Pathfinder AVHRR Land Research Articles

Comparison of Eight Techniques for Reconstructing Multi-Satellite Sensor Time-Series NDVI Data Sets in the Heihe River Basin, China

More than 20 techniques have been developed to de-noise time-series vegetation index data from different satellite sensors to reconstruct long time-series data sets. Although many studies have compared Normalized Difference Vegetation Index (NDVI) noise-reduction techniques, few studies have compared these techniques systematically and comprehensively. This study tested eight techniques for smoothing different vegetation types using different types of multi-temporal NDVI data (Advanced Very High Resolution Radiometer (AVHRR) (Global Inventory Modeling and Map Studies (GIMMS) and Pathfinder AVHRR Land (PAL), Satellite Pour l’ Observation de la Terre (SPOT) VEGETATION (VGT), and Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra)) with the ultimate purpose of determining the best reconstruction technique for each type of vegetation captured with four satellite sensors. These techniques include the modified best index slope extraction (M-BISE) technique, the Savitzky-Golay (S-G) technique, the mean value iteration filter (MVI) technique, the asymmetric Gaussian (A-G) technique, the double logistic (D-L) technique, the changing-weight filter (CW) technique, the interpolation for data reconstruction (IDR) technique, and the Whittaker smoother (WS) technique. These techniques were evaluated by calculating the root mean square error (RMSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). The results indicate that the S-G, CW, and WS techniques perform better than the other tested techniques, while the IDR, M-BISE, and MVI techniques performed worse than the other techniques. The best de-noise technique varies with different vegetation types and NDVI data sources. The S-G performs best in most situations. In addition, the CW and WS are effective techniques that were exceeded only by the S-G technique. The assessment results are consistent in terms of the three evaluation indexes for GIMMS, PAL, and SPOT data in the study area, but not for the MODIS data. The study will be very helpful for choosing reconstruction techniques for long time-series data sets.

Global evaluation of four AVHRR–NDVI data sets: Intercomparison and assessment against Landsat imagery

Advanced Very High Resolution Radiometer (AVHRR) data with their long-term (1981–current) global coverage at frequent intervals provide unique opportunities to explore vegetation dynamics related to climate variability, climate change, and land-use driven changes of land cover. Several AVHRR-derived Normalized Difference Vegetation Index (NDVI) data sets exist, each based on the AVHRR Global Area Coverage archive but differing in their processing to correct for sensor and atmospheric effects. This paper presents a global comparative analysis for the land surface involving four AVHRR-derived NDVI data sets: (1) Pathfinder AVHRR Land (PAL); (2) Global Inventory Modeling and Mapping Studies (GIMMS); (3) Land Long Term Data Record (LTDR) version 3 (V3); and (4) Fourier-Adjustment, Solar zenith angle corrected, Interpolated Reconstructed (FASIR). Our aims are two-fold: (1) to assess the level of agreement of the medians, trends, and variances, as well as the correlation between the four AVHRR–NDVI data sets from 1982 to 1999; and (2) to independently assess the performance of each AVHRR–NDVI data set, and that of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI, using 11,764 Landsat samples of 20 × 20 km 2 located globally covering every major land-cover type. For the AVHRR–NDVI intercomparison equal medians, variance, and trends, and no correlation between all the respective AVHRR–NDVI data sets were found for 9.9%, 45.5%, 48.1% and 38.4% of the total land surface, respectively ( p ≥ 0.05). For the four AVHRR–NDVI data sets we found: (1) consistent trends for the tundra and particularly Australia; (2) inconsistent trends for Europe, Africa, and the Sahel; and (3) moderately consistent trends for the rest of the terrestrial land surface including North America and China. The PAL and LTDR V3 data sets lack calibration, as evidenced by the presence of apparent trends in desert areas. In the Landsat–NDVI vs. AVHRR–NDVI comparison of absolute values the LTDR V3 data set performed best, whereas in the comparison of temporal-change values the GIMMS data set performed best. In both analyses MODIS–NDVI performed better than any AVHRR–NDVI data set. The simple average of the four AVHRR–NDVI data sets produced better results than either AVHRR–NDVI data set alone, indicating that the errors between the data sets are at least partially unrelated. This research emphasizes the implications of AVHRR–NDVI data set choice for studies assessing the vegetation response to climate change and modeling of the terrestrial carbon balance.

