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Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index
Remote Sensing of Environment 97 (2005) 519 – 525 www.elsevier.com/locate/rse
Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index Hideki Kobayashi *, Dennis G. Dye Ecosystem Change Research Program, Frontier Research Center for Global Change (FRCGC), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25, Showa-machi, Kanazawa-ku Yokohama, Kanagawa, 236-0001, Japan Received 6 January 2005; received in revised form 3 June 2005; accepted 10 June 2005
Abstract 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. D 2005 Elsevier Inc. All rights reserved. Keywords: NDVI; NOAA/AVHRR; Biomass-burning; Phenology; Amazon forest
1. Introduction The analyses of the temporal vegetation dynamics in the Amazon region and their relation to climate are of particular interest to understand atmosphere – ecosystem interaction. The time-series observations of the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-series of polar orbiting environmental satellites, which is computed from visible and near infrared spectral reflectance, are often used to monitor the vegetation dynamics. * Corresponding author. Tel.: +81 45 778 5666. E-mail address: [email protected] (H. Kobayashi). 0034-4257/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2005.06.007
A potential obstacle to reliable interpretation of the observed temporal changes in the satellite-derived NDVI is the influence of aerosols from biomass-burning (BB) (Miura et al., 1998) and clouds (Goward et al., 1991), both which can cause the NDVI to decrease relative to its true (clearsky) value. The creation of time-composite images, in which the maximum NDVI value at each pixel location during a given time period is selected, is a simple, common method for minimizing atmospheric effects on the NDVI. However, the method can be ineffective in areas of the Amazon where clouds and BB aerosols persist. For example, daily measurements collected during the dry season in 1999 at Alta Floresta, Brazil (S9-55V, W56-01V) indicate a background value of aerosol optical thickness at 670 nm
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correlated with temporal dynamics in BB aerosol load and there are few effective (clear-sky) NDVI in the end of dry season (September to December). (2) In central Amazon, temporal dynamics in NDVI are well correlated with temporal dynamics in cloudiness and there is little indication of NDVI seasonality if cloud-affected data are masked.
2. Data and methods 2.1. Study area Fig. 1. Day to day variations in ground-based aerosol optical thickness (AOT670 nm) measurements and aerosol index (AI) at Alta Floresta (S9-55V, W56-01V) in 1999. From the regression analysis after removal of one outlier (Julian day = 230), we obtained the following relationship; AOT670 nm = 0.30AI + 0.22 (R 2 = 0.64).
(AOT670) of approximately 0.05, but markedly larger values between days 210 and 300 (Fig. 1). These data demonstrate that maximum-value NDVI composites for this location, even for monthly periods, would contain substantial artifacts as a result of persistent aerosols and/or clouds. Attention has been given in the past studies to remove the atmospheric effect on NDVI or interpret vegetation dynamics from time-composite NDVI data (e.g., Asner et al., 2000; Batista et al., 1997; Dessay et al., 2004; Gurgel & Ferreira, 2003; Poveda & Salazar, 2004). Asner et al. (2000) selected the maximum NDVI value within an 8 8 array (64 km2) as the most probable value for all cells in the array. Potter et al. (2001) used a Fourier smoothing algorithm to empirically correct the NDVI for both atmospheric and orbital drift effects. Despite general recognition of BB aerosol and clouds effects on time-series NDVI data, our review of the literature revealed little quantitative information about this phenomenon for the Amazon region. Such information is important as a basis for advanced correction and interpretation of the phenological information contained in multiyear, time-series NDVI data sets for tropical forest regions. We address this issue by examining the BB aerosol and clouds effects on time-series NDVI data from the NOAA AVHRR, with supporting data obtained from the SPOT/ VEGETATION sensor, the Total Ozone Mapping Spectrometer (TOMS), and radiative transfer simulation. In this study, we do not include the effect of atmospheric water vapor effect on NDVI. Water vapor is generally low in the dry season and high in the wet season. However seasonal and interannual NDVI analyses in past studies and our own analysis indicate that NDVI in Amazon region is usually low in dry season and high in wet season. This pattern is opposite of the expected water vapor effect on NDVI. Therefore we do not consider the water vapor effect on NDVI to be a primary factor regulating NDVI variations in this region. The results of our analysis show that: (1) In south and eastern Amazon, temporal dynamics in NDVI are well
Our analysis focuses on five 5 5 degree study areas (A, B, C, D, and E) in geographically diverse locations of the Amazon region (Fig. 2). The region A (Fonte Boa) and E (Tapajos National Forest) are located in the central and northeastern Amazon region that has usually large amounts of rainfall. The regions B (Rondonia), C (Alta Floresta), and D (Maraba) are located in the Southern and Eastern Amazon regions that have a distinct cycle of wet (November to April) and dry (May to October) seasons. 2.2. NDVI data We create monthly composite images by extracting the maximum NDVI from the each monthly set of three 10-day composites for pathfinder AVHRR land (PAL) NDVI data (James & Kalluri, 1994) and SPOT VEGETATION sensor (VGT S10), and two 15-day composites for Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data (Pinzon, 2002; Pinzon et al., 2004; Tucker et al., in press). We use GIMMS NDVI data and PAL NDVI data for years from 1982 to 1999. The PAL NDVI data are calculated from reflectance values after corrections for ozone absorption and Rayleigh scattering. GIMMS NDVI data are calculated from apparent reflectance at the top of atmosphere. Corrections are applied for the effects of stratospheric aerosols from two volcanic eruptions (El Chichon and Mt. Pinatubo).
