Geology topics

Shuguang Liu

Publications and source records attributed to Shuguang Liu.

At least 37 records · Page 2Linked to original sources

USGS2014

Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687 g C m −2 yr −1 and total NPP in the range of 318–490 Tg C yr −1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m −2 yr −1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m −2 yr −1 . MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. We suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.

Illinois, Indiana, Iowa, Kansas, Michigan, Minneso↗

USGS2014

Evaluation of a model framework to estimate soil and soil organic carbon redistribution by water and tillage using 137 Cs in two U.S. Midwest agricultural fields

Cultivated lands in the U.S. Midwest have been affected by soil erosion, causing soil organic carbon (SOC) redistribution in the landscape and other environmental and agricultural problems. The importance of SOC redistribution on soil productivity and crop yield, however, is still uncertain. In this study, we used a model framework, which includes the Unit Stream Power-based Erosion Deposition (USPED) and the Tillage Erosion Prediction (TEP) models, to understand the soil and SOC redistribution caused by water and tillage erosion in two agricultural fields in the U.S. Midwest. This model framework was evaluated for different digital elevation model (DEM) spatial resolutions (10-m, 24-m, 30-m, and 56-m) and topographic exponents ( m = 1.0–1.6 and n = 1.0–1.3) using soil redistribution rates from 137 Cs measurements. The results showed that the aggregated 24-m DEM, m = 1.4 and n = 1.0 for rill erosion, and m = 1.0 and n = 1.0 for sheet erosion, provided the best fit with the observation data at both sites. Moreover, estimated average SOC redistributions were 1.3 ± 9.8 g C m − 2 yr − 1 in field site 1 and 3.6 ± 14.3 g C m − 2 yr − 1 in field site 2. Spatial distribution patterns showed SOC loss (negative values) in the eroded areas and SOC gain (positive value) in the deposition areas. This study demonstrated the importance of the spatial resolution and the topographic exponents to estimate and map soil redistribution and the SOC dynamics throughout the landscape, helping to identify places where erosion and deposition from water and tillage are occurring at high rates. Additional research is needed to improve the application of the model framework for use in local and regional studies where rainfall erosivity and cover management factors vary. Therefore, using this model framework can help to improve the information about the spatial distribution of soil erosion across agricultural landscapes and to gain a better understanding of SOC dynamics within eroding and previously eroded fields.

Iowa↗

USGS2013

Forest cutting and impacts on carbon in the eastern United States

Forest cutting is a major anthropogenic disturbance that affects forest carbon (C) storage and fluxes. Yet its characteristics and impacts on C cycling are poorly understood over large areas. Using recent annualized forest inventory data, we estimated cutting-related loss of live biomass in the eastern United States was 168 Tg C yr −1 from 2002 to 2010 (with C loss per unit forest area of 1.07 Mg ha −1 yr −1 ), which is equivalent to 70% of the total U.S. forest C sink or 11% of the national annual CO 2 emissions from fossil-fuel combustion over the same period. We further revealed that specific cutting-related C loss varied with cutting intensities, forest types, stand ages, and geographic locations. Our results provide new insights to the characteristics of forest harvesting activities in the eastern United States and highlight the significance of partial cutting to regional and national carbon budgets.

Scientific Reports↗

USGS2013

Land use and carbon dynamics in the southeastern United States from 1992 to 2050

Land use and land cover change (LUCC) plays an important role in determining the spatial distribution, magnitude, and temporal change of terrestrial carbon sources and sinks. However, the impacts of LUCC are not well understood and quantified over large areas. The goal of this study was to quantify the spatial and temporal patterns of carbon dynamics in various terrestrial ecosystems in the southeastern United States from 1992 to 2050 using a process-based modeling system and then to investigate the impacts of LUCC. Spatial LUCC information was reconstructed and projected using the FOREcasting SCEnarios of future land cover (FORE-SCE) model according to information derived from Landsat observations and other sources. Results indicated that urban expansion (from 3.7% in 1992 to 9.2% in 2050) was expected to be the primary driver for other land cover changes in the region, leading to various declines in forest, cropland, and hay/pasture. The region was projected to be a carbon sink of 60.4 gC m −2 yr −1 on average during the study period, primarily due to the legacy impacts of large-scale conversion of cropland to forest that happened since the 1950s. Nevertheless, the regional carbon sequestration rate was expected to decline because of the slowing down of carbon accumulation in aging forests and the decline of forest area.

Environmental Research Letters↗

USGS2013

Reconstructing satellite images to quantify spatially explicit land surface change caused by fires and succession: A demonstration in the Yukon River Basin of interior Alaska

Land surface change caused by fires and succession is confounded by many site-specific factors and requires further study. The objective of this study was to reveal the spatially explicit land surface change by minimizing the confounding factors of weather variability, seasonal offset, topography, land cover, and drainage. In a pilot study of the Yukon River Basin of interior Alaska, we retrieved Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) from a postfire Landsat image acquired on August 5th, 2004. With a Landsat reference image acquired on June 26th, 1986, we reconstructed NDVI, albedo, and LST of 1987–2004 fire scars for August 5th, 2004, assuming that these fires had not occurred. The difference between actual postfire and assuming-no-fire scenarios depicted the fires and succession impact. Our results demonstrated the following: (1) NDVI showed an immediate decrease after burning but gradually recovered to prefire levels in the following years, in which burn severity might play an important role during this process; (2) Albedo showed an immediate decrease after burning but then recovered and became higher than prefire levels; and (3) Most fires caused surface warming, but cooler surfaces did exist; time-since-fire affected the prefire and postfire LST difference but no absolute trend could be found. Our approach provided spatially explicit land surface change rather than average condition, enabling a better understanding of fires and succession impact on ecological consequences at the pixel level.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2013

Modeling spatially explicit fire impact on gross primary production in interior Alaska using satellite images coupled with eddy covariance

In interior Alaska, wildfires change gross primary production (GPP) after the initial disturbance. The impact of fires on GPP is spatially heterogeneous, which is difficult to evaluate by limited point-based comparisons or is insufficient to assess by satellite vegetation index. The direct prefire and postfire comparison is widely used, but the recovery identification may become biased due to interannual climate variability. The objective of this study is to propose a method to quantify the spatially explicit GPP change caused by fires and succession. We collected three Landsat images acquired on 13 July 2004, 5 August 2004, and 6 September 2004 to examine the GPP recovery of burned area from 1987 to 2004. A prefire Landsat image acquired in 1986 was used to reconstruct satellite images assuming that the fires of 1987–2004 had not occurred. We used a light-use efficiency model to estimate the GPP. This model was driven by maximum light-use efficiency (E max ) and fraction of photosynthetically active radiation absorbed by vegetation (F PAR ). We applied this model to two scenarios (i.e., an actual postfire scenario and an assuming-no-fire scenario), where the changes in E max and F PAR were taken into account. The changes in E max were represented by the change in land cover of evergreen needleleaf forest, deciduous broadleaf forest, and shrub/grass mixed, whose E max was determined from three fire chronosequence flux towers as 1.1556, 1.3336, and 0.5098 gC/MJ PAR. The changes in F PAR were inferred from NDVI change between the actual postfire NDVI and the reconstructed NDVI. After GPP quantification for July, August, and September 2004, we calculated the difference between the two scenarios in absolute and percent GPP changes. Our results showed rapid recovery of GPP post-fire with a 24% recovery immediately after burning and 43% one year later. For the fire scars with an age range of 2–17 years, the recovery rate ranged from 54% to 95%. In addition to the averaging, our approach further revealed the spatial heterogeneity of fire impact on GPP, allowing one to examine the spatially explicit GPP change caused by fires.

Remote Sensing of Environment↗

USGS2013

Simulating the water budget of a Prairie Potholes complex from LiDAR and hydrological models in North Dakota, USA

Hydrological processes of the wetland complex in the Prairie Pothole Region (PPR) are difficult to model, partly due to a lack of wetland morphology data. We used Light Detection And Ranging (LiDAR) data sets to derive wetland features; we then modelled rainfall, snowfall, snowmelt, runoff, evaporation, the “fill-and-spill” mechanism, shallow groundwater loss, and the effect of wet and dry conditions. For large wetlands with a volume greater than thousands of cubic metres (e.g. about 3000 m 3 ), the modelled water volume agreed fairly well with observations; however, it did not succeed for small wetlands (e.g. volume less than 450 m 3 ). Despite the failure for small wetlands, the modelled water area of the wetland complex coincided well with interpretation of aerial photographs, showing a linear regression with R 2 of around 0.80 and a mean average error of around 0.55 km 2 . The next step is to improve the water budget modelling for small wetlands.

