• 한국공간정보시스템학회:학술대회논문집
• /
• 2007.06a
• /
• pp.169-174
• /
• 2007

식생지수 시계열 자료를 이용한 식생 및 토지피복 모니터링을 수행하기 위해서는 구름으로 인한 누락 및 왜곡된 식생지수 문제를 먼저 해결해야만 한다. 특히 한반도와 같이 여름철 집중 호우기에 대부분의 영상에 구름이 존재하는 경우 이들 구름화소를 제거하거나 복원하지 않을 경우, 분석 결과에 상당한 왜곡이 발생하거나 특정 시기의 영상자료를 분석에 반영할 수 없는 경우가 발생하게 된다. HANTS 알고리즘은 이 같은 구름 화소 문제를 해결하기 위한 알고리즘으로 연중 식생지수의 변화는 비교적 단순한 반복적 주기함수의 형태를 가지므로 소수의 cos 함수를 이용한 푸리에 근사식으로 전체 연중 식생지수를 표현할 수 있다는 가정에서 출발한다. 이 때 구름화소로 인한 원식생지수와의 차이가 특정 임계값을 초과하였을 경우 해당 관측치를 근사과정에서 제외함으로써 구름의 영향을 받지 않은 식생지수 시계열 자료만을 이용하게 된다. 이 과정을 수행하기 위해서는 몇몇 제어변수의 설정이 필요한데, 본 연구에서는 한반도와 같이 특정 시기에 장기간 구름이 분포하는 상황에서 최적의 식생지수 복원을 위한 HANTS 알고리즘의 제어변수를 선정하고 재구축된 식생지수를 평가하였다. 이를 위한 실험으로 2002년 대전 지역의 MODIS Terra 식생지수 시계열 영상을 대상으로 HANTS 알고리즘을 주요 식생피복별로 적용해 보았다.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.329-332
• /
• 2007

Annual vegetation growth patterns are determined by the intrinsic phenological characteristics of each land cover types. So, if typical growth patterns of each land cover types are well-estimated, and a NDVI time-series data of a certain area is compared to those estimated patterns, we can implement more advanced analyses such as a land surface-type classification or a land surface type change detection. In this study, we utilized Terra MODIS NDVI 250m data and compressed full annual NDVI time series data into several indices using the Harmonic Analysis of Time Series(HANTS) algorithm which extracts the most significant frequencies expected to be presented in the original NDVI time-series data. Then, we found these frequencies patterns, described by amplitude and phase data, were significantly different from each other according to vegetation types and these could be used for land cover classification. However, in spite of the capabilities of the HANTS algorithm for detecting and interpolating cloud-contaminated NDVI values, some distorted NDVI pixels of June, July and August, as well as the long rainy season in Korea, are not properly corrected. In particular, in the case of two or three successive NDVI time-series data, which are severely affected by clouds, the HANTS algorithm outputted wrong results.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.17 no.3
• /
• pp.20-38
• /
• 2014

Vegetation phenology is the most important indicator of ecosystem response to climate change. Therefore it is necessary to continuously monitor forest phenology. This paper analyzes the phenological characteristics of forests in South Korea using the MODIS vegetation index with error from clouds or other sources removed using the HANTS algorithm. After using the HANTS algorithm to reduce the noise of the satellite-based vegetation index data, we were able to confirm that phenological transition dates varied strongly with altitudinal gradients. The dates of the start of the growing season, end of the growing season and the length of the growing season were estimated to vary by +0.71day/100m, -1.33day/100m and -2.04day/100m in needleleaf forests, +1.50day/100m, -1.54day/100m and -3.04day/100m in broadleaf forests, +1.39day/100m, -2.04day/100m and -3.43day/100m in mixed forests. We found a linear pattern of variation in response to altitudinal gradients that was related to air temperature. We also found that broadleaf forests are more sensitive to temperature changes compared to needleleaf forests.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.19 no.1
• /
• pp.30-42
• /
• 2016

Climate change are known to have had enormous impacts on plant phenology and thus to have damage on other species which are interacted within ecosystem. In Korea, however, it is difficult to analyze the relationship between climate and phenology due to the limitation of measurement data of plant phenological records. In this study, to be effective analysis of SOG(start of growing season), we used phenological transition dates by using satellite data. Then, we identified the most influential variable in variation of SOG throughout the relationship between SOG and temperature factors. As a result, there is a strong correlation between the SOG and April temperature, TSOGmin($3^{\circ}C$, 12days). This study is expected to be used for predicting plant phenological change using climate change scenario data.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.1B
• /
• pp.89-98
• /
• 2000

This paper presents an active noise cancelling model using wavelet transform and subband filter banks based on adaptive filter. The analysis filter banks decompose input and error signals into QMF filter banks of lowpass and highpass bands. Each filter bank uses wavelet filter with dyadic tree structure. The decomposed input and error signals are iterated by adaptive filter coefficients of each subband using filtered-X LMS algorithm. The synthesis filter banks make output signal of wideband with perfect reconstruction to prepare adaptive filter output signals of each subband. The analysis and synthesis niter hants use conjugate quadrature filters for Pefect reconstruction. Also, The delayed LMS algorithm model for on-line identification of error path transfer characteristics is used gain and acoustic time delay factors. The proposed adaptive active noise cancelling modelis suggested by system retaining the computational and convergence speed advantage using wavelet subband filter banks.