Fig. 2. Analysis windows defined in this study. A= Fonte Boa (S2-30V, W66-00V), B = Rondonia (S9-00V, W63-00V), C = Alta Floresta (S9-55V, W56-01V), D = Maraba (S6-00V, W50-00V), and E = Tapajos National Forest (S2-52V, W54-54V).
H. Kobayashi, D.G. Dye / Remote Sensing of Environment 97 (2005) 519 – 525 Table 1 Summary of the parameters in radiative transfer simulations Parameter
Value
Atmospheric profile Aerosol type Aerosol optical thickness at 550 nm Solar zenith angle View zenith angle Relative azimuth angle LAI Leaf reflectance/transmittance
Tropical profile Biomass-burninga 0.0 – 2.0 22.522.5900.5 – 7.0 0.079/0.073 (Red), 0.43/0.43 (NIR)b Spherical 0.06 (Red), 0.11 (NIR)c
Leaf angle distribution Soil reflectance a b c
Procopio et al. (2003). Myneni et al. (1997). Miura et al. (1998).
We also employ 10-day maximum-value NDVI composite data from VGT S10 for our analysis of the BB aerosol and cloud effects. Atmospheric corrections are applied to the data following the method of Rahman and Dedieu (1994), which uses fixed values of aerosol optical thickness (AOT). The data from April 1998 to March 1999 were compared with the two AVHRR-based NDVI data sets. In addition, we use the reflectance in blue channel (430 –470 nm) to detect pixels affected by BB aerosol and clouds. To identify the VGT S10 NDVI affected by BB aerosol and clouds, we select NDVI with Digital Count (DC) < 30 in the blue channel. Minimum blue and red reflectance thresholds are likely to select the cloud-shadowed pixels. However we first extract monthly NDVI using monthly maximum-value composite algorithm, which does not preferentially select the shadowed pixels since NDVI for cloud-shadowed vegetation is typically lower than for vegetation under clear-sky. Thus we assume that the contaminations of shadowed pixels are small enough in the 5 5 degree study areas.
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to the site. Seasonal variations of AI and AOT670 during the 1999 dry season show generally good agreement (Fig. 1). On this basis, we used monthly averaged AI from TOMS as an indicator of the BB aerosol conditions. 2.4. Radiative transfer simulation Radiative transfer between the atmosphere and the vegetation land cover is simulated by combining the 6S model of atmospheric radiative transfer (Vermote et al., 1997) with the SAIL model of canopy radiative transfer (Verhoef, 1984). Model parameters used for the simulation are summarized in Table 1. Previous studies for aerosol properties in Amazon region indicate that the aerosol size distribution varies with change in AOT (Procopio et al., 2003; Remer et al., 1998). Procopio et al. (2003) modeled the aerosol optical properties such as single scattering albedo, extinction efficiency, and asymmetry factor as a function of AOT. We use their modeled values in this simulation. Values for leaf reflectance and transmittance are from Myneni et al. (1997). Soil reflectance at the nadir view angle was set to the value for a burnt land surface as measured by Miura et al. (1998). NDVI is calculated from apparent reflectance at the top of atmosphere in AVHRR channels 1 and 2 as simulated with the 6S and SAIL models.
