Biogeosciences [B]

B31A   MCW:Level 2   Wednesday  0800h

Land Surface Phenology, Seasonality, and the Water Cycle I Posters

Presiding: G M Henebry, South Dakota State University; M A Friedl, Boston University; M A White, Utah State University

B31A-1060

Understanding Monthly Land Surface Relationships at the Continental Scale Using Remotely Sensed Data

* Robertson, R D (rdrobert@uiuc.edu) , Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
Mehra, V (vmehra2@uiuc.edu) , Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
Kumar, P (kumar1@uiuc.edu) , Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
Bajcsy, P (pbajcsy@ncsa.uiuc.edu) , National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
Tcheng, D (dtcheng@ncsa.uiuc.edu) , National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States

In the past few decades, remotely sensed Earth observation data has been gathered at rates now on the order of tens of terabytes per day. These collections of data are valuable reserves of "scientific ore." However, mining the ore for useful science has been challenging due to the sheer volume of data, esoteric formats, varying temporal scales, and varying spatial scales. Regardless, the wide geographic and temporal ranges allow investigations at scales inaccessible by other presently existing methods. We developed a technology called GeoLearn to facilitate data preparation and basic exploration so this data can be more readily available for scientific purposes. GeoLearn is used to prepare the data which we examine for relationships between several land surface variables across the entire continental USA during each month in the summer of 2004. We employ two approaches: k- means style clustering and regression tree approaches. Using k-means, we try to identify geographic regions of similarity using only remotely sensed characteristics. The resulting geographic regions often, but not always, correspond to EPA ecoregion boundaries. Using regressions trees, we try to predict a greenness index (EVI) based on other characteristics. In this case, the differing resolutions of the datasets became important. EVI is the most detailed variable we use. Since regression trees are capable of quite detailed approximations, the best naive model turns out to be based on the one or two most detailed explanatory variables. This results in a model which merely uses the explanatory variables as ID numbers rather than identifying any general relationships. We are able to develop alternative models which maintain flexibility without succumbing to the ID number'' problem as easily. These models allow us to identify what variables are most important for determining vegetation greenness at continental scales as well as how those relationships changed throughout the summer of 2004.

B31A-1061

Phenology model from weather station meteorology does not predict satellite-based onset

* Fisher, J I (jeremy.fisher@unh.edu) , Complex Systems Research Center, 461 Morse Hall University of New Hampshire 39 College Road, Durham, NH 03824, United States
Richardson, A D (andrew.richardson@unh.edu) , Complex Systems Research Center, 461 Morse Hall University of New Hampshire 39 College Road, Durham, NH 03824, United States
Mustard, J F (John_Mustard@brown.edu) , Geological Sciences, Box 1846 Brown University, Providence, RI 02912, United States

Seasonal temperature changes in temperate forests are known to trigger the start of spring growth, and both interannual and spatial variations in spring growth have been tied to climatic variability. Satellite data are finding increased use in regional and global phenological studies, but to date there have been few efforts to rigorously tie remotely sensed phenology to surface climate records. Where satellite records have been compared to broad- scale climate patterns, broadleaf deciduous forests have typically been characterized as a single functional type and differences between communities ignored. We used a simple two-parameter spring warming model to explore the relationship between interannual climate variability and satellite-based phenology in New England broadleaf temperate forests. We employed daily air temperature records between 2000 and 2005 from 171 NOAA meteorological stations to parameterize a simple spring warming model predicting the date of MODIS half- maximum greenness (spring onset). We find that the best model starts accumulating heating degree days (HDD) after March 20th and when average daily temperatures exceed 5$\deg$C. Critical heat sums to reach onset range from 150 to 300 degree-days, with increasing requirements southward and in coastal regions. In our findings, the spring warming model offers little improvement on the photoperiod null model (i.e. the average date of onset). However, differences between the relative goodness-of-fit of the spring warming model compared to the null (coined the ï¿½climate sensitivity ratio', or CSR) displayed unexpected spatial coherency. The spatial variation in CSR appears to be related to differences in forest composition, with clear differences between northern (beech-maple-birch) and central (oak-hickory) hardwood forests. The two forest types may respond to climate differently, with disparate sensitivities to the minimum temperature initiating spring growth (3 and 6$\deg$C, respectively). We conclude that spatial location and species composition are critical factors which regulate the phenological response to climate. Regardless of model choice, satellite observations of temperate phenology cannot be effectively tied to climate without regard to community composition.

B31A-1062

Challenges in Estimating the Vegetation Phenology With Remote Sensed Data

* Tan, B (tanbin@bu.edu) , Boston University, Rm 457, 675 Commonwealth Ave, Boston, ma 02215, United States
Friedl, M (friedl@bu.edu) , Boston University, Rm 457, 675 Commonwealth Ave, Boston, ma 02215, United States
Zhang, X (xiaoyang.zhang@noaa.gov) , NOAA/NESDIS/Center for Satellite Applications and Research, Rm 712, 5200 Auth Road, Camp Springs, md 20746, United States

At seasonal to interannual time scales, vegetation phenology reflects dynamics of the Earth's climate and hydrologic regimes, and is diagnostic of coupling between the Earth's biosphere and atmosphere. Information related to large scale phenology is therefore useful for studies of seasonal and interannual variability in carbon exchange and vegetation-climate interactions. Remote sensing provides a key means of measuring and monitoring phenology at continental to global scales and vegetation indices derived from satellite data are now commonly used for this purpose. Since April of 2000, the Moderate Resolution Imaging Spectroradiometer (MODIS) has been providing imagery of the Earth that is highly suitable for studies of phenology. At the same time, the use of MODIS data presents a number of significant technical challenges. In particular, residual atmospheric effects, clouds, and the presence of snow all introduce substantial uncertainty to estimates of phenology from MODIS. In this paper, we examine several key factors that affect the quality of phenological metrics derived from MODIS. These factors include: (1) the choice of vegetation index; (2) the method used to screen and correct the effects of snow; and (3) the technique used to fill time series gaps caused by clouds. We examined each of these factors using the MODIS land cover dynamics algorithm (MOD12Q2) for a set of locations that span a range of climate and ecosystem types. Our results show that the sensitivity of MOD12Q2 algorithm to the choice of vegetation index depends on the biome type. Specifically, we observed distinct differences in phenology inferred from EVI versus NDVI in densely vegetated areas caused by saturation of the NDVI. Conversely, in semi-arid areas we observed large differences arising from the different sensitivity of each index to sparse vegetation cover. Finally, our results indicate that the method used to account for snow has a pronounced effect on remotely sensed estimates for the beginning of the growing season.

B31A-1063

Intraseasonal Interactions between Climate and Vegetation Variability over the Boreal Forests

* Wang, W (weile.wang@gmail.com) , Department of Geography and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215
Anderson, B T , Department of Geography and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215
Entekhabi, D , Ralph M. Parsons Laboratory, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 15 Vassar Street, Cambridge, MA 02139
Huang, D , Department of Geography and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215
Kaufmann, R K , Department of Geography and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215
Myneni, R B , Department of Geography and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215
Nemani, R R , Ecosystem Science and Technology Branch, NASA Ames Research Center, NASA Ames Research Center, Mail Stop 242, Moffett Field, CA 94035

This study uses statistical techniques and analytical methodologies to investigate intraseasonal interactions between temperature and vegetation (surrogated by normalized difference vegetation index, or NDVI) over the boreal forests. Results indicate that interactions between the two fields may be approximated as a coupled second-order system, in which the variability of NDVI and temperature of the current month is significantly regulated by lagged NDVI anomalies from the preceding two months. In particular, the influence from the one- month lagged NDVI anomalies is generally positive, but the influence from the second-month lagged NDVI anomalies is often negative. Such regulations lead to an intrinsic oscillatory variability of vegetation at growing- season time scales, which are identified in NDVI anomalies almost everywhere across the study domain. On the other hand, the regulations of NDVI anomalies on temperature variability are found most significant over interior Asia (Siberia), suggesting strong vegetation/atmosphere couplings over these regions. Physical mechanisms for these statistical results are further investigated with a stochastic model. It suggests that the oscillatory variability of the temperature-NDVI system may reflect the dynamic adjustments between the two fields to maintain a thermal balance within the soil and lower boundary layer of the atmosphere; and the particular role vegetation plays in this scenario is mainly to dissipate heat and therefore reduce surface temperatures.

