B31B-0206 0800h
Interannual Variability and Decadal Trend of Global Fractional Vegetation Cover Since 1982
Fractional vegetation cover (FVC) is one of the most important variables in land surface modeling and also provides a continuous field to complement discrete land cover classification. A global 8-km FVC dataset from 1982-2000 is derived using the NOAA/NASA land pathfinder normalized difference vegetation index data. The confidence in the dataset is provided by the insensitivity of our algorithm to the data resolution (between 1 km and 8 km), the good agreement of our results with the field survey data over Germany, the consistency of our results with previous observational studies over the savannas in North Africa and the forests in Bolivia, and the robustness of our algorithm as demonstrated by the small interannual variability of FVC over areas where anthropogenic land cover change is expected to be small based on the 30-m Landsat data analysis. Significant interannual variability is found over shrubland, savanna, and grassland, while both positive and negative trends exist over different areas of the same region in many parts of the world. In particular, our trend analysis pinpoints areas with statistically significant trends (i.e., `hotspots') for further study using higher resolution satellite data and field survey data. Additional results using both AVHRR and MODIS data will also be presented.
B31B-0207 0800h
Intercomparison of interannual changes in NDVI from PAL and GIMMS in relation to evapotranspiration over northern Asia
Vegetation over an extensive area influences actual evapotranspiration (ET) from the land to the atmosphere mainly through transpiration activity. The authors' previous study (Suzuki and Masuda, 2004. J Meteor Soc Japan, 82, 1233 -- 1241) found an interannual covariability between ET and the Normalized Difference Vegetation Index (NDVI) over a continental-scale land surface. This result suggested that vegetation controls interannual variation in ET, and therefore vegetation change must be considered to predict future climate. In this prior study, NDVI data from the Pathfinder AVHRR Land (PAL) dataset were analyzed. However, studies of NDVI interannual change are subject to uncertainty, because NDVI data often contain errors associated with sensor- and atmosphere-related effects. This study is aimed toward reducing this uncertainty by employing another major NDVI dataset, from the Global Inventory Monitoring and Modeling Studies (GIMMS) group, in addition to PAL. GIMMS-NDVI data were produced with a calibration method that differs from the one employed for PAL-NDVI data. An intercomparison of the PAL-NDVI and GIMMS-NDVI datasets provide an effective basis for further analysis of the covariability of NDVI and ET interannual changes. The analysis was carried out for the northern Asia region from 1982 to 2000. 19-year interannual changes (monthly anomalies) in the PAL-NDVI and GIMMS-NDVI values were compared. The correlation coefficient (R) in summer months exhibits high positive values (over 0.8 in June). This result indicates that PAL-NDVI and GIMMS-NDVI display similar interannual variation for active growing season months. Interannual change in PAL-NDVI and GIMMS-NDVI were both compared with interannual change in model-assimilated ET. Although the R between GIMMS-NDVI and ET is slightly less than for PAL-NDVI and ET, for both NDVI datasets the annual maximum correlation with ET occurs in June, which is near the central period of the growing season. A positive correlation between GIMMS-NDVI and ET was observed over most of the vegetated land area in June, and a similar result was obtained with PAL-NDVI. These results reinforce the authors' prior research that indicates the control of interannual change in ET is dominated by interannual change in vegetation activity.
