MODIS NDVI Time Series Data Research Articles

Comparative Analysis of MODIS Time-Series Classification Using Support Vector Machines and Methods Based upon Distance and Similarity Measures in the Brazilian Cerrado-Caatinga Boundary

We have mapped the primary native and exotic vegetation that occurs in the Cerrado-Caatinga transition zone in Central Brazil using MODIS-NDVI time series (product MOD09Q1) data over a two-year period (2011–2013). Our methodology consists of the following steps: (a) the development of a three-dimensional cube composed of the NDVI-MODIS time series; (b) the removal of noise; (c) the selection of reference temporal curves and classification using similarity and distance measures; and (d) classification using support vector machines (SVMs). We evaluated different temporal classifications using similarity and distance measures of land use and land cover considering several combinations of attributes. Among the classification using distance and similarity measures, the best result employed the Euclidean distance with the NDVI-MODIS data by considering more than one reference temporal curve per class and adopting six mapping classes. In the majority of tests, the SVM classifications yielded better results than other methods. The best result among all the tested methods was obtained using the SVM classifier with a fourth-degree polynomial kernel; an overall accuracy of 80.75% and a Kappa coefficient of 0.76 were obtained. Our results demonstrate the potential of vegetation studies in semiarid ecosystems using time-series data.

Spatio-temporal analysis of trends in seasonal vegetation productivity across Uttarakhand, Indian Himalayas, 2000–2014

Himalayan mountain system in the Indian sub-continent are among the most ecologically sensitive environments and are also a repository of biodiversity, water and ecosystem services. Over the last two decades, land transformation related to exploitative land uses is among the main drivers of changing vegetation cover and productivity in western Himalayas. In a region where field based research is challenging due to heterogenous relief and high altitude, quantifying changes in vegetation photosynthetic activity using remote sensing can provide essential information regarding trends in vegetation cover and its linkages with anthropogenic impacts. We conducted seasonal trend analysis (STA) on MODIS NDVI time-series data (2000–2014) over Uttarakhand Himalayas and examined spatio-temporal patterns in vegetation trends and its association with altitudinal gradient and land use land cover (LULC) dynamics. In STA the first step determines the annual mean and seasonal NDVI patterns and the second step analyzes the non-parametric trend in magnitude and timing of the annual mean and seasonal NDVI cycle. To provide insights on the role of changing land use to detected changes in vegetation trend, we linked MODIS derived trends to land transformation processes using multi-temporal high spatial resolution imagery in Google Earth (GE). In total 3286.82 km2 (6.9% vegetated area of Uttarakhand) showed significant trend (p < 0.01) in mean annual greenness. While areas <800 m elevation showed dominant negative trend in mean annual greenness (browning)), those between 800 and 1600 m showed mostly positive trend (greening) and majority of areas >1600 m were characterized by negative trend in mean annual greenness. Majority of intensively cultivated irrigated croplands in the Himalayan foothills as well as areas around growing urban centers showed widespread browning, which was contrastingly different from rainfed cultivation areas that showed dominant greening trend. Browning trend was observed to be consistent with increasing altitude, particularly in closed needle leaf forests and alpine shrublands, except areas where human impacts has led to more mixed patterns. Trends in the annual seasonal timing of NDVI indicated an earlier green-up for most parts of the Uttarakhand Himalayas. These results highlight fine scale spatial variations in seasonal vegetation trends and are in partial agreement with previous studies that report only increasing brownness detected at broader scale using much coarser spatial resolution time-series.

Detection of Rice Distribution Using NDVI Time-Series Similarity Measure

Rice cultivation is essential for agricultural production in Mekong Delta. Rice production estimation is based on the measurement of paddy field area and plantation system. Detection of paddy rice area and the plantation system is significant to food security decision. Based on the MODIS NDVI time-series data and the Dynamic Time Wrapping (DTW) similarity measure we detected the single and multiple rice distribution in Mekong Delta in 2010. Firstly, a combination of replacement and filter method was applied to denoising the MODIS NDVI time-series. Secondly, extracted the standard NDVI time-series cycle of single and multiple rice on MODIS NDVI time-series image. Thirdly, compare each pixel’s NDVI time-series cycle with each standard NDVI time-series cycle based on DTW distance. Finally, choose threshold in DTW distance and determine paddy rice’s distribution. Results were examined by sampling point and high resolution image of Mekong Delta; it shows that the extraction accuracy is 86%, relatively high for MODIS data classification outcome.

Drought stress monitoring by laboratory and satellite spectral methods in an apple orchard

An orchard can be examined on the basis of spectral data, using such methods with which the reflected radiation can be divided into a large number of (several hundreds) small spectral channel (some nm). Based on the spectral characteristics of the canopy, or the different index numbers calculated from hyperspectral data the water supply conditions of foliage can be well characterized. The research site is an intensive apple orchard, which located in Debrecen University, Centre for Agricultural and Applied Economic Sciences, Farm and Regional Research Institute at Pallag. During our experiments the evaluation of spectral, non-invasive measurement method are carried out for detecting stress symptoms caused by drought. Furthermore, MODIS NDVI time series data were analyzed for orchards situated in North-Eastern part of Hungary in drought effected and wet years in order to detect differences. Significant differences in NDVI values were detected after the end of June.

