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L.L. Tieszen

Publications and source records attributed to L.L. Tieszen.

At least 19 recordsLinked to original sources
USGS2011

Evidence of shift in C 4 species range in central Argentina during the late Holocene

Aim Millennial-scale biogeographic changes are well understood in many parts of the world, but little is known about long-term vegetation dynamics in subtropical regions. Here we investigate shifts in C 3 /C 4 plant abundance occurred in central Argentina during the past few millennia Methods We determined present day soil organic matter δ 13 C signatures of grasslands, shrublands and woodlands, containing different mixtures of C 3 and C 4 plants. We measured past changes in the relative cover of C 3 /C 4 plants by comparing δ 13 C values in soil profiles with present day δ 13 C signatures. We analyzed 14 C activity in soil depths that showed major changes in vegetation. Results Present day relative cover of C 3 /C 4 plants determines whole ecosystem δ 13 C signatures integrated as litter and superficial soil organic matter (R 2 = 0.78; p < 0.01). Deeper soils show a consistent shift in δ 13 C, indicating a continuous replacement of C 4 by C 3 plants since 3,870 (±210) YBP. During this period, the relative abundance of C 3 plants increased 32% (average across sites) with significant changes being observed in all studied ecosystems. Conclusions Our results show that C 4 species were more abundant in the past, but C 3 species became dominant during the late Holocene. We identified increases in the relative C 3 /C 4 cover in grasslands, shrublands and woodlands, suggesting a physiological basis for changes in vegetation. The replacement of C 4 by C 3 plants coincided with changes in climate towards colder and wetter conditions and could represent a climatically driven shift in the C 4 species optimum range.

Plant and Soil↗
USGS2010

Impacts of precipitation seasonality and ecosystem types on evapotranspiration in the Yukon River Basin, Alaska

Evapotranspiration (ET) is the largest component of water loss from terrestrial ecosystems; however, large uncertainties exist when estimating the temporal and spatial variations of ET because of concurrent shifts in the magnitude and seasonal distribution of precipitation as well as differences in the response of ecosystem ET to environmental variabilities. In this study, we examined the impacts of precipitation seasonality and ecosystem types on ET quantified by eddy covariance towers from 2002 to 2004 in three ecosystems (grassland, deciduous broadleaf forest, and evergreen needleleaf forest) in the Yukon River Basin, Alaska. The annual precipitation changed greatly in both magnitude and seasonal distribution through the three investigated years. Observations and model results showed that ET was more sensitive to precipitation scarcity in the early growing season than in the late growing season, which was the direct result of different responses of ET components to precipitation in different seasons. The results demonstrated the importance of seasonal variations of precipitation in regulating annual ET and overshadowing the function of annual precipitation. Comparison of ET among ecosystems over the growing season indicated that ET was largest in deciduous broadleaf, intermediate in evergreen needleleaf, and lowest in the grassland ecosystem. These ecosystem differences in ET were related to differences in successional stages and physiological responses.

Water Resources Research↗
USGS2009

Historical and simulated ecosystem carbon dynamics in Ghana: Land use, management, and climate

We used the General Ensemble biogeochemical Modeling System (GEMS) to simulate responses of natural and managed ecosystems to changes in land use and land cover, management, and climate for a forest/savanna transitional zone in central Ghana. Model results show that deforestation for crop production during the 20th century resulted in a substantial reduction in ecosystem carbon (C) stock from 135.4 Mg C ha −1 in 1900 to 77.0 Mg C ha −1 in 2000, and in soil organic C stock within the top 20 cm of soil from 26.6 Mg C ha −1 to 21.2 Mg C ha −1 . If no land use change takes place from 2000 through 2100, low and high climate change scenarios (increase in temperature and decrease in precipitation over time) will result in losses of soil organic C stock by 16% and 20%, respectively. A low nitrogen (N) fertilization rate is the principal constraint on current crop production. An increase in N fertilization under the low climate change scenario would lead to an increase in the average crop yield by 21% with 30 kg N ha −1 and by 42% with 60 kg N ha −1 (varying with crop species), accordingly, the average soil C stock would decrease by 2% and increase by 17%, in all cropping systems by 2100. The results suggest that a reasonable N fertilization rate is critical to achieve food security and agricultural sustainability in the study area through the 21st century. Adaptation strategies for climate change in this study area require national plans to support policies and practices that provide adequate N fertilizers to sustain soil C and crop yields and to consider high temperature tolerant crop species if these temperature projections are exceeded.