B31A-1064

Monitoring Start of Season in Alaska

* Robin, J , University of Maryland-College Park, 2181 LeFrak Hall, College Park, MD 20742
Dubayah, R , University of Maryland-College Park, 2181 LeFrak Hall, College Park, MD 20742
Sparrow, E , University of Alaska-Fairbanks, 317 O'Neill Building, Fairbanks, AK 99775
Levine, E , NASA Goddard Space Flight Center, Code 614.4, Greenbelt, MD 20771

In biomes that have distinct winter seasons, start of spring phenological events, specifically timing of budburst and green-up of leaves, coincides with transpiration. Seasons leave annual signatures that reflect the dynamic nature of the hydrologic cycle and link the different spheres of the Earth system. This paper evaluates whether continuity between AVHRR and MODIS normalized difference vegetation index (NDVI) is achievable for monitoring land surface phenology, specifically start of season (SOS), in Alaska. Additionally, two thresholds, one based on NDVI and the other on accumulated growing degree-days (GDD), are compared to determine which most accurately predicts SOS for Fairbanks. Ratio of maximum greenness at SOS was computed from biweekly AVHRR and MODIS composites for 2001 through 2004 for Anchorage and Fairbanks regions. SOS dates were determined from annual green-up observations made by GLOBE students. Results showed that different processing as well as spectral characteristics of each sensor restrict continuity between the two datasets. MODIS values were consistently higher and had less inter-annual variability during the height of the growing season than corresponding AVHRR values. Furthermore, a threshold of 131-175 accumulated GDD was a better predictor of SOS for Fairbanks than a NDVI threshold applied to AVHRR and MODIS datasets. The NDVI threshold was developed from biweekly AVHRR composites from 1982 through 2004 and corresponding annual green-up observations at University of Alaska-Fairbanks (UAF). The GDD threshold was developed from 20+ years of historic daily mean air temperature data and the same green-up observations. SOS dates computed with the GDD threshold most closely resembled actual green-up dates observed by GLOBE students and UAF researchers. Overall, biweekly composites and effects of clouds, snow, and conifers limit the ability of NDVI to monitor phenological changes in Alaska.

B31A-1065

MODIS and AMSR-E vegetation indices for land surface phenology studies in the Eurasia semi-arid grain belt

* Doubkova, M (marcela.doubkova@sdstate.edu) , South Dakota State University, Geographic Information Science Center of Excellence (GIScCE), 1021 Medary Ave., Wecota Hall 506B, Brookings, SD 57007-3510, United States
Henebry, G M (geoffrey.henebry@sdstate.edu) , South Dakota State University, Geographic Information Science Center of Excellence (GIScCE), 1021 Medary Ave., Wecota Hall 506B, Brookings, SD 57007-3510, United States

Alterations in land surface phenology (LSP) can affect land-atmosphere exchanges of energy and water. Monitoring and modeling LSP can be accomplished using image time series from remote sensing product streams. From MODIS onboard the Terra and Aqua platforms, these products and derivatives include, NPP, LAI, fPAR, vegetation indices, (NDVI, WDRVI, EVI/EVI2), and indicators of vegetation wetness (NDWI, LSWI). From AMSR-E onboard the Aqua platform, the principal product is the vegetation water content derived from multifrequency microwave brightness temperatures. Here we investigate the synergistic use of MODIS and AMSR-E products for LSP studies. Optical vegetation products are restricted for daytime acquisition and do not provide information on the diel canopy energy and moisture exchanges. Passive microwave remote sensing, on the other hand, senses the "cool earthlight" - the energy emitted at longer wavelengths (0.34 ï¿½ 4.3 cm) during both day and night. The sensitivity of microwave emittance to the moisture status of vegetation has been amply demonstrated. We construct LSP models based using various MODIS products and indices and for the period 2003-2005 we assess (1) the value at the beginning of the observational period, (2) the seasonal peak, (3) the quantity of accumulated growing degree- days at the peak value, and (4) the seasonal dynamic range in value. We evaluate how the AMSR-E vegetation water content (VWC) product that is available at coarse spatial but finer temporal resolutions corresponds to the LSP models based on MODIS data. The region is interest is the semi-arid grain belt of Eurasia that covers about 200 million hectares and extends eastward from eastern Ukraine across southern Russia and northern Kazakhstan and northward of the Irano- Turanian deserts to the foothills of the Tian-Shan and the Altai Mountains. The western portions of this region served as the breadbasket of the Russian Empire. There are three principal grain crops grown within the region: barley, winter wheat, and spring wheat. Cropping predominantly follows a rain-fed fallow rotational system. Differential LSPs are observed across the region and the diel difference of the VWC is able to track significant changes in LSP at a quicker tempo than MODIS products.

http://globalmonitoring.sdstate.edu/eurasia- LSP/

B31A-1066

Phenological Mixture Models: Using MODIS to Identify Key Phenological Endmembers and Their Spatial Distribution in the Northern Eurasian Semi-Arid Grain Belt.

* de Beurs, K M (debeurs@wisc.edu) , University of Wisconsin - Madison, Department of Forest Ecology and Management 1630 Linden Drive, 120 Russell Lab., Madison, WI 53706, United States
Henebry, G M (Geoffrey.Henebry@sdstate.edu) , South Dakota State University, Geographic Information Science Center of Excellence (GIScCE) Wecota Hall, Box 506B, Brookings, SD 57007-3510, United States

Land surface phenology (LSP) is the study of the spatio-temporal patterns of the vegetated land surface as observed by synoptic sensors. LSP necessarily deals with mixtures of vegetation types and land covers, each with potentially different phenological cycles. MODIS data from the Terra and Aqua platforms currently provide a new dataset with a 32 times finer resolution than the often used global AVHRR NDVI composites. This finer resolution increases the possibility of identifying and extracting image time series that exhibit exemplary spectral- temporal behaviors, which we call "phenological endmembers". Most linear mixture models used in remote sensing analysis assume a linear superposition of spectral endmembers acquired simultaneously. However, here we use phenological endmembers as linear superpositions of vegetation index temporal profiles. Using the MODIS/Terra BRDF/Albedo Model-1 16-Day L3 Global 1km ISIN Grid data (MOD43B4) from the Land Processes Distributed Active Archive Center between from 2001 to 2005, we first apply a series of flexible seasonal windows to determine the best fitting phenology model for each pixel. The parameter coefficients of these optimized phenology models then give us estimates for five phenological characteristics: 1) the NDVI at the beginning of the growing season; 2) the maximum NDVI at the peak of the growing season; 3) seasonal dynamic range of NDVI; 4) the quantity of Accumulated Growing Degree Days (AGDD) necessary to reach the peak of the growing season; and 5) the day of the year of the peak of the growing season. These characteristics and the phenological behavior of each individual pixel aligned by DOY as well as by AGDD scale allows us to identify six key phenological endmembers in the Northern Eurasian semi-arid grain belt: cropland, grassland, forests (mixed and deciduous), desert, and urban area. The results show not only a distribution of available land cover types for each pixel, but also a distribution of land cover behaviors for each land cover type. These distributions allow us to demonstrate how a potential change in land cover types could affect the final land surface phenology signal in that particular pixel. Finally, we show that we can use the MODIS change patterns to interpret AVHRR change patterns. This can enhance the informational value of the historical AVHRR archive and demonstrates the power of recurrent observations.

