NDVI Time Series Data Research Articles

Temporal divergence in cropping pattern and its implications on geospatial drought assessment

Time series data on cropping pattern at disaggregated level were analysed and its implications on geospatial drought assessment were demonstrated. An index of Cropping Pattern Dissimilarity (CP-DI) between a pair of years, developed in this study, proved that the cropping pattern of a year has a higher degree of similarity with that of recent past years only and tends to be dissimilar with longer time difference. The temporal divergence in cropping pattern has direct implications on geospatial approach of drought assessment, in which, time series NDVI data are compared for drought interpretation. It was found that, seasonal NDVI profiles of drought year and normal year did not show any anomaly when the cropping patterns were dissimilar and two normal years having dissimilar cropping pattern showed different NDVI profiles. Therefore, it is suggested that such temporal comparisons of NDVI are better restricted to recent past years to achieve more objective interpretation.

Analysing the vegetation cover variation of China from AVHRR‐NDVI data

In this paper, the characteristics of vegetation cover and variation in China have been studied by using the AVHRR NDVI time‐series data from 1981 to 2001. The Harmonic Analysis of Time Series (HANTS) method was successfully applied to eliminate the clouds on remote sensing data and reconstruct cloud‐free time series images. Then, the Fourier components of NDVI time series data were calculated. Finally, the physical meaning of Fourier components was analysed, and the relationship between Fourier components and land vegetation cover variation was investigated. The mean NDVI, or zeroth‐order harmonic, indicates overall vegetation cover level. The first harmonics of the HANTS summarizes the amplitude and phase of annual values of NDVI data, and the second harmonics of the HANTS summarizes those of biannual values of NDVI data. The amplitude of the first harmonic indicates the variability of vegetation productivity over the year. The phase of the first harmonic summarizes the timing of vegetation green‐up, while the second harmonic indicates the strength and timing of biannual vegetation cover variation. The Fourier components calculated by HANTS algorithm reveal the vegetation distribution and growing cycle characteristics. The physical meaning of Fourier components are significant to the land‐surface vegetation variation study of China. The methodology proposed in this paper is an effective method for the processing, analysis and application of long‐time‐series remote sensing data.

Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data

Crop identification is the basis of crop monitoring using remote sensing. Remote sensing the extent and distribution of individual crop types has proven useful to a wide range of users, including policy-makers, farmers, and scientists. Northern China is not merely the political, economic, and cultural centre of China, but also an important base for grain production. Its main grains are wheat, maize, and cotton. By employing the Fourier analysis method, we studied crop planting patterns in the Northern China plain. Then, using time-series EOS-MODIS NDVI data, we extracted the key parameters to discriminate crop types. The results showed that the estimated area and the statistics were correlated well at the county-level. Furthermore, there was little difference between the crop area estimated by the MODIS data and the statistics at province-level. Our study shows that the method we designed is promising for use in regional spatial scale crop mapping in Northern China using the MODIS NDVI time-series.

植物フェノロジー観測における時系列ＮＤＶＩデータの雑音除去手法

Vegetation phenology is closely related to seasonal dynamics of the lower atmosphere, and important elements in global models and vegetation monitoring. Time-series NDVI data based on imagery from AVHRR or MODIS are suitable for phenological monitoring, because these sensors provide data with a high temporal frequency. In order to reduce noises caused by cloud contamination or atmospheric variability, Maximum Value Composite (MVC) is applied to the data. However, composite data have undesirable noises due to remained cloud contamination and inequality of observation intervals. Though MVC technique is applied, these noises disturb phenological monitoring. This paper proposed new noise reduction algorithm which integrates Best Index Slope Extraction (BISE) and Maximum Value Interpolated (MVI) algorithms. Integrated algorithm was applied to timeseries NDVI data consists of NOAA AVHRR composite images. This algorithm worked well in areas dominated by vegetation such as cropland including double cropping area, deciduous broadleaf forest and evergreen needleleaf forest. We confirmed that the algorithm reduced effects of cloud contamination, and equalized each observation interval. Therefore, applying developed algorithm to time-series NDVI data allows us to phenological monitoring more precisely.