3. Results and discussion 3.1. Simulation results The results from the radiative transfer simulation are displayed in Fig. 3. The sensitivity of the NDVI to variation in LAI tends to saturate for LAI values above 4, which are typical for dense tropical forests (Roberts et al., 1996) (Fig. 3A). In contrast, NDVI sensitivity to aerosol conditions is consistently strong, as NDVI decreases linearly with
2.3. Aerosol data Aerosol index (AI) data derived from radiances at two UV-A wavelengths by the TOMS sensor (340 and 380 nm for Nimbus-7, 331 and 360 nm for Earth Probe) are used for evaluation of the BB (smoke) aerosol effect (Herman et al., 1997) on the NDVI data sets. AI data for January 1982 to May 1993 are from Nimbus-7/TOMS, and data for August 1996 to December 1999 are from Earth Probe/TOMS. The spatial resolution of TOMS level-3 daily AI products is 1latitude 1.25- longitude (McPeters et al., 1998). AI is usually proportional to AOT at 380 nm (AOT380) in the Amazon (Hsu et al., 1999). To confirm this relation for the visible wavelengths observed by the AVHRR, we compared AI with AOT at 670 nm (AOT670). Only measurements around the TOMS observation time (11:00 – 13:00 LST) were included in this analysis. AIs at Alta Floresta were calculated by the bilinear method using the 4 pixels nearest
Fig. 3. Results of the radiative transfer simulations. (A) Relationship between NDVI and Leaf Area Index (LAI), (B) AOT550 as a function of NDVI with different LAI conditions. NDVIs were calculated from apparent reflectance at the top of atmosphere simulated by the 6S and SAIL model. The model parameters used in this study are summarized in Table 1.
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Fig. 4. Upper figures: the seasonal variations NDVI data. Closed circle: SPOT/VGT, closed rectangle: SPOT/VGT after blue reflectance threshold, closed diamond: GIMMS, cross: PAL. Bottom figures: the ratio of selected NDVI to total NDVI data (NDVIclear-sky) after applying to the blue channel threshold.
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increasing AOT at 550 nm (AOT550) across a wide range of sample LAI values (0.5 – 7.0) (Fig. 3B). In the Amazon, the dry season is typically from the beginning of May (day 120) to the end of October (day 304). AOT550 is usually higher at the end of the dry season and at the transition period to the wet season (days 210 to 320) than at the beginning of the dry season (¨day 210) (Fig. 1). Although the monthly maximum NDVI composite procedure would tend to select the NDVI associated with the clearest atmospheric conditions, NDVI values are nevertheless reduced in the presence of persistent BB aerosols. In August 1999 (days 213 to 243, Fig. 1), the average AOT670 is 0.27 for the 4 days with the lowest AOT670 values. Assuming an angstrom exponent of 1.8, this AOT is equivalent to an AOT at 550 nm (AOT550) of 0.39. At the beginning of the dry season, AOT550 is approximately equal to 0.05. When we estimate the difference in NDVI for these two BB aerosol conditions for LAI = 3.0 (Fig. 3) it becomes 0.07 (a 10.6% difference). In contrast, when LAI is higher than 4, a 30% decrease in LAI becomes less than 6.3% (0.04) decrease in NDVI (Fig. 3). These simulation results suggest that NDVI from the high-LAI forests of the Amazon are highly sensitive to seasonal variations in BB aerosol. 3.2. Smoke and clouds effects on the NDVI seasonal and interannual variations Fig. 4 (upper figures for each region) shows the seasonal variations in area-averaged VGT S10, GIMMS, and PALderived NDVI from April 1998 to March 1999. Although systematic differences among PAL, GIMMS, and VGT exist
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due to the difference of the data processing method and the response function of the AVHRR and VGT sensors, the pattern of seasonal variation of the NDVI in VGT S10, GIMMS, and PAL data is similar. Therefore it is possible to infer the effects of BB aerosols and clouds on the AVHRRbased NDVI from the SPOT/VGT NDVI. Fig. 4 (upper figures for each region) show the areaaveraged NDVI after masking the BB aerosol- and cloudsaffected NDVI using the threshold described in 2.2. Fig. 4 (bottom figures for each 5 region) shows the ratio of selected NDVI to total NDVI data (NDVIclear-sky) after applying to the blue channel threshold. Results in Fig. 4 suggest that NDVI tends to increase as the ratio of clear ratio increases. In region A and E, where there is a large amount of rainfall, NDVIclear-sky exhibits little seasonality. Our results in E (Tapajos) are consistent with the pixel-based analysis obtained from Terra/MODIS and 10 VGT S10 (Huete et al., 2002; Xiao et al., 2005). Our results and past two studies (Huete et al., 2002; Xiao et al., 2005) suggest that it may not be possible to observe change in leaf area index or other forest phonological change such as leaf aging from NDVI in E. In regions B, C, and D (southern and eastern Amazon), where rainfall varies through the year, seasonal NDVI variations are generally less than 0.1. In these areas, NDVI between September and December are only 0– 20% of total data. In contrast, almost all data are selected between April and July. Previous studies (e.g., Ferreira & Huete, 2004; Roberts et al., 1998) of the Amazon have shown the relation between NDVI or near infrared reflectance seasonality and climate through pixel-based analysis. In these cases, it may be
Fig. 5. Interannual variations in GIMMS and PAL NDVI and AI.