B31A-1067

Change and Persistence in Land Surface Phenologies in the Don and Dnieper River Basins

* Kovalskyy, V (Valeriy.Kovalskyy@sdstate.edu) , South Dakota State University, Geographic Information Center of Excellence (GIScCE), 1021 Medary Ave., Wecota Hall 506B, Brookings, SD 57007-3510, United States
Henebry, G M (Geoffrey.Henebry@sdstate.edu) , South Dakota State University, Geographic Information Center of Excellence (GIScCE), 1021 Medary Ave., Wecota Hall 506B, Brookings, SD 57007-3510, United States

The formal collapse of the Soviet Union at the end of 1991 produced major socioeconomic and institutional dislocations across the agricultural sector. We examine the patterns of land surface phenology (LSP) within two key river basins in Ukraine and Russia from 1982-2000 using AVHRR datasets (PAL and GIMMS) and from 2001- 2005 using MODIS products. Originating south of Moscow, the Don River flows for nearly 2000 km south through the Central Russian Upland and discharges into the Gulf of Taganrog at the northern end of the Sea of Azov. The Don River Basin covers more than 45,000,000 ha of which roughly 83% is used for agricultural purposes. The Dnieper stretches from Russia through Belarus and Ukraine before flowing into the northern Black Sea at Kherson. The Dnieper River Basin covers more than 53,000,000 ha of which roughly 87% is used for agricultural purposes. Five very large surface water reservoirs occur in these basins; they cover at total of 862,000 ha or 17% of the impounded surface area in the Former Soviet Union. We report the temporal persistence of LSPs modeled using NDVI, WDRVI, and EVI2 within MODIS/IGBP land cover classes and map where significant shifts in LSPs have occurred during the past two decades.

B31A-1068

Simulation Of Growing Season Length Of A Deciduous Forest In A Dry Tropical Area

* Tanaka, K (ktanaka@jamstec.go.jp) , Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showamachi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan, Yokohama, 240-0001 Japan
Yoshifuji, N (natsuko@fr.a.u-tokyo.ac.jp) , Graduate School of Agriculture and Life Science, University of Tokyo, Yayoi, Tokyo 113-8657, Japan, Tokyo, 113-8657 Japan
Tanaka, N (tanaka@fr.a.u-tokyo.ac.jp) , Graduate School of Agriculture and Life Science, University of Tokyo, Yayoi, Tokyo 113-8657, Japan, Tokyo, 113-8657 Japan
Tantasirin, C (fforcct@ku.ac.th) , Faculty of Forestry, Kasetsart University, Chatuchuk, Bangkok 10903, Thailand, Bangkok, 10903 Thailand
Suzuki, M (suzuki@fr.a.u-tokyo.ac.jp) , Graduate School of Agriculture and Life Science, University of Tokyo, Yayoi, Tokyo 113-8657, Japan, Tokyo, 113-8657 Japan

The growing season length of trees can be predicted in temperate regions only with climate factors, such as temperature, while in dry tropical areas soil water availability must be examined to predict the length of the growing season during which trees can assimilate carbon. A soilÃƒ¢Ã¢â€š¬Ã¢â‚¬Å“plantÃƒ¢Ã¢â€š¬Ã¢â‚¬Å“air continuum multi-layer model was used to simulate evapotranspiration and canopy net assimilation (An) in a deciduous forest in northern Thailand and to examine temporal changes in soil water availability for carbon gain. An was also simulated with a constant LAI, to investigate, through the value of An, whether soil water is temporally available for tree assimilation. The model incorporating temporal changes in the leaf area index (LAI) captured seasonal changes in soil surface moisture, and the simulated transpiration agreed with the seasonal changes in heat pulse velocity, corresponding to water use of individual trees. A numerical experiment with a constant LAI showed that the simulated value of An became negative in the dry season because of the assimilation limitations caused by stomatal conductance being reduced by severe soil drought, and because the simultaneous rise in leaf temperature increased dark respiration. Thus, the experiment emphasized the unfavorable carbon gain conditions in the dry season. The start of the longest duration of simulated positive An in a year approached the timing of leaf flushing after the spring equinox, and the end appeared earlier than that of all canopy duration periods. Therefore, the model likely predicts the growing season length of deciduous trees in dry tropical areas. However, when leaf flushing occurred around the spring equinox, before the rainy season, other factors such as increasing day length may have been responsible, in addition to well-watered soil conditions; however, this hypothesis needs to be tested in future research. The numerical experiments with a constant LAI suggest that a smaller LAI and slower maximum rate of carboxylation likely extend the growing season length because soil water from the surface to the rooting depth is maintained at levels adequate for carbon gain longer by decreased canopy transpiration. These experiments also suggest that lower soil hydraulic conductivity and deeper rooting depth can extend the growing season by increasing soil water retention and soil water capacity, respectively. We discuss the distribution of deciduous and evergreen trees in a dry tropical region based on these results.

B31A-1069

Seasonal Variation of Nutrient Resorption in Nine Canopy Trees of a Wet Tropical Forest

* Wood, T E (tana@virginia.edu)
Lawrence, D (dl3c@virginia.edu)

Withdrawal of nutrients at the time of leaf abscission (nutrient resorption) is a nutrient conserving mechanism that could play an important role in stand-level nutrient economy. Currently data on nutrient resorption in wet tropical forests and on how this process varies temporally are sparse. We evaluated the N and P resorption efficiency of nine rain forest canopy tree species in both wet and dry season months. In addition, we measured short-term (bi-weekly) variation in nutrient resorption in the two dominant tree species, {\it Pentaclethra macroloba} and {\it Laetia procera}, over a 4-month period. We hypothesized that nutrient resorption would be more efficient during the dry season months and that resorption would be low during periods of high rainfall. Contrary to expectations, P resorption efficiency was higher in the wet season for four of the nine canopy tree species, while N resorption did not differ seasonally. The low dry season P resorption efficiency found in this study may be the result of drought stress during short periods of low rainfall, leading to incomplete nutrient resorption from senescing leaves. Nutrient resorption also varied significantly over the short-term. Both P and N resorption efficiency increased in {\it P. macroloba} and {\it L. procera} as the wet season progressed. The variability in resorption was not related to rainfall or temperature. Instead, the senesced leaf concentrations were a simple proportion of green leaf nutrient concentrations, with short punctuated periods of high resorption efficiency that may be reflective of species-specific phenological events, such as fruit and leaf production. The different timing of the seasonal increase in nutrient resorption between {\it L. procera} and {\it P. macroloba} supports this hypothesis, deserving of further study.

B31A-1070

Using MODIS Data as the Remote Sensing Input to the Fire Potential Index

* Schneider, P (phil@geog.ucsb.edu) , UCSB Department of Geography, Ellison Hall, University of California, Santa Barbara, Santa Barbara, CA 93106, United States
Roberts, D A (dar@geog.ucsb.edu) , UCSB Department of Geography, Ellison Hall, University of California, Santa Barbara, Santa Barbara, CA 93106, United States
Kyriakidis, P C (phaedon@geog.ucsb.edu) , UCSB Department of Geography, Ellison Hall, University of California, Santa Barbara, Santa Barbara, CA 93106, United States

