15arspc Submission 136

8/8/2019 15arspc Submission 136

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REMOTE SENSING OF TROPICAL FOREST PHENOLOGY:ISSUES AND CONTROVERSIES

Alfredo Huete1

and Scott Saleska2

1Plant Functional Biology and Climate Change Cluster

University of Technology SydneyP.O. Box 123, Broadway, NSW 2007

Tel. +61295144084, Fax [email protected]

2Dept. Ecology & Evolutionary Biology, University of Arizona, Tucson, AZ USA,[email protected]

Abstract

Different spatial and spectral resolution satellite products collected at hightemporal frequencies from the AVHRR and Terra- MODIS sensors showvariable and inconsistent seasonal patterns over tropical forests with somesensor products showing canopy drying in the dry season and negative forestresponses to seasonal drought, while other products show leaf flushing andenhanced greening in the dry season with a positive response to seasonaldrought. Large inconsistencies have been reported in cross- satellite productcomparisons (that include MODIS and AVHRR) for tropical evergreen broadleafforests and inconsistencies are found among MODIS products and tropical field

observations for LAI-related products. In this study, we investigate variousremote sensing processing methods, from leaf to canopy scale, for derivingspatial and temporal patterns of photosynthetic activity in tropical environments,using the Brazilian Amazon and Monsoon Asia tropical forests as study areas.We also consider the role and uncertainty played by atmospheric artifactsassociated with seasonal cloud cover and atmospheric aerosols, which renderthe task of decoupling surface vegetation phenology from atmosphereinfluences quite challenging.

1. Introduction

The vulnerability of tropical forest systems to climate change depends not juston the physical climate system, but on the biological response of forests toinitial climate changes. While much attention has focused on tropical forest die-back and savannization scenarios (Betts et al. 2004), other models imply forestresilience. Differing modeled fates of the forest are due to model differences inrepresentation of forest function, not just differences in representations ofclimate and current knowledge is insufficient to determine which representationsof vegetation function are most consistent with real forest ecosystems, butcontinuing observations from satellites and from the network of eddy flux towersprovide tools that can rigorously test mechanisms of forest-climate interactions

on observable timescales.


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Recent studies using MODIS remote sensing products have detected positivegreening vegetation responses to seasonal drying (Huete et al. 2006; Myneniet al. 2007) and interannual drought (Saleska et al. 2007), which if accurate,suggest that tropical vegetation, by apparently responding to increased dry-

period light availability, is more complex, and possibly more robust than mostecosystem models suggest, with implications for long-term vulnerability toclimate change. Prior to these studies, models have assumed that tropicalforest canopies exhibited no seasonality, in part, because seasonal remotesensing in the tropics is undermined by poor atmospheric conditions (Kobayashi& Dye, 2005). Other models represented browning vegetation and decreasedactivity in dry seasonal periods and interannual droughts, such as ENSO (Tianet al. 1998; Botta et al. 2002). Similar to many modeled results, the long-termAVHRR- NDVI time series show decreases in photosynthetic activity andproductivity with seasonal drought and interannual drought events such asENSO (Fig. 1) (Asner et al. 2000).

Figure 1. Seasonal precipitation and AVHRR NDVI (20 year averages) across an eco-

climatic gradient from south (Brasilia savanna) to Araguaia ecotone to the seasonallydry rainforests to the north (Tapajos). All biomes are found to dry down during

seasonal drought periods (credit, Humberto Barbosa).

Various hypotheses for forest response to climatic variability include: (1) Lightlimitation, in which tropical forest growth is primarily limited by availability ofphotosynthetically-active radiation (PAR), with access to deep soil water viadeep tree roots minimizing the typical modeled water stresses; (2) Diffuseradiation enhancement of Light-Use Efficiency (LUE), which is the amount ofphotosynthesis that takes place per amount of PAR absorbed by the forestcanopy; (3) Leaf growth phenology.

However, issues remain in the interpretation and understanding of remotesensing data. Different coarse resolution satellite products collected at high


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temporal frequencies from the AVHRR and MODIS platforms show variable andinconsistent seasonal and interannual patterns over tropical rainforests, withsome sensor products showing canopy drying in the dry season and negativeforest responses to drought, while other products show leaf flushing and

greening in the dry season and a positive response to drought (Fig. 2, Huete etal. 2008). The MODIS NDVI product, on the other hand, show widespreadsaturation over rainforest ecosystems (Huete et al. 2002).

Figure 2. Seasonal variations in satellite products from MODIS (GPP, FPAR, and EVI),compared to eddy tower GPP (Pg) from SE Asia tropical forests (MaeKlong Station,

Thailand). The satellite products (all intended to relate to photosynthesis) show

divergent performance. FPAR (fraction absorbed PAR) shows no seasonality, MODIS

GPP shows a seasonality opposite to the tower, and only EVI shows a seasonality inphase with the tower (Huete et al. 2008).

1.1 Vegetation Indices

The main scientific requirement of a VI measurement is that it combine thechlorophyll-absorbing red spectral region with the non-absorbing, leafreflectance signal in the near-infrared (NIR) to depict vegetation greenness orarea-averaged canopy photosynthetic capacity. There are a variety of ways inwhich the red and NIR bands may be combined to estimate vegetation

properties and greenness amounts, and this has resulted in a multitude of VIequations, including band ratios, normalized differences, linear bandcombinations, and optimized band combinations. All of VIs have beenempirically related with various vegetation canopy properties, however, thereare significant differences in how they depict greenness and their suitability forglobal, moderate resolution algorithms. The NDVI is computed as,

NDVI = [NIR red] / [NIR + red] (1)

The MODIS enhanced vegetation index (EVI) product, on the other hand, hasminimal saturation problems through greater weighing of the near-infrared band.


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The EVI gains its heritage primarily from the soil adjusted vegetation index(SAVI, Huete 1988) and is an optimized combination of red and NIR bands,designed to extract canopy greenness, independent of biases from differencesin soil background. The EVI also incorporates an aerosol resistance term

developed in the atmosphere resistance vegetation index (ARVI) equations thatutilize the blue reflectance to stabilize aerosol influences and biased correctionsin red reflectances,

EVI = 2.5 [NIR red] / [L + NIR +C1red C2blue] (2)

where L is the canopy background adjustment factor, is reflectance, and C1

and C2 are the aerosol resistance weights. The coefficients of the EVI equationare L=1; C1=6 and C2 =7.5.

Since the role of the blue-band in the EVI does not provide additionalbiophysical information on vegetation properties, but rather is aimed at reducingnoise and uncertainties associated with highly variable atmospheric aerosols, a2-band adaptation of EVI should be compatible. A simple, 2-band EVI has beenformulated as a backup algorithm to the MODIS EVI product, and is used incases when the blue band yields problematic VI values, mainly in dense snowand over certain clouds. It is derived by setting C1 = 2.4 and C2 = 0 (Jiang et al.2008),

EVI2 = 2.5 [NIR - red] / [L + NIR + 2.4 red] (3)

where L=1. Although the EVI2 is computed without a blue band, it remainsfunctionally-equivalent to the EVI, although slightly more prone to aerosol noise,which may become less significant with continuing advancements inatmosphere correction. This also provides one method to generate a backwardcompatibility of the EVI to the historical AVHRR record, and complement theNDVI long-term record.

The primary difference between the NDVI and EVI is in the L parameter in EVI,which attempts to optimize the measure of greenness through a simple, first-

order application of Beer-Lamberts law to describe differential red and NIRextinction through vegetation canopies, according to,

C = v + t2Cs (4)

where canopy reflectance (C) is the sum of the vegetation layer reflectance (v)

and the two-way canopy transmitted (t2C) soil reflectance (s). The NDVI has a

very low optical depth penetration into canopies due to its heavier reliance onthe red channel, while the EVI will have a higher canopy penetration throughgreater weighting on the NIR channel, hence allowing extended sensitivity overhigher LAI/ biomass areas where the NDVI saturates. EVI may provide a more

direct relationship with photosynthesis (GPP) in dense canopies by relying on


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the more sensitive bio-structural NIR canopy reflectance which is less prone tosaturate. This is also supported through theoretical analyses concluding thatspectral indices that are more functional on the NIR best describe area-averaged canopy photosynthetic capacities and gross primary productivity

(Sellers, 1987).When L is optimized correctly, there is no (or minimal) soil backgroundinfluences in the VI signal as the relative optical depths of the two bands aremore closely adjusted to only see the greenness of the canopy. As the NDVIrepresents the special case of L=0, in contrast to L=1 in the EVI, the impact ofL on the seasonality of the greenness signal can be shown by varying L from0 to 1 (Fig. 3). As can be seen, the shift in L has a significant impact onobserved seasonality/ phenology. In the Tapajos rainforest, the change in Lvalue from 0 (NDVI) to 1 (EVI) resulted in seasonal profiles completely out ofphase with one another, and emphasizes the importance of fusion of satellite VI

measurements with ground, in situ observations for more accurate inputs tomodels.

Figure 3. (top) Sensitivity of vegetation index seasonal profiles to the L parameter.