North Dakota↗

USGS2012

Estimations of evapotranspiration and water balance with uncertainty over the Yukon River Basin

In this study, the revised Remote Sensing-Penman Monteith model (RS-PM) was used to scale up evapotranspiration (ET) over the entire Yukon River Basin (YRB) from three eddy covariance (EC) towers covering major vegetation types. We determined model parameters and uncertainty using a Bayesian-based method in the three EC sites. The 95 % confidence interval for the aggregate ecosystem ET ranged from 233 to 396 mm yr −1 with an average of 319 mm yr −1 . The mean difference between precipitation and evapotranspiration ( W ) was 171 mm yr −1 with a 95 % confidence interval of 94–257 mm yr −1 . The YRB region showed a slight increasing trend in annual precipitation for the 1982–2009 time period, while ET showed a significant increasing trend of 6.6 mm decade −1 . As a whole, annual W showed a drying trend over YRB region.

Alaska↗

USGS2012

The General Ensemble Biogeochemical Modeling System (GEMS) and its applications to agricultural systems in the United States: Chapter 18

The General Ensemble Biogeochemical Modeling System (GEMS) was es in individual models, it uses multiple site-scale biogeochemical models to perform model simulations. Second, it adopts Monte Carlo ensemble simulations of each simulation unit (one site/pixel or group of sites/pixels with similar biophysical conditions) to incorporate uncertainties and variability (as measured by variances and covariance) of input variables into model simulations. In this chapter, we illustrate the applications of GEMS at the site and regional scales with an emphasis on incorporating agricultural practices. Challenges in modeling soil carbon dynamics and greenhouse emissions are also discussed.

Book chapter↗

USGS2011

Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site‐level synthesis

[1] Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program's site‐level intercomparison. This study expands upon previous single‐site and single‐model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20–120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2–20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate‐scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model‐by‐band effect but also a nonsignificant model‐by‐site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.

Journal of Geophysical Research: Biogeosciences↗

USGS2011

Simulating the impacts of disturbances on forest carbon cycling in North America: Processes, data, models, and challenges

Forest disturbances greatly alter the carbon cycle at various spatial and temporal scales. It is critical to understand disturbance regimes and their impacts to better quantify regional and global carbon dynamics. This review of the status and major challenges in representing the impacts of disturbances in modeling the carbon dynamics across North America revealed some major advances and challenges. First, significant advances have been made in representation, scaling, and characterization of disturbances that should be included in regional modeling efforts. Second, there is a need to develop effective and comprehensive process‐based procedures and algorithms to quantify the immediate and long‐term impacts of disturbances on ecosystem succession, soils, microclimate, and cycles of carbon, water, and nutrients. Third, our capability to simulate the occurrences and severity of disturbances is very limited. Fourth, scaling issues have rarely been addressed in continental scale model applications. It is not fully understood which finer scale processes and properties need to be scaled to coarser spatial and temporal scales. Fifth, there are inadequate databases on disturbances at the continental scale to support the quantification of their effects on the carbon balance in North America. Finally, procedures are needed to quantify the uncertainty of model inputs, model parameters, and model structures, and thus to estimate their impacts on overall model uncertainty. Working together, the scientific community interested in disturbance and its impacts can identify the most uncertain issues surrounding the role of disturbance in the North American carbon budget and develop working hypotheses to reduce the uncertainty

Journal of Geophysical Research: Biogeosciences↗

USGS2011

Are soils of Iowa USA currently a carbon sink or source? Simulated changes in SOC stock from 1972 to 2007