Albedo-rainfall Feedback Over Indian Monsoon Region Using Long Term Observations Between 1981 to 2000

Albedo determines radiation balance of land (soil-canopy complex) surface and influence boundary layer structure of the atmosphere. Accurate surface albedo determination is important for weather forecasting, climate projection and ecosystem modelling. Albedo-rainfall feedback relationship has not been studied so far using observations on spatial scale over Indian monsoon region due to lack of consistent, systematic and simultaneous long-term measurements of both. The present study used dekadal (ten-day) composite of satellite (e.g. NOAA) based Pathfinder AVHRR Land (PAL) datasets between 1981 and 2000 over India (68–100°E, 5–40°N) at 8 km spatial resolution. Land surface albedo was computed using linear transformation of red and near infrared (NIR) surface reflectances. The cloud effects were removed using a smoothening filter with harmonic analysis applied to time series data in each year. The monthly, annual and long term means were computed from dekadal reconstructed albedo. The mean per year and coefficient of variation (CV) of surface albedo over seventeen years, averaged over Indian land region, were found to show a significantly decreasing (0.15 to 0.14 and 60 to 40%, respectively) trend between 1981 and 2000. Among all the land use patterns, the inter-annual variation of albedo of Himalayan snow cover showed a significant and the steepest reducing trend (0.42 – 0.35) followed by open shurbland, grassland and cropland. No significant change was noticed over different forest types.. This could be due to increase in snow melting period and snow melt area. A strong inverse exponential relation (correlation coefficient r = 0.95, n = 100) was found between annual rainfall and annual albedo over seven rainfall zones. The decreasing trend in snow-albedo of accumulation period (September to March) follows the declining trend in measured south-west monsoon rainfall between 1988 (980 mm) to 1998 (880 mm) over India. This finding perhaps suggests the possible reversal of reported coupling of increased snowfall followed by lower monsoon rainfall.

Assessing the performance of NDVI as a proxy for plant biomass using non-linear models: a case study on the Kerguelen archipelago

Numerous ecological studies, including of the polar environment, are now using the remotely sensed normalized difference vegetation index (NDVI, e.g. PAL-NDVI or MODIS-NDVI) as a proxy of vegetation productivity rather than performing direct vegetation assessments. Even though previous data strongly suggested a saturation of NDVI at high biomass values, few studies have explicitly included this characteristic in the modelling process. Here, we developed a generalized non-linear model to explicitly model the relationship between temporal variations of NDVI (Pathfinder AVHRR Land 8 km dataset) and empirical field data. We illustrated our approach on the Kerguelen archipelago by using a green biomass index (point-intercept protocol) sampled at a small scale relative to PAL-NDVI data, and in presence of spatial (water) and temporal (cloud contamination, snow) heterogeneity, i.e. field conditions encountered in many ecological studies. We showed a strong relationship (r pred.obs = 0.89 [0.77; 0.95]95%) between this index and the seasonal component of NDVI time series (NDVIcomp). Despite the absence of lignified species in the stand, the NDVIcomp reached an asymptote (0.54 ± 0.05) for high values of green biomass index stressing the need to account for non-linearity when relating NDVI and plant measurements. We provided here a new methodological framework to standardize comparisons between studies assessing performance of NDVI as a proxy of vegetation data.

