluation. The 4 years (2001-2004) MODIS LAI data were prepared for the evaluation of continuous hydrological model, SLURP (Semi-distributed Land Use-based Runoff Processes). The model was applied for simulating the dam inflow of Chungjudam watershed ($6661.58\;km^2$) located in the upstream of Han river basin of South Korea. From the model results, the FAO Penman Monteith ET was affected by the MODIS LAIs. Especially for the ET of deciduous forest, the Total ET was 33.9 % lager than coniferous forest for the 3.8 % lager of LAI. The watershed average LAI caused a 7.0 % decrease in average soil moisture of the watershed and 14.3 % decrease of ground water recharge.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.325-338
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• 2023

Korean fir (Abies koreana Wilson) is one of the most important environmental indicator tree species for assessing climate change impacts on coniferous forests in the Korean Peninsula. However, due to the nature of alpine and subalpine regions, it is difficult to conduct regular field surveys of Korean fir, which is mainly distributed in regions with altitudes greater than 1,000 m. Therefore, this study analyzed the vegetation change trend of Korean fir using regularly observed remote sensing data. Specifically, normalized difference vegetation index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS), land surface temperature (LST), and precipitation data from Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievalsfor GPM from September 2003 to 2020 for Hallasan and Jirisan were used to analyze vegetation changes and their association with environmental variables. We identified a decrease in NDVI in 2020 compared to 2003 for both sites. Based on the NDVI difference maps, areas for healthy vegetation and high mortality of Korean fir were selected. Long-term NDVI time-series analysis demonstrated that both Hallasan and Jirisan had a decrease in NDVI at the high mortality areas (Hallasan: -0.46, Jirisan: -0.43). Furthermore, when analyzing the long-term fluctuations of Korean fir vegetation through the Hodrick-Prescott filter-applied NDVI, LST, and precipitation, the NDVI difference between the Korean fir healthy vegetation and high mortality sitesincreased with the increasing LST and decreasing precipitation in Hallasan. Thissuggests that the increase in LST and the decrease in precipitation contribute to the decline of Korean fir in Hallasan. In contrast, Jirisan confirmed a long-term trend of declining NDVI in the areas of Korean fir mortality but did not find a significant correlation between the changes in NDVI and environmental variables (LST and precipitation). Further analyses of environmental factors, such as soil moisture, insolation, and wind that have been identified to be related to Korean fir habitats in previous studies should be conducted. This study demonstrated the feasibility of using satellite data for long-term monitoring of Korean fir ecosystems and investigating their changes in conjunction with environmental conditions. Thisstudy provided the potential forsatellite-based monitoring to improve our understanding of the ecology of Korean fir.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.441-448
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• 2015

Lack of agricultural information for food supply and demand in Democratic People's republic Korea(DPRK) make people sometimes confused for right and timely decision for policy support. We carried out a study to estimate paddy rice yield in DPRK using MODIS NDVI reflecting rice growth and climate data. Mean of MODIS $NDVI_{max}$ in paddy rice over the country acquired and processed from 2002 to 2014 and accumulated rainfall collected from 27 weather stations in September from 2002 to 2014 were used to estimated paddy rice yield in DPRK. Coefficient of determination of the multiple regression model was 0.44 and Root Mean Square Error(RMSE) was 0.27 ton/ha. Two-way analysis of variance resulted in 3.0983 of F ratio and 0.1008 of p value. Estimated milled rice yield showed the lowest value as 2.71 ton/ha in 2007, which was consistent with RDA rice yield statistics and the highest value as 3.54 ton/ha in 2006, which was not consistent with the statistics. Scatter plot of estimated rice yield and the rice yield statistics implied that estimated rice yield was higher when the rice yield statistics was less than 3.3 ton/ha and lower when the rice yield statistics was greater than 3.3 ton/ha. Limitation of rice yield model was due to lower quality of climate and statistics data, possible cloud contamination of time-series NDVI data, and crop mask for rice paddy, and coarse spatial resolution of MODIS satellite data. Selection of representative areas for paddy rice consisting of homogeneous pixels and utilization of satellite-based weather information can improve the input parameters for rice yield model in DPRK in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.329-335
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• 2018

Satellite images, which are widely used in various applications, are very useful for monitoring the surface of the earth. Since satellite data is obtained from a remote sensor, it contains a lot of noise and errors depending on observation weather conditions during data acquisition and sensor malfunction status. Since the accuracy of the data affects the accuracy and reliability of the data analysis results, noise removal and data restoration for high quality data is important. In this study, we propose a reconstruction system that models the time dependent dynamic characteristics of satellite data using a multi-period harmonic model and performs adaptive data restoration considering the spatial correlation of data. The proposed method is a real-time restoration method and thus can be employed as a preprocessing algorithm for real-time reconstruction of satellite data. The proposed method was evaluated with both simulated data and MODIS NDVI data for six years from 2011 to 2016. Experimental results show that the proposed method has the potentiality for reconstructing high quality satellite data.