MODIS NDVI 시계열 자료의 하모닉 분석을 통한 지표 식생 변화 탐지

MODIS NDVI 시계열 자료에 하모닉 분석을 적용하면 계절에 따른 식생의 연간 변화 패턴을 이해할 수 있다. 하모닉 분석은 시간에 따라 형성된 시계열 자료의 복잡한 파형의 형태를 일련의 정현파 파형(sinusoidal waves)의 합으로 분해하고 진폭과 위상으로 정의되는 각 파형의 특성을 통해 시계열 자료의 패턴을 분석하는 방법이다. 본 논문은 NDVI 시계열 자료에 하모닉 모형을 적용하여 각 구성 성분의 진폭과 위상을 측정하고 이러한 파라미터들의 변화를 분류하여 식생의 연간 변화를 탐지하는 방법론을 제안하고 있다. 이를 통해 장기간에 걸친 식생 변화 지역을 모니터링할 수 있고 또한, 이 과정에서 하모닉 모형을 통해 미관측 자료나 노이즈 자료를 복원하여 시계열자료를 재구축할 수 있는 장점도 있다. 본 연구에서는 시뮬레이션 자료를 통해 하모닉 분석의 유용성에 대해 테스트하였고, 2006년부터 2012년까지 총 7년간 북한 개마고원 부근의 MODIS NDVI 식생 자료에 하모닉 모형을 적용하여 연간 변화 지역을 탐지하고 연간 식생변화지역맵을 작성하였다. 이렇게 작성된 연간 식생변화지역맵은 장기적인 식생변화 모니터링을 위한 기초 맵으로 활용될 수 있을 것으로 기대된다. 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.

Extraction of Planting Areas of Major Crops and Crop Growth Monitoring in Northeast China

Abstract This paper presents a method used in China Agriculture Remote Sensing Monitoring System (CHARMS) for automatically identifying crop planting areas and monitoring crop growth conditions at a large scale based on time-series of MODIS NDVI Datasets. In doing that, the characteristics of NDVI time series of spring wheat, spring corn, soybean and rice in Northeastern China were firstly analyzed to determine the threshold values used for extracting different crops. Then using these thresholds, extraction models for above-mentioned four major crops were established and applied to obtain the spatial distribution of these four crops in Northeastern China in 2009. In comparison with the average statistic data of several years, the total extraction accuracy is over 87%, which suggests its feasibility to extract planting areas of different crops at a large scale using MODIS data. Based on the extracted crop planting areas, the same MODIS NDVI time series data were used to monitor crop growth conditions in 20...

Classification of rice cropping systems by empirical mode decomposition and linear mixture model for time-series MODIS 250 m NDVI data in the Mekong Delta, Vietnam

Estimating the area of rice planting is vital for production prediction. This study utilizes time-series MODIS NDVI data from 2002 to 2007 to discriminate rice cropping systems in the Mekong Delta (MD), Vietnam. Data are processed using Empirical Mode Decomposition (EMD) and the Linear Mixture Model (LMM). Various spatial and non-spatial data are also collected for accuracy validation. The results indicate that EMD acts as a well-fitted filter for noise reduction of the time-series NDVI data. The classification results derived from the LMM for 2002 showed an overall classification accuracy of 71.6% and a Kappa coefficient of 0.6. The provincial level area estimates were strongly correlated with the rice statistics. An examination of the change in cropping patterns between 2002 and 2007 showed that 29.0% of the triple irrigated-rice cropping systems had been changed to double irrigated-rice cropping systems and that 12.0% and 9.0% of the double irrigated and rainfed-rice cropping systems, respectively, had been changed to triple rice cropping systems. These changes were verified by visual comparisons with Landsat images.

A Comparison Between the Algorithms for Removing Cloud Pixel from MODIS NDVI Time Series Data

Although composite data present lower atmospheric contamination than raw time series,MODIS NDVI products are still contaminated by clouds,especially when cloud cover lasts longer than the composite period.e.g.,in the rainy season.The long-time cloud cover will weaken the application of MODIS NDVI time series data.To remove the effect of these clouds from NDVI data and reconstruct high-quality NDVI data,the authors propose three algorithms for cloud removal,namely SPLINE function,HANTS and Savizky-Golay.The capabilities of the three algorithms in cloud removal was compared with each other in this study,with the MODIS NDVI time series data in Shandong province serving as the test data.The results show that the three algorithms can remove the effect of cloud from NDVI time series data effectively,with each algorithm having its own advantages and disadvantages.For the algorithm of SPLINE function,the result of cloud removal mainly depends on the quality of cloud data and sometimes extreme values will occur;this algorithm fails to change the values of pixels which have not been contaminated atmospherically.When HANTS and Savizky-Golay algorithms are used,most of the pixels will lose their original values,and the parameters have to be determined after conducting many experiments because there is no objective rule to set them.

Classification of vegetation in North Tibet Plateau based on MODIS time-series data

Based on the 16d-composite MODIS (moderate resolution imaging spectroradiometer)-NDVI(normalized difference vegetation index) time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.