Fourier analysis of historical NOAA time series data to estimate bimodal agriculture

In the present study, NDVI time‐series 10‐day composites derived from NOAA AVHRR data were used to estimate bimodal agriculture areas (where there are two seasons of cultivation per annum) using Fourier approach. The NDVI sequence was transformed into harmonic signals and the amplitude and phase of first and second harmonics were used for the analysis. A classification was applied, using a decision tree, to discriminate bimodal agriculture area from other land cover types, principally over the Asian sub‐region. When the amplitude of second harmonics in a sample region, where bimodal agriculture is predominant, was compared with the irrigated area statistics developed by FAO‐UF, a linear relationship was determined. The derived function was applied to transform the amplitude of second harmonics to bimodal agriculture area estimates. Thus large‐scale irrigation projects appear on the map and provide an encouraging initial result. This result indicates that estimating bimodal agriculture area that is one of the main sources of information for irrigated area mapping at regional or global scale, with improved accuracy possible if greater spatial, temporal resolution is achieved, for instance from MODIS or SPOT vegetation time series NDVI data, combined with (1) an improved decision tree classification algorithm and (2) a greater precision and geographical distribution of ground‐truth data. The principle merits of this approach are automation and repeatability.

Classification of tropical seasonal forests using time-series NDVI data with the tree model classification approach.

Classification of tropical seasonal forests using time-series NDVI data with the tree model classification approach.

Using USDA Crop Progress Data for the Evaluation of Greenup Onset Date Calculated from MODIS 250-Meter Data

Identification of the onset of vegetation greenup is a key factor in characterizing and monitoring vegetation dynamics over large areas. However, the relationship between greenup onset dates estimated from satellite imagery and the actual growth stage of vegetation is often unclear. Herein, we present an approach for comparing pixel-level onset dates to regional planting and emergence information for agricultural crops, with the goal of drawing reliable conclusions regarding the physical growth stage of the vegetation of interest at the time of greenup onset. To accomplish this, we calculated onset of greenup using MODIS 250 m, 16-day composite NDVI time series data for Kansas for 2001 and a recently proposed methodology for greenup detection. We then evaluated the estimated greenup dates using the locations of 1,417 large field sites that were planted to corn, soybeans, or sorghum in 2001, in conjunction with United States Department of Agriculture (USDA) weekly crop progress reports containing crop planting and emergence percentage estimates. Average greenup onset dates calculated for the three summer crops showed that the dates were consistent with the relative planting order of corn, sorghum, and soybeans across the state. However, the influence of pre-crop vegetation (weeds and “volunteer” crops) introduced an early bias for the greenup onset dates calculated for many field sites. This pre-crop vegetation signal was most pronounced for the later planted summer crops (soybeans and sorghum) and in areas of Kansas that receive higher annual precipitation. The most reliable results were obtained for corn in semi-arid western Kansas, where pre-crop vegetation had considerably less influence on the greenup onset date calculations. The greenup onset date calculated for corn in western Kansas was found to occur 23 days after 50 percent of the crop had emerged. Corn’s greenup onset was detected, on average, at the agronomic stage where plants are 15 to 45 cm (6 to 18 inches) tall and the crop begins its rapid growth.

LANDUSE AND CROPPING PETTERN CLASSIFICATION USING SATELLITE DERIVED VEGETATION INDICES AND SURFACE METEOROLOGICAL DATA

The Huaihe River Lasin is vast granary in China, therefore landuse and cropping cycle information is important for water usage and hydrological analysis. In this study, landuse and cropping pattern classification in the Huaihe River Basin is produced from satellite derived vegetation indices (30sec resolution) and surface meteorological data. And agricultural statistic data of 187 prefectures in Henan Province is used for validation. Firstly, using some notable features in the NDVI and NDWI time series, all meshes (1320 × 600) are classified into 7 categories. Secondly, accumulated degree-days temperature data during growing period is calculated from NDVI time series and surface air temperature data, and it is used for cropping type classification. Lastly, VSW (Vegetation, Soil and Water) index is applied for evaluation of the mixed-cell effect. The results of this study show the importance of mixed-cell effect and the availability of VSW index for cropland classification.

