Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images

Leaf phenology of tropical evergreen forests affects carbon and water fluxes. In an earlier study of a seasonally moist evergreen tropical forest site in the Amazon basin, time series data of Enhanced Vegetation Index (EVI) from the VEGETATION and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors showed an unexpected seasonal pattern, with higher EVI in the late dry season than in the wet season. In this study we conducted a regional-scale analysis of tropical evergreen forests in South America, using time series data of EVI from MODIS in 2002. The results show a large dynamic range and spatial variations of annual maximum EVI for evergreen forest canopies in the region. In tropical evergreen forests, maximum EVI in 2002 typically occurs during the late dry season to early wet season. This suggests that leaf phenology in tropical evergreen forests is not determined by the seasonality of precipitation. Instead, leaf phenological process may be driven by availability of solar radiation and/or avoidance of herbivory.     

Annual Total Gross Primary Production Estimation based on Vegetation Indices Seasonal Variation Curve

For the estimation of annual Gross Primary Productivity(GPP),it is proposed an estimation method with simple parameters and small errors.By taking each type of vegetation in the area of Three-North Shelterbelt Program(TNSP) as the research subject,the MODIS vegetation indices were obtained,and the seasonal variation curve of vegetation indices were built.Then,the fitting relation between the integral of time series vegetation indices(ΣVIs) and GPP products of MODIS was established,so as to realize a simple GPP estimation method and study the applicable ΣVIs for estimating the GPP of all vegetation types.The results show that：(1) ΣVIs is suitable for estimating the annual total GPP in research area and significantly correlated with MODIS GPP at the confidence level of p<0.01;(2) ΣEVI2 is applicable to estimate the GPP of evergreen needleleaf forest,decidious needleleaf forest,decidious broadleaf forest,mixed forest,woody savannas,savannas,permanent wetlands,croplands,croplands/natural vegetation mosaic,while the effect of ΣNDVI for estimating the GPP of closed shrublands,open shrublands,grasslands,croplands,and barren or sparsely vegetated is superior to ΣEVI andΣEVI2;(3) Since the NDVI itself is saturated in the area of high Leaf Area Index(LAI),the error of estimating the GPP of high LAI vegetation type by ΣNDVI is larger,while using ΣEVI and ΣEVI2 to estimate them has better accuracy,and the limitation from blue band of EVI2 reduces compared with EVI,which can be applied to the GPP research of long time series better.     

On intra-annual EVI variability in the dry season of tropical forest: A case study with MODIS and hyperspectral data

Considerable controversy is associated with dry season increases in the Enhanced Vegetation Index (EVI), observed using the Moderate Resolution Imaging Spectroradiometer (MODIS), compared with field-based estimates of decreasing plant productivity. Here, we investigate potential causes of intra-annual variability by comparing EVI from mature forest with field-measured Leaf Area Index (LAI) to validate space-based observations. EVI was calculated from 19 nadir and off-nadir Hyperion images in the 2005 dry season, and inspected for consistency with MODIS observations from 2004 to 2009. The objective was to evaluate the possible influence of the view-illumination geometry and of canopy foliage and leaf flush on the EVI. Spectral mixture models were used to evaluate the relationship between EVI and the shade fraction, a measure that varies with pixel brightness. MODIS LAI values were compared with LAI estimated using hemispherical photographs taken in two field campaigns in the dry season. To keep LAI and leaf flush conditions as constant variables and vary solar illumination, we used airborne Hyperspectral Mapper (Hymap) data acquired over mature forest from another region on the same day but with two distinct solar zenith angles (SZA) (29° and 53°). Results showed that intra-annual variability in MODIS and nadir Hyperion EVI in the dry season of tropical forest were driven by solar illumination effects rather than changes in LAI. The reflectance of the MODIS and Hyperion blue, red and near infrared (NIR) bands was higher at the end of the dry season because of the predominance of sunlit canopy components for the sensors due to decreasing SZA from June (44°) to September (26°). Because EVI was highly correlated with the reflectance of the NIR band used to generate it (r of + 0.98 for MODIS and + 0.88 for Hyperion), this vegetation index followed the general NIR pattern, increasing with smaller SZA towards the end of the dry season. Hyperion EVI was inversely correlated with the shade fraction (r = − 0.93). Changes in canopy foliage detected from MODIS LAI data were not consistent with LAI estimates from hemispherical photographs. Although further research is necessary to measure the impact of leaf flush on intra-annual EVI variability in the Querência region, analysis of Hymap data with fixed LAI and leaf flush conditions confirmed the influence of the illumination effects on the EVI.     

Sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in Northern Asia

Satellite observations play an important role in characterization of the interannual variation of vegetation. Here, we report anomalies of two vegetation indices for Northern Asia (40°N-75°N, and 45°E-179°E), using images from the SPOT-4 VEGETATION (VGT) sensor over the period of April 1, 1998 to November 20, 2001. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), which are correlated to a number of vegetation properties (e.g., net primary production, leaf area index), were compared. The results show that there is a large disagreement between NDVI and EVI anomalies in 1998 and 1999 for Northern Asia. The NDVI anomaly in 1998 was largely affected by atmospheric contamination, predominantly aerosols from extensive forest fires in that year. The EVI anomaly in 1998 was less sensitive to residual atmospheric contamination, as it is designed to be, and thus EVI is a useful alternative vegetation index for the large-scale study of vegetation. The EVI anomaly also suggests that potential vegetation productivity in Northern Asia was highest in 1998 but declined substantially in 2001, consistent with precipitation data from 1998-2001.     

Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest

A CO₂ eddy flux tower study has recently reported that an old-growth stand of seasonally moist tropical evergreen forest in Santarém, Brazil, maintained high gross primary production (GPP) during the dry seasons [Saleska, S. R., Miller, S. D., Matross, D. M., Goulden, M. L., Wofsy, S. C., da Rocha, H. R., de Camargo, P. B., Crill, P., Daube, B. C., de Freitas, H. C., Hutyra, L., Keller, M., Kirchhoff, V., Menton, M., Munger, J. W., Pyle, E. H., Rice, A. H., & Silva, H. (2003). Carbon in amazon forests: Unexpected seasonal fluxes and disturbance-induced losses. Science, 302, 1554-1557]. It was proposed that seasonally moist tropical evergreen forests have evolved two adaptive mechanisms in an environment with strong seasonal variations of light and water: deep roots system for access to water in deep soils and leaf phenology for access to light. Identifying tropical forests with these adaptive mechanisms could substantially improve our capacity of modeling the seasonal dynamics of carbon and water fluxes in the tropical zone. In this paper, we have analyzed multi-year satellite images from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite (4/1998-12/2002) and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite (2000-2003). We reported temporal analyses of vegetation indices and simulations of the satellite-based vegetation photosynthesis model (VPM). The Enhanced Vegetation Index (EVI) identified subtle changes in the seasonal dynamics of leaf phenology (leaf emergence, leaf aging and leaf fall) in the forest, as suggested by the leaf litterfall data. The land surface water index (LSWI) indicated that the forest experienced no water stress in the dry seasons of 1998-2002. The VPM model, which uses EVI, LSWI and site-specific climate data (air temperature and photosynthetically active radiation, PAR) for 2001-2002, predicted high GPP in the late dry seasons, consistent with observed high evapotranspiration and estimated GPP from the CO₂ eddy flux tower.     

In situ examination of the relationship between various vegetation indices and canopy phenology in an evergreen coniferous forest,Japan

We examined the relationship between four vegetation indices and tree canopy phenology in an evergreen coniferous forest in Japan based on observations made using a spectral radiometer and a digital camera at a daily time step during a 4 year period. The colour of the canopy surface of Japanese cedar (Cryptomeria japonica) changed from yellowish-green to whitish-green from late May to July and turned reddish-green in winter. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and plant area index (PAI) showed no seasonality. In contrast, the green–red ratio vegetation index (GRVI) increased from March to June and then decreased gradually from July to December, resulting in a bell-shaped curve. GRVI revealed seasonal changes in the colour of the canopy surface. GRVI correlated more positively with the evaluated maximum photosynthetic rate for the whole forest canopy, A _max, than did NDVI or EVI. These results suggest the possibility that GRVI is more useful than NDVI and EVI for capturing seasonal changes in photosynthetic capacity, as the green and red reflectances are strongly influenced by changes in leaf pigments in this type of forest.     