We evaluated Moderate Resolution Imaging Spectrometer (MODIS) remote sensing imagery for wildfire susceptibility assessment in southern California. Traditionally, wildfire danger estimation has been performed using meteorological data from weather stations. The Fire Potential Index (FPI) introduced the use of remote sensing imagery for assessing the state of live fuels. However, all current implementations of the FPI use the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor and are therefore limited to one near-infrared and red wavelength. The MODIS sensor offers finer spatial and spectral resolution and thus has the potential to improve wildfire susceptibility assessment in several ways, particularly through the use of more appropriate vegetation indices than the traditional Normalized Difference Vegetation Index (NDVI). In this study the Visible Atmospherically Resistant Index (VARI) was utilized to compute relative greenness (RG) of vegetation from a 6- year time series of 16-day MODIS composites. The RG images were validated through regression analysis with a set of ground-sampled observations of live fuel moisture at 14 sites within Los Angeles County. The results indicate that for southern California test sites, the MODIS/VARI-derived relative greenness has a much stronger correlation with live fuel moisture than NDVI. At 12 out of 14 test sites VARI-based RG showed a stronger relationship with live fuel moisture than NDVI-based RG. Depending on the site, the increase in R$^{2}$ ranged from 5% to 110%. The second part of the study used MODIS data and several other data sources to implement a prototype of a second generation FPI for Southern California. In addition to the use of VARI, advancements included spatial interpolation of meteorological data using kriging with external drift based on auxiliary data sets. The fire susceptibility maps derived using this system were validated against those obtained from the traditional approach. The MODIS-based method correlated closely with the operational approach, but overall showed slightly higher FPI values. A direct comparison of VARI- and NDVI-derived FPI points towards a higher seasonal sensitivity of the VARI approach to fire susceptibility. While the overall VARI-FPI values are slightly lower than the corresponding NDVI-FPI values in winter, they are significantly higher in summer. This higher sensitivity may better highlight areas susceptible to extreme events. Overall MODIS imagery appears to be a great asset for wildfire susceptibility assessment in southern California, providing improved estimates of life fuel moisture through indices such as VARI.

B31A-1071

Retrieving Subpixel Fire Sizes From MODIS Using Multiple Endmember Spectral Mixture Analysis

* Eckmann, T C (ted@geog.ucsb.edu) , Department of Geography and Institute for Computational Earth System Science, University of California at Santa Barbara, Santa Barbara, CA 93106-4060, United States
Dennison, P E , Center for Technological and Natural Hazards, Department of Geography, University of Utah, Salt Lake City, UT 84112, United States
Roberts, D A , Department of Geography and Institute for Computational Earth System Science, University of California at Santa Barbara, Santa Barbara, CA 93106-4060, United States

The size and temperature of fires strongly influence trace gas and aerosol emissions, and their effects on ecosystems. The Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors on NASA's Terra and Aqua satellites provide a means of monitoring fires over much of the Earth's surface multiple times each day, providing information that cannot be practically acquired using other means. Unfortunately, current fire products from MODIS and other sensors leave large uncertainties in measurements of fire sizes and temperatures. This study shows how multiple endmember spectral mixture analysis (MESMA) can retrieve subpixel fire sizes and temperatures from MODIS and other sensors, and overcome many limitations of existing methods for pixel-level and subpixel fire measurements. This study also shows how images of fires from sensors with high spatial resolution, such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), can be used to validate subpixel retrievals of fire properties from MODIS. For example, comparison of subpixel retrievals of fire size from near-simultaneous images acquired by AVIRIS and MODIS of the October 2003 Simi Fire in southern California showed agreement between the two sensors with a root mean squared error of 0.9348% of the size of a MODIS pixel. Prior to this work, few studies, if any, had validated subpixel retrievals of fire size from MODIS. Subsequent research will explore how MESMA fire retrievals may improve initialization and validation of fire spread models. These improvements in fire measurement and modeling could help to support fire management, reduce hazards that fires pose to property and health, and enhance scientific understanding of fires and their roles within the Earth system.

B31A-1072

Validating the MODerate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF)/Albedo

* Roman, M O (mroman@bu.edu) , Boston University, 675 Commonwealth Ave STO-457, Boston, MA 02215, United States
Schaaf, C B (schaaf@bu.edu) , Boston University, 675 Commonwealth Ave STO-457, Boston, MA 02215, United States
Morisette, J (jeff.morisette@nasa.gov) , NASA-GSFC, Terrestrial Information Systems Branch, Greenbelt, MD 20771, United States
Strahler, A H (alan@bu.edu) , Boston University, 675 Commonwealth Ave STO-457, Boston, MA 02215, United States
Hodges, J C (jcfh@bu.edu) , Boston University, 675 Commonwealth Ave STO-457, Boston, MA 02215, United States
Phillips, N (nathan@bu.edu) , Boston University, 675 Commonwealth Ave STO-457, Boston, MA 02215, United States
Garrigues, S (Sebastien.Garrigues@gsfc.nasa.gov) , NASA-GSFC, Terrestrial Information Systems Branch, Greenbelt, MD 20771, United States
Nickeson, J E (Jaime.Nickeson@gsfc.nasa.gov) , NASA-GSFC, Terrestrial Information Systems Branch, Greenbelt, MD 20771, United States
Cook, B (brucecook@umn.edu) , University of Minnesota, 1530 Cleveland Ave N, St. Paul, MN 55108, United States
Williamson, D (dwilliamson@eng.ua.edu) , University of Alabama, Box 870205, Tuscaloosa, AL 35487, United States
McArthur, B (bruce.mcarthur@ec.gc.ca) , Meteorological Service of Canada, 4905 Dufferin Street, North York, ON M3H 5T4 Canada

We present the results from the MODerate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF)/Albedo 2006 validation campaign. Surface albedo serves as a fundamental physical property for measuring land surface change and its reliable prediction directly supports global monitoring efforts that require producing estimates of regional carbon balance with quantifiable uncertainty. Field measurements of surface albedo at local solar noon and averaged every 16 days are compared to the MODIS BRDF/Albedo satellite product results across five well-calibrated field stations. We also developed qualitative intercomparisons between high-spatial resolution scenes derived from ASTER (15-m) and IKONOS (4-m) sensors and 7km x 7km grids of MODIS-derived surface albedos. Our preliminary results using MODIS 500-m BRDF/Albedo Collection 5 data from the LTER-Harvard Forest, BU Sargent Camp, and the BSRN Bratt's Lake stations verifies the effectiveness of previous accuracy assessments and validation strategies. The results from our spatial intercomparisons for the Ameriflux-ChEAS and BSRN-Bondville stations further demonstrate how upscaling from ground point measurements to the MODIS 500-m resolution using high-resolution scenes is a necessary and critical step-- particularly when assessing the effects of spatial heterogeneity across large sampling areas. The validation protocols adapted on these and future MODIS BRDF/Albedo validation campaigns will be used as a primary framework in direct support to the NPOESS Preparatory Project (NPP) mission's calibration and validation activities. This NASA-sponsored project provides the tools to develop and evaluate global monitoring models that require characterization of the land surface, and will ultimately increase our skill in land surface analysis, weather, and climate prediction.

http://www-modis.bu.edu/brdf/

B31A-1073

Multi-sensor Translation of Remotely Sensed Spectral Vegetation Indices for Long-term Monitoring

* Miura, T (tomoakim@hawaii.edu) , University of Hawaii at Manoa, 1910 East-West Rd, Sherman 101, Honolulu, HI 96822, United States
Yoshioka, H (yoshioka@ist.aichi-pu.ac.jp) , Aichi Prefectural University, 1522-3 Kumabari, Nagakute, 480-1198 Japan
Fujiwara, K (kayof@hawaii.edu) , University of Hawaii at Manoa, 1680 East-West Rd., Honolulu, HI 96822, United States
Huete, A (ahuete@ag.arizona.edu) , University of Arizona, 1200 E. South Campus Dr., Tucson, AZ 85721, United States
Eidenshink, J (eidenshink@usgs.gov) , USGS, Earth Resources Observation and Science (EROS), Sioux Falls, SD 57198, United States
Reed, B (reed@usgs.gov) , USGS Flagstaff Field Center, 2255 N. Gemini Dr., Flagstaff, AZ 86001, United States

The development of long-term data records from multi-satellite sensors is a key requirement to improve our understanding of ecosystem functions and their responses to natural and human-induced changes in regional to global scales. Multi-sensor continuity/compatibility of spectral vegetation indices (VIs) is, however, a complicated issue due to differences in both sensor characteristics and product generating algorithms. Multi-sensor VI translation equations were derived based on the physics of atmosphere-vegetation-photon interactions to address the issue. In this study, we applied the theory and derived equations to VI data sets obtained from global remote sensors, including Moderate Resolution Imaging Spectroradiometer (MODIS), SPOT Vegetation, and Advanced Very High Resolution Radiometer (AVHRR). In comparison with the results obtained by conventional linear cross-calibration, the newly-developed approach modeled the complicated cross-sensor VI relationships well, reducing variability by 50% for the best case. These results have indicated a great potential of generating a seamless, long-term VI data record from multi-satellites that can be used to monitor longer-term changes in terrestrial vegetation.