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Fig. 6. Spatial patterns of correlation coefficient between AI and GIMMS NDVI in Amazon. Only statistically significant pixels ( p < 0.01) are shown.
possible to remove the erroneous data with careful selection of effective data. However our analysis suggests that the large-scale seasonal variation in the monthly maximumvalue NDVI composite data for the Amazon forest region is mainly regulated by BB aerosol and cloud cover. Thus one cannot easily scale these locally observed vegetation dynamics up to large-scale (basin-scale) dynamics. Fig. 5 shows the relationship between interannual variations in NDVI (both PAL and GIMMS) and AI in regions A and C. The PAL-based NDVI from 1992 to 1994 has large amplitude. There are two reasons for this pattern. The large decrease in 1992 may be caused by the stratospheric aerosol effect by Mt. Pinatubo eruption. The large increase in 1994 may be primary caused by the large error of NOAA-7 sensor because NOAA-7/AVHRR observation time in 1994 is very late afternoon (nearly sunset). Therefore the data obtained from NOAA-7/AVHRR in 1992 – 1994 has much uncertainty. NDVI in region A does not exhibit interannual variations and is not correlated with AI variations. On the other hand, NDVI in region C exhibits clear interannual variations, and depression periods in NDVI are well correlated with the increasing periods in AI. The correlation coefficients between NDVI and AI are spatially shown in Fig. 6. The NDVI in south and east Amazon are well correlated with AI. Thus, large-scale interannual NDVI variations in south and east Amazon are mainly regulated by the interannual variations in BB aerosol.
4. Summary and conclusion Our analysis indicates that temporal variations in NDVI in the PAL and GIMMS datasets at the southern and eastern regions of the Amazon forest are primarily associated with atmospheric variations such as BB aerosol and clouds. In these regions, reliable NDVI data can be mainly acquired from April to July, whereas from September to December substantial contamination by persistent BB aerosols severely reduces the NDVI fidelity. In the central Amazon forest where large-scale biomass-burning is less common, NDVI seasonality tends to be influenced primarily by seasonal cloud cover variations, as the NDVI exhibits little seasonality when the cloud-affected data are masked. Although
previous pixel-based analysis have shown the NDVI seasonality related to vegetation dynamics, one cannot easily extrapolate these locally observed vegetation dynamics up to large-scale (basin-scale) dynamics due to a paucity of effective NDVI data. In this study, we focused on the Amazon region. However NDVI in other tropical forest regions may be similarly affected by BB aerosols and clouds. Caution is warranted when interpreting large-scale phenological information in the Amazon and other tropical forest on the basis of apparent seasonal variation in timecomposite NDVI data.
Acknowledgements Thanks are extended to the two NDVI datasets distributors; NASA GIMMS team (Compton Tucker, Molly Brown, Jorge Pinzon, and colleagues) for their NDVI datasets and Distributed Active Archive Center at the Goddard Space Flight Center for NOAA/NASA Pathfinder Land data. And thanks are extended to Brent Holben and those who are engaged in the AERONET project for providing the aerosol optical thickness measurements. Also we thank the Ozone Processing Team (OPT) for the TOMS ozone mapping, and the NASA/Goddard Space Flight Center for providing the Aerosol Index.