Effects of atmospheric variation on AVHRR NDVI data

The AVHRR (Advanced Very High Resolution Radiometer) series of instruments has frequently been used for vegetation studies. The 25+ year record has enabled important time-series studies. Many applications use NDVI (Normalized Difference Vegetation Index), or derivatives of it, as their operational variable. However, most AVHRR datasets have incomplete atmospheric correction, because of which there is considerable, but largely unknown, uncertainty in the significance of differences in NDVI and other short wave observations from AVHRR instruments. The purpose of this study was to gain better understanding of the impact of incomplete or lack of atmospheric correction in widely-used, publicly available processed AVHRR-NDVI long-term datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AERONET (AErosol RObotic NETwork) sunphotometer sites in 1999. The datasets included in this study are: TOA (Top Of Atmosphere) that is with no atmospheric correction; PAL (Pathfinder AVHRR Land); and an early version of the new LTDR (Long Term Data Record) NDVI. The other publicly available datasets like GIMMS (Global Inventory Modeling and Mapping studies) and GVI (Global Vegetation Index) have atmospheric error budget similar to that of TOA, because no atmospheric correction is used in either processing stream. Of the three datasets, LTDR was found to have least errors (accuracy = 0.0064 to − 0.024, precision = 0.02 to 0.037 for clear and average atmospheric conditions) followed by PAL (accuracy = − 0.145 to − 0.035, precision = 0.0606 to 0.0418), and TOA (accuracy = − 0.0791 to − 0.112, precision = 0.0613 to 0.0684). It was also observed that temporal maximum value compositing technique does not cause significant improvement of precision in regions experiencing persistently high AOT (Aerosol Optical Thickness).

Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVIs of northern Asia

This study examined the covariability between interannual changes in the normalized difference vegetation index (NDVI) and actual evapotranspiration (ET). To reduce possible uncertainty in the NDVI time series, two NDVI datasets derived from Pathfinder AVHRR Land (PAL) data and the Global Inventory Monitoring and Modeling Studies (GIMMS) group were used. Analyses were conducted using data over northern Asia from 1982 to 2000. Interannual changes over 19 years in the PAL-NDVI and GIMMS-NDVI were compared with interannual changes in ET estimated from model-assimilated atmospheric data and gridded precipitation data. For both NDVI datasets, the annual maximum correlation with ET occurred in June, which is the beginning of the vegetation growing season. The PAL and GIMMS datasets showed a significant, positive correlation between interannual changes in the NDVI and ET over most of the vegetated land area in June. These results suggest that interannual changes in vegetation activity predominantly control interannual changes in ET in June. Based on analyses of interannual changes in temperature, precipitation, and the NDVI in June, the study area can be roughly divided into two regions, the warmth-dominated northernmost region and the wetness-dominated southern region, indicating that interannual changes in vegetation and the resultant interannual changes in ET are controlled by warmth and wetness in these two regions, respectively.

Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999

We used land surface temperature (LST) algorithms and NDVI values to estimate changes in vegetation in the European continent between 1982 and 1999 from the Pathfinder AVHRR Land (PAL) dataset. These two parameters are monitored through HANTS (Harmonic ANalysis of Time Series) software, which allows the simultaneous observation of mean value, first harmonic amplitude and phase behaviors in the same image. These results for each complete year of data show the effect of volcanic aerosols and orbital drift on PAL data. Comparison of time series of HANTS cloud-free time series with the original time series for various land cover proves that this software is useful for LST analysis, although primarily designed for NDVI applications. Comparison of yearly averages of HANTS LST over the whole Europe with air temperature confirms the validity of the results. Maps of the evolution for both parameters between periods 1982/1986 and 1995/1999 have been elaborated: NDVI data show the well confirmed trend of increase over Europe (up to 0.1 in NDVI), Southern Europe seeing a decrease in NDVI (− 0.02). LST averages stay stable or slightly decrease (up to − 1.5 K) over the whole continent, except for southern areas for which the increase is up to 2.5 K. These results evidence that arid and semi-arid areas of Southern Europe have become more arid, the rest of Europe seeing an increase in its wood land proportion, while seasonal amplitude in Northern Europe has decreased.