B31A-1074

Quantification of Uncertainty due to Atmosphere in Long-Term AVHRR-NDVI Data Record

* Nagol, J (jnagol@umd.edu) , Department of Geography, University of Maryland at COllege Park, 2181 LeFrak Hall, College Park, MD 20742, United States
Prince, S (sprince@geog.umd.edu) , Department of Geography, University of Maryland at COllege Park, 2181 LeFrak Hall, College Park, MD 20742, United States
Vermote, E (eric@ltdri.org) , Department of Geography, University of Maryland at COllege Park, 2181 LeFrak Hall, College Park, MD 20742, United States

Vegetation index data derived from the AVHRR (Advanced Very High Resolution Radiometer) series of instruments has been extensively used for monitoring global and regional vegetation dynamics for last 25+ years. Many of these studies make use of NDVI (Normalized Difference Vegetation Index) or products derived from NDVI as their operational variable. The AVHRR data lacks onboard calibration for visible and near infrared channels, has an incomplete atmospheric correction, and makes Lambertian assumption about surface reflectance. Quantification of uncertainty is therefore necessary for setting informed confidence intervals for change detection studies. Theoretical estimates of uncertainty due to atmosphere for TOA (Top of Atmosphere) NDVI ranged from 0.08 for desert to 0.3 for tropical forest. In order to assess the uncertainty due to atmosphere in the three publicly available NDVI long term data sets: 1) PAL (Pathfinder AVHRR Land), 2) GIMMS-g (Global Inventory Modeling and Mapping Studies) and 3) LTDR (Long Term Data Record), they have been compared with NDVI from extensive correction at AERONET (AErosol RObotic NETwork) sites. The results for the year 1999 show that the errors for these data sets can be as high ~0.3 NDVI units.

B31A-1075

Estimating global specific leaf area from MODIS leaf area index and model-simulated foliage mass

* Baruah, P J (pjbaruah@iis.u-tokyo.ac.jp) , The University of Tokyo, Institute of Industrial Science C-block, Ce509 4-6-1 Komaba, Meguro, Tokyo, 1538505 Japan
Yasuoka, Y (yyasuoka@iis.u-tokyo.ac.jp) , The University of Tokyo, Institute of Industrial Science C-block, Ce509 4-6-1 Komaba, Meguro, Tokyo, 1538505 Japan
Ito, A (itoh@nies.go.jp) , National Institute for Environmental Studies, Center for Global Environmental Research 16-2 Onogawa, Tsukuba, 3058506 Japan
Dye, D (dye@jamstec.go.jp) , Frontier Research Center for Global Change, Ecosystem Change Research Program Showamachi, Kanazawa-ku, Yokohama, 317325 Japan

Specific leaf area (SLA) is an important leaf trait that is universally correlated positively to leaf nitrogen, leaf turnover rates, relative growth rate and most importantly, photosynthetic capacity. Though SLA is genetically encoded, it is often spatially variable within a species and within a single biome due to variable environmental conditions. However, without a global SLA map, global ecosystem models that use SLA, generally fix a single value for a particular biome. In this study, we develop a methodology to estimate global SLA from a remote sensing-derived key ecosystem variable, leaf area index and foliage mass estimated by a terrestrial ecosystem model SimCYCLE. SimCYCLE uses climatic inputs, land-cover data and biomass-allocation to estimate leaf biomass in a process-based scheme. Model-estimated foliage mass and MODIS leaf area index are assumed to represent the most-accurate ground condition to estimate SLA for the entire globe at 0.5 degree resolution. Validation of estimated specific leaf area is done with a published field-sampled global dataset, and additional field-sampled SLA data collected from published literatures. The validation data is also used for rectification of unrealistic values of estimated SLA to produce a global SLA map, which we strongly believe, would be valuable to improve estimates of carbon dynamic across individual biomes upon assimilation with the ecosystem models.

B31A-1076

Water-Vegetation Interaction in Mediterranean Climate Zones Under Global Warming

* Krakauer, N Y (niryk@berkeley.edu) , Department of Earth and Planetary Science, University of California, Berkeley, CA 94720
Fung, I F (inez@atmos.berkeley.edu) , Department of Earth and Planetary Science, University of California, Berkeley, CA 94720

Water shortage controls biotic production over much of the land surface. Global warming will exacerbate water limitation in arid and semiarid areas, but high CO$_{2}$ concentrations should increase plant water use efficiency; the net impact on vegetation productivity and water availability is uncertain. We analyzed the response of vegetation to historic and future (SRES A2) fossil CO$_{2}$ emissions in the Climate System Model (CSM1.4) run with coupled ocean, atmosphere and land-surface components, focusing on areas with a Mediterranean climate (defined by the occurrence of precipitation mostly during the cool season, with consequent summer drought). By the 2090s, Mediterranean regions were modeled to have warmed by almost 1 ${\deg}$C more than the land surface average. Precipitation increased modestly, while NPP rose by more than the world average possibly because of warmer conditions in the spring growing season, but mean water vapor pressure deficits increased and water use efficiency fell slightly despite the favorable impact of high CO$_{2}$. More work is needed to understand the contribution of vegetation responses to regional climate change, and to test the process mechanisms assumed in climate models.

B31A-1077

Prediction of the onset of greenness from historical satellite and climate data in California

* Hashimoto, H (hirofumi.hashimoto@gmail.com) , California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955
* Hashimoto, H (hirofumi.hashimoto@gmail.com) , NASA Ames Research Center, Moffett Field, Moffett Field, CA 94035
Ichii, K (kichii@arc.nasa.gov) , NASA Ames Research Center, Moffett Field, Moffett Field, CA 94035
Ichii, K (kichii@arc.nasa.gov) , San Jose State University, One Washington Square, San Jose, CA 95192
Nemani, R R (rnemani@arc.nasa.gov) , NASA Ames Research Center, Moffett Field, Moffett Field, CA 94035
White, M A (mikew@cc.usu.edu) , Utah State University, 0160 Old Main Hill, Logan, UT 84322

Predicting the onset of growing season (GS) has significant applications in health, agriculture and rangeland management. In order to forecast the onset of GS, California was divided into the 20 phenoregions by using maximum-likelihood method applied with climatological monthly MODIS 15 FPAR and MODIS 11 LST data. For each phenoregion, onset was calculated as the date when the percent above threshold (PAT) of FPAR became larger than the phenoregion threshold. These MODIS-derived onset dates were used as validation data to develop the prediction model. We tested two approaches for predicting onset depending on the prediction range, that is the short-term and the mid-term prediction. Both short-term and mid-term predictions were based on the concept that the historical time order of the onset could be the same ranking with other historical data. MODIS 15 FPAR data were used for short-term prediction and Daymet climate data were used for mid-term prediction. The rank correlation coefficient between FPAR and onset ware close to -1 when the lag time was less than 1-month. The high correlation between climate components and onset suggested that onset can be predicted well from ranking of climate components in historical record. Forecasting based on climate components depends on the accuracy of weather/climate forecasts. Better onset forecasts are possible by extending our analysis to longer time periods by using historical records such as those from AVHRR.