Biogeosciences↗
USGS2009

Simulated dynamics of carbon stocks driven by changes in land use, management and climate in a tropical moist ecosystem of Ghana

Sub-Saharan Africa is large and diverse with regions of food insecurity and high vulnerability to climate change. This project quantifies carbon stocks and fluxes in the humid forest zone of Ghana, as a part of an assessment in West Africa. The General Ensemble biogeochemical Modeling System (GEMS) was used to simulate the responses of natural and managed systems to projected scenarios of changes in climate, land use and cover, and nitrogen fertilization in the Assin district of Ghana. Model inputs included historical land use and cover data, historical climate records and projected climate changes, and national management inventories. Our results show that deforestation for crop production led to a loss of soil organic carbon (SOC) by 33% from 1900 to 2000. The results also show that the trend of carbon emissions from cropland in the 20th century will continue through the 21st century and will be increased under the projected warming and drying scenarios. Nitrogen (N) fertilization in agricultural systems could offset SOC loss by 6% with 30 kg N ha −1 year −1 and by 11% with 60 kg N ha −1 year −1 . To increase N fertilizer input would be one of the vital adaptive measures to ensure food security and maintain agricultural sustainability through the 21st century.

Agriculture, Ecosystems and Environment↗
USGS2008

Climate controls on C3 vs. C4 productivity in North American grasslands from carbon isotope composition of soil organic matter

We analyzed the ??13 C of soil organic matter (SOM) and fine roots from 55 native grassland sites widely distributed across the US and Canadian Great Plains to examine the relative production of C3 vs. C4 plants (hereafter %C4) at the continental scale. Our climate vs. %C4 results agreed well with North American field studies on %C4, but showed bias with respect to %C4 from a US vegetation database (statsgo) and weak agreement with a physiologically based prediction that depends on crossover temperature. Although monthly average temperatures have been used in many studies to predict %C4, our analysis shows that high temperatures are better predictors of %C4. In particular, we found that July climate (average of daily high temperature and month's total rainfall) predicted %C4 better than other months, seasons or annual averages, suggesting that the outcome of competition between C3 and C4 plants in North American grasslands was particularly sensitive to climate during this narrow window of time. Root ??13 C increased about 1??? between the A and B horizon, suggesting that C 4 roots become relatively more common than C3 roots with depth. These differences in depth distribution likely contribute to the isotopic enrichment with depth in SOM where both C3 and C4 grasses are present. ?? 2008 The Authors Journal compilation ?? 2008 Blackwell Publishing Ltd.

Global Change Biology↗
USGS2008

Integrating remotely sensed land cover observations and a biogeochemical model for estimating forest ecosystem carbon dynamics

Land cover change is one of the key driving forces for ecosystem carbon (C) dynamics. We present an approach for using sequential remotely sensed land cover observations and a biogeochemical model to estimate contemporary and future ecosystem carbon trends. We applied the General Ensemble Biogeochemical Modelling System (GEMS) for the Laurentian Plains and Hills ecoregion in the northeastern United States for the period of 1975-2025. The land cover changes, especially forest stand-replacing events, were detected on 30 randomly located 10-km by 10-km sample blocks, and were assimilated by GEMS for biogeochemical simulations. In GEMS, each unique combination of major controlling variables (including land cover change history) forms a geo-referenced simulation unit. For a forest simulation unit, a Monte Carlo process is used to determine forest type, forest age, forest biomass, and soil C, based on the Forest Inventory and Analysis (FIA) data and the U.S. General Soil Map (STATSGO) data. Ensemble simulations are performed for each simulation unit to incorporate input data uncertainty. Results show that on average forests of the Laurentian Plains and Hills ecoregion have been sequestrating 4.2 Tg C (1 teragram = 1012 gram) per year, including 1.9 Tg C removed from the ecosystem as the consequences of land cover change. ?? 2008 Elsevier B.V.

Ecological Modelling↗
USGS2008

An improved state-parameter analysis of ecosystem models using data assimilation

Much of the effort spent in developing 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 lowered if the temporal evolution of parameter values is not incorporated. This research develops a smoothed ensemble Kalman filter (SEnKF) by combining ensemble Kalman filter with kernel smoothing technique. SEnKF has following characteristics: (1) to estimate simultaneously the model states and parameters through concatenating unknown parameters and state variables into a joint state vector; (2) to mitigate dramatic, sudden changes of parameter values in parameter sampling and parameter evolution process, and control narrowing of parameter variance which results in filter divergence through adjusting smoothing factor in kernel smoothing algorithm; (3) to assimilate recursively data into the model and thus detect possible time variation of parameters; and (4) to address properly various sources of uncertainties stemming from input, output and parameter uncertainties. The SEnKF is tested by assimilating observed fluxes of carbon dioxide and environmental driving factor data from an AmeriFlux forest station located near Howland, Maine, USA, into a partition eddy flux model. Our analysis demonstrates that model parameters, such as light use efficiency, respiration coefficients, minimum and optimum temperatures for photosynthetic activity, and others, 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 the 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 the understanding and quantification of carbon cycle parameters and processes. ?? 2008 Elsevier B.V.

Ecological Modelling↗
USGS2008

Land cover and forest formation distributions for St. Kitts, Nevis, St. Eustatius, Grenada and Barbados from decision tree classification of cloud-cleared satellite imagery

Satellite image-based mapping of tropical forests is vital to conservation planning. Standard methods for automated image classification, however, limit classification detail in complex tropical landscapes. In this study, we test an approach to Landsat image interpretation on four islands of the Lesser Antilles, including Grenada and St. Kitts, Nevis and St. Eustatius, testing a more detailed classification than earlier work in the latter three islands. Secondly, we estimate the extents of land cover and protected forest by formation for five islands and ask how land cover has changed over the second half of the 20th century. The image interpretation approach combines image mosaics and ancillary geographic data, classifying the resulting set of raster data with decision tree software. Cloud-free image mosaics for one or two seasons were created by applying regression tree normalization to scene dates that could fill cloudy areas in a base scene. Such mosaics are also known as cloud-filled, cloud-minimized or cloud-cleared imagery, mosaics, or composites. The approach accurately distinguished several classes that more standard methods would confuse; the seamless mosaics aided reference data collection; and the multiseason imagery allowed us to separate drought deciduous forests and woodlands from semi-deciduous ones. Cultivated land areas declined 60 to 100 percent from about 1945 to 2000 on several islands. Meanwhile, forest cover has increased 50 to 950%. This trend will likely continue where sugar cane cultivation has dominated. Like the island of Puerto Rico, most higher-elevation forest formations are protected in formal or informal reserves. Also similarly, lowland forests, which are drier forest types on these islands, are not well represented in reserves. Former cultivated lands in lowland areas could provide lands for new reserves of drier forest types. The land-use history of these islands may provide insight for planners in countries currently considering lowland forest clearing for agriculture. Copyright 2008 College of Arts and Sciences.

Caribbean Journal of Science↗
USGS2007

Mangrove forest distributions and dynamics (1975–2005) of the tsunami-affected region of Asia

Aim We aimed to estimate the present extent of tsunami-affected mangrove forests and determine the rates and causes of deforestation from 1975 to 2005. Location Our study region covers the tsunami-affected coastal areas of Indonesia, Malaysia, Thailand, Burma (Myanmar), Bangladesh, India and Sri Lanka in Asia. Methods We interpreted time-series Landsat data using a hybrid supervised and unsupervised classification approach. Landsat data were geometrically corrected to an accuracy of plus-or-minus half a pixel, an accuracy necessary for change analysis. Each image was normalized for solar irradiance by converting digital number values to the top-of-the atmosphere reflectance. Ground truth data and existing maps and data bases were used to select training samples and also for iterative labelling. We used a post-classification change detection approach. Results were validated with the help of local experts and/or high-resolution commercial satellite data. Results The region lost 12% of its mangrove forests from 1975 to 2005, to a present extent of c . 1,670,000 ha. Rates and causes of deforestation varied both spatially and temporally. Annual deforestation was highest in Burma ( c . 1%) and lowest in Sri Lanka (0.1%). In contrast, mangrove forests in India and Bangladesh remained unchanged or gained a small percentage. Net deforestation peaked at 137,000 ha during 1990–2000, increasing from 97,000 ha during 1975–90, and declining to 14,000 ha during 2000–05. The major causes of deforestation were agricultural expansion (81%), aquaculture (12%) and urban development (2%). Main conclusions We assessed and monitored mangrove forests in the tsunami-affected region of Asia using the historical archive of Landsat data. We also measured the rates of change and determined possible causes. The results of our study can be used to better understand the role of mangrove forests in saving lives and property from natural disasters such as the Indian Ocean tsunami, and to identify possible areas for conservation, restoration and rehabilitation.

Journal of Biogeography↗
USGS2007

Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes

The quantitative simulation of gross primary production (GPP) at various spatial and temporal scales has been a major challenge in quantifying the global carbon cycle. We developed a light use efficiency (LUE) daily GPP model from eddy covariance (EC) measurements. The model, called EC-LUE, is driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux (used to calculate moisture stress). The EC-LUE model relies on two assumptions: First, that the fraction of absorbed PAR (fPAR) is a linear function of NDVI; Second, that the realized light use efficiency, calculated from a biome-independent invariant potential LUE, is controlled by air temperature or soil moisture, whichever is most limiting. The EC-LUE model was calibrated and validated using 24,349 daily GPP estimates derived from 28 eddy covariance flux towers from the AmeriFlux and EuroFlux networks, covering a variety of forests, grasslands and savannas. The model explained 85% and 77% of the observed variations of daily GPP for all the calibration and validation sites, respectively. A comparison with GPP calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS) indicated that the EC-LUE model predicted GPP that better matched tower data across these sites. The realized LUE was predominantly controlled by moisture conditions throughout the growing season, and controlled by temperature only at the beginning and end of the growing season. The EC-LUE model is an alternative approach that makes it possible to map daily GPP over large areas because (1) the potential LUE is invariant across various land cover types and (2) all driving forces of the model can be derived from remote sensing data or existing climate observation networks.

Agricultural and Forest Meteorology↗
USGS2007

Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000

Mangrove forests in many parts of the world are declining at an alarming rate—possibly even more rapidly than inland tropical forests. The rate and causes of such changes are not known. The forests themselves are dynamic in nature and are undergoing constant changes due to both natural and anthropogenic forces. Our research objective was to monitor deforestation and degradation arising from both natural and anthropogenic forces. We analyzed multi-temporal satellite data from 1970s, 1990s, and 2000s using supervised classification approach. Our spatio-temporal analysis shows that despite having the highest population density in the world in its periphery, areal extent of the mangrove forest of the Sundarbans has not changed significantly (approximately 1.2%) in the last ∼25 years. The forest is however constantly changing due to erosion, aggradation, deforestation and mangrove rehabilitation programs. The net forest area increased by 1.4% from the 1970s to 1990 and decreased by 2.5% from 1990 to 2000. The change is insignificant in the context of classification errors and the dynamic nature of mangrove forests. This is an excellent example of the co-existence of humans with terrestrial and aquatic plant and animal life. The strong commitment of governments under various protection measures such as forest reserves, wildlife sanctuaries, national parks, and international designations, is believed to be responsible for keeping this forest relatively intact (at least in terms of area). While the measured net loss of mangrove forest is not that high, the change matrix shows that turnover due to erosion, aggradation, reforestation and deforestation was much greater than net change. The forest is under threat from natural and anthropogenic forces leading to forest degradation, primarily due to top-dying disease and over-exploitation of forest resources.

Estuarine, Coastal and Shelf Science↗
USGS2006

Analysis of ecosystem controls on soil carbon source-sink relationships in the northwest Great Plains

Our ability to forecast the role of ecosystem processes in mitigating global greenhouse effects relies on understanding the driving forces on terrestrial C dynamics. This study evaluated the controls on soil organic C (SOC) changes from 1973 to 2000 in the northwest Great Plains. SOC source-sink relationships were quantified using the General Ensemble Biogeochemical Modeling System (GEMS) based on 40 randomly located 10 × 10 km 2 sample blocks. These sample blocks were aggregated into cropland, grassland, and forestland groups based on land cover composition within each sample block. Canonical correlation analysis indicated that SOC source-sink relationship from 1973 to 2000 was significantly related to the land cover type while the change rates mainly depended on the baseline SOC level and annual precipitation. Of all selected driving factors, the baseline SOC and nitrogen levels controlled the SOC change rates for the forestland and cropland groups, while annual precipitation determined the C source-sink relationship for the grassland group in which noticeable SOC sink strength was attributed to the conversion from cropped area to grass cover. Canonical correlation analysis also showed that grassland ecosystems are more complicated than others in the ecoregion, which may be difficult to identify on a field scale. Current model simulations need further adjustments to the model input variables for the grass cover-dominated ecosystems in the ecoregion.

Global Biogeochemical Cycles↗
USGS2005

Soil organic carbon dynamics as related to land use history in the northwestern Great Plains

Strategies for mitigating the global greenhouse effect must account for soil organic carbon (SOC) dynamics at both spatial and temporal scales, which is usually challenging owing to limitations in data and approach. This study was conducted to characterize the SOC dynamics associated with land use change history in the northwestern Great Plains ecoregion. A sampling framework (40 sample blocks of 10 × 10 km 2 randomly located in the ecoregion) and the General Ensemble Biogeochemical Modeling System (GEMS) were used to quantify the spatial and temporal variability in the SOC stock from 1972 to 2001. Results indicate that C source and sink areas coexisted within the ecoregion, and the SOC stock in the upper 20-cm depth increased by 3.93 Mg ha −1 over the 29 years. About 17.5% of the area was evaluated as a C source at 122 kg C ha −1 yr −1 . The spatial variability of SOC stock was attributed to the dynamics of both slow and passive fractions, while the temporal variation depended on the slow fraction only. The SOC change at the block scale was positively related to either grassland proportion or negatively related to cropland proportion. We concluded that the slow C pool determined whether soils behaved as sources or sinks of atmospheric CO 2 , but the strength depended on antecedent SOC contents, land cover type, and land use change history in the ecoregion.

Global Biogeochemical Cycles↗
USGS2004

Intra-seasonal mapping of CO 2 flux in rangelands of northern Kazakhstan at one-kilometer resolution

Algorithms that establish relationships between variables obtained through remote sensing and geographic information system (GIS) technologies are needed to allow the scaling up of site-specific CO2 flux measurements to regional levels. We obtained Bowen ratio-energy balance (BREB) flux tower measurements during the growing seasons of 1998-2000 above a grassland steppe in Kazakhstan. These BREB data were analyzed using ecosystem light-curve equations to quantify 10-day CO2 fluxes associated with gross primary production (GPP) and total respiration (R). Remotely sensed, temporally smoothed normalized difference vegetation index (NDVIsm) and environmental variables were used to develop multiple regression models for the mapping of 10-day CO2 fluxes for the Kazakh steppe. Ten-day GPP was estimated (R 2 = 0.72) by day of year (DOY) and NDVIsm, and 10-day R was estimated (R2 = 0.48) with the estimated GPP and estimated 10-day photosynthetically active radiation (PAR). Regression tree analysis estimated 10-day PAR from latitude, NDVIsm, DOY, and precipitation (R2 = 0.81). Fivefold cross-validation indicated that these algorithms were reasonably robust. GPP, R, and resulting net ecosystem exchange (NEE) were mapped for the Kazakh steppe grassland every 10 days and summed to produce regional growing season estimates of GPP, R, and NEE. Estimates of 10-day NEE agreed well with BREB observations in 2000, showing a slight underestimation in the late summer. Growing season (May to October) mean NEE for Kazakh steppe grasslands was 1.27 Mg C/ha in 2000. Winter flux data were collected during the winter of 2001-2002 and are being analyzed to close the annual carbon budget for the Kazakh steppe. ?? 2004 Springer-Verlag New York, LLC.

Environmental Management↗
USGS2004

Evaluation of land performance in Senegal using multi-temporal NDVI and rainfall series

Time series of rainfall data and normalized difference vegetation index (NDVI) were used to evaluate land cover performance in Senegal, Africa, for the period 1982–1997, including analysis of woodland/forest, agriculture, savanna, and steppe land cover types. A strong relationship exists between annual rainfall and season-integrated NDVI for all of Senegal ( r =0.74 to 0.90). For agriculture, savanna, and steppe areas, high positive correlations portray ‘normal’ land cover performance in relation to the rainfall/NDVI association. Regions of low correlation might indicate areas impacted by human influence. However, in the woodland/forest area, a negative or low correlation (with high NDVI) may reflect ‘normal’ land cover performance, due in part to the saturation effect of the rainfall/NDVI association. The analysis identified three areas of poor performance, where degradation has occurred over many years. Use of the ‘Standard Error of the Estimate’ provided essential information for detecting spatial anomalies associated with land degradation.

Journal of Arid Environments↗
USGS2004

Assessing land cover performance in Senegal, West Africa using 1-km integrated NDVI and local variance analysis

The researchers calculated seasonal integrated normalized difference vegetation index (NDVI) for each of 7 years using a time-series of 1-km data from the Advanced Very High Resolution Radiometer (AVHRR) (1992-93, 1995) and SPOT Vegetation (1998-2001) sensors. We used a local variance technique to identify each pixel as normal or either positively or negatively anomalous when compared to its surroundings. We then summarized the number of years that a given pixel was identified as an anomaly. The resulting anomaly maps were analysed using Landsat TM imagery and extensive ground knowledge to assess the results. This technique identified anomalies that can be linked to numerous anthropogenic impacts including agricultural and urban expansion, maintenance of protected areas and increased fallow. Local variance analysis is a reliable method for assessing vegetation degradation resulting from human pressures or increased land productivity from natural resource management practices. ?? 2004 Published by Elsevier Ltd.

Journal of Arid Environments↗
USGS2004

Land use change and terrestrial carbon stocks in Senegal

Environmental degradation resulting from long-term drought and land use change has affected terrestrial carbon (C) stocks within Africa's Sahel. We estimated Senegal's terrestrial carbon stocks in 1965, 1985, and 2000 using an inventory procedure involving satellite images revealing historical land use change, and recent field measurements of standing carbon stocks occurring in soil and plants. Senegal was divided into eight ecological zones containing 11 land uses. In 2000, savannas, cultivated lands, forests, and steppes were the four largest land uses in Senegal, occupying 70, 22, 2.7, and 2.3 percent of Senegal's 199,823 km 2 . System C stocks ranged from 9 t C ha −1 in degraded savannas in the north, to 113 t C ha −1 in the remnant forests of the Senegal River Valley. This approach resulted in estimated total C stocks of 1019 and 727 MT C between 1965 and 2000, respectively, indicating a loss of 292 MT C over 35 years. The proportion of C residing in biomass decreased with time, from 55 percent in 1965 to 38 percent in 2000. Calculated terrestrial C flux for 1993 was −7.5 MT C year −1 and had declined by 17 percent over the previous 18 years. Most of the terrestrial C flux in 1993 was attributed to biomass C reduction. Human disturbance accounted for only 22 percent of biomass C loss in 1993, suggesting that the effects of long-term Sahelian drought continue to play an overriding role in ecosystem change. Some carbon mitigation strategies for Senegal were investigated, including potential C sequestration levels. Opportunities for C mitigation exist but are constrained by available knowledge and access to resources.

Journal of Arid Environments↗