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 km² 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.     

Impact of orthorectification and spatial sampling on maximum NDVI composite data in mountain regions

Topography and accuracy of image geometric registration significantly affect the quality of satellite data, since pixels are displaced depending on surface elevation and viewing geometry. This effect should be corrected for through the process of accurate image navigation and orthorectification in order to meet the geolocation accuracy for systematic observations specified by the Global Climate Observing System (GCOS) requirements for satellite climate data records. We investigated the impact of orthorectification on the accuracy of maximum Normalized Difference Vegetation Index (NDVI) composite data for a mountain region in north-western Canada at various spatial resolutions (1 km, 4 km, 5 km, and 8 km). Data from AVHRR on board NOAA-11 (1989 and 1990) and NOAA-16 (2001, 2002, and 2003) processed using a system called CAPS (Canadian AVHRR Processing System) for the month of August were considered. Results demonstrate the significant impact of orthorectification on the quality of composite NDVI data in mountainous terrain. Differences between orthorectified and non-orthorectified NDVI composites (ΔNDVI) adopted both large positive and negative values, with the 1% and 99% percentiles of ΔNDVI at 1 km resolution spanning values between − 0.16 < ΔNDVI < 0.09. Differences were generally reduced to smaller numbers for coarser resolution data, but systematic positive biases for non-orthorectified composites were obtained at all spatial resolutions, ranging from 0.02 (1 km) to 0.004 (8 km). Analyzing the power spectra of maximum NDVI composites at 1 km resolution, large differences between orthorectified and non-orthorectified AVHRR data were identified at spatial scales between 4 km and 10 km. Validation of NOAA-16 AVHRR NDVI with MODIS NDVI composites revealed higher correlation coefficients (by up to 0.1) for orthorectified composites relative to the non-orthorectified case. Uncertainties due to the AVHRR Global Area Coverage (GAC) sampling scheme introduce an average positive bias of 0.02 ± 0.03 at maximum NDVI composite level that translates into an average relative bias of 10.6% ± 19.1 for sparsely vegetated mountain regions. This can at least partially explain the systematic average positive biases we observed relative to our results in AVHRR GAC-based composites from the Global Inventory Modeling and Mapping Studies (GIMMS) and Polar Pathfinder (PPF) datasets (0.19 and 0.05, respectively). With regard to the generation of AVHRR long-term climate data records, results suggest that orthorectification should be an integral part of AVHRR pre-processing, since neglecting the terrain displacement effect may lead to important biases and additional noise in time series at various spatial scales.     

Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A

This work extends the previous study of Trishchenko et al. [Trishchenko, A. P., Cihlar, J., & Li, Z. (2002). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment 81 (1), 1-18] that analyzed the spectral response function (SRF) effect for the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites NOAA-6 to NOAA-16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). The developed approach is now applied to cover three new AVHRR sensors launched in recent years on NOAA-17, 18, and METOP-A platforms. As in the previous study, the results are provided relative to the reference sensor AVHRR NOAA-9. The differences in reflectance among these three radiometers relative to the AVHRR NOAA-9 are similar to each other and range from − 0.015 to 0.015 (− 20% to + 2% relative) for visible (red) channel, and from − 0.03 to 0.02 (− 5% to 5%) for the near infrared (NIR) channel. The absolute change in the Normalized Difference Vegetation Index (NDVI) ranged from − 0.03 to + 0.06. Due to systematic biases of the visible channels toward smaller values and the NIR channels toward slightly larger values, the overall systematic biases for NDVI are positive. The polynomial approximations are provided for the bulk spectral correction with respect to the AVHRR NOAA-9 for consistency with previous study. Analysis was also conducted for the SRF effect only among the AVHRR-3 type of radiometer on NOAA-15, 16, 17, 18 and METOP-A using AVHRR NOAA-18 as a reference. The results show more consistency between sensors with typical correction being under 5% (or 0.01 in absolute values). The AVHRR METOP-A reveals the most different behavior among the AVHRR-3 group with generally positive bias for visible channel (up to + 5%, relative), slightly negative bias for the NIR channel (1%-2% relative), and negative NDVI bias (− 0.02 to + 0.005). Polynomial corrections are also suggested for normalization of AVHRR on NOAA-15, 16, 17 and METOP-A to AVHRR NOAA-18.     