B31A-1078

Refinement of rooting depths using satellite-based evapotranspiration seasonality and ecosystem modeling in California

* Ichii, K (kichiijp@yahoo.co.jp) , San Jose State University, One Washinton Square, San Jose, CA 95192, United States
Hashimoto, H (hirofumi.hashimoto@gmail.com) , California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955, United States
Yang, F (feihuayang@wisc.edu) , University of Wisconsin, Madison, 550 North Park Street, Madison, WI 53706, United States
Votava, P (pvotava@arc.nasa.gov) , California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955, United States
Michaelis, A (amac@hyperplane.org) , California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955, United States
Nemani, R (rama.nemani@nasa.gov) , NASA Ames Research Center, MS242-5, Moffett Field, CA 94035, United States

Accurate modeling of terrestrial water cycle largely depends on model parameters, meteorological data, vegetation phenology, and soil physical properties. Although many studies evaluated meteorological data, and vegetation phenology, evaluation of soil properties is rare. Some studies showed the existence of deep rooting system especially in the seasonally water-limited ecosystems, and importance of deep rooting depth for accurate simulation of carbon and water cycle. In this study, we estimated rooting depth in California using satellite-based evapotranspiration (ET) and an ecosystem model by minimizing differences of simulated and satellite-based ET, and analyzed the impacts on simulated ET seasonality. Large differences in estimated and soil survey (STATSGO) based rooting depth are found in northern forest regions. In these regions, much deeper rooting depth (e.g. >3m) estimated in the study could successfully reproduce satellite-based ET seasonality, which peaks in summer, however the shallow rooting depth based on STATSGO (e.g. <1.5m) could not sustain high ET in summer. In grass and shrub regions in central and southern California, estimated rooting depth are similar to the one from STATSGO, probably due to shallow rooting depth in these ecosystem or small seasonal ET variation which is difficult to be captured by model and satellite-based method. Our analysis suggests that setting of rooting depth is important for ecosystem modeling, and satellite-based data can help to constrain the spatial variability of rooting depth.

http://ecocast.arc.nasa.gov

B31A-1079

Characterizing the Phenology of Semi-desert Grassland Dominated by Non-native Eragrostis lehmanniana or Native Grasses Using MODIS NDVI and Brightness Data

* Huang, C (chuang@email.arizona.edu) , School of Natural Resources, 325 Biological Sciences East University of Arizona, Tucson, AZ 85721, United States
* Huang, C (chuang@email.arizona.edu) , Office of Arid Lands Studies, 1955 E. 6th St. University of Arizona, Tucson, AZ 85719, United States
Geiger, E (elg@email.arizona.edu) , School of Natural Resources, 325 Biological Sciences East University of Arizona, Tucson, AZ 85721, United States
van Leeuwen, W J (leeuw@ag.arizona.edu) , Office of Arid Lands Studies, 1955 E. 6th St. University of Arizona, Tucson, AZ 85719, United States
van Leeuwen, W J (leeuw@ag.arizona.edu) , Department of Geography and Regional Development, Harvill Building, Box #2 University of Arizona, Tucson, AZ 85721, United States
Marsh, S (smarsh@ag.arizona.edu) , Office of Arid Lands Studies, 1955 E. 6th St. University of Arizona, Tucson, AZ 85719, United States
Marsh, S (smarsh@ag.arizona.edu) , Department of Geography and Regional Development, Harvill Building, Box #2 University of Arizona, Tucson, AZ 85721, United States

In the past several decades, one of the most significant changes in semi-desert grasslands of southern Arizona is invasion of an exotic perennial grass from South Africa, Lehmann lovegrass (ERLE: {\it Eragrostis lehmanniana}). Understanding the seasonal variations of ERLE phenology is pivotal for managing these systems where dominance by ERLE is associated with decreases in native species richness and changes in fire regimes. Therefore, the objective of this study is to derive the phenology of vegetation dominated by ERLE or native grasses, using the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and brightness [red and near-infrared (NIR) reflectance] time-series data from 2000 to 2005. Field campaigns were conducted in spring (2000-2004) and fall (1999-2003) to collect the biomass of ERLE in the semi-desert grassland of southern Arizona. There were 18 sites each comprised of three 1-ha subplots. NDVI and brightness time-series data for each site were extracted and re-grouped into three categories based on average abundance of ERLE over 10 sampling periods: ERLE (153-229 g/m$^{2}$ of ERLE), Mixed (82-134 g/m$^{2}$) and Native (2-30 g/m$^{2}$) sites. The results show that the season of distinct segregation of NDVI time-series data among these three groups is spring (mid-March to April) and/or early summer (May to July) depending on the amount and timing of precipitation. However, no significant separation was found in brightness time-series data. Annual temporal variation of NDVI time-series data at Native sites was lower than Mixed and ERLE sites, but within group variation of NDVI through time was higher. A similar trend was found in the red reflectance but no apparent trend was discerned in the NIR reflectance perhaps due to substantial amounts of variation in the data. The lower NDVI time-series data in ERLE infested sites (Mixed/ERLE), especially during spring/early summer may be caused by: (1) upper layers of bright-colored inflorescent material and standing litter of ERLE which obstructed the photosynthetically active radiation from shoots at the lower stratum; (2) less production of annual grasses and herbaceous dicots in the spring/early summer at these sites. The mitigation of temporal variation of NDVI in Native sites was due to the growth of annual grasses and herbaceous dicots in the non-summer growing seasons. High production of ERLE generated more homogeneous surfaces with dense graminoid and litter layers in growing and dormant periods, respectively. Hence, within plant community group variation of NDVI and red reflectance through time have a negative relationship with the abundance of ERLE. This work demonstrates the feasibility of integrating field observations and MODIS vegetation and spectral data to characterize landscapes dominated by ERLE or native grasses. The outcome provides opportunities to monitor the spread of ERLE in semi-arid environments at large spatial scales by utilizing remotely sensed data from sensors with large footprints.

B31A-1080

Dynamically Characterizing a Variety of Phenological Responses of Semi-Arid Areas to Hydrological Inputs using Multi-Year AVHRR NDVI Time Series and HYDRO1k-Type Terrain Parameters

* Hermance, J F (John_Hermance@Brown.Edu) , Brown University, Department of Geological Sciences, Providence, RI 02912, United States
Jacob, R W (Robert_Jacob@Brown.Edu) , Brown University, Department of Geological Sciences, Providence, RI 02912, United States
Bradley, B A (BethanyB@Princeton.Edu) , Princeton University, Woodrow Wilson School, Princeton, NJ 08544, United States
Mustard, J F (John_Mustard@Brown.Edu) , Brown University, Department of Geological Sciences, Providence, RI 02912, United States