L=0 is the case of NDVI, while L=1 represents the EVI, EVI2; L=0.5 (dark blue) as inSAVI; L=0.25 (light blue) and L=0.05 (orange); (bottom) simultaneous in situtower

derived GPP monthly fluxes at the Tapajos Amazon rainforest site.

Large inconsistencies have been reported in cross- satellite productcomparisons (that include MODIS and AVHRR) for tropical evergreen broadleafforests (Garrigues et al. 2008). At local tower site scales, Doughty and Goulden(2008) showed in situ LAI measurements differed markedly from seasonalcycles of the MODIS LAI product (MOD15). Hutrya et al. (2007) showed MODISLAI and EVI products not in phase with each other. There is a great need tobetter relate satellite products with canopy functional processes in tropicalforests in order to understand what changes in greenness signify.

Remote sensing methods directly detect photosynthetic pigments in vegetation,and could be a powerful tool for investigating spatial and temporal patterns of


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photosynthetic metabolism in challenging tropical environments, but uncertaintyremains about atmospheric artefacts from highly seasonal cloud cover andaerosol loads in tropical atmospheres. Some argue that the observed remotesensing responses are due to artifact. Kobayashi and Dye (2005) analyzed the

GIMMS AVHRR- NDVI time series over the Amazon and found the seasonalsignal to be primarily dominated by cloud and aerosol contamination.

We have previously argued (Huete et al. 2006; Saleska et al. 2007) that bychoosing satellite products that correlate with measurements on the ground(e.g. satellite EVI with ecosystem scale photosynthetic fluxes measured fromtowers), and by a combination of appropriate selection of high quality data fromrelatively uncontaminated pixels and correction for residual contamination, wearrive at observations that are robust to the problems cited above (for example,because MODIS EVI uses atmospherically-corrected surface reflectances andan aerosol resistance term, we believe it is minimally affected by residual

aerosols up to optical depths of over 1).

1.2. Interannual Drought

In 2005, large areas of the southwestern Amazon basin experienced one of themost intense droughts of the last 100 years (Marengo et al. 2008). During the2005 drought, satellite observations showed short-term (3 mos) increases inMODIS EVI, suggesting increased forest photosynthetic capacity (Fig. 4,Saleska et al. 2007), while Phillips et al. (2009), using field measurements,showed a longer-term decline in the rate of biomass accumulation, slowing orreversing previous trends, primarily due to an increase in tree mortality. It is not

currently understood whether these seemingly divergent responses (short termincrease in photosynthetic capacity during the peak months of drought,encompassed by excess mortality integrated over several years) may in fact bereconciled by accounting for differing time scales and effects of time lags.

The divergent responses, however, pose specific questions, including: (1) Whatis the role of light vs. water limitation in controlling vegetation response todrought; (2) What are the causes for variation in forest responses to the samedrought event (while most areas greened up, distinct brown-down regionswere also observed (Fig. 4); (3) How does a drought response interact withnominal patterns of seasonal drought, given the varying nature of seasonal

drought responses (Fig.2).Samanta et al (2010) suggest that apparent tropical forest greening during theAmazon drought of 2005 (as reported in Saleska et al. 2007) is in fact due toatmospheric aerosol contamination of the surface reflectance and EVI, ratherthan a true vegetation response. Some of the possible differences fordiscrepancies between the Saleska et al. paper and the Samanta et al. papersmay be due to different MODIS Collections (the former study was conductedwith Coll. 4, while the more recent study with Coll. 5). Other causes of differingresults may be due to processing methods, statistical methods, andinterpretations. For example, the 2 studies may be based on different sets ofhypotheses, and hence conclusions.


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Figure 4. 2005 TRMM-based precipitation anomaly (left) and corresponding MODISEVU anomaly (right), with anomalies expressed as standard deviations. Contrary to

model predictions, short term drought caused an increase in EVI over 34% of thedrought affected area. Forests may be adapted to drought to take advantage of

increased sunlight (Saleska et al. 2007).

2. Methods

We analyzed the spatio-temporal tropical forest responses to seasonal andinterannual drought events with 9 years of MODIS enhanced vegetation index(EVI) satellite data and 10 years of TRMM precipitation data (3B43 monthlyproduct at 25 km resolution). We used the 1 km and CMG versions of MODISEVI (MOD13A2 and MOD13C1) and used the quality assurance (QA) filters toscreen for and remove cloud and aerosol contaminated pixels, as in Saleska etal. (2007). To be conservative, we aggregated the 16-day C5 EVI product to aquarterly anomaly time series according to the restrictive method of Samanta etal. (2010). EVI and precipitation anomalies are defined relative to the2000-2008 mean (excluding 2005). The 2005 drought area is defined as thoseforested areas within the Amazon basin where July-Aug-Sept precipitationanomalies < -1 standard deviation relative to the 1998-2006 mean (excluding2005).