B31B-0208 0800h
Analysis of smoke and cloud impact on seasonal and interannual variations in normalized difference vegetation index in Amazon
Normalized difference vegetation index (NDVI) derived from National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) is a unique measurement of long-term variations in global vegetation dynamics. The NDVI data have been used for the detection of the seasonal and interannual variations in vegetation. However, as reported in several studies, NDVI decreases with the increase in clouds and/or smoke aerosol contaminated in the pixels. This study assesses the smoke and clouds effect on long-term Global Inventory Modeling and Mapping Studies (GIMMS) and Pathfinder AVHRR Land (PAL) NDVI data in Amazon. This knowledge will help developing the correction method in the tropics in the future. To assess the smoke and cloud effects on GIMMS and PAL, we used another satellite-derived data sets; NDVI derived from SPOT/VEGETATION (VGT) data and Aerosol Index (AI) derived from Total Ozone Mapping Spectrometer (TOMS). Since April 1998, VGT has measured the earth surface globally including in Amazon. The advantage of the VGT is that it has blue channel where the smoke and cloud can be easily detected. By analyzing the VGT NDVI and comparing with the AVHRR-based NDVI, we inferred smoke and cloud effect on the AVHRR-based NDVI. From the results of the VGT analysis, we found the large NDVI seasonality in South and Southeastern Amazon. In these areas, the NDVI gradually increased from April to July and decreased from August to October. However the sufficient NDVI data were not existed from August to November when the smoke and cloud pixels were masked using blue reflectance. Thus it is said that the smoke and clouds mainly cause the large decreases in NDVI between August and November and NDVI has little vegetation signature in these months. Also we examined the interannual variations in NDVI and smoke aerosol. Then the decrease in NDVI is well consistent with the increase in the increase in AI. Our results suggest that the months between April and July are the most reliable season to monitor the vegetation.
B31B-0209 0800h
Temporal Dynamics of Vegetation Phenological in Mongolia Using NOAA-AVHRR Data -- the Saw-tooth Pattern
The objective of this study was to examine the temporal trend of the Mongolian natural vegetation phenology during 18 years between 1981 and 1999, in various ecosystems, by using two Pathfinder NOAA-AVHRR Land (PAL) products -- the Normalized Different Vegetation Index (NDVI) and Land Surface Temperature (LST). Mongolia was selected as a study area for implementing the above objectives since it enables a regional research (rather than continental or global scale). The north-south cross section is relatively short (ca. 500 km) between latitude 42$^\circ$ to 52$^\circ$ N, and covers 6 different ecosystems -- Tundra, Mountain, Forest Steppe, Steppe, Desert Steppe, and Desert. Along this cross section, precipitation ranges from more than 350 (in the north) to less than 75 mm. The entire territory consists only on natural vegetation without anthropogenic influences such as urban heat island, industry, agricultural crops etc. For estimating the growing season dynamics, an accurate determination of the beginning (greenup onset) and ending (senescence, or decline) dates of the vegetation phenology were computed. For achieving these dates four combined criteria were calculated based on the NDVI and LST datasets. Main finding of the project shows that no significant results are achieved when analyzing the entire study period over 18 years. However, when breaking the period into two sub-periods, from 1982 to 1991 and from 1992 to 1999, phenology parameters can be easily detected and results are more significant. It is shown that on the average of the entire territory, the onset starts 10 and 16 days earlier, and the decline occurs 6 and 3 days later, during the first and second sub-periods, respectively. Consequently, on the average the phenology cycle of the growing season lasts 17--19 days longer. The above-mentioned sub-periods can be visualized as a saw-tooth pattern. It is due to the eruption of Mount Pinatubo in the Philippines in June 1991, led to a global cooling of 0.5$^\circ$C due to aerosols injected into the stratosphere. The three phenological parameters were interrupted by the event for 1--2 years, but both continued afterwards.
http://www.bgu.ac.il/BIDR/research/phys/remote/index.html
B31B-0210 0800h
Sensitivity Of MODIS Global Terrestrial Primary Production To The Accuracy Of Meteorological Data Sets
Abstract: MODIS provides a dramatic improvement in our ability to accurately monitor global terrestrial primary production. The global weekly terrestrial primary production (MOD17) is significant for scientific research and natural resource management. The near real-time MOD17 requires regularly daily gridded assimilation meteorological data set as input, and the accuracy of this meteorological data set shows marked difference in their spatial and temporal distribution. This study compares five global surface meteorological data sets - Data Assimilation Office (DAO) GEOS402, NCEP-NCAR reanalysis 1(NCEP), ECMWF 40 (ERA-40) years reanalysis, Climate Research Unit of University of East Anglia (CRU) and NASA Surface meteorology and Solar Energy (SSE) at vegetated land area - to assess the sensitivity of MODIS global terrestrial primary production to the uncertainties of meteorological inputs. Compared with SSE and CRU, NCEP tends to overestimate surface solar radiation, underestimate temperature and Vapor Pressure Deficit (VPD), and SRA-40 is closer to SSE and CRU, but its radiation tend to lower in tropical region. DAO is closer to ERA-40, which suggests its magnetite may be closer to real value. Global daily observations from WMO weather stations also are used to directly compare with DAO, NCEP and ERA-40. The larger discrepancies among different meteorological data sets occur in low latitude region. Global total GPP and NPP driven by DAO, NCEP and ERA-40 show large differences, and spatially, large difference occurs in tropical region due to higher production, larger vegetated areas and higher uncertainties in meteorological data sets in tropics. MOD17 driven by DAO, NCEP and ECMWF also are compared with it driven by observed surface meteorological data set with solar radiation from over 300 weather stations across USA, respectively. The results demonstrate the need for improving the accuracy of global surface meteorological data set, especially for tropical regions, to better understand and predict global carbon cycle under global climate change.
http://www.ntsg.umt.edu
B31B-0211 0800h
Prognostic/Diagnostic Analysis of Ecosystem Processes: Comparison of MODIS and Biome-BGC Based Land Surface Products Over United States From 2000 to 2003
Terrestrial ecosystem models can simulate terrestrial carbon, water, energy cycles, and have a potential to forecast future status of them. However, we need to conduct validation and comparison studies with observed data for improving model capabilities and performances. Four-years of MODIS products, such as snow cover, LAI, and GPP provide the opportunity for validating the terrestrial ecosystem models. In this study, we used Biome-BGC terrestrial ecosystem model and MODIS based land surface products to evaluate the model performance. We analyzed spatial patterns and interannual variations over continental United States from model-based and satellite-based products. We focused on GPP, LAI and snow cover over US from 2000 to 2003. Spatial patterns (4 years average) of GPP, LAI, and Snow Cover (number of days covered by snow in a year) are consistent between MODIS and Biome-BGC. Although magnitudes are different (Biome-BGC underestimates LAI, and overestimates GPP), spatial patterns of GPP and LAI are consistent in forest ecosystems. However, Biome-BGC estimates very low GPP and LAI in grassland ecosystems, and we need to improve them. Snow cover shows very similar spatial pattern with a slight overestimation by Biome-BGC. Seasonal and interannual variations in GPP show very similar patterns except for grassland ecosystems. Both methodologies show relatively low GPP in 2002, which corresponds with low precipitation year. Seasonal and interannual variations in Biome-BGC based LAI show smaller amplitudes, however, both of them show similar year to year variations including negative anomaly in 2002. Snow cover from Biome-BGC and MODIS show very good agreement in terms of both seasonal and interannual variations. Overall, prognostic and diagnostic analyses show consistent results, suggesting capabilities of future prediction.
http://ecocast.arc.nasa.gov
B31B-0212 0800h
MODIS LAI and FPAR: Product Analysis
Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation [400-700 nm] absorbed by vegetation (FPAR) are key environmental variables used in climate research to characterize exchange of fluxes of energy, mass and momentum in Earth system. MODerate resolution Imaging Spectroradiometer (MODIS) provide set of global remote sensing measurements to generate 16 land geophysical products including LAI and FPAR. Current MODIS LAI and FPAR research integrates three major components: LAI and FPAR algorithm development, product analysis and product validation. Product analysis is the central activity, which is closely interconnected with algorithm development and validation. The objective of this presentation is to illustrate this interconnection, targeted for continuous product refinement from Collection 3 through current Collection 4 and into future Collection 5 of the LAI and FPAR products. Product analysis is presented at global scale, regional scale (MODIS tile) and local scale (validation). At global scale we routinely analyzed spatial coverage of the main algorithm, studied seasonality of LAI and FPAR products, and analyzed impact of snow and cloud conditions on product retrievals. MODIS tile scale is convenient for detailed study of product retrievals as function of the algorithm and its input uncertainties and provide direct feedback to algorithm refinement. Finally we summarized reported in peer-reviewed literature results of the validation activities, performed by LAI/FPAR team as well as external validators.