Land‐cover mixing and spectral vegetation indices

Vegetation indices have been widely used as indicators of seasonal and inter‐annual variations in vegetation caused by either human activities or climate, with the overall goal of observing and documenting changes in the Earth system. While existing satellite remote sensing systems, such as NASA's Multi‐angle Imaging SpectroRadiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS), are providing improved vegetation index data products through correcting for the distortions in surface reflectance caused by atmospheric particles as well as ground covers below vegetation canopy, the impact of land‐cover mixing on vegetation indices has not been fully addressed. In this study, based on real image spectral samples for two‐component mixtures of forest and common nonforest land‐cover types directly extracted from a 1.1?km MISR image by referencing a 30?m land‐cover classification, the effect of land‐cover mixing on the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) has been quantitatively evaluated. When the areal fraction of forest was lower than 80%, both NDVI and EVI varied greatly with mixed land‐cover types, although EVI varied less than NDVI. Such a phenomenon can cause errors in applications based on use of these vegetation indices. This study suggests that methods that reduce land‐cover mixing effects should be introduced when developing new spectral vegetation indices.     

Predicting Enhanced Vegetation Index (EVI) curves for ecosystem modeling applications

Vegetation indices derived from remote sensing data provide information about the variability in stature, growth and vigor of the vegetation across a region, and have been used to model plant processes. For example, the Enhanced Vegetation Index (EVI) provides a measure of greenness of the vegetation that can be used to predict net primary production. However, ecosystem models relying on remote sensing data for EVI or other vegetation indices are limited by the time series of the satellite data record. Our objective was to develop a statistical model to predict EVI in order to extend the time series for modeling applications. To explain the functional behavior of the seasonal EVI curves, a two-stage multiple regression fitting procedure within a semi-parametric mixed effect (SPME) model framework was used. First, a linear mixed effect (LME) model was fitted to the EVI with climate indexes, crop and irrigation information as predictor variables. Second, Penalized B-splines were used to explain the behavior of the smooth residuals, which result from a smooth model fit to the smooth EVI data curve, in order to describe the uncertainty of the EVI curve. Individual models were fit within individual Major Land Resources Areas (MLRAs). Predicted seasonal EVI, derived from our regression equations, showed a strong agreement with the observed EVI and was able to capture the site by site and year by year variation in the EVI curve. Out-of-sample prediction produced excellent results for a majority of the sites, except for sites without clear seasonal patterns, which may have resulted from cloud contamination and/or snow cover. Therefore, given the appropriate climate, crop, and irrigation information, the proposed approach can be used to predict seasonal EVI curves for extending the time series into the past and future.     

Response of seasonal vegetation development to climatic variations in eastern central Asia

Meteorological records show that central Asia has experienced one of the strongest warming signals in the world over the last 30 years. The objective of this study was to examine the seasonal vegetation response to the recent climatic variation on the Mongolian steppes, the third largest grassland in the world. The onset date of green-up for central Asia was estimated using time-series analysis of advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) biweekly composite data collected between January 1982 and December 1991. Monthly precipitation and mean temperature data (1982-1990) were acquired from 19 meteorological stations throughout the grasslands of the eastern Mongolian steppes in China. Our results showed that while the taiga forest north of the Mongolian steppes (>50°N) experienced an earlier onset of green-up during the study period, a later onset was observed at the eastern and northern edges of the Gobi Desert (40°N-50°N). Responses of different vegetation types to climatic variability appeared to vary with vegetation characteristics and spring soil moisture availability of specific sites. Plant stress caused by drought was the most significant contributor to later vegetation green-up as observed from satellite imagery over the desert steppe. Areas with greater seasonal soil moisture greened up earlier in the growing season. Our results suggested that water budget limitations determine the pattern of vegetation responses to atmospheric warming.     

Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study

Landscapes containing differing amounts of ecological disturbance provide an excellent opportunity to validate and better understand the emerging Moderate Resolution Imaging Spectrometer (MODIS) vegetation products. Four sites, including 1‐year post‐fire coniferous, 13‐year post‐fire deciduous, 24‐year post‐fire deciduous, and >100 year old post‐fire coniferous forests, were selected to serve as a post‐fire chronosequence in the central Siberian region of Krasnoyarsk (57.3°N, 91.6°E) with which to study the MODIS leaf area index (LAI) and vegetation index (VI) products. The collection 4 MODIS LAI product correctly represented the summer site phenologies, but significantly underestimated the LAI value of the >100 year old coniferous forest during the November to April time period. Landsat 7‐derived enhanced vegetation index (EVI) performed better than normalized difference vegetation index (NDVI) to separate the deciduous and conifer forests, and both indices contained significant correlation with field‐derived LAI values at coniferous forest sites (r ² = 0.61 and r ² = 0.69, respectively). The reduced simple ratio (RSR) markedly improved LAI prediction from satellite measurements (r ² = 0.89) relative to NDVI and EVI. LAI estimates derived from ETM+ images were scaled up to evaluate the 1 km resolution MODIS LAI product; from this analysis MODIS LAI overestimated values in the low LAI deciduous forests (where LAI<5) and underestimated values in the high LAI conifer forests (where LAI>6). Our results indicate that further research on the MODIS LAI product is warranted to better understand and improve remote LAI quantification in disturbed forest landscapes over the course of the year.     