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.     

Multi-temporal analysis of deforestation in Rondônia state in Brazil using Landsat MSS,TM, ETM+ and NOAA AVHRR imagery and its relationship to changes in the local hydrological environment

Deforestation in Rondônia state in the south-western part of the Brazilian Legal Amazon was analysed using Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), National Oceanic & Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and hydrological data. The Landsat sensor data coverage was supplemented with Pathfinder AVHRR Land (PAL) Normalized Difference Vegetation Index (NDVI) datasets. The results from the Landsat-based analysis show that more than 30% of the natural vegetation in the study area was subject to deforestation between 1973 and 1999, a finding reinforced by analysis of the PAL NDVI data. In addition, it was established that trends in the PAL NDVI datasets were coincident with the pattern of deforestation. Apart from imagery analysis, time variations in the hydrological data between 1982 and 1988 were used to estimate the evapotranspiration. A decreasing trend was identified in the rate of evapotranspiration, suggesting that deforestation has a significant impact on the local hydrological cycle.     

Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products

Global NDVI data are routinely derived from the AVHRR, SPOT-VGT, and MODIS/Terra earth observation records for a range of applications from terrestrial vegetation monitoring to climate change modeling. This has led to a substantial interest in the harmonization of multisensor records. Most evaluations of the internal consistency and continuity of global multisensor NDVI products have focused on time-series harmonization in the spectral domain, often neglecting the spatial domain. We fill this void by applying variogram modeling (a) to evaluate the differences in spatial variability between 8-km AVHRR, 1-km SPOT-VGT, and 1-km, 500-m, and 250-m MODIS NDVI products over eight EOS (Earth Observing System) validation sites, and (b) to characterize the decay of spatial variability as a function of pixel size (i.e. data regularization) for spatially aggregated Landsat ETM+ NDVI products and a real multisensor dataset. First, we demonstrate that the conjunctive analysis of two variogram properties - the sill and the mean length scale metric - provides a robust assessment of the differences in spatial variability between multiscale NDVI products that are due to spatial (nominal pixel size, point spread function, and view angle) and non-spatial (sensor calibration, cloud clearing, atmospheric corrections, and length of multi-day compositing period) factors. Next, we show that as the nominal pixel size increases, the decay of spatial information content follows a logarithmic relationship with stronger fit value for the spatially aggregated NDVI products (R² = 0.9321) than for the native-resolution AVHRR, SPOT-VGT, and MODIS NDVI products (R² = 0.5064). This relationship serves as a reference for evaluation of the differences in spatial variability and length scales in multiscale datasets at native or aggregated spatial resolutions. The outcomes of this study suggest that multisensor NDVI records cannot be integrated into a long-term data record without proper consideration of all factors affecting their spatial consistency. Hence, we propose an approach for selecting the spatial resolution, at which differences in spatial variability between NDVI products from multiple sensors are minimized. This approach provides practical guidance for the harmonization of long-term multisensor datasets.     

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.     

Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS

The long term Advanced Very High Resolution Radiometer (AVHRR)‐Normalized Difference Vegetation Index (NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non‐stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor‐specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at 1° is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product.     

Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series

A method is developed to separate Normalised Difference Vegetation Index (NDVI) time series data into contributions from woody (perennial) and herbaceous (annual) vegetation, and thereby to infer their separate leaf area indices and cover fractions. The method is formally consistent with fundamental linearity requirements for such a decomposition, and is capable of rejecting contaminated NDVI data. In this study, estimates of annual averaged woody cover and monthly averaged herbaceous cover over Australia are determined using Pathfinder AVHRR Land series (PAL) Global Area Coverage (GAC) Advanced Very High Resolution Radiometer (AVHRR) NDVI data from 1981 to 1994, together with ground-based measurements of leaf area index (LAI) and foliage projective cover (FPC).     