B31B-0213 0800h
Estimating Leaf Area Index From Fine Resolution Satellite Data Over a Boreal Forest
The leaf area index (LAI) is a key structural characteristic of vegetation due to the role of green leaves in controlling many biological and physical processes driving the exchange of matter and energy flow. LAI is the most important functional parameter of green vegetation and at the same time the main determinant of the spectral signal of the Earth's vegetated areas. The amount of leaf area responds rapidly to changes in climatic conditions and the subsequent decrease or increase in LAI is directly reflected in future biomass production. Monitoring of LAI is important in order to track changes in the size of the area covered by vegetation, to characterise the condition and growth potential of the terrestrial ecosystem as well as to identify carbon sinks. Our poster presents results from a MODIS LAI/FPAR field campaign organized at a boreal coniferous test site in northern Sweden in 2002. During the field campaign, an extensive data set comprising canopy LAI and structural and spectral properties was obtained to develop new methods for estimating LAI from fine resolution satellite data and to validate the MODIS LAI product. Here we present results from testing and applying the new methods over the study region.
B31B-0214 0800h
A GIS and Remote Sensing Investigation of the Relationship of Terrain, Soil, and other Physiographic Factors on the Pine Community of Lincoln National Park in the Sacramento Mountains of Southwest New Mexico
The health of forests is important to a community on several levels. Forests provide economic viability for people, habitat for wildlife, and natural beauty for all to enjoy. Loss of forest ecosystems impact communities greatly. In this study we will utilize GIS and Remote Sensing applications to better understand the dynamics of White Pine Blister Rust (WPBR) infestation in the White Pine Community of the Sacramento Mountains of Southwest New Mexico. Both field spectral sampling of the needles as well as imagery analysis will be incorporated to better understand the infestation, progression and vulnerability of the forest to this and other diseases. Use of ancillary data such as topography, geology, hydrology, and soil composition will be used to construct a GIS database. The data produced from this study will be incorporated with the existing USDA Forest Service database to produce a more inclusive catalog for supervisors, researchers, and the public to use.
B31B-0215 0800h
Comparative Analysis of AVHRR and MODIS NDVI Time Series for Developing Long Term Records of Terrestrial Vegetation
Long-term continuity of the moderate resolution satellite data record is critical for documenting effects on the land surface that may be related to anthropogenic influences or climate variability. Since the data record began with the advanced very high resolution radiometer (AVHRR) in the 1980's and continued to the moderate resolution imaging spectroradiometer (MODIS) beginning in 2000, variations in the satellite signal may be attributed to changes in both the imaging platform and imaging sensor. The US Geological Survey Earth Resources Observation Systems (EROS) Data Center (EDC) has created contemporaneous data sets of AVHRR and MODIS vegetation indices from 2001 through 2003 in order to characterize and resolve differences in the instrument signals and derived indices to ensure the continuity of a long-term record or vegetation state and condition. The comparison data sets consist of 16-day normalized difference vegetation index (NDVI) at 1-kilometer resolution data covering the conterminous United States. Early results indicate strong agreement (r2 = 0.92) between the AVHRR and MODIS NDVI over the data set as a whole, although agreement may be weaker for specific land cover types, such as southeastern evergreen needle-leaf forest (r2 = 0.65). While there is generally very strong agreement between the AVHRR and MODIS NDVI, phenological metrics derived from each data set exhibited significant differences that were attributed to aerosol corrections in the MODIS VI data. In the case of the MODIS NDVI data, considerable post-processing and analysis of the embedded quality assessment information is necessary in order to determine whether departures from the corresponding AVHRR NDVI records are due to differences in radiometric response of the instruments, methods of atmospheric correction, cloud cover assessment, or decision rules for constructing image composites.