Locating Amazonian Dark Earths (ADE) using vegetation vigour as a surrogate for soil type

Amazonian Dark Earths (ADE) are patches of archaeological soils scattered throughout the Amazon Basin. These soils are a mixture of charcoal, nutrient vegetable matter and the underlying Oxisol soil. ADE are extremely fertile in comparison to the surrounding soils and they are sought after by local residents for agricultural food production. Research is being conducted to learn how ADE were created and to explore the possibility of replicating them to sequester carbon and to reclaim depleted soils in the Amazon Basin. A factor limiting the success of this research is our current inability to locate ADE sites hidden beneath the tropical forest canopy. We use annual time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) satellite imagery from 2001 to 2005 and harmonic analysis (HA) to examine the spectral differences between forest vegetation growing on ADE and forest vegetation growing on non-ADE. There is a significant difference between the reflectances of vegetation growing on the two soil types, due primarily to lower EVI values over ADE during the dry season (multiple analysis of variance (MANOVA) p-value?=?0.040). A logistic model is used to create a predictive map of ADE location.     

Assessing the response of the MODIS vegetation indices to landscape disturbance in the forested areas of the legal Brazilian Amazon

In this study we assessed the impacts of forest fragmentation on the Amazon landscape using remote sensing techniques. Landscape disturbance, obtained for an area of approximately 3.5 × 10⁶ km² through simple spatial metrics (i.e. number of fragments, mean fragment area and border size) and principal component transformation were then compared to the MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) seasonal responses. As expected, higher disturbance values prevail in the southern border of the Amazon, near the intensively converted deforestation arc, and close to the major roads. NDVI seasonal responses more closely follow human-induced patterns, i.e. forest remnants from areas more intensively converted were associated with higher NDVI seasonal values. The significant correlation between NDVI seasonal responses and landscape disturbances were corroborated through analysis of geographically weighted regression (GWR) parameters and predictions. On the other hand, EVI seasonal responses were more complex with significant variations found over intact, less fragmented forest patches, thus restricting its utility to assess landscape disturbance. Although further research is needed, our results suggest that the degree of fragmentation of the forest remnants can be remotely sensed with MODIS vegetation indices. Thus, it may become possible to upscale field-based data on overall canopy condition and fragmentation status for basin-wide extrapolations.     

Comparison between vegetation change analysis in Kenya based on AVHRR and SeaWiFS images

An assessment of the suitability of the Advanced Very High Resolution Radiometer (AVHRR) vegetation index to estimate land degradation in semi‐arid areas has been carried out, comparing its behaviour with that of vegetation indices based on Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) images. Notwithstanding the importance of the classic Normalized Difference Vegetation Index (NDVI) indicator, based on red–NIR channels, several studies have identified some limitations related to its use, such as its dependence on the atmospheric profile, saturation problems, non‐linearity in biophysical coupling with Leaf Area Index (LAI) and canopy background contamination. The relatively recent Enhanced Vegetation Index (EVI) overcomes these limits, using the information related to the blue channel and a soil adjustment factor. SeaWiFS data allow the computation of both vegetation indices. On the other hand, the NDVI based on AVHRR can be computed back in time to the 1980s, allowing a sufficient time span to obtain information on the desertification trend of the considered region (northern Kenya). In conclusion, taking advantage of both datasets, the accuracy of a change detection technique based on the classic NDVI has been confirmed as suitable for revealing any desertification trend.     

Comparison of multiple models for remote sensing of carbon exchange using MODIS data in conifer-dominated forests

Boreal forests in the northern hemisphere provide important sinks for storing carbon dioxide (CO₂). However, the size and distribution of these sinks remain uncertain. In particular, many remote-sensing models show a strong bias in the simulation of carbon fluxes for evergreen needleleaf forest. The objective of this study is to improve these predictive models for accurately quantifying temporal changes in the net ecosystem exchange (NEE) of conifer-dominated forest solely based on satellite remote sensing, including the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daytime land-surface temperature (LST), night-time LST′, enhanced vegetation index (EVI), land–surface water index (LSWI), fraction of absorbed photosynthetically active radiation (FPAR), and leaf area index (LAI). Considering that the component fluxes, gross primary production (GPP), and ecosystem respiration (R_e), are strongly influenced by vegetation phenology, seasonality information was extracted from time-series MODIS EVI data based on non-linear least-squares fits of asymmetric Gaussian model functions with a software package for analysing the time-series of satellite sensor data (TIMESAT). The results indicated that models directly incorporating phenological information failed to improve their performance for temperate deciduous forest. Instead, three methods to retrieve the component fluxes – GPP and R_e – including direct estimates, models incorporating the phenological information, and models developed based on the threshold value (LST 273 K), were explored respectively. All methods improved NEE estimates markedly and models developed based on the threshold value performed best, and provided a future framework for accurate remote sensing of NEE in evergreen forest.     

Relationship between spatio-temporal characteristics of leaf-fall phenology and seasonal variations in near surface- and satellite-observed vegetation indices in a cool-temperate deciduous broad-leaved forest in Japan

We examined the relationship between the spatio-temporal distribution of leaf litter for each species and the seasonal patterns of in situ and satellite-observed daily vegetation indices in a cool-temperate deciduous broad-leaved forest. The timing and distribution of leaf-fall revealed spatio-temporal relationships with species and topography. Values of the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and green–red vegetation index (GRVI), measured both in situ and by satellite, and those of the in situ-measured leaf area index (LAI), rapidly declined at the peak of leaf-fall. At the late stage of leaf-fall, in situ-measured values of NDVI, EVI, and LAI declined but those of GRVI changed from decreasing to increasing. The peak timing of leaf-fall, when 50–73% of the leaf litter had fallen, corresponds to LAI = 1.80–0.81, NDVI = 0.61–0.54, EVI = 0.29–0.25, and GRVI = 0.01 ～ ?0.07. Although the distribution of leaf litter among species displayed spatial characteristics at the peak of leaf-fall, spatial heterogeneity of amount of leaf litter at the peak timing of leaf-fall was less than that at the beginning and end. These facts suggest that the criterion for determining the timing of leaf-fall from vegetation indices should be a value corresponding to the peak of leaf-fall rather than its end. In a high-biodiversity forest, such as this study forest, the effect of spatial heterogeneity on the timing and patterns of leaf-fall on vegetation indices can be reduced by observing only the seasonal variation in colour on the canopy surface by using GRVI, which consists of visible reflectance bands, rather than that of both leaf area and colour of the canopy surface by using NDVI and EVI, which consist of visible and near-infrared reflectance bands.     

Effect of the current major insect outbreaks on decadal phenological and LAI trends in southern Rocky Mountain forests

Remote sensing is a valuable tool for monitoring the impact of landscape-scale disturbances on ecosystem structure and function. We explore the impact of the ongoing insect outbreaks (Dendroctonus ponderosae, D. rufipennis, and Ips spp.) on southern Rocky Mountain forests, with the goal of assessing the sensitivity of leaf area index (LAI) and phenology metrics to different disturbance severities. Specifically, we investigate the influence of the outbreaks on two important ecosystem metrics, LAI, and phenology (e.g. green-up date, green-up speed, amplitude, etc.). Both were assessed via MODIS: 1000 m LAI (MOD15A2) and 250 m NDVI (MYD13Q1) for the phenology assessment. Trends (2002–2010) in phenology metrics and LAI were compared to different cumulative severities and timing of tree mortality, as determined from aerial surveys. Trends in phenology were significantly correlated with disturbance severity but with very low predictive power. This seems likely due to yearly variations in the onset of snow-fall and snow-melt, which dominate the phenologic signal at the regional scale of this study. Trends in LAI were associated more strongly with both disturbance severity and timing, with landscapes disturbed early in the observation period showing recovery (e.g. a positive trend) in LAI. The LAI, which is related to various vital ecosystem properties like water use and gas exchange, seems to be fairly resilient to even heavy mortality. Further work determining the relative contribution of the various functional groups (trees, shrubs, and grasses) to the LAI recovery is needed to better understand the implications of this large-scale, pervasive disturbance on forest structure and function.     

A simple and effective radiometric correction method to improve landscape change detection across sensors and across time

Satellite data offer unrivaled utility in monitoring and quantifying large scale land cover change over time. Radiometric consistency among collocated multi-temporal imagery is difficult to maintain, however, due to variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle that can obscure surface change detection. To detect accurate landscape change using multi-temporal images, we developed a variation of the pseudoinvariant feature (PIF) normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on June 9, 1990 (Landsat 4), June 20, 2000 (Landsat 7), and August 26, 2001 (Landsat 7) to analyze boreal forests near the Siberian city of Krasnoyarsk using the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and reduced simple ratio (RSR). The temporally invariant cluster (TIC) centers were identified via a point density map of collocated pixel VIs from the base image and the target image, and a normalization regression line was created to intersect all TIC centers. Target image VI values were then recalculated using the regression function so that these two images could be compared using the resulting common radiometric scale. We found that EVI was very indicative of vegetation structure because of its sensitivity to shadowing effects and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. Conversely, because NDVI reduced the radiometric influence of shadow, it did not allow for distinctions among these vegetation types. After normalization, correlations of NDVI and EVI with forest leaf area index (LAI) field measurements combined for 2000 and 2001 were significantly improved; the r² values in these regressions rose from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI “cancellation effect” where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a post fire chronosequence with normalized data. These findings indicate that the TIC method provides a simple, effective and repeatable method to create radiometrically comparable data sets for remote detection of landscape change. Compared to some previous relative radiometric normalization methods, this new method does not require high level programming and statistical skills, yet remains sensitive to landscape changes occurring over seasonal and inter-annual time scales. In addition, the TIC method maintains sensitivity to subtle changes in vegetation phenology and enables normalization even when invariant features are rare. While this normalization method allowed detection of a range of land use, land cover, and phenological/biophysical changes in the Siberian boreal forest region studied here, it is necessary to further examine images representing a wide variety of ecoregions to thoroughly evaluate the TIC method against other normalization schemes.     

Satellite-based modeling of gross primary production in an evergreen needleleaf forest

The eddy covariance technique provides valuable information on net ecosystem exchange (NEE) of CO₂, between the atmosphere and terrestrial ecosystems, ecosystem respiration, and gross primary production (GPP) at a variety of CO₂ eddy flux tower sites. In this paper, we develop a new, satellite-based Vegetation Photosynthesis Model (VPM) to estimate the seasonal dynamics and interannual variation of GPP of evergreen needleleaf forests. The VPM model uses two improved vegetation indices (Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI)). We used multi-year (1998-2001) images from the VEGETATION sensor onboard the SPOT-4 satellite and CO₂ flux data from a CO₂ eddy flux tower site in Howland, Maine, USA. The seasonal dynamics of GPP predicted by the VPM model agreed well with observed GPP in 1998-2001 at the Howland Forest. These results demonstrate the potential of the satellite-driven VPM model for scaling-up GPP of forests at the CO₂ flux tower sites, a key component for the study of the carbon cycle at regional and global scales.     

Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data

Net ecosystem exchange (NEE) of CO₂ between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO₂ flux measurements at individual CO₂ eddy flux sites provide valuable information on the seasonal dynamics of GPP. In this paper, we developed and validated the satellite-based Vegetation Photosynthesis Model (VPM), using site-specific CO₂ flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA. The VPM model is built upon the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation (NPV) within the leaf and canopy. It estimates GPP, using satellite-derived Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), air temperature and photosynthetically active radiation (PAR). Multi-year (1998-2001) data analyses have shown that EVI had a stronger linear relationship with GPP than did the Normalized Difference Vegetation Index (NDVI). Two simulations of the VPM model were conducted, using vegetation indices from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The predicted GPP values agreed reasonably well with observed GPP of the deciduous broadleaf forest at Harvard Forest, Massachusetts. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the VPM model for scaling-up of GPP of deciduous broadleaf forests.     

Fire-induced changes in green-up and leaf maturity of the Canadian boreal forest

Recent studies of vegetation phenology of northern forests using satellite data suggest that the observed earlier spring increase and peak amplitude of the normalized difference vegetation index (NDVI) are a result of climate warming. In addition to undergoing an increase in temperature, the northern forests of Canada have also seen a dramatic increase in area burned by wildfire over the same time period. Using the Canadian Large Fire Database, we analyzed the impact fire had on the phenological dates derived from fitting a logistical model to yearly data from 2004 for several different subsets of both AVHRR-NDVI and MODIS LAI in wildfire dominated terrestrial ecozones. Fire had a significant but complex effect on estimated phenology dates. The most recently burned areas (1994–2003) had later green-up dates in two ecozones for AVHRR data and all ecozones for MODIS. However, older forested (not burned during 1980–2003) had estimated green-up dates 1 to 9 days earlier than the entire forested area in the MODIS LAI data. These data corroborate studies in Canada and demonstrate that fire history is influencing boreal forest phenology and growing season LAI.