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H. Kobayashi, D.G. Dye / Remote Sensing of Environment 97 (2005) 519 – 525 Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195 – 213. James, M. E., & Kalluri, S. N. V. (1994). The pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring. International Journal of Remote Sensing, 15(17), 3347 – 3363. McPeters, R. D., Bhartia, P. K., Krueger, A. J., Herman, J. R., Wellemeyer, C. G., Seftor, C. J., et al. (1998). Earth probe total ozone mapping spectrometer (TOMS) data products user’ guide. NASA Tech. Publ., vol. 1998-206895 (pp. 1 – 64). Miura, T., Huete, A. R., van Leeuwen, W. J. D., & Didan, K. (1998). Vegetation detection through smoke-filled AVIRIS images: An assessment using MODIS band passes. Journal of Geophysical Research, 103(D24), 32001 – 32011. Myneni, R. B., Nemani, R. R., & Running, S. W. (1997). Estimation of global leaf area index and absorbed par using radiative transfer models. IEEE Transactions on Geoscience and Remote Sensing, 35(6), 1380 – 1393. Pinzon, J. (2002). Using HHT to successfully uncouple seasonal and interannual components in remotely sensed data. Conference proceedings, Jul 14 – 18. Orlando, Florida. SCI. Pinzon, J., Brown, M. E., & Tucker, C. J. (2004). Satellite time series correction of orbital drift artifacts using empirical mode decomposition. In N. Huang (Ed.), Hilbert – Huang transform: Introduction and applications. Chapter 10, Part II. Applications. Potter, C., Klooster, S., de Carvalho, C. R., Genovese, V. B., Torregrosa, A., Dungan, J., et al. (2001). Modeling seasonal and interannual variability in ecosystem carbon cycling for the Brazilian Amazon region. Journal of Geophysical Research, 106(D10), 10423 – 10446. Poveda, G., & Salazar, L. (2004). Annual and interannual (ENSO) variability of spatial scaling properties of a vegetation index (NDVI) in Amazonia. Remote Sensing of Environment, 93, 391 – 401.
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Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index Hideki Kobayashi *, Dennis G. Dye Ecosystem Change Research Program, Frontier Research Center for Global Change (FRCGC), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25, Showa-machi, Kanazawa-ku Yokohama, Kanagawa, 236-0001, Japan Received 6 January 2005; received in revised form 3 June 2005; accepted 10 June 2005
Abstract 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. D 2005 Elsevier Inc. All rights reserved. Keywords: NDVI; NOAA/AVHRR; Biomass-burning; Phenology; Amazon forest
1. Introduction The analyses of the temporal vegetation dynamics in the Amazon region and their relation to climate are of particular interest to understand atmosphere – ecosystem interaction. The time-series observations of the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-series of polar orbiting environmental satellites, which is computed from visible and near infrared spectral reflectance, are often used to monitor the vegetation dynamics. * Corresponding author. Tel.: +81 45 778 5666. E-mail address: [email protected] (H. Kobayashi). 0034-4257/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2005.06.007
A potential obstacle to reliable interpretation of the observed temporal changes in the satellite-derived NDVI is the influence of aerosols from biomass-burning (BB) (Miura et al., 1998) and clouds (Goward et al., 1991), both which can cause the NDVI to decrease relative to its true (clearsky) value. The creation of time-composite images, in which the maximum NDVI value at each pixel location during a given time period is selected, is a simple, common method for minimizing atmospheric effects on the NDVI. However, the method can be ineffective in areas of the Amazon where clouds and BB aerosols persist. For example, daily measurements collected during the dry season in 1999 at Alta Floresta, Brazil (S9-55V, W56-01V) indicate a background value of aerosol optical thickness at 670 nm
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correlated with temporal dynamics in BB aerosol load and there are few effective (clear-sky) NDVI in the end of dry season (September to December). (2) In central Amazon, temporal dynamics in NDVI are well correlated with temporal dynamics in cloudiness and there is little indication of NDVI seasonality if cloud-affected data are masked.
2. Data and methods 2.1. Study area Fig. 1. Day to day variations in ground-based aerosol optical thickness (AOT670 nm) measurements and aerosol index (AI) at Alta Floresta (S9-55V, W56-01V) in 1999. From the regression analysis after removal of one outlier (Julian day = 230), we obtained the following relationship; AOT670 nm = 0.30AI + 0.22 (R 2 = 0.64).