ESTIMATION OF AGRICULTURAL TYPE IN THE HUAIHE RIVER BASIN CHINA AND APPLIED TO THE LAND-SURFACE SCHEME

In this study, agricultural cycle was estimated from satellite data for the evaluation of agricultural water use in the Huaihe river basin, China. Using SPOT-NDVI data from April 1998 to March 1999, all cropland are categorized into four classes (wheat+soybean, wheat+maize, rape+rice, wheat+rice). Then the harvesting time of summer harvesting crop, the planting and harvesting time of autmun harvesting crop are estimated from the time series of NDVI data at each mesh. Based on these results and water requirement data, appropriate water level and soil moisture condition is defined depending on the growing stage. In the numerical simulation, irrigation water is supplied or drained to meet this appropriate condition. Due to this method, water use of this basin is faithfully reflected in the land sueface scheme.

Mapping crop key phenological stages in the North China Plain using NOAA time series images

Six key phenological stages were defined based on NOAA/AVHRR NDVI time series data collected in the Huang-Huai-Hai (HHH) Plain of China from 1990 through 2000. In a winter wheat-summer maize rotation, the recovering, heading and maturity stages of winter wheat and the emergence, tasseling and maturity stages of summer maize were recorded using 6km resolution 10-day composite NDVI. The satellite-derived data proved to be consistent with the ‘green wave’ moving through the HHH Plain in spring. The recovering stage of winter wheat recorded by satellite was closely correlated to the temperatures measured in February whereas summer maize yields (at zone level) were correlated well with the satellite-derived length of the crop cycle. Comparison with synchronous phenological observations on the ground confirmed the coherence of satellite-derived phenology data. It is expected that satellite data with greater spatial and temporal resolutions and improved smoothing methods will increase the precision of the estimated data still further.

Relationship between land cover and monsoon interannual variations in east Asia

Asian monsoon have multiple forms of variations such as seasonal variation, intra-seasonal variation, interannual variation, etc. The interannual variations have not only yearly variations but also variations among several years. In general, the yearly variations are described with winter temperature and summer precipitation, and the variations among several years are reflected by circulation of ENSO events. In this study, at first, we analyze the relationship between land cover and interannual monsoon variations represented by precipitation changes using Singular Value Decomposition method based on the time series precipitation data and 8km NOAA AVHRR NDVI data covering 1982 to 1993 in east Asia. Furthermore, after confirmation and reclassification of ENSO events which are recognized as the strong signal of several year monsoon variation, using the same time series NDVI data during 1982 to 1993 in east Asia, we make a Principle Component Analysis and analyzed the correlation of the 7th component eigenvectors and Southern Oscillation Index (SOI) that indicates the characteristic of ENSO events, and summed up the temporal-spatial distribution features of east Asian land cover’s inter-annual variations that are being driven by changes of ENSO events.

Application of NOAA-AVHRR NDVI time-series data to assess changes in Saudi Arabia's rangelands