This report explores the interaction of a variety of phenological responses in a semi-arid environment to hydrological forcing terms. We analyze the interannual departure of AVHRR NDVI values from normal seasonal variations in the context of local climate, hydrological and topographic controls. We have recently reported on several new techniques to smoothly interpolate and minimize noise in weekly and biweekly NDVI data for the purpose of characterizing both average annual variations and interannual variations in the time series of 1 km x 1 km pixels. These least-squares procedures tend to fit the upper data envelope to minimize the effects of atmospheric obfuscations, while minimizing model roughness to allow higher order models to better track subtle time variations in the raw data. We apply these techniques to mapping the dynamic behavior of multi-year NDVI time series from the NOAA-14 and NOAA-16 satellites for a 150 x 150 km study area of the Great Basin in west-central Nevada. These time series, as currently produced and delivered by the U.S. Geological Survey's National Center for Earth Observation and Science, have been compensated for sensor drift and atmospheric water vapor, and in this area are georegistered to an accuracy of better than 0.5 km. Our study area provides a microcosm of a broad range of vegetation classes including irrigated agriculture with peak annual NDVI values of up to 0.7, semi-arid grasslands having NDVI values of 0.6 or higher during "wet" years, and non-vegetated playas (alkali salt flats) with typical annual NDVI values of 0.07. We show that the high precision of georegistration, combined with the higher resolution of our smoothing algorithms and the refined level of digital elevation models (DEMs) for this area, allows one to draw obvious and meaningful conclusions on the impact of elevation, hill slope and hydrology on a variety of local phenologies over the scale of the intermontane valleys. Adopting the USGS procedures for defining the HYDRO1k metrics of aspect, flow direction, slope etc., we refine the grid scale from the current HYDRO1k GTOPO30 DEM dimension of 1 km to a local DEM for our study area having a grid scale of 0.25 km. We employ higher-order 9 point finite differences to compute local topographic gradients, then aggragate (or integrate) the "HYDRO1k-type" parameters to the 1 km pixel dimensions of the NDVI data. We then perform a multivariate comparison of the derived-hydrologic parameters with characteristic phenological behaviors from the interannual NDVI modeled time series. For example, as one would expect, in spite of similarities of peak NDVI values in a particularly "wet" year, irrigated agricultural sites are well-discriminated from natural semi-arid grassland due to the multivariate controls from observed precipitation, surface water runoff, topographic slope, and the intrinsic fine structure in the behavior of the interannual NDVI time series. NDVI time series from montane areas provide interesting insight into the time of disappearance of snow cover, as well as the relation of summertime phenology to elevation and slope. A striking pattern emerges regarding the similitude between seasonal surface water runoff and interannual trends in phenology that corroborates the potential of NDVI data to monitor and characterize long term trends in the response of phenology to hydrological processes.

http://www.geo.brown.edu/research/Hydrolog y/NDVI_reports/

B31A-1081

Interpreting 250m Moderate Resolution Imaging Spectroradiometer Vegetation Indices in the Colorado Plateau, USA

* Hamar, M (mhamar@gmail.com) , Utah State University Department of Watershed Sciences, 5210 Old Main Hill, Logan, UT 84322, United States
White, M (mikew@cc.usu.edu) , Utah State University Department of Watershed Sciences, 5210 Old Main Hill, Logan, UT 84322, United States
Lauver, C (Chris_Lauver@nps.gov) , Southern Colorado Plateau Network, NPS Northern Arizona University, P.O. Box 5765, Flagstaff, AZ 86011, United States
Garman, S (Steve_Garman@nps.gov) , Northern Colorado Plateau Network, NPS, P.O. Box 848, Moab, UT 84532

Semi-arid ecosystems, which are inherently vulnerable to small changes in the amount and distribution of precipitation, are frequently co-located with extensive human habitations and vulnerable species distributions. Important societal considerations identified by GEOSS, such as human health and well-being, ecological services, environmental protection, genetic resources, and disaster risk reduction and mitigation, may be under particular strain from invasive species, demographic changes, and/or climate change in semi-arid ecosystems. Satellite-based remote sensing may therefore be an important tool for monitoring the status of semi-arid ecosystems and habitats. Yet to date, the ability of current generation sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), to accurately represent within-season phenological variability in semi-arid ecosystems - a crucial precursor for long-term monitoring - is largely unknown. Several programs worldwide seek information on this relationship. As part of one such effort, the Vital Signs Monitoring program for the National Park Service (NPS), we evaluated the ability of the MODIS 250m Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and red, near infra-red, and blue channels to represent relative changes in ground-measured vegetation structure. Using an AccuPAR LP-80 PAR/LAI Ceptometer, LAI-2000 Plant Canopy Analyzer, and First Growth digital camera, we sampled plant area index (PAI) and green fractional cover (GFC) at 4 sites along a bioclimatic gradient in the Colorado Plateau, USA, representing semi-arid woodland, mixed grassland/shrubland, and grassland plant functional types. In 14 visits from June 2005 to October 2005, we intensively sampled each site at a spatial resolution directly comparable to 4 MODIS pixels. At each site, PAI was always less than 1.0 and often less than 0.4 and GFC was rarely greater than 5%. Likely due to low plant cover and instrument noise, correlation coefficients between instruments were rarely significant. The woodland site showed little evidence of phenological variability from both the ground and MODIS data. For the other sites, all with a deciduous grassland component, the LP-80 was significantly and consistently related to NDVI (r2 0.57 to 0.79, slope 0.76 to 0.89) and less consistently to EVI. In spite of minimal base and amplitude of PAI and NDVI, it appears that MODIS NDVI is capable of resolving extremely subtle changes in herbaceous plant canopies and is therefore a promising tool for monitoring extensive and remote semi-arid environments.

B31A-1082

Water Availability Does Not Control Timing of Bud Break in African Savannas

Bazzaz, F A (fbazzaz@oeb.harvard.edu) , Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, United States
* Richer, R A (rricher@aua.am) , American University of Armenia, 40 Baghramian Avenue, Yerevan, 375019 Armenia

The timing of bud break can have wide ranging ecological impacts on water table depth, forage availability and ultimately global carbon cycling. In seasonally dry savannas water availability is thought to be a significant factor in determining leaf phenological patterns. We tested this hypothesis by recording the timing of bud break in relation to rainfall in seven leguminous tree species over three years in Northwestern Zimbabwe. In a single year, the date of bud break varied between species by an average of 30 days. The average date of bud break within species varied between years by 20 days. Despite high inter-annual variation in rainfall from 470mm in1999 to 869mm in 2001, the timing of bud break was not correlated with stem water content, pre-dawn leaf water potential or soil water content. These data question the generality of the assumption that water availability is an important determinant of leaf phenology in this system. In this case bud break is likely to be a result of other environmental factors such as temperature extremes.

B31A-1083

Using Rainfall-NDVI Relationship In An Hydrological Modelling Of A West African Sahel Watershed.

MAHE, G (gil.mahe@msem.univ-montp2.fr)
PATUREL, J (jean-emmanuel.paturel@msem.univ-montp2.fr)
DEZETTER, A (alain.dezetter@msem.univ-montp2.fr)

B31A-1084

Sediment Records of Recent Climate Variability From the Badain Jaran Desert, North West China

* Young, A A (Adam.Young@ucl.ac.uk) , Environmental Change Research Centre, University College London, Pearson Building, Gower Street, London, WC1E 6BT United Kingdom
Holmes, J A (J.Holmes@geog.ucl.ac.uk) , Environmental Change Research Centre, University College London, Pearson Building, Gower Street, London, WC1E 6BT United Kingdom
Leng, M J (mjl@nigl.nerc.ac.uk) , NERC Isotope Geosciences Laboratory, Kingsley Dunham Centre, Keyworth, Nottingham, NG12 5GG United Kingdom
Leng, M J (mjl@nigl.nerc.ac.uk) , School of Geography, University of Nottingham, Nottingham, NG7 2RD United Kingdom
Zhang, J (jwzhang@lzu.edu.cn) , Department of Geography, College of Earth and Environmental Science, Lanzhou University, 222 South Tianshui Road, Lanzhou, 730000 China

The Badain Jaran Desert, which is located in north-west China, is close to the northernmost limit of East Asian summer monsoon precipitation. Climatic change in the region is marked by changes in effective moisture, possibly in response to the monsoon intensity. The desert contains numerous shallow, groundwater fed lakes that vary between brackish and hyper saline. Due to the remote nature of the region information regarding limnological response to climatic variation is extremely limited. However, recent lake monitoring has shown that the lake water balance across the desert is primarily driven by differences in local evaporation rates. Short sediment cores, covering approximately the last 200 years, have been collected from a number of lake basins. Biological, geochemical and isotopic methods are used to reconstruct the palaeolimnology of the lakes. Chronologies are reconstructed using 137-Cs and 210-Pb. Modern data, collected during the monitoring study, are used to calibration the sediment records. Each individual basin responses differently to climatic variation; however comparisons between basins should permit a regional palaeoenvironmental reconstruction to be made for the region. This work is of importance to help understand the long term hydrological trajectory across semi- arid north-west China, an area where domestic and agricultural demands for water are increasing, especially in the Minchin Basin which is a major agricultural centre to the south-east of the Badain Jaran Desert.