B31B-0216 0800h
Comparing FAPAR by canopy, FAPAR by leaves, FAPAR by chlorophyll in leaves, NDVI, and EVI of Harvard Forest using MODIS data
Moderate Resolution Imaging Spectroradiometer (MODIS) provides an improved potential (more and finer spectral bands, and finer spatial resolution) for better characteristics of vegetation at global scale than AVHRR (advanced very high resolution radiometer). We calculated fraction of photosynthetically active radiation (PAR) absorbed (FAPAR) by canopy, by leaves, and by chlorophyll in leaves of Harvard Forest using the PROSPECT+SAIL (PROSAIL) model and MODIS daily data. The FAPAR by canopy, FAPAR by leaves, FAPAR by chlorophyll in leaves,normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from the same MODIS daily data were compared. We concluded that, under atmospherically clear condition for Harvard Forest, EVI is closer to FAPAR by chlorophyll in leaves than other two FAPAR, and NDVI is closer to FAPAR by leaves than other two FAPAR, at the MODIS scale ( about 500m). We plan to investigate their relationships under both atmospherically polluted condition and atmospherically clear condition and for other forest sites in the future.
B31B-0217 0800h
Understanding the TVX Relationship between NDVI and Surface Temperature for the Varying Landscape of Oklahoma
The relationship between the Normalized Difference Vegetation Index (NDVI) and surface skin temperature (Ts), the Temperature-Vegetation Index (TVX), generally exhibits a negative relationship where vegetation has a high NDVI and low temperature and bare soil has a low NDVI and higher temperature. This relationship has been utilized to study air temperature, soil moisture and surface moisture conditions as part of biospheric and hydrologic studies. A form of this relationship is part of the MOD 16 MODIS Evaporative Fraction (EF) product. We have found that the TVX relationship exhibits unusual behavior in some situations. We derived TVX slope and air temperature estimations using AVHRR data and compared the output to data from the Oklahoma Mesonet for the period of May 6 to August 20, 1999. This study shows that positive NDVI/Ts slopes were possibly caused by subpixel water bodies. In addition, NDVI/Ts slopes become increasingly negative after winter wheat harvest in July in Central Oklahoma producing errors in air temperature estimation up to 30 K. The largest errors occurred as a drought intensified through August.
B31B-0218 0800h
Production of MODIS Land Science Products
The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are aboard the NASA's polar orbiting Terra and Aqua Earth Observing System (EOS) satellites. Since launch MODIS/Terra has acquired over 4.5 years of earth observation data and MODIS/Aqua over two years of data. On each satellite MODIS acquires daily global data in 36 spectral bands - 29 with 1 km, 5 with 500 m and 2 with 250 m nadir pixels. The MODIS Land Science products have been specially tailored to the land research community needs. The MODIS science team has continually improved their algorithms over time and is now planning for the third major reprocessing. A summary of the improvements to the algorithms will be presented as well as some of the factors involved in reliably producing and archiving high quality land science products.
http://modis-land.gsfc.nasa.gov/
B31B-0219 0800h
Analysis of Multi-sensor Continuity of Reflectance and Vegetation Indices Using Radiative Transfer Models
Long term observations of global vegetation from multiple satellites require much effort to ensure continuity and compatibility due to different sensor characteristics (e.g., band pass filters and spatial resolutions) and product generation algorithms (e.g., atmospheric correction and compositing schemes) as well as different observation geometries. While it is important to investigate and correct for each of these factors individually, it is also of great importance to develop a systematic understanding of simultaneous impacts of multiple factors on data continuity for translation. In this study, a coupled canopy-atmosphere radiative transfer model (SAIL+6S) was employed to investigate the relative impacts of the multiple factors on reflectance and vegetation index (VI) product continuity. Reflectance spectra for a variety of canopy, atmosphere, and geometry conditions were simulated for a grassland biome based on the observed ranges of the parameters. These data were then spectrally convolved to simulate AVHRR, MODIS, ETM+, and ASTER band passes and analyzed to determine the relative impacts on continuity. We found that inter-sensor relationships of reflectance and VIs vary systematically with canopy, atmosphere, and geometry conditions and that multi-sensor data sets can be made interchangeably useable by modeling these systematic behaviors as translation equations.
