Geology topics

Shengli Huang

Publications and source records attributed to Shengli Huang.

11 recordsLinked to original sources

USGS2018

Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests

Timely and accurate knowledge of species-level biomass is essential for forest managers to sustain forest resources and respond to various forest disturbance regimes. In this study, maps of species-level biomass in Chinese boreal forests were generated by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) images with forest inventory data using k nearest neighbor ( k NN) methods and evaluated at different scales. The performance of 630 k NN models based on different distance metrics, k values, and temporal MODIS predictor variables were compared. Random Forest (RF) showed the best performance among the six distance metrics: RF, Euclidean distance, Mahalanobis distance, most similar neighbor in canonical correlation space, most similar neighbor computed using projection pursuit, and gradient nearest neighbor. No appreciable improvement was observed using multi-month MODIS data compared with using single-month MODIS data. At the pixel scale, species-level biomass for larch and white birch had relatively good accuracy (root mean square deviation < 62.1%), while the other species had poorer accuracy. The accuracy of most species except for willow and spruce was improved up to the ecoregion scale. The maps of species-level biomass captured the effects of disturbances including fire and harvest and can provide useful information for broad-scale forest monitoring over time.

Canadian Journal of Forest Research↗

USGS2016

Annual accumulation over the Greenland ice sheet interpolated from historical and newly compiled observation data

The estimation of ice/snow accumulation is of great significance in quantifying the mass balance of ice sheets and variation in water resources. Improving the accuracy and reducing uncertainty has been a challenge for the estimation of annual accumulation over the Greenland ice sheet. In this study, we kriged and analyzed the spatial pattern of accumulation based on an observation data series including 315 points used in a recent research, plus 101 ice cores and snow pits and newly compiled 23 coastal weather station data. The estimated annual accumulation over the Greenland ice sheet is 31.2 g cm −2 yr −1 , with a standard error of 0.9 g cm −2 yr −1 . The main differences between the improved map developed in this study and the recently published accumulation maps are in the coastal areas, especially southeast and southwest regions. The analysis of accumulations versus elevation reveals the distribution patterns of accumulation over the Greenland ice sheet.

Geografiska Annaler, Series A: Physical Geography↗

USGS2016

Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA

High-resolution site index (SI) and mean annual increment (MAI) maps are desired for local forest management. We integrated field inventory, Landsat, and ecological variables to produce 30 m SI and MAI maps for the Tahoe National Forest (TNF) where different tree species coexist. We converted species-specific SI using adjustment factors. Then, the SI map was produced by ( i ) intensifying plots to expand the training sets to more climatic, topographic, soil, and forest reflective classes, ( ii ) using results from a stepwise regression to enable a weighted imputation that minimized the effects of outlier plots within classes, and ( iii ) local interpolation and strata median filling to assign values to pixels without direct imputations. The SI (reference age is 50 years) map had an R 2 of 0.7637, a root-mean-square error (RMSE) of 3.60, and a mean absolute error (MAE) of 3.07 m. The MAI map was similarly produced with an R 2 of 0.6882, an RMSE of 1.73, and a MAE of 1.20 m 3 ·ha −1 ·year −1 . Spatial patterns and trends of SI and MAI were analyzed to be related to elevation, aspect, slope, soil productivity, and forest type. The 30 m SI and MAI maps can be used to support decisions on fire, plantation, biodiversity, and carbon.

Canadian Journal of Forest Research↗

USGS2015

Projecting the spatiotemporal carbon dynamics of the Greater Yellowstone Ecosystem from 2006 to 2050

Background Climate change and the concurrent change in wildfire events and land use comprehensively affect carbon dynamics in both spatial and temporal dimensions. The purpose of this study was to project the spatial and temporal aspects of carbon storage in the Greater Yellowstone Ecosystem (GYE) under these changes from 2006 to 2050. We selected three emission scenarios and produced simulations with the CENTURY model using three General Circulation Models (GCMs) for each scenario. We also incorporated projected land use change and fire occurrence into the carbon accounting. Results The three GCMs showed increases in maximum and minimum temperature, but precipitation projections varied among GCMs. Total ecosystem carbon increased steadily from 7,942 gC/m 2 in 2006 to 10,234 gC/m 2 in 2050 with an annual rate increase of 53 gC/m 2 /year. About 56.6% and 27% of the increasing rate was attributed to total live carbon and total soil carbon, respectively. Net Primary Production (NPP) increased slightly from 260 gC/m 2 /year in 2006 to 310 gC/m 2 /year in 2050 with an annual rate increase of 1.22 gC/m 2 /year. Forest clear-cutting and fires resulted in direct carbon removal; however, the rate was low at 2.44 gC/m 2 /year during 2006–2050. The area of clear-cutting and wildfires in the GYE would account for 10.87% of total forested area during 2006–2050, but the predictive simulations demonstrated different spatial distributions in national forests and national parks. Conclusions The GYE is a carbon sink during 2006–2050. The capability of vegetation is almost double that of soil in terms of sequestering extra carbon. Clear-cutting and wildfires in GYE will affect 10.87% of total forested area, but direct carbon removal from clear-cutting and fires is 109.6 gC/m 2 , which accounts for only 1.2% of the mean ecosystem carbon level of 9,056 gC/m 2 , and thus is not significant.

Greater Yellowstone Ecosystem↗

USGS2015

Spatial variations in immediate greenhouse gases and aerosol emissions and resulting radiative forcing from wildfires in interior Alaska

Boreal fires can cool the climate; however, this conclusion came from individual fires and may not represent the whole story. We hypothesize that the climatic impact of boreal fires depends on local landscape heterogeneity such as burn severity, prefire vegetation type, and soil properties. To test this hypothesis, spatially explicit emission of greenhouse gases (GHGs) and aerosols and their resulting radiative forcing are required as an important and necessary component towards a full assessment. In this study, we integrated remote sensing (Landsat and MODