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Larry L. Tieszen

Publications and source records attributed to Larry L. Tieszen.

35 records · Page 2Linked to original sources
USGS2008

Modeling and dynamic monitoring of ecosystem performance in the Yukon River Basin

Central Alaska is ecologically sensitive and experiencing stress in response to marked regional warming. Resource managers would benefit from an improved ability to monitor ecosystem processes in response to climate change, fire, insect damage, and management policies and to predict responses to future climate scenarios. We have developed a method for analyzing ecosystem performance as represented by the growing season integral of normalized difference vegetation index (NDVI), which is a measure of greenness that can be interpreted in terms of plant growth or photosynthetic activity (gross primary productivity). The approach illustrates the status and trends of ecosystem changes and separates the influences of climate and local site conditions from the influences of disturbances and land management. We emphasize the ability to quantify ecosystem processes, not simply changes in land cover, across the entire period of the remote sensing archive (Wylie and others, 2008). The method builds upon remotely sensed measures of vegetation greenness for each growing season. By itself, however, a time series of greenness often reflects annual climate variations in temperature and precipitation. Our method seeks to remove the influence of climate so that changes in underlying ecological conditions are identified and quantified. We define an "expected ecosystem performance" to represent the greenness response expected in a particular year given the climate of that year. We distinguish "performance anomalies" as cases where the ecosystem response is significantly different from the expected ecosystem performance. Maps of the performance anomalies (fig. 1) and trends in the anomalies give valuable information on the ecosystems for land managers and policy makers at a resolution of 1 km to 250 m.

Fact Sheet↗
USGS2007

Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands

Two spatially-explicit estimates of gross primary production (GPP) are available for the Northern Great Plains. An empirical piecewise regression (PWR) GPP model was developed from flux tower measurements to map carbon flux across the region. The Moderate Resolution Imaging Spectrometer (MODIS) GPP model is a process-based model that uses flux tower data to calibrate its parameters. Verification and comparison of the regional PWR GPP and the global MODIS GPP are important for the modeling of grassland carbon flux. This study compared GPP estimates from PWR and MODIS models with five towers in the grasslands. Among them, PWR GPP and MODIS GPP showed a good agreement with tower-based GPP at three towers. The global MODIS GPP, however, did not agree well with tower-based GPP at two other towers, probably because of the insensitivity of MODIS model to regional ecosystem and climate change and extreme soil moisture conditions. Cross-validation indicated that the PWR model is relatively robust for predicting regional grassland GPP. However, the PWR model should include a wide variety of flux tower data as the training data sets to obtain more accurate results. In addition, GPP maps based on the PWR and MODIS models were compared for the entire region. In the northwest and south, PWR GPP was much higher than MODIS GPP. These areas were characterized by the higher water holding capacity with a lower proportion of C 4 grasses in the northwest and a higher proportion of C 4 grasses in the south. In the central and southeastern regions, PWR GPP was much lower than MODIS GPP under complicated conditions with generally mixed C 3 /C 4 grasses. The analysis indicated that the global MODIS GPP model has some limitations on detecting moisture stress, which may have been caused by the facts that C 3 and C 4 grasses are not distinguished, water stress is driven by vapor pressure deficit (VPD) from coarse meteorological data, and MODIS land cover data are unable to differentiate the sub-pixel cropland components.

Remote Sensing of Environment↗
USGS2007

Land Cover Applications, Landscape Dynamics, and Global Change

The Land Cover Applications, Landscape Dynamics, and Global Change project at U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) seeks to integrate remote sensing and simulation models to better understand and seek solutions to national and global issues. Modeling processes related to population impacts, natural resource management, climate change, invasive species, land use changes, energy development, and climate mitigation all pose significant scientific opportunities. The project activities use remotely sensed data to support spatial monitoring, provide sensitivity analyses across landscapes and large regions, and make the data and results available on the Internet with data access and distribution, decision support systems, and on-line modeling. Applications support sustainable natural resource use, carbon cycle science, biodiversity conservation, climate change mitigation, and robust simulation modeling approaches that evaluate ecosystem and landscape dynamics.

Fact Sheet↗
USGS2007

Estimating soil erosion using the USPED model and consecutive remotely sensed land cover observations

Intensified soil erosion contributes to the degradation of ecosystems. Better estimation of soil erosion across landscapes is a necessary part of understanding ecosystem biogeochemical cycles and ecosystem sustainability. In this study, we used the Unit Stream Power-based Erosion Deposition (USPED) model to estimate the lateral movement of soils across Fort Benning, a military training installation in western Georgia, USA. A land cover weight factor was used in the calculation of surface flow accumulation. The simulation results were compared with observations of the total suspended sediments in stream water for ten watersheds, and showed a significant linear relationship (R 2 = 0.72). Erosion estimates of the ten watersheds are also related to the land disturbance index that is a measure of the intensity of military training disturbances. Results suggest that the USPED model is an effective tool to quantify erosion and deposition at military installations.

Georgia↗
USGS2006

State-parameter estimation of ecosystem models using a smoothed ensemble Kalman filter

Much of the effort in data assimilation methods for carbon dynamics analysis has focused on estimating optimal values for either model parameters or state variables. The main weakness of estimating parameter values alone (i.e., without considering state variables) is that all errors from input, output, and model structure are attributed to model parameter uncertainties. On the other hand, the accuracy of estimating state variables may be reduced if the temporal evolution of parameter values is not incorporated. This research develops a smoothed ensemble Kalman filter (SEnKF) to estimate simultaneously the system states and model parameters of an eddy flux partition model. The approach is used to assimilate observed fluxes of carbon and major driving forces at an AmeriFlux forest station: Howland, Maine, USA. The aim of applying a kernel-smoothing algorithm to an ensemble Kalman filter is to overcome the dramatic, sudden change of parameter values in time and the loss of continuity between two consecutive points in time. Our analysis demonstrates that model parameters, such as light use efficiency, respiration coefficients, minimum and optimum temperatures for photosynthetic activity, and so on, are highly constrained by eddy flux data at daily-to-seasonal time scales. The SEnKF stabilizes parameter values quickly regardless of the initial values of the parameters. Potential ecosystem light use efficiency demonstrates a strong seasonality. Results show that the simultaneous parameter estimation procedure significantly improves model predictions. Results also show that the SEnKF can dramatically reduce variance in state variables stemming from the uncertainty of parameters and driving variables. The SEnKF is a robust and effective algorithm in evaluating and developing ecosystem models and in improving understanding and quantification of carbon cycle parameters and processes.

Conference Paper↗
USGS2006

Scaling-up of CO2 fluxes to assess carbon sequestration in rangelands of Central Asia

Flux towers provide temporal quantification of local carbon dynamics at specific sites. The number and distribution of flux towers, however, are generally inadequate to quantify carbon fluxes across a landscape or ecoregion. Thus, scaling up of flux tower measurements through use of algorithms developed from remote sensing and GIS data is needed for spatial extrapolation of carbon fluxes and to identify regional sinks and sources of carbon. Spatial and temporal quantification of carbon dynamics are useful in understanding the biophysical factors that cause regions to be sinks or sources of carbon. We analyzed data sets from the Northern Great Plains and the Kazakh Steppe and found similarities in latitude, precipitation, and carbon fluxes between the two regions. These similarities allowed us to pool carbon flux data, remotely sensed data, and GIS data from these two regions to map gross primary productivity (Pg), total ecosystem respiration (Re), and net ecosystem exchange (NEE) for Kazakh Steppe for 2001 using regression tree techniques. We estimated 10-day Pg and Re with mean absolute errors of 3.2 and 2.7 g CO 2 /m 2 /day, respectively. The NEE for grasslands in the Kazakh Steppe during the growing season (April through October 2001) was 0.79 t C/ha. Localized carbon sinks and sources were positively correlated with growing season precipitation and Pg. The regression tree technique provided an effective method for the regional mapping of carbon dynamics as seasonally quantified by flux towers in the Northern Great Plains of North America and the Kazakh Steppe of Central Asia.

Conference Paper↗
USGS2005

Integration of CO2 flux and remotely-sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: potential for quantitative spatial extrapolation

Aim  Extrapolation of tower CO 2 fluxes will be greatly facilitated if robust relationships between flux components and remotely sensed factors are established. Long-term measurements at five Northern Great Plains locations were used to obtain relationships between CO 2 fluxes and photosynthetically active radiation ( Q ), other on-site factors, and Normalized Difference Vegetation Index ( NDVI ) from the SPOT VEGETATION data set. Location  CO 2 flux data from the following stations and years were analysed: Lethbridge, Alberta 1998–2001; Fort Peck, MT 2000, 2002; Miles City, MT 2000–01; Mandan, ND 1999–2001; and Cheyenne, WY 1997–98. Results  Analyses based on light-response functions allowed partitioning net CO 2 flux ( F ) into gross primary productivity ( P g ) and ecosystem respiration ( R e ). Weekly averages of daytime respiration, γ day , estimated from light responses were closely correlated with weekly averages of measured night-time respiration, γ night ( R 2 0.64 to 0.95). Daytime respiration tended to be higher than night-time respiration, and regressions of γ day on γ night for all sites were different from 1 : 1 relationships. Over 13 site-years, gross primary production varied from 459 to 2491 g CO 2 m −2 year −1 , ecosystem respiration from 996 to 1881 g CO 2 m −2 year −1 , and net ecosystem exchange from −537 (source) to +610 g CO 2 m −2 year −1 (sink). Maximum daily ecological light-use efficiencies, ɛ d , max = P g /Q , were in the range 0.014 to 0.032 mol CO 2 (mol incident quanta) −1 . Main conclusions  Ten-day average P g was significantly more highly correlated with NDVI than 10-day average daytime flux, P d ( R 2 = 0.46 to 0.77 for P g -NDVI and 0.05 to 0.58 for P d -NDVI relationships). Ten-day average R e was also positively correlated with NDVI , with R 2 values from 0.57 to 0.77. Patterns of the relationships of P g and R e with NDVI and other factors indicate possibilities for establishing multivariate functions allowing scaling-up local fluxes to larger areas using GIS data, temporal NDVI, and other factors.

Global Ecology and Biogeography↗
USGS2004

Impacts of land use and climate change on carbon dynamics in south-central Senegal

Total carbon stock in vegetation and soils was reduced 37% in south-central Senegal from 1900 to 2000. The decreasing trend will continue during the 21st century unless forest clearing is stopped, selective logging dramatically reduced, and climate change, if any, relatively small. Developing a sustainable fuelwood and charcoal production system could be the most feasible and significant carbon sequestration project in the region. If future climate changes dramatically as some models have predicted, cropland productivity will drop more than 65% around 2100, posing a serious threat to food security and the efficiency of carbon sequestration projects.

Journal of Arid Environments↗
USGS2004

Sequestration of carbon in soil organic matter in Senegal: an overview

Sequestration of Carbon in Soil Organic Matter ( SOCSOM ) in Senegal is a multi-disciplinary development project planned and refined through two international workshops. The project was implemented by integrating a core of international experts in remote sensing, biogeochemical modeling, community socio-economic assessments, and carbon measurements in a fully collaborative manner with Senegal organizations, national scientists, and local knowledge and expertise. The study addresses the potential role developing countries in semi-arid areas can play in climate mitigation activities. Multiple benefits to smallholders could accrue as a result of management practices to re-establish soil carbon content lost because of land use changes or management practices that are not sustainable. The specific importance for the Sahel is because of the high vulnerability to climate change in already impoverished rural societies. The project focuses on four objectives in specific locations across the agroecological zones of Senegal. These objectives are: use of soil sampling and biogeochemical modeling to quantify the biophysical potential for carbon sequestration and to determine the sensitivity of the carbon stocks to various management and climate scenarios, to evaluate the socio-economic and cultural requirements necessary for successful project implementation directed toward an aggregation of smallholders to sequester around 100,000 t carbon (C), to support capacity building to develop a Carbon Specialist Team, and to initiate extrapolation from site-specific project areas to the Sahel region and the national level.