(AOT670) of approximately 0.05, but markedly larger values between days 210 and 300 (Fig. 1). These data demonstrate that maximum-value NDVI composites for this location, even for monthly periods, would contain substantial artifacts as a result of persistent aerosols and/or clouds. Attention has been given in the past studies to remove the atmospheric effect on NDVI or interpret vegetation dynamics from time-composite NDVI data (e.g., Asner et al., 2000; Batista et al., 1997; Dessay et al., 2004; Gurgel & Ferreira, 2003; Poveda & Salazar, 2004). Asner et al. (2000) selected the maximum NDVI value within an 8 8 array (64 km2) as the most probable value for all cells in the array. Potter et al. (2001) used a Fourier smoothing algorithm to empirically correct the NDVI for both atmospheric and orbital drift effects. Despite general recognition of BB aerosol and clouds effects on time-series NDVI data, our review of the literature revealed little quantitative information about this phenomenon for the Amazon region. Such information is important as a basis for advanced correction and interpretation of the phenological information contained in multiyear, time-series NDVI data sets for tropical forest regions. We address this issue by examining the BB aerosol and clouds effects on time-series NDVI data from the NOAA AVHRR, with supporting data obtained from the SPOT/ VEGETATION sensor, the Total Ozone Mapping Spectrometer (TOMS), and radiative transfer simulation. In this study, we do not include the effect of atmospheric water vapor effect on NDVI. Water vapor is generally low in the dry season and high in the wet season. However seasonal and interannual NDVI analyses in past studies and our own analysis indicate that NDVI in Amazon region is usually low in dry season and high in wet season. This pattern is opposite of the expected water vapor effect on NDVI. Therefore we do not consider the water vapor effect on NDVI to be a primary factor regulating NDVI variations in this region. The results of our analysis show that: (1) In south and eastern Amazon, temporal dynamics in NDVI are well
Our analysis focuses on five 5 5 degree study areas (A, B, C, D, and E) in geographically diverse locations of the Amazon region (Fig. 2). The region A (Fonte Boa) and E (Tapajos National Forest) are located in the central and northeastern Amazon region that has usually large amounts of rainfall. The regions B (Rondonia), C (Alta Floresta), and D (Maraba) are located in the Southern and Eastern Amazon regions that have a distinct cycle of wet (November to April) and dry (May to October) seasons. 2.2. NDVI data We create monthly composite images by extracting the maximum NDVI from the each monthly set of three 10-day composites for pathfinder AVHRR land (PAL) NDVI data (James & Kalluri, 1994) and SPOT VEGETATION sensor (VGT S10), and two 15-day composites for Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data (Pinzon, 2002; Pinzon et al., 2004; Tucker et al., in press). We use GIMMS NDVI data and PAL NDVI data for years from 1982 to 1999. The PAL NDVI data are calculated from reflectance values after corrections for ozone absorption and Rayleigh scattering. GIMMS NDVI data are calculated from apparent reflectance at the top of atmosphere. Corrections are applied for the effects of stratospheric aerosols from two volcanic eruptions (El Chichon and Mt. Pinatubo).
Fig. 2. Analysis windows defined in this study. A= Fonte Boa (S2-30V, W66-00V), B = Rondonia (S9-00V, W63-00V), C = Alta Floresta (S9-55V, W56-01V), D = Maraba (S6-00V, W50-00V), and E = Tapajos National Forest (S2-52V, W54-54V).
H. Kobayashi, D.G. Dye / Remote Sensing of Environment 97 (2005) 519 – 525 Table 1 Summary of the parameters in radiative transfer simulations Parameter
Value
Atmospheric profile Aerosol type Aerosol optical thickness at 550 nm Solar zenith angle View zenith angle Relative azimuth angle LAI Leaf reflectance/transmittance
Tropical profile Biomass-burninga 0.0 – 2.0 22.522.5900.5 – 7.0 0.079/0.073 (Red), 0.43/0.43 (NIR)b Spherical 0.06 (Red), 0.11 (NIR)c
Leaf angle distribution Soil reflectance a b c
Procopio et al. (2003). Myneni et al. (1997). Miura et al. (1998).
We also employ 10-day maximum-value NDVI composite data from VGT S10 for our analysis of the BB aerosol and cloud effects. Atmospheric corrections are applied to the data following the method of Rahman and Dedieu (1994), which uses fixed values of aerosol optical thickness (AOT). The data from April 1998 to March 1999 were compared with the two AVHRR-based NDVI data sets. In addition, we use the reflectance in blue channel (430 –470 nm) to detect pixels affected by BB aerosol and clouds. To identify the VGT S10 NDVI affected by BB aerosol and clouds, we select NDVI with Digital Count (DC) < 30 in the blue channel. Minimum blue and red reflectance thresholds are likely to select the cloud-shadowed pixels. However we first extract monthly NDVI using monthly maximum-value composite algorithm, which does not preferentially select the shadowed pixels since NDVI for cloud-shadowed vegetation is typically lower than for vegetation under clear-sky. Thus we assume that the contaminations of shadowed pixels are small enough in the 5 5 degree study areas.
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to the site. Seasonal variations of AI and AOT670 during the 1999 dry season show generally good agreement (Fig. 1). On this basis, we used monthly averaged AI from TOMS as an indicator of the BB aerosol conditions. 2.4. Radiative transfer simulation Radiative transfer between the atmosphere and the vegetation land cover is simulated by combining the 6S model of atmospheric radiative transfer (Vermote et al., 1997) with the SAIL model of canopy radiative transfer (Verhoef, 1984). Model parameters used for the simulation are summarized in Table 1. Previous studies for aerosol properties in Amazon region indicate that the aerosol size distribution varies with change in AOT (Procopio et al., 2003; Remer et al., 1998). Procopio et al. (2003) modeled the aerosol optical properties such as single scattering albedo, extinction efficiency, and asymmetry factor as a function of AOT. We use their modeled values in this simulation. Values for leaf reflectance and transmittance are from Myneni et al. (1997). Soil reflectance at the nadir view angle was set to the value for a burnt land surface as measured by Miura et al. (1998). NDVI is calculated from apparent reflectance at the top of atmosphere in AVHRR channels 1 and 2 as simulated with the 6S and SAIL models.