Normalization of the directional effects in NOAA–AVHRR reflectance measurements for an improved monitoring of vegetation cycles

This paper describes a correction of directional effects in AVHRR reflectance time series. The method relies on a priori directional signatures, in the red and near infrared, for various surface covers. The reflectance normalization is applied to the historical Pathfinder AVHRR Land (PAL) data set acquired between 1981 and 1999. The high frequency variability in the reflectance time series, which is interpreted as a noise due to varying observation geometry and directional effects, is reduced by a factor greater than 1.7 for most vegetated surfaces. For the analysis of the vegetation annual cycle and its inter-annual variations, we recommend the use of the Difference Vegetation Index (DVI), following a prior correction of the directional effects, instead of the commonly used NDVI. Indeed, DVI appears more robust than NDVI to noise resulting from atmospheric perturbations, which makes it a good candidate for long-term ecological surveys and climate change studies.

Influence of solar zenith angles on observed trends in the NOAA/NASA 8‐km Pathfinder normalized difference vegetation index over the African Sahel

The strong systematic change in solar zenith angles (SZA) due to annual orbital drift of the NOAA satellites has raised the suspicion of the influence of residual illumination on the calibrated normalized difference vegetation index (NDVI) derived from the Pathfinder AVHRR Land (PAL) database. The aim of this work is to analyse if trends in AVHRR NDVI from 1982 to 2000 over the Sahel region in Africa depend on variations in SZA. The analysis uses both ordinary least squares regression and cointegration to analyse possible linear dependencies between NDVI and SZA on a per satellite basis. Tests for integration and cointegration fail to find any significant evidence for either. This, together with the ability of simple deterministic models to explain primarily SZA constitutes evidence against integration and cointegration, indicating that linear relationships can be examined using ordinary linear regression. Regression gives no consistent relationship between NDVI and SZA and the explanatory power (R 2) of the regression is low (on average 0.08). However there is some evidence for downward bias in NDVI due to nonlinear interactions between NDVI and SZA when SZA is large (⩾80°) leading to the conclusion that PAL data from the year 2000 should not be used for analyses in these environments.

Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada

The boreal forest biome is one of the largest on Earth, covering more than 14% of the total land surface. Fire disturbance plays a dominant role in boreal ecosystems, altering forest succession, biogeochemical cycling, and carbon sequestration. We used two time-series data sets of Advanced Very High Resolution Radiometer (AVHRR) Normalized Differenced Vegetation Index (NDVI) imagery for North America to analyze vegetation recovery after fire. The Canadian Forest Service Large Fire Database was used to identify the location of fires and calculate scaled NDVI statistics from the Pathfinder AVHRR Land (PAL) and the Global Inventory Modeling and Mapping Studies (GIMMS) AVHRR data sets. Unburned areas were also identified, based on interannual variability metrics, in order to reduce the effects of factors other than fire on the temporal behavior of scaled NDVI. Burned and unburned areas were stratified by ecoregion to ensure the presence of comparable land cover types and account for influences of local environmental variability. Temporal anomalies in NDVI for burned and unburned areas show the impacts of fire and the recovery of the forest to pre-burn levels, and indicate changes in variability that might be associated with vegetation compositional changes consistent with early successional species. The rate of recovery varied in the three episodic fire years on which we focused our analysis (1981, 1989, and 1995), but were consistently shorter than previous studies that emphasized the most impacted areas within fires. Temporal variability in the time series, represented by the difference of burned and unburned area anomalies, increased beyond the observed post-fire recovery period. This indicates residual effects of fire disturbance over the regrowth period, perhaps associated with early successional vegetation and increased susceptibility to drought. Distinct differences were noted between the PAL and GIMMS data sets, with evidence for systematic data processing artifacts remaining in the PAL time series.