Journal of Arid Environments↗
USGS2003

Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush–steppe ecosystem

The net ecosystem exchange (NEE) of carbon flux can be partitioned into gross primary productivity (GPP) and respiration ( R ). The contribution of remote sensing and modeling holds the potential to predict these components and map them spatially and temporally. This has obvious utility to quantify carbon sink and source relationships and to identify improved land management strategies for optimizing carbon sequestration. The objective of our study was to evaluate prediction of 14-day average daytime CO 2 fluxes ( F day ) and nighttime CO 2 fluxes ( R n ) using remote sensing and other data. F day and R n were measured with a Bowen ratio&ndash;energy balance (BREB) technique in a sagebrush ( Artemisia spp.)&ndash;steppe ecosystem in northeast Idaho, USA, during 1996&ndash;1999. Micrometeorological variables aggregated across 14-day periods and time-integrated Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (iNDVI) were determined during four growing seasons (1996&ndash;1999) and used to predict F day and R n . We found that iNDVI was a strong predictor of F day ( R 2 =0.79, n =66, P <0.0001). Inclusion of evapotranspiration in the predictive equation led to improved predictions of F day ( R 2 =0.82, n =66, P <0.0001). Crossvalidation indicated that regression tree predictions of F day were prone to overfitting and that linear regression models were more robust. Multiple regression and regression tree models predicted R n quite well ( R 2 =0.75&ndash;0.77, n =66) with the regression tree model being slightly more robust in crossvalidation. Temporal mapping of F day and R n is possible with these techniques and would allow the assessment of NEE in sagebrush&ndash;steppe ecosystems. Simulations of periodic F day measurements, as might be provided by a mobile flux tower, indicated that such measurements could be used in combination with iNDVI to accurately predict F day . These periodic measurements could maximize the utility of expensive flux towers for evaluating various carbon management strategies, carbon certification, and validation and calibration of carbon flux models.

Idaho↗
USGS1998

An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. northern and central Great Plains

Time-integrated normalized difference vegetation index (TI NDVI) derived from the multitemporal satellite imagery (1989–1993) was used as a surrogate for primary production to investigate climate impacts on grassland performance for central and northern Great Plains grasslands. Results suggest that spatial and temporal variability in growing season precipitation, potential evapotranspiration, and growing degree days are the most important controls on grassland performance and productivity. When TI NDVI and climate data of all grassland land cover classes were examined as a whole, a statistical model showed significant positive correlation between the TI NDVI and accumulated spring and summer precipitation, and a negative correlation between TI NDVI and spring potential evapotranspiration. The coefficient of determination (R 2 ) of the general model was 0.45. When the TI NDVI-climate relationship was examined by individual land cover type, the relationship was generally better defined in terms of the variance accounted for by class-specific models . The photosynthetic pathway is an important determinant of grassland performance with northern mixed prairie (mixture of C 3 and C 4 grassland) TI NDVI affected by both thermal and moisture conditions during the growing season while southern plains grasslands (primarily C 4 grassland) were predominantly influenced by spring and summer precipitation. Grassland land cover classes associated with sandy soils also demonstrated a strong relationship between TI NDVI and growing season rainfall. Significant impact of interannual climate variability on the TI NDVI–climate relationship was also observed. The study suggests an integrated approach involving numerical models, satellite remote sensing, and field observations to monitor grassland ecosystem dynamics on a regional scale.

Remote Sensing of Environment↗
USGS1996

Grassland canopy parameters and their relationships to remotely sensed vegetation indices in the Nebraska Sand Hills

Relationships among spectral vegetation indices and grassland biophysical parameters including the effects of varying levels of standing dead vegetation, range sites, and range plant communities were examined. Range plant communities consisting of northern mixed grass prairie and a smooth brome field as well as range sites and management in a Sand Hills bluestem prairie were sampled with a ground radiometer and for LAI, biomass, chlorophy]] and nitrogen amounts. Live above ground (herbaceous and shrub leaf) biomass quadrat estimates in early June, 1994, ranged from a mean value of 35,9 g/m 2 in the burned area to 128.0 g/m 2 in the mixed grass prairie. Shrubs, when present, had a stronger effect on the ground radiometer NDVI and MSAVI relationships with live biomass than either standing dead vegetation or plant community composition. Predictive relationships for live biomass from ground radiometer spectral data allowed rapid nondestructive estimation of live biomass for eleven 30 m by 30 m plots. Strong (r 2 ‐ 0.81 to 0.87) predictive relationships for live biomass and SPOT vegetation indices at the 30 m by 30 m scale were developed and applied to estimate live biomass for entire site areas.

Geocarto International↗