3. Results and discussion 3.1. Simulation results The results from the radiative transfer simulation are displayed in Fig. 3. The sensitivity of the NDVI to variation in LAI tends to saturate for LAI values above 4, which are typical for dense tropical forests (Roberts et al., 1996) (Fig. 3A). In contrast, NDVI sensitivity to aerosol conditions is consistently strong, as NDVI decreases linearly with
2.3. Aerosol data Aerosol index (AI) data derived from radiances at two UV-A wavelengths by the TOMS sensor (340 and 380 nm for Nimbus-7, 331 and 360 nm for Earth Probe) are used for evaluation of the BB (smoke) aerosol effect (Herman et al., 1997) on the NDVI data sets. AI data for January 1982 to May 1993 are from Nimbus-7/TOMS, and data for August 1996 to December 1999 are from Earth Probe/TOMS. The spatial resolution of TOMS level-3 daily AI products is 1latitude 1.25- longitude (McPeters et al., 1998). AI is usually proportional to AOT at 380 nm (AOT380) in the Amazon (Hsu et al., 1999). To confirm this relation for the visible wavelengths observed by the AVHRR, we compared AI with AOT at 670 nm (AOT670). Only measurements around the TOMS observation time (11:00 – 13:00 LST) were included in this analysis. AIs at Alta Floresta were calculated by the bilinear method using the 4 pixels nearest
Fig. 3. Results of the radiative transfer simulations. (A) Relationship between NDVI and Leaf Area Index (LAI), (B) AOT550 as a function of NDVI with different LAI conditions. NDVIs were calculated from apparent reflectance at the top of atmosphere simulated by the 6S and SAIL model. The model parameters used in this study are summarized in Table 1.
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Fig. 4. Upper figures: the seasonal variations NDVI data. Closed circle: SPOT/VGT, closed rectangle: SPOT/VGT after blue reflectance threshold, closed diamond: GIMMS, cross: PAL. Bottom figures: the ratio of selected NDVI to total NDVI data (NDVIclear-sky) after applying to the blue channel threshold.
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increasing AOT at 550 nm (AOT550) across a wide range of sample LAI values (0.5 – 7.0) (Fig. 3B). In the Amazon, the dry season is typically from the beginning of May (day 120) to the end of October (day 304). AOT550 is usually higher at the end of the dry season and at the transition period to the wet season (days 210 to 320) than at the beginning of the dry season (¨day 210) (Fig. 1). Although the monthly maximum NDVI composite procedure would tend to select the NDVI associated with the clearest atmospheric conditions, NDVI values are nevertheless reduced in the presence of persistent BB aerosols. In August 1999 (days 213 to 243, Fig. 1), the average AOT670 is 0.27 for the 4 days with the lowest AOT670 values. Assuming an angstrom exponent of 1.8, this AOT is equivalent to an AOT at 550 nm (AOT550) of 0.39. At the beginning of the dry season, AOT550 is approximately equal to 0.05. When we estimate the difference in NDVI for these two BB aerosol conditions for LAI = 3.0 (Fig. 3) it becomes 0.07 (a 10.6% difference). In contrast, when LAI is higher than 4, a 30% decrease in LAI becomes less than 6.3% (0.04) decrease in NDVI (Fig. 3). These simulation results suggest that NDVI from the high-LAI forests of the Amazon are highly sensitive to seasonal variations in BB aerosol. 3.2. Smoke and clouds effects on the NDVI seasonal and interannual variations Fig. 4 (upper figures for each region) shows the seasonal variations in area-averaged VGT S10, GIMMS, and PALderived NDVI from April 1998 to March 1999. Although systematic differences among PAL, GIMMS, and VGT exist
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due to the difference of the data processing method and the response function of the AVHRR and VGT sensors, the pattern of seasonal variation of the NDVI in VGT S10, GIMMS, and PAL data is similar. Therefore it is possible to infer the effects of BB aerosols and clouds on the AVHRRbased NDVI from the SPOT/VGT NDVI. Fig. 4 (upper figures for each region) show the areaaveraged NDVI after masking the BB aerosol- and cloudsaffected NDVI using the threshold described in 2.2. Fig. 4 (bottom figures for each 5 region) shows the ratio of selected NDVI to total NDVI data (NDVIclear-sky) after applying to the blue channel threshold. Results in Fig. 4 suggest that NDVI tends to increase as the ratio of clear ratio increases. In region A and E, where there is a large amount of rainfall, NDVIclear-sky exhibits little seasonality. Our results in E (Tapajos) are consistent with the pixel-based analysis obtained from Terra/MODIS and 10 VGT S10 (Huete et al., 2002; Xiao et al., 2005). Our results and past two studies (Huete et al., 2002; Xiao et al., 2005) suggest that it may not be possible to observe change in leaf area index or other forest phonological change such as leaf aging from NDVI in E. In regions B, C, and D (southern and eastern Amazon), where rainfall varies through the year, seasonal NDVI variations are generally less than 0.1. In these areas, NDVI between September and December are only 0– 20% of total data. In contrast, almost all data are selected between April and July. Previous studies (e.g., Ferreira & Huete, 2004; Roberts et al., 1998) of the Amazon have shown the relation between NDVI or near infrared reflectance seasonality and climate through pixel-based analysis. In these cases, it may be
Fig. 5. Interannual variations in GIMMS and PAL NDVI and AI.