B31B-0220 0800h
A Daily AVHRR Land Surface Temperature Data Set: Evidence of Directional Biases
The NOAA AVHRR instrument has been monitoring the brightness temperature of the planet for more than 20 years. Split window algorithms convert these measurements to land surface temperature (LST) by correcting for atmospheric and surface emissivity effects. However, the low precision of LST retrievals -- associated with intractable variability -- has often hindered its wide use. In this study, we developed a 6-year daily (day and night) NOAA-14 AVHRR LST data set over continental Africa and investigate it for the presence of directional effects. By combining vegetation structural data available in the literature and a geometric optics model, we estimated the fractions of sunlit and shaded endmembers observed by AVHRR for each pixel of each overpass. Although our simplistic approach requires many assumptions (e.g., only four endmember types per scene), we demonstrate through correlation that significant AVHRR LST variability can be attributed to angular effects imposed by AVHRR orbit and sensor characteristics, in combination with vegetation structure. These angular effects lead to systematic LST biases, including `hot spot' effects when no shadows are seen by the sensor. For example, a woodland case showed that LST measurements within the `hot-spot' geometry were about 9 K higher than those at other geometries. We describe the general patterns of these biases as a function of tree cover fraction, season, and satellite drift (time past launch).
B31B-0221 0800h
Vegetation Change in West Africa as Assessed by Surface-Based and Satellite Observations
The semi-arid Sahel of West Africa has experienced a remarkable change of climate over the last century. Long-term means of precipitation for 30 year periods have declined by 30% to 50% between 1931-60 and 1968-97. During this time there has been anecdotal evidence of major shifts in the vegetation zones. We have attempted to document these shifts by using maps and transcript studies from the 1950s to reconstruct the vegetation patterns that prevailed earlier in the century. AVHRR and MODIS have been used to determine vegetation classes and other characteristics for the more recent decades. The climatic variables that are associated with various types are assessed. A climate/vegetation analysis has been carried out for both the wetter conditions earlier in the century and for the recent dry decades.
B31B-0222 0800h
A multi-scale Analysis of Dynamic Optical Signals in a Southern California Chaparral Ecosystem: a Comparison of Field, AVIRIS and MODIS Data
Using field data, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) imagery, and Moderate-Resolution Imaging SpectroRadiometer (MODIS) data, a multi-scale analysis of ecosystem optical properties was performed for Sky Oaks, a Southern California chaparral ecosystem in the SpecNet and FLUXNET networks. The study covered a four-year period (2000-2004), which included a severe drought in 2002 and a subsequent wildfire in July 2003, leading to extreme perturbation in ecosystem optical properties. Two vegetation greenness indices (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) and a measure of the fraction of photosynthetically active radiation absorbed by vegetation (fPAR) were compared across sampling platforms, which ranged in pixel size from 1 meter (tram system in the field) to 1000 m (MODIS satellite sensor). For the EVI, there was excellent agreement between MODIS, AVIRIS and the ground measurements (tram system). AVIRIS and tram-based NDVI and fPAR values were in close agreement. However, MODIS NDVI and fPAR values were consistently higher than those determined from the field and the aircraft sensor, and these differences could not be entirely attributed to differences in sampling scale. Interestingly, MODIS fPAR derived from backup algorithms (NDVI driven) was closer to the AVIRIS and tram fPAR under the cloudy conditions. This suggests that derivation of fPAR directly from vegetation indices could work better than the currently deployed dominant algorithms incorporating look-up tables for biome type. These results appear consistent with other recently published results that indicate that MODIS overestimates fPAR and thus NPP for terrestrial ecosystems, and demonstrates the need for proper validation of MODIS terrestrial biospheric products by direct comparison against optical signals at other spatial scales. The study also demonstrates the utility of in-situ field sampling (e.g. tram systems) and hyperspectral aircraft imagery for proper interpretation of satellite data taken at coarse spatial scales.