Validation of clear-sky radiances over oceans simulated with MODTRAN4.2 and global NCEP GDAS fields against nighttime NOAA15-18 and MetOp-A AVHRR data

An accurate and globally representative forward radiative transfer model (RTM) is needed to explore improvements in sea surface temperature (SST) retrievals from spaceborne infrared observations. This study evaluates the biases in top-of-atmosphere (TOA) brightness temperatures (BT) modeled with the moderate resolution transmission (MODTRAN4.2) band RTM, bounded by a Fresnel's reflective flat sea surface. This model is used to simulate global clear-sky Advanced Very High Resolution Radiometer (AVHRR) nighttime BTs from NOAA-15 through 18 and MetOp-A platforms for one full day of 18 February 2007. Inputs to RTM (SST fields and vertical profiles of atmospheric relative humidity, temperature, pressure, and geopotential height) are specified from the National Centers for Environmental Prediction's (NCEP) Global Data Assimilation System (GDAS) data. Model BTs in AVHRR channels 3B (3.7 μm), 4 (11 μm), and 5 (12 μm) are then compared with their respective measured counterparts, available in the NESDIS operational SST files. Ideally, the RTM should match the observations, but in fact, the modeled BTs are biased high with respect to the AVHRR BTs. The “Model minus Observation” (M − O) bias ranges from about 0 to 2 K, depending upon spectral band, view zenith angle, and sea and atmosphere state at the retrieval point. The bias asymptotically decreases towards confidently clear-sky conditions, but it never vanishes and invariably shows channel-specific dependencies on view zenith angle and geophysical conditions (e.g., column water vapor and sea-air temperature difference). Fuller exploration of the potential of the current RTM (e.g., adding global vertical aerosol profiles) or improvements to its input (NCEP SST and atmospheric profiles) may reduce this bias, but they cannot fully reconcile its spectral and angular structure. The fact that the M − O biases are closely reproducible for five AVHRR sensors flown onboard different platforms adds confidence in the validation approach employed in this study. We emphasize the need for establishing a globally adequate forward RTM for the use in SST modeling and retrievals. A first test of the RTM adequacy is its ability, when used in conjunction with the global fields from the numerical weather prediction models, to reproduce the TOA clear-sky radiances measured by satellite sensors.     

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 CO₂ exchange in the Amazon region.     

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.     

Evaluation of AVHRR PAL and GIMMS 10‐day composite NDVI time series products using SPOT‐4 vegetation data for the African continent

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10‐day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT‐4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT‐4 vegetation (VGT) 10‐day composite (S10) NDVI data. Three years of AVHRR PAL (1998–2000) and seven years of GIMMS (1998–2004) have been compared to 8 km resampled SPOT‐4 VGT (1998–2004) data. The dynamic range of SPOT‐4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT‐4 VGT NDVI. Ortho‐regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT‐4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT‐4 VGT compared to the PAL/SPOT‐4 VGT.

Analyses on decade data likewise show that a linear relation exists between Spot‐4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot‐4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot‐4 VGT NDVI compared to AVHRR PAL versus Spot‐4 VGT NDVI (in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composite data, however, reveal intra‐annual variations in the correlation between SPOT‐4 VGT and the two AVHRR data sets, which are attributed to different cloud masking algorithms. The SPOT‐4 VGT cloud‐screening algorithm is insufficient, thereby suppressing the rainy season NDVI.     