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Fig. 6. Spatial patterns of correlation coefficient between AI and GIMMS NDVI in Amazon. Only statistically significant pixels ( p < 0.01) are shown.
possible to remove the erroneous data with careful selection of effective data. However our analysis suggests that the large-scale seasonal variation in the monthly maximumvalue NDVI composite data for the Amazon forest region is mainly regulated by BB aerosol and cloud cover. Thus one cannot easily scale these locally observed vegetation dynamics up to large-scale (basin-scale) dynamics. Fig. 5 shows the relationship between interannual variations in NDVI (both PAL and GIMMS) and AI in regions A and C. The PAL-based NDVI from 1992 to 1994 has large amplitude. There are two reasons for this pattern. The large decrease in 1992 may be caused by the stratospheric aerosol effect by Mt. Pinatubo eruption. The large increase in 1994 may be primary caused by the large error of NOAA-7 sensor because NOAA-7/AVHRR observation time in 1994 is very late afternoon (nearly sunset). Therefore the data obtained from NOAA-7/AVHRR in 1992 – 1994 has much uncertainty. NDVI in region A does not exhibit interannual variations and is not correlated with AI variations. On the other hand, NDVI in region C exhibits clear interannual variations, and depression periods in NDVI are well correlated with the increasing periods in AI. The correlation coefficients between NDVI and AI are spatially shown in Fig. 6. The NDVI in south and east Amazon are well correlated with AI. Thus, large-scale interannual NDVI variations in south and east Amazon are mainly regulated by the interannual variations in BB aerosol.
4. Summary and conclusion Our analysis indicates that temporal variations in NDVI in the PAL and GIMMS datasets at the southern and eastern regions of the Amazon forest are primarily associated with atmospheric variations such as BB aerosol and clouds. In these regions, reliable NDVI data can be mainly acquired from April to July, whereas from September to December substantial contamination by persistent BB aerosols severely reduces the NDVI fidelity. In the central Amazon forest where large-scale biomass-burning is less common, NDVI seasonality tends to be influenced primarily by seasonal cloud cover variations, as the NDVI exhibits little seasonality when the cloud-affected data are masked. Although
previous pixel-based analysis have shown the NDVI seasonality related to vegetation dynamics, one cannot easily extrapolate these locally observed vegetation dynamics up to large-scale (basin-scale) dynamics due to a paucity of effective NDVI data. In this study, we focused on the Amazon region. However NDVI in other tropical forest regions may be similarly affected by BB aerosols and clouds. Caution is warranted when interpreting large-scale phenological information in the Amazon and other tropical forest on the basis of apparent seasonal variation in timecomposite NDVI data.
Acknowledgements Thanks are extended to the two NDVI datasets distributors; NASA GIMMS team (Compton Tucker, Molly Brown, Jorge Pinzon, and colleagues) for their NDVI datasets and Distributed Active Archive Center at the Goddard Space Flight Center for NOAA/NASA Pathfinder Land data. And thanks are extended to Brent Holben and those who are engaged in the AERONET project for providing the aerosol optical thickness measurements. Also we thank the Ozone Processing Team (OPT) for the TOMS ozone mapping, and the NASA/Goddard Space Flight Center for providing the Aerosol Index.
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