B31B-0223 0800h
Tamarisk (Salt Cedar) Infestations in Northwestern Nevada Mapped Using Landsat TM Imagery and GIS Layers
Tamarisk, also known as salt cedar (Tamarix sp.) is a prevalent invasive species that has infested many riparian areas in the southwestern United States. Mature salt cedar plants are resistant to high stress environments and fare well in drought conditions, mainly due to their extensive root systems that derive much of their sustenance from the water table rather than surface water and precipitation. The salt cedar root systems have altered hydrological patterns by tapping into underlying aquifers. This has decreased water available for recreational use, regional ecology and plant diversity. Many states have implemented salt cedar monitoring programs at the local level, but the problem of large-scale mapping of this invasive species has continued to be a challenge to land management agencies. Furthermore, inaccessible and unexplored areas continue to be absent in the mapping process. In August 2004, using field data consisting of large areas as training sets for classification of Landsat TM imagery, the DEVELOP student research team at NASA Ames Research Center generated a preliminary map of areas that that were susceptible to salt cedar growth for a region in northwestern Nevada. In addition to the remote sensing-based classification of satellite imagery, the team used the variables of elevation and estimated distance to the water table in conjunction with collected field data and knowledge of salt cedar growth habits to further refine the map. The team has further extended the mapping of key environmental factors of water availability for salt cedar, soil types and species distribution in regions infested by salt cedar. The investigation was carried out by 1) improving an existing GIS layer for water access using a suitable interpolation method, 2) including a GIS layer for soils associated with salt cedar growth and 3) completing field work to evaluate species distribution and regions of presence or absence of salt cedar. The outcome of this project served to improve the salt cedar mapping methods already in place in Nevada, to create a guideline for future salt cedar management efforts and to evaluate the usefulness of satellite imagery in the detection of an invasive species. The results will be presented through both the final maps and visualization.
http://develop.larc.nasa.gov
B31B-0224 0800h
Remote Sensing of Forest Floor and Upper Layer LAI Evaluated With IKONOS Satellite in East Siberian Taiga
Remote sensing of forest floor types and upper layer LAI was evaluated with IKONOS imagery in east Siberian larch forest. Siberian taiga is characterized by occurrences of forest fire. The area contained various age forest stands and burned forest differing in fire severity. The taiga is also characterized by sparse upper soil layer and patched forest ground vegetation that may include burned scars. By taking account of these characteristics, we built and evaluated radiative transfer models for estimation of forest floor types and upper layer LAI that contribute to carbon budget estimation. Forest floor plant species and tree crowns were mapped and component spectrums were observed in a 30m X 30m observation plot in a mature larch stand (N62?_q20', E129?_q30') in east Siberia, from Yakutsk. IKONOS imagery was taken over the plot on 11 July, 2001 from 45 degrees zenith angle. The forest floor plant species and tree crowns were mapped by sketching and interpretation of photos taken on the forest floors, and height and diameter at breast height of all the trees were measured. 1m X 1m sized 900 and 400 photographs were taken for mapping of the forest floor plant species in the larch and grassland plots, respectively. The data was integrated as spatial data set and used for evaluation of forest floor types and upper layer LAI estimation from IKONOS satellite data.
B31B-0225 0800h
Sensitivity of key climate parameters to global NPP using a process-based model integrated with remote sensing data
A sensitivity analysis is performed for the terrestrial carbon cycle model SimCYCLE upon integration with the MODIS sensor derived LAI (Leaf Area Index). The analysis is carried in two different phases based on the objectives. The first phase consists of simple analysis involving key climatic parameters driving the model by simply varying each of the parameter independently and analyzing the latitude-wise and biome-wise changes to locate any untoward effects on the model upon `nudging' with the MODIS-LAI. The results from the first phase produced expected global estimates. However, the biome-wise analysis was able to point out the possible mismatch between remote sensing derived LAI and the old landcover map used in SimCYCLE, which has been good enough for estimating potential NPP (Net Primary Production). This led to tuning of the model to a new landcover product. The objective of the second phase of sensitivity analysis is to see the effectiveness of the model to diagnose the changing climate change and land cover scenarios. Decadal global change patterns were used to analyze the sensitivity of NPP to changing air temperature, surface temperature, precipitation and cloud cover, both biome-wise and across latitudes with emphasis on tropical and boreal regions. Cloud cover, which essentially represents the amount of radiation received by vegetation, is found to be the most sensitive parameter for global estimates of NPP. For landcover, several recent global landcover products have been considered and the differences in NPP estimates while using them in the model presents the sensitivity of landcover on global NPP estimates.