Temporal stability of some global NDVI products derived from NOAA/AVHRR GVI

The Normalized DilTerence Vegetation Index (NDVI) derived from NOAA's Advanced Very High Resolution Radiometer (AVHRR) has been widely used in monitoring continental and global vegetation distribution and dynamics, drought severity and location, and environmental deterioration. Since 1982, NOAA has produced the Weekly Global Vegetation Index (GVI) product from AVHRR. The analyses of the GVI product have revealed many problems due to the simplified radiometric correction involved in the processing. Those limitations have inspired several elTorts to reprocess the NOAA GVI data sets to produce an improved representation of global NDVI patterns. In this paper, the quality of three Global NDVI products resulting from very simple to rather sophisticated reprocessing was examined by using a global approach. In general, the quality of data improves with increasing sophistication of radiometric correction. However, this study reveals some significant errors common in all three products assessed. The problems include a systematic annual increase in values computed from a single satellite and jumps between consecutive satellites. These errors are large enough to alTect results of the long term time-series analyses. This pattern suggests an additional radiometric distortion in NOAA/ AVHRR data. It is found that the values computed from data of the first year after satellite launch are roughly the same statistically for NOAA satellites. Thus, the discontinuity ofNDVls between satellites appears to be mainly caused by the systematic drift. Therefore, data collected in the first year of satellite launch might be considered as a baseline for correcting the systematic errors. By comparing NDVI from the first year of satellites in space, it is also found that NDVI increases at higher latitude and decreases or keeps constant at lower latitude. This change of NDVI with time might signal the change of global climate.     

Evaluation of earth observation based long term vegetation trends — Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data

AVHRR (Advanced Very High Resolution Radiometer) GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference vegetation Index) data is available from 1981 to present time. The global coverage 8 km resolution 15-day composite data set has been used for numerous local to global scale vegetation time series studies during recent years. Several aspects however potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. More recent NDVI data sets from both Terra MODIS and SPOT VGT data are considered an improvement over AVHRR and these products in theory provide a possibility to evaluate the accuracy of GIMMS NDVI time series trend analysis for the overlapping period of available data. In this study the accuracy of the GIMMS NDVI time series trend analysis is evaluated by comparison with the 1 km resolution Terra MODIS (MOD13A2) 16-day composite NDVI data, the SPOT Vegetation (VGT) 10-day composite (S10) NDVI data and in situ measurements of a test site in Dahra, Senegal. Linear least squares regression trend analysis on eight years of GIMMS annual average NDVI (2000-2007) has been compared to Terra MODIS (1 km and 8 km resampled) and SPOT VGT NDVI data 1 km (2000-2007). The three data products do not exhibit identical patterns of NDVI trends. SPOT VGT NDVI data are characterised by higher positive regression slopes over the 8-year period as compared to Terra MODIS and AVHRR GIMMS NDVI data, possibly caused by a change in channels 1 and 2 spectral response functions from SPOT VGT1 to SPOT VGT2 in 2003. Trend analysis of AVHRR GIMMS NDVI exhibits a regression slope range in better agreement with Terra MODIS NDVI for semi-arid areas. However, GIMMS NDVI shows a tendency towards higher positive regression slope values than Terra MODIS in more humid areas. Validation of the different NDVI data products against continuous in situ NDVI measurements for the period 2002-2007 in the semi-arid Senegal revealed a good agreement between in situ measurements and all satellite based NDVI products. Using Terra MODIS NDVI as a reference, it is concluded that AVHRR GIMMS coarse resolution NDVI data set is well-suited for long term vegetation studies of the Sahel-Sudanian areas receiving < 1000 mm rainfall, whereas interpretation of GIMMS NDVI trends in more humid areas of the Sudanian-Guinean zones should be done with certain reservations.     