3. Results and Discussion

We tested a wide variety of methods to process the MODIS EVI time seriesdata and found consistent patterns throughout our analysis, further confirmingthe Amazon green-up hypothesis. All datasets, time periods, and methods ofanalysis show that valid pixels in the drought region exhibited a statisticallysignificant anomalous increase in EVI, and MODIS EVI anomalies of the 2005drought remain statistically significantly skewed towards greenness.


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Figure 5. (A) Collection 5 EVI anomalies for July-Sept. of 2005 for the Amazon basin,

with QA filtering of 1km product (MOD13A2) by the method used in both Saleska et al(2007) and Samanta et al (2010). Gray pixels contain no valid 2005 data and/or

insufficient valid data to calculate a mean and standard deviation across other years;(B)same as in A, but aggregated according to the more restrictive method of Samantaet al, which discards a majority of all valid observations in the aggregation. Gray pixels

(which cover most of the basin) have one month or more (out of 21 in the 7 -year timeseries) without a valid observation; (C, D): EVI anomalies (as on left), but masked toinclude only forested areas within the basin that were in the 2005 drought area (where

July-Sept precipitation anomalies < -1 standard deviation relative to the 1998-2006mean, excluding 2005). (insets to C,D)Corresponding histograms of EVI standardized

anomaly frequencies of valid pixels in the 2005 drought area (credit, Kamel Didan).

The method of Samanta et al. (2010) essentially eliminates much of the drought

region with stricter criteria for the time series pixels to be valid, but even so, adisproportionate number of remaining pixels are anomalously green. We furthershow that the full time series shows that the strongest positive EVI anomaly ofthe entire satellite record corresponds to the strongest drought of the entirerecord (2005). This observed time series stands in strong contrast to modelsimulations (Fig. 6).


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Figure 6. (A) Model simulations, by the Hadley Center, of forest photosynthesis andprecipitation in central Amazonia in years relative to a hypothetical modeled drought

(Jones et al., 2001); (B) Satellite observations (from the third -quarter in each year), of

the percent of valid pixels in the area defined by the 2005 drought with EVI anomaliesexceeding +1 standard deviations, and (lower panel) the percent of pixels in the area

defined by the 2005 drought in precipitation deficit (i.e. with TRMM precipitation

anomalies more than 1 standard deviation below the mean). Upper right panel fromSamanta et al. (2010) with the yellow point added from this analysis.

Separate tests were made to determine whether it was possible for aerosols to

have contaminated our results. We found that even in the worst-case periods,surface reflectances flagged as contaminated by high aerosols hardlyintersected the drought region at all, and played no role in the outcome of thisanalyses. Thus, high aerosols were, for the most part, not in the drought area.We further found high aerosol resistance in the EVI through the use of the blueband.

In conclusion, the patterns of anomalous greening were found to be robustacross different versions and processings of the MODIS dataset, and across arange of analysis methods. If drought had the expected negative effect oncanopy photosynthesis, it should have been especially observable duringextreme drought events, when anomalous interannual drought coincided withthe already seasonally low precipitation. The observations of intact forestcanopy greenness in the affected areas, however, are dominated by asignificant increase, not a decline.


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4. Discussion

The extraction of meaningful forest canopy information from coarse resolutionsatellite data is severely limited by clouds (primarily in the wet season) and byaerosols (primarily in the dry season). Despite this limitation, there remainssufficiently high quality pixels to derive meaningful biophysical information. Inthe highly restrictive and strict quality criteria of Samanta et al. (2010), 33% ofthe Amazon basin forests exhibited the highest quality pixels over a span of > 8years. Thus, one third of the population of pixels were useful for interpretation ofvegetation dynamics and responses to seasonal and interannual droughtperiods. Since more than two-thirds of the Amazon basin were unobservabledue to pixel contamination, Samanta et al (2010) classified such areas an non-green, therefore satisfying their perceived hypothesis of showing that theAmazon basin did not green up. However, the same data and results areinterpreted as greening in the Amazon by Saleska et al. since a statistically

significant proportion of the pixels that were observable (25-35%) did green up,when in fact it was hypothesized that Amazon forests should have browned-down as the models show and predict. By this reasoning, the large proportionof unobservable area does not become classified as green or non-green, sincethe satellites could not accurately image such areas. Lastly, between the twosets of studies, there is the added factor of mixing of Coll. 4 and Coll. 5 MODISdata. One cannot readily separate the greening differences from the 2 studiesfrom the myriad of other changes that occurred between the 2 Collections,including significant changes to the surface reflectance product, the primaryinput to the MODIS vegetation indices.