The primary objective of this study was to assess the condition of a portion of Saudi Arabia's rangelands and evaluate the effects of grazing by the animal herds of indigenous nomads over the last decade. Because of the desertic condition of these rangelands, changes in vegetation cover are more subtle than would be the case for other, less arid areas. Consequently, a new analytic methodology for the detection of desertification of arid and hyper-arid rangelands was developed specifically for this project. The conceptual framework for the analysis is the use of the coefficient of variation (COV) of the monthly Normalized Difference Vegetation Index (NDVI, maximum-value composite) as a measure of vegetative biomass change. A higher NDVI COV for a given pixel (excluding cases of changes in land use) represents a greater change in vegetation biomass in the ground area represented by that pixel. Linear regression was used to determine the trend in COV values for each pixel over the 12-year period for which data was available; pixels with a negative slope are considered to represent ground areas with decreasing amounts of vegetation. Results were validated by tests of statistical significance and by comparison of the theoretical results to vegetation change and land-cover data from the remote sensing systems and from reconnaissance flights over select areas. These desertification trend results were then combined with land-cover information to provide an assessment of desertification status.

Maximizing land cover classification accuracies produced by decision trees at continental to global scales

Classification of land cover from remotely sensed data at continental to global scales requires sophisticated algorithms and feature selection techniques to optimize classifier performance. The authors examine methods to maximize classification accuracies using decision trees to map land cover from multitemporal AVHRR imagery at continental and global scales. As part of their analysis they test the utility of boosting, a new technique developed to increase classification accuracy by forcing the learning (classification) algorithm to concentrate on those training observations that are most difficult to classify. Their results show that boosting consistently reduces misclassification rates by 20-50% depending on the data set in question, and that most of the benefit gained by boosting is achieved after seven boosting iterations. They also assess the utility of including phenological metrics and geographic position as additional features to the classification algorithm. They find that using derived phenological metrics produces little improvement in classification accuracy relative to using an annual time series of NDVI data, but that geographic position provides substantial power for predicting land cover types at continental and global scales. However, in order to avoid generating spurious classification accuracies using geographic position, training data must be distributed evenly in geographic space.

Compatibility of FAO-ARTEMIS and NASA Pathfinder AVHRR Land NDVI data archives for the African continent

There is a need for a consistent time-series of NDVI data for vegetation monitoring in Africa. In this work, we explore the compatibility of the FAOARTEMIS and NASA Pathfinder AVHRR Land NDVI archives. Values from the two archives were found to be significantly and systematically different even for the recently corrected form of the Pathfinder archive. Differences are attributable to the different processing chains used on essentially the same raw data. The two archives should not be mixed together for time-series analysis of NDVI, without correction. Although differences varied for periods of the record covered by different satellites, these were practically small and we present a single linear relation to correct one series values to the same basis as the other.

Calibrating long-term AVHRR-derived NDVI imagery

Several studies have confirmed that the radiometric calibration of AVHRR instruments changes following launch, and drifts over time. These changes cause spurious trends to appear in NDVI data that confound the reliable detection of vegetation change. It has previously been established that approximate additive corrections can be applied to an entire NDVI image, if the postlaunch calibration coefficients are known. However, the results of postlaunch calibration studies published in the literature vary, leading to different NDVI corrections depending on which coefficients are used. We acquired ten years (1981–1991) of monthly NDVI images covering Western Australia (Latitude 13°S to 36°S) in which the NDVI had been derived using prelaunch calibration coefficients. To address the calibration issue, we have computed theoretical corrections to the NDVI based on the prelaunch coefficients used to compute the NDVI in our data set, and postlaunch calibration coefficients reported by Che and Price (1992) and Kaufman and Holben (1993). Large differences were found in the NDVI corrections, depending on which set of postlaunch calibrations was adopted. To resolve the differences, we assumed that the entire image was a seasonally invariant target, and that the trend in the mean NDVI time series was solely due to changes in sensor calibration. The results obtained using this “time series” approach agreed with those computed in theoretical simulations using the calibration coefficients of Kaufman and Holben (1993). It is concluded that the time series technique described in this article can be used to remove sensor-induced trends from a time series of NDVI data without any knowledge of the postlaunch calibration coefficients. To apply the time series calibration technique, the dates of any changes in the sensor prelaunch calibration coefficients (including changes in the satellite platform) must be known. No other information is necessary, offering a significant advantage over existing methods for users of NDVI time series image data.