A method to convert AVHRR Normalized Difference Vegetation Index time series to a standard viewing and illumination geometry

The bi-directional reflectance distribution function (BRDF) alters the seasonal and inter-annual variations exhibited in Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data and this hampers the detection and, consequently, the interpretation of temporal variations in land-surface vegetation. The magnitude and sign of bi-directional effects in commonly used AVHRR data sets depend on land-surface properties, atmospheric composition and the type of atmospheric correction that is applied to the data. We develop an approach to estimate BRDF effects in AVHRR NDVI time series using the Moderate Resolution Imaging Spectrometer (MODIS) BRDF kernels and subsequently adjust NDVI time series to a standard illumination and viewing geometry. The approach is tested on NDVI time series that are simulated for representative AVHRR viewing and illumination geometry. These time series are simulated with a canopy radiative transfer model coupled to an atmospheric radiative transfer model for four different land cover types—tropical forest, boreal forest, temperate forest and grassland – and five different atmospheric conditions – turbid and clear top-of-atmosphere, turbid and clear top-of-atmosphere with a correction for ozone absorption and Rayleigh scattering applied (Pathfinder AVHRR Land data) and ground-observations (fully corrected for atmospheric effects). The simulations indicate that the timing of key phenological stages, such as start and end of growing season and time of maximum greenness, is affected by BRDF effects. Moreover, BRDF effects vary with latitude and season and increase over the time of operation of subsequent NOAA satellites because of orbital drift. Application of the MODIS kernels on simulated NVDI data results in a 50% to 85% reduction of BRDF effects. When applied to the global 18-year global Normalized Difference Vegetation Index (NDVI) Pathfinder data we find BRDF effects similar in magnitude to those in the simulations. Our analysis of the global data shows that BRDF effects are especially large in high latitudes; here we find that in at least 20% of the data BRDF errors are too large for accurate detection of seasonal and interannual variability. These large BRDF errors tend to compensate, however, when averaged over latitude.

Changing patterns of global‐scale vegetation photosynthesis, 1982–1999

The primary objective of this research was to assess changes in global vegetation photosynthesis between 1982 and 1999. Global‐scale Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data from the Pathfinder AVHRR Land (PAL) and Global Inventory Modeling and Mapping Studies (GIMMS) datasets were analysed for 96% of the non‐Antarctic land area of the Earth. The results showed that between 1982 and 1999 over 30% of the Earth's land surface increased and less than 5% decreased in annual average photosynthesis greater than 4%. Although both the PAL and GIMMS datasets produced broadly similar patterns of change, there were distinct differences between the two datasets. Changes in vegetation photosynthesis were occurring in spatial clusters across the globe and were being driven by climate change, El Niño–Southern Oscillation (ENSO) events and human activity.

Reconstructing pathfinder AVHRR land NDVI time-series data for the Northwest of China

Vegetation typically elicits dynamics at the seasonal and annual level. Time-series of normalized difference vegetation index (NDVI) datasets, such as the pathfinder AVHRR land (PAL) NDVI dataset, have proven to be appropriate for the detection of long-term vegetation cover changes. It has been applied in modelling experiments for terrestrial ecosystems at the global, continental, and regional scales. But some PAL NDVI time series remain significant residual effects and noise levels. A simple method, the mean-value iteration filter (MVI), has been developed to reduce the noise and to enable the reconstruction of high quality NDVI time-series. A comparison between the newly developed method and other existing methods (the modified BISE algorithm and a fast Fourier transform algorithm) indicates that the newly developed method is an effective tool for reconstructing high-quality time series of PAL NDVI time series.

Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index

This study examined the effect of biomass-burning aerosols and clouds on the temporal dynamics of the normalized difference vegetation index (NDVI) exhibited by two widely used, time-series NDVI data products: the Pathfinder AVHRR land (PAL) dataset and the NASA Global Inventory Monitoring and Modeling Studies (GIMMS) dataset. The PAL data are 10-day maximum-value NDVI composites from 1982 to 1999 with corrections for Rayleigh scattering and ozone absorption. The GIMMS data are 15-day maximum-value NDVI composites from 1982 to 1999. In our analysis, monthly maximum-value NDVI was extracted from these datasets. The effects were quantified by comparing time-series of NDVI from PAL and GIMMS with observations from the SPOT/VEGETATION sensor and aerosol index data from the Total Ozone Mapping Spectrometer (TOMS), and results from radiative transfer simulation. Our analysis suggests that the substantial large-scale NDVI seasonality observed in the south and east Amazon forest region with PAL and GIMMS is primarily caused by variations in atmospheric conditions associated with biomass-burning aerosols and cloudiness. Reliable NDVI data can be typically acquired from April to July when such effects are relatively low, whereas there is a few effective NDVI data from September to December. In the central Amazon forest region, where aerosol loads are relatively low throughout the year, large-scale NDVI seasonality results primarily from seasonal variations in cloud cover. Careful treatment of these aerosol and cloud effects is required when using NDVI from PAL and GIMMS (or other source) to determine large-scale seasonal and interannual dynamics of vegetation greenness and ecosystem–atmosphere CO 2 exchange in the Amazon region.

Evaluating vegetation recovery following large-scale forest fires in Borneo and northeastern China using multi-temporal NOAA/AVHRR images

This study evaluated whether a normalized difference vegetation index (NDVI), derived from 8-km-resolution National Oceanic Atmospheric Administration (NOAA) Pathfinder AVHRR (advanced very high resolution radiometer) land (PAL) data, is appropriate for monitoring recovery following large-scale forest fires. Recovery processes were examined after fires on the island of Borneo and in northeastern China in 1983 and 1987, respectively. Based on pre- and post-fire NDVI differences (NDVId), six damage classes were established. Post-fire changes in land cover were monitored using (1) the average NDVI of all pixels corresponding to each damage class (A-NDVI) and (2) the ratio of a fire-affected A-NDVI to a non-fire-affected A-NDVI (QNDVI). Burn areas located by an NDVId threshold value were similar to reported burn areas. Both A-NDVI and QNDVI values signaled vegetation recovery, but the QNDVI gave much better results. For both the 1983 Borneo and 1987 northeastern China fires, QNDVI values dropped at the time of the fire and increased for about 4 years afterwards, although a 4-year period is obviously less than the time required for biomass recovery. Trends at the two study sites diverged after this period, however. The QNDVI values for multiple fire events in Borneo (in 1983, 1987, 1991, 1997, and 1998) showed that recovery times varied with the size of the burn area, but not with the damage class of the same event, whereas the severe-class QNDVI values for the fire in northeastern China in 1987 were still lower than the control value 10 years after the fire.

Landscape, soil and meteorological influences on canopy dynamics of northern flooding Pampa grasslands, Argentina

AbstractQuestion: How do meteorological variations at seasonal, interannual scales differentially affect the canopy dynamics of four contrasting landscape units within a region?Location: Flooding Pampa, Buenos Aires, Argentina. 5000 km2. Central point: 35°15′S, 57°45′W.Methods: We used a 19‐year series of the normalized difference vegetation index (NDVI) derived from NOAA‐AVHRR PAL (Pathfinder AVHRR Land) images and meteorological data provided by a nearby weather station. The NDVI was used as surrogate of canopy photosynthetic status. The relationship between annually integrated NDVI and meteorological conditions was explored by stepwise multiple regressions for each defined unit. PC A was performed to compare units and growing seasons on a multivariate basis.Results: Mean seasonal NDVI curve was similarly shaped among landscapes. However, the absolute values differed widely. There was high interannual variation so that the mean seasonal pattern was seldom observed in any particular year. Annually integrated NDVI of all landscapes was negatively associated with summer temperature and positively with previous year precipitation. It was also directly related with current year winter precipitation in two landscapes and with summer precipitation in the others. NDVI response to September and March precipitation accounted for some of the differences in interannual variation among landscapes.Conclusions: Our results revealed a strong intra‐regional variation of canopy dynamics, closely linked to landscape (vegetation‐soil) and water availability (mainly in summer and during the previous year). These links may be used to predict forage production rates for livestock.