Upscaling the spatial and temporal changes in carbon (C) stocks and fluxes from sites to regions is a critical and challenging step toward improving our understanding of the dynamics of C sources and sinks over large areas. This study simulated soil organic C (SOC) dynamics within 0–100 cm depth of soils across the state of Iowa in the USA from 1972 to 2007 using the General Ensemble biogeochemical Modeling System (GEMS). The model outputs with variation coefficient were analyzed and assembled from simulation unit to the state scale based upon major land use types at annual step. Results from this study indicate that soils (within a depth of 0–100 cm) in Iowa had been a SOC source at a rate of 190 ± 380 kg C ha −1 yr −1 . This was likely caused by the installation of a massive drainage system which led to the release of SOC from deep soil layers previously protected under poor drainage conditions. The annual crop rotation was another major force driving SOC variation and resulted in spatial variability of annual budgets in all croplands. Annual rate of change of SOC stocks in all land types depended significantly on the baseline SOC levels; soils with higher SOC levels tended to be C sources, and those with lower levels tended to be C sinks. Management practices (e.g., conservation tillage and residue management practices) slowed down the C emissions from Iowa soils, but could not reverse the general trend of net SOC loss in view of the entire state due mainly to a high level of baseline SOC stocks.

Agriculture, Ecosystems and Environment↗

USGS2011

Estimating aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus using data from the Forest Inventory and Analysis program, Landsat, and LANDFIRE

The concentrations of CO 2 and other greenhouse gases in the atmosphere have been increasing and greatly affecting global climate and socio-economic systems. Actively growing forests are generally considered to be a major carbon sink, but forest wildfires lead to large releases of biomass carbon into the atmosphere. Aboveground forest biomass carbon (AFBC), an important ecological indicator, and fire-induced carbon emissions at regional scales are highly relevant to forest sustainable management and climate change. It is challenging to accurately estimate the spatial distribution of AFBC across large areas because of the spatial heterogeneity of forest cover types and canopy structure. In this study, Forest Inventory and Analysis (FIA) data, Landsat, and Landscape Fire and Resource Management Planning Tools Project (LANDFIRE) data were integrated in a regression tree model for estimating AFBC at a 30-m resolution in the Utah High Plateaus. AFBC were calculated from 225 FIA field plots and used as the dependent variable in the model. Of these plots, 10% were held out for model evaluation with stratified random sampling, and the other 90% were used as training data to develop the regression tree model. Independent variable layers included Landsat imagery and the derived spectral indicators, digital elevation model (DEM) data and derivatives, biophysical gradient data, existing vegetation cover type and vegetation structure. The cross-validation correlation coefficient ( r value) was 0.81 for the training model. Independent validation using withheld plot data was similar with r value of 0.82. This validated regression tree model was applied to map AFBC in the Utah High Plateaus and then combined with burn severity information to estimate loss of AFBC in the Longston fire of Zion National Park in 2001. The final dataset represented 24 forest cover types for a 4 million ha forested area. We estimated a total of 353 Tg AFBC with an average of 87 MgC/ha in the Utah High Plateaus. We also estimated that 8054 Mg AFBC were released from 2.24 km 2 burned forest area in the Longston fire. These results demonstrate that an AFBC spatial map and estimated biomass carbon consumption can readily be generated using existing database. The methodology provides a consistent, practical, and inexpensive way for estimating AFBC at 30-m resolution over large areas throughout the United States.

Ecological Indicators↗

USGS2011

Integration of Palmer Drought Severity Index and remote sensing data to simulate wetland water surface from 1910 to 2009 in Cottonwood Lake area, North Dakota

Spatiotemporal variations of wetland water in the Prairie Pothole Region are controlled by many factors; two of them are temperature and precipitation that form the basis of the Palmer Drought Severity Index (PDSI). Taking the 196 km 2 Cottonwood Lake area in North Dakota as our pilot study site, we integrated PDSI, Landsat images, and aerial photography records to simulate monthly water surface. First, we developed a new Wetland Water Area Index (WWAI) from PDSI to predict water surface area. Second, we developed a water allocation model to simulate the spatial distribution of water bodies at a resolution of 30 m. Third, we used an additional procedure to model the small wetlands (less than 0.8 ha) that could not be detected by Landsat. Our results showed that i) WWAI was highly correlated with water area with an R 2 of 0.90, resulting in a simple regression prediction of monthly water area to capture the intra- and inter-annual water change from 1910 to 2009; ii) the spatial distribution of water bodies modeled from our approach agreed well with the water locations visually identified from the aerial photography records; and iii) the R 2 between our modeled water bodies (including both large and small wetlands) and those from aerial photography records could be up to 0.83 with a mean average error of 0.64 km 2 within the study area where the modeled wetland water areas ranged from about 2 to 14 km 2 . These results indicate that our approach holds great potential to simulate major changes in wetland water surface for ecosystem service; however, our products could capture neither the short-term water change caused by intensive rainstorm events nor the wetland change caused by human activities.