B31A-1085

Global Impacts of Long-Term Land Cover Changes Within China's Densely Populated Rural Regions

* Ellis, E C (ece@umbc.edu) , University of Maryland, Baltimore County, Dept. of Geography & Environmental Systems 1000 Hilltop Circle, Baltimore, MD 21250, United States

Long-term changes in land cover are usually investigated in terms of large-scale change processes such as urban expansion, deforestation and land conversion to agriculture. Yet China's densely populated agricultural regions, which cover more than 2 million square kilometers of Monsoon Asia, have been transformed profoundly over the past fifty years by fine-scale changes in land cover caused by unprecedented changes in population, technology and social conditions. Using a regional sampling and upscaling design coupled with high-resolution landscape change measurements at five field sites, we investigated long-term changes in land cover and ecological processes, circa 1945 to 2002, within and across China's densely populated agricultural regions. As expected, the construction of buildings and roads increased impervious surface area over time, but the total net increase was surprising, being similar in magnitude to the total current extent of China's cities. Agricultural land area declined over the same period, while tree cover increased, by about 10%, driven by tree planting and regrowth around new buildings, the introduction of perennial agriculture, improved forestry, and declines in annual crop cultivation. Though changes in impervious surface areas were closely related to changes in population density, long-term changes in agricultural land and tree cover were unrelated to populated density and required explanation by more complex models with strong regional and biophysical components. Moreover, most of these changes occurred primarily at fine spatial scales (< 30 m), under the threshold for conventional global and regional land cover change measurements. Given that these changes in built structures and vegetation cover have the potential to contribute substantially to regional and global changes in biogeochemistry, hydrology, and land-atmosphere interactions, future investigations of these changes and their impacts across Monsoon Asia would benefit from models that incorporate fine-scale landscape structure and its changes over time.

http://www.ecotope.org/projects/china_2000.htm

B31A-1086

Cloud Water Path Over China: An Analysis Using ISCCP Data During 1984-2004

* Li, X (lxy@mail.iap.ac.cn) , Institute of Atmospheric Physics, Chinese Academy of Sciences, Qi Jia Huo Zi, De Sheng Men Wai Street, Chao Yang District, Beijing, 100029 China
Guo, X (guoxl@mail.iap.ac.cn) , Institute of Atmospheric Physics, Chinese Academy of Sciences, Qi Jia Huo Zi, De Sheng Men Wai Street, Chao Yang District, Beijing, 100029 China
Zhu, J (jzhu@mail.iap.ac.cn) , Institute of Atmospheric Physics, Chinese Academy of Sciences, Qi Jia Huo Zi, De Sheng Men Wai Street, Chao Yang District, Beijing, 100029 China

Analysis of cloud water path (CWP) data over China available by the International Satellite Cloud Climatology Project (ISCCP) is performed for the period 1984-2004. The climatology, trends, and variability of CWP are examined. The climatological distribution and variation of CWP are dependent on the circulation, especially the monsoon circulation, topography and atmospheric moisture. Influenced by the Asia monsoon, China's CWP exhibits very large seasonal variations. All-China average shows the maximum CWP in June and the minimum CWP in October. Under the influences of the Tibetan Plateau and the westerly flow, the largest CWP is found in winter and early spring in the southeastern China. Linear regression analysis is used to characterize seasonal and annual trends in CWP. Increasing trends in CWP are observed over much of China. The northwestern China, especially over the Tibetan Plateau, and the Inner Mongolia show significant increases of CWP. The largest increase in CWP is in winter and the increasing trend is weakest in spring. These increases in CWP are primarily dependent on the enhanced updraft deduced by the variation of circulation, including the weakening of the summer monsoon system. According to the EOF analysis, step-like increase in CWP is also found (EOF1) during 1984-2004 and the variation of CWP is statistically significant correlated with the North Atlantic Oscillation (NAO) in EOF2. Interannual variation and trends in CWP and water vapor are closely correlated in China, confirming the enhanced hydrological cycle under the background of global warming. The correlations among CWP, water vapor and precipitation in the southeastern and the northwestern China are investigated. In summertime the higher correlation are found between CWP and precipitation than that between water vapor and precipitation in the both regions.

B31A-1088

The Role of land surface conditions in 1998 Southeast Asia Monsoon Onset

* Dairaku, K (dairaku@bosai.go.jp) , National Research Institute for Earth Science and Disaster Prevention, 3-1 Tennodai, Tsukuba, Iba 305-0006 Japan

The first transitions into the Asian Summer Monsoon (ASM) occur between late April and early May over inland Indochina, before any transitions occur along the coast. This study used a regional climate model to elucidate the influence of orography and ground wetness on subcontinental-scale hydrological processes. The model reproduced many elements of the onset of the Southeast Asia Monsoon (SEAM) associated with land surface conditions, including the abrupt transitions observed when mountain effects and relatively dry soil conditions were combined in the model simulations. The nonlinear effects of mountains and ground wetness, combined with realistic increases in precipitation, can modify the hydrological cycle through changes in the surface energy budget. A positive feedback between soil moisture and precipitation increases the moisture source for further precipitation in the first transition period.

B31A-1089

Assessment of the Effect of Climate Change on Grain Yields in China

* Chou, J (jiejieming@hotmail.com) , Institute of Amospheric Physics, Chinese Academy of Sciences, 40# Huayanli, Beichen West Road, Chaoyang District, Beijing, 100029 China

The paper elaborates the social background and research background; makes clear what the key scientific issues need to be resolved and where the difficulties are. In the research area of parasailing the grain yield change caused by climate change, massive works have been done both in the domestic and in the foreign. It is our upcoming work to evaluate how our countrywide climate change information provided by this pattern influence our economic and social development; and how to make related policies and countermeasures. the main idea in this paper is that the grain yield change is by no means the linear composition of social economy function effect and the climatic change function effect. This paper identifies the economic evaluation object, proposes one new concept - climate change output. The grain yields change affected by the social factors and the climatic change working together. Climate change influences the grain yields by the non C linear function from both climate change and social factor changes, not only by climate change itself. Therefore, in my paper, the appraisal object is defined as: The social factors change based on actual social changing situations; under the two kinds of climate change situation, the invariable climate change situation and variable climate change situation; the difference of grain yield outputs is called " climate change output ", In order to solve this problem, we propose a method to analyze and imitate on the historical materials. Giving the condition that the climate is invariable, the social economic factor changes cause the grain yield change. However, this grain yield change is a tentative quantity index, not an actual quantity number. So we use the existing historical materials to exam the climate change output, based on the characteristic that social factor changes greater in year than in age, but the climate factor changes greater in age than in year. The paper proposes and establishes one economy - climate model (C-D-C model) to appraise the grain yield change caused by the climatic change. Also the preliminary test on this model has been done. In selection of the appraisal methods, we take the C-D production function model, which has been proved more mature in the economic research, as our fundamental model. Then, we introduce climate index (arid index) to the C-D model to develop one new model. This new model utilizes the climatic change factor in the economical model to appraise how the climatic change influence the grain yield change. The new way of appraise should have the better application prospect. The economy - climate model (The C-D-C model) has been applied on the eight Chinese regions that we divide; it has been proved satisfactory in its feasibility, rationality and the application prospect. So we can provide the theoretical fundamentals for policy-making under the more complex and uncertain climate change. Therefore, we open a new possible channel for the global climate change research moving toward the actual social, economic life.

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