We believe that an important goal of remote sensing in the tropics is to devise

statistically meaningful schemes to retrieve information of ecosystem properties,processes, and phenology. However, coarse resolution satellite imagery wouldstill suffer from the complexities in separating biophysical from biochemicalcanopy factors as well as how to interpret dynamic signals from numerousspecies with asynchronies in their seasonal patterns. As an example,enhanced tree mortality associated with prolonged droughts may result in anincrease in undergrowth and herbaceous species that may appear as overallenhanced greening in both moderate and coarse resolution data.

5. References

Asner,G.P., Townsend, A.R., and Braswell, B.H., 2000, Satellite observation ofEl Nio effects on Amazon Forest phenology and productivity. GeophysicalResearch Letters, 27: 981-984.

Betts, R.A, Cox, P.M, Collins, M., et al., 2004, The role of ecosystem-atmosphere interactions in simulated Amazonian precipitation decrease andforest dieback under global climate warming. Theoretical and AppliedClimatology, 78:157-175.

Botta, A., Ramankutty, N., and Foley, J.A., 2002, Long-term variations of


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climate and carbon fluxes over the Amazon basin. Geophysical ResearchLetters, 29:1319.

Doughty, C. E., and Goulden, M.L., 2008, Seasonal patterns of tropical forest

leaf area index and CO2 exchange, Journal of Geophysical Research, 113,G00B06, doi:10.1029/2007JG000590.

Garrigues, S., Lacaze, R., Baret, F., 2008, Validation and intercomparison ofglobal Leaf Area Index products derived from remote sensing data. Journal ofGeophysical Research, 113:doi:10.1029/2007JG000635.

Huete, A., 1988, A soil-adjusted vegetation index (SAVI). Remote Sensing ofEnvironment, 25:295.

Huete, A.R., Didan, K., Shimabukuro, Y.E., et al., 2006, Amazon rainforestsgreen-up with sunlight in dry season. Geophysical Research Letters,

33:L06405.

Huete, A.R, Restrepo-Coupe, N., Ratana, P., et al., 2008, Multiple site towerflux and remote sensing comparisons of tropical forest dynamics in MonsoonAsia.Agricultural and Forest Meteorology, 148:748-760.

Hutyra, L.R.. Munger, J.W., Saleska, S.R., et al., 2007, Seasonal controls onthe exchange of carbon and water in an Amazonian rainforest. JGR-Biogeoscience, 112, G03008, doi:10.1029/2006JG000365.

Jiang, Z., Huete, A.R., Didan, K., Miura, T., 2008, Development of a 2-band

Enhanced Vegetation Index without a blue band. Remote Sensing ofEnvironment, 112:3833-3845.

Kobayashi, H., and Dye, D.G., 2005, Atmospheric conditions for monitoring thelong-term vegetation dynamics in the Amazon using normalized differencevegetation index. Remote Sensing of Environment, 97:519-525.

Marengo, J.A., Nobre, C., Tomasella, J., et al., 2008, The drought of Amazoniain 2005. Journal of Climate, 21:495-516.

Myneni, R.B., Yang, W., Nemani, R.R., et al., 2007, Large seasonal swings inleaf area of Amazon rainforests. Proceedings of the National Academy of

Science, doi:10.1073/pnas.0611338104.

Phillips, O.L, Arago, L.E., Lewis, S.L., et al., 2009, Drought sensitivity of theAmazon rainforest. Science, 323:1344-1347.

Saleska, S. R., Didan, K., Huete, A.R., and da Rocha, H.R., 2007, Amazonforests green up during 2005 drought. Science, DOI: 10.1126/science.1146663.

Samanta, H., Ganguly, S.A., Hashimoto, Y. et al., 2010, Amazon forests did notgreen-up during 2005 drought. Geophysical Research Letters, 37, L05401,doi:10.1029/2009GL042154.

Sellers, P., 1987, Canopy reflectance, photosynthesis, and transpiration, II. The


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role of biophysics in the linearity of their interdependence. Remote Sensing ofEnvironment, 21:1.

Tian, H., Melillo, J.M., Kicklighter, D.W., et al., 1998, Effect of interannual

climate variability on carbon storage in Amazonian Ecosystems. Nature, 396:664-667.

15arspc Submission 136

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