North Dakota↗

USGS2011

Integrating estimates of ecosystem services from conservation programs and practices into models for decision makers

Most government agencies involved in land management are seeking consistent approaches to evaluate the effects of specific management actions on ecological processes and concurrent changes on ecosystem services. This is especially true within the context of anthropogenic influences, such as land use and climate change. The Conservation Effects Assessment Project—Wetlands National Component (CEAP–Wetlands) was developed by the U.S. Department of Agriculture (USDA) to evaluate effects of conservation practices on ecosystem services including carbon sequestration for climate stability, groundwater recharge, runoff and flood attenuation, water storage, nutrient and contaminant retention, and wildlife habitat. A primary purpose of CEAP–Wetlands is to provide science‐based information in an adaptive monitoring framework for use by the USDA to facilitate policy and management decisions, and to document effects of conservation programs and practices to the federal Office of Management and Budget. Herein, we propose a modeling framework to allow estimation of conservation practice and program effects on various ecosystem services at different temporal and spatial scales. This modeling approach provides the broad view needed by decision‐makers to avoid unintended negative environmental outcomes, and to communicate to society the positive effects of conservation actions on a broad suite of ecosystem services.

Ecological Applications↗

USGS2010

A model‐data intercomparison of CO2 exchange across North America: Results from the North American Carbon Program site synthesis

Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO 2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO 2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO 2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.

Journal of Geophysical Research: Biogeosciences↗

USGS2010

A method for land surveying sampling optimization strategy

At present, how to select a limited but representative sample dataset from the existing land information database to guide the new round of land survey and assessment sampling is a critical issue for land sampling strategy study. As a case study to determine and analyze the sample capacity and sample spatial location of land survey sampling for the study area, Panyu District in Guangzhou, the paper developed the strategy based on the combination of classical sampling technique and geographical model under a certain confidence level and estimation accuracy requirement, and the performance of the sampling strategy was then evaluated by the Global Geary's C and the Quick-BP neural network model respectively. The test result showed that, compared with traditional c-means clustering sampling method, the accuracy of the sampling prediction based on local Moran index spatial clustering sampling method was increased by 13.57% which abstracted better the land information in the database.

Guangzhou↗

USGS2010

Erosion and vegetation restoration impacts on ecosystem carbon dynamics in South China

To quantify the consequences of erosion and vegetation restoration on ecosystem C dynamics (a key element in understanding the terrestrial C cycle), field measurements were collected since 1959 at two experimental sites set up on highly disturbed barren land in South China. One site had received vegetation restoration (the restored site) while the other received no planting and remained barren (the barren site). The Erosion-Deposition Carbon Model (EDCM) was used to simulate the ecosystem C dynamics at both sites. The on-site observations in 2007 showed that soil organic C (SOC) storage in the top 80-cm soil layer at the barren site was 50.3 ± 3.5 Mg C ha −1 , half that of the restored site. The SOC and surface soil loss by erosion at the restored site from 1959 to 2007 was 3.7 Mg C ha −1 and 2.2 cm, respectively—one-third and one-eighth that of the barren site. The on-site C sequestration in SOC and vegetation at the restored site was 0.67 and 2.5 Mg C ha −1 yr −1 , respectively, from 1959 to 2007, driven largely by tree growth and high atmospheric N deposition in the study area. Simulated findings suggested that higher N deposition resulted in higher on-site SOC storage in the soil profile (with SOC in the top 20-cm layer increasing more significantly), and higher on-site ecosystem C sequestration as long as N saturation was not reached. Lacking human-induced vegetation recovery, the barren site remained as barren land from 1959 to 2007 and the on-site C decrease was 0.28 Mg C ha −1 yr −1 Our study clearly indicated that vegetation restoration and burial by soil erosion provide a large potential C sink in terrestrial ecosystems.

Soil Science Society of America Journal↗