Landscape, soil and meteorological influences on canopy dynamics of northern flooding Pampa grasslands, Argentina

Abstract Question: How do meteorological variations at seasonal, interannual scales differentially affect the canopy dynamics of four contrasting landscape units within a region? Location: Flooding Pampa, Buenos Aires, Argentina. 5000 km2. Central point: 35°15′ S, 57°45′ W. Methods: We used a 19-year series of the normalized difference vegetation index (NDVI) derived from NOAA-AVHRR PAL (Pathfinder AVHRR Land) images and meteorological data provided by a nearby weather station. The NDVI was used as surrogate of canopy photosynthetic status. The relationship between annually integrated NDVI and meteorological conditions was explored by stepwise multiple regressions for each defined unit. PCA was performed to compare units and growing seasons on a multivariate basis. Results: Mean seasonal NDVI curve was similarly shaped among landscapes. However, the absolute values differed widely. There was high interannual variation so that the mean seasonal pattern was seldom observed in any particular year. Annually i...

Spatio-temporal change analysis to identify anomalous variation in the vegetated land surface: ENSO effects in tropical South America

[1] Seasonal variation of the vegetated land surface across tropical South America was evaluated using Trajectory Analysis (TA) on the Pathfinder AVHRR Land (PAL) NDVI data. These 8 km 10-day maximum-value composite images of the Normalized Difference Vegetation Index (NDVI) span nearly two decades (7/81–12/99) that include several ENSO warm/cold phases. The derived trajectory established a baseline to assess the effect of climatic events related to the El Nino/Southern Oscillation (ENSO) on the temporal development of the spatial dependence structure of the NDVI image time series. Results indicate that ENSO phases have significant effects on the spatial dependence structure of the land surface in Tropical South America that would be undetected, if the spatial domain of remotely sensed data were neglected. As such, TA provides an important technique for the assessment of the effects of global change and long-term land use/land cover transformations on phenologies of the vegetated land surface.

TIMESAT—a program for analyzing time-series of satellite sensor data

Three different least-squares methods for processing time-series of satellite sensor data are presented. The first method uses local polynomial functions and can be classified as an adaptive Savitzky–Golay filter. The other two methods are more clear cut least-squares methods, where data are fit to a basis of harmonic functions and asymmetric Gaussian functions, respectively. The methods incorporate qualitative information on cloud contamination from ancillary datasets. The resulting smooth curves are used for extracting seasonal parameters related to the growing seasons. The methods are implemented in a computer program, TIMESAT, and applied to NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index data over Africa, giving spatially coherent images of seasonal parameters such as beginnings and ends of growing seasons, seasonally integrated NDVI and seasonal amplitudes. Based on general principles, the TIMESAT program can be used also for other types of satellite-derived time-series data.

Studies on Surface Temperature using Remote Sensing Technique in the NW Part of Bangladesh

A simple and straightforward technique based on remote sensing was applied for the estimation of surface emissivity and surface temperature at different dates in the northwestern part of Bangladesh for the summer and autumn period (March through October). Surface temperature was estimated from Pathfinder AVHRR Land (PAL) daily data. For this estimation different split window algorithms were employed. Estimated results show a discrepancy ranging from 0.04°C to 4.0°C. Surface emissivity was calculated based on fractionation of vegetation cover and NDVI at pixel level calculated from TM data. TM data was corrected for atmospheric effect. PAL data set was provided after atmospheric and geometric correction. Both air temperature and estimated surface temperature shows a decreasing trend from south to farther north. However, no distinct trend could be recognized in the temporal variation in surface temperature and in measured air temperature. A general trend of negative relationship could be concluded between NDVI and surface temperature from this study.