Trends in vegetation NDVI from 1 km AVHRR data over Canada for the period 1985–2006

Long‐term changes in the Normalized Difference Vegetation Index (NDVI) have been evaluated in several studies but results have not been conclusive due to differences in data processing as well as the length and time of the analysed period. In this research a newly developed 1 km Advanced Very High Resolution Radiometer (AVHRR) satellite data record for the period 1985–2006 was used to rigorously evaluate NDVI trends over Canada. Furthermore, climate and land cover change as potential causes of observed trends were evaluated in eight sample regions. The AVHRR record was generated using improved geolocation, cloud screening, correction for sun‐sensor viewing geometry, atmospheric correction, and compositing. Results from both AVHRR and Landsat revealed an increasing NDVI trend over northern regions where comparison was possible. Overall, 22% of the vegetated area in Canada was found to have a positive NDVI trend based on the Mann–Kendal test at the 95% confidence level. Of these, 40% were in northern ecozones. The mean absolute difference of NDVI measurements between AVHRR and Landsat data was <7%. When compared with results from other studies, similar trends were found over northern areas, while in southern regions the results were less consistent. Local assessment of potential causes of trends in each region revealed a stronger influence of climate in the north compared to the south. Southern regions with strong positive trends appeared to be most influenced by land cover change.     

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.     

不同辐射校正水平下水稻植被指数监测对比分析

归一化植被指数(NDVI)是反映植被长势特征的重要参数之一。获取准确的植被指数对揭示植被长势变化等定量遥感分析至关重要。基于不同辐射校正水平(辐射定标与大气校正),分别利用Landsat ETM+影像的灰度值(DN)、表观(TOA)反射率与地表(Surface)反射率计算相应NDVI,并根据鄱阳湖区野外定点观测数据,从农田、景观尺度揭示不同辐射校正水平下水稻生育期内NDVI动态变化特征。结果表明,根据DN、TOA反射率与Surface反射率提取的NDVI基本上可以反映出年内水稻不同熟制种植信息变化特征,即移栽期NDVI处于谷值,孕穗抽穗期NDVI达到峰值。但相应NDVI逐渐增加,且波动范围逐渐增大。就不同熟制水稻生育期而言,根据DN值计算并构建的NDVI曲线差异较小,而根据TOA反射率与Surface反射率反演的NDVI曲线差异明显。在植被定量遥感研究中,通过大气校正反演地表反射率计算植被指数相对客观准确。     

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.     

Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework

Long-term vegetation dynamics associated with climatic changes can be assessed using Advanced Very High Resolution Radiometer (AVHRR) red and near-infrared reflectance data provided that the data have been processed to remove the effects of non-target signal variability, such as atmospheric and sensor calibration effects. Here we present a new method that performs a relative calibration of reflectance data to produce consistent long-term vegetation information. It is based on a simple biological framework that assumes that the position of the vegetation cover triangle is invariant in reflectance space. This assumption is in fact an intrinsic assumption behind the commonly used Normalised Difference Vegetation Index (NDVI) and is violated when the NDVI is calculated from inadequately corrected reflectance data. In this new method, any temporal variability in the position of the cover triangle is removed by geometrically transforming the observed reflectance data such that two features of the triangle—the soil line and the dark point—are stationary in reflectance space. The fraction of Photosynthetically Active Radiation absorbed by vegetation (fPAR; 0.0-0.95) is then calculated, via the NDVI, from calibrated reflectances. This method was tested using two distinct, monthly AVHRR products for Australia: (i) the coarse-resolution, fully calibrated, partially atmospherically corrected PAL data (1981-1994); and (ii) the fine-resolution, fully calibrated, non-atmospherically corrected HRPT data (1992-2004). Results show that, in the 20-month period when the two datasets overlap (1992-1994), the Australia-wide, root mean square difference between the two datasets improved from 0.098 to 0.027 fPAR units. The calibrations have produced two approximately equivalent datasets that can be combined as a single input into time-series analyses. The application of this method is limited to areas that have a wide-enough variety of land-cover types so that the soil line and dark point are evident in the cover triangle in every image of the time-series. Another limitation is that the methodology performs only bulk, relative calibrations and does not remove the absolute effects of observation uncertainties. The simplicity of the method means that the calibration procedure can be easily incorporated into near-real-time operational remote-sensing environments. Vegetation information produced using this invariant-cover-triangle method is expected to be well suited to the analysis of long-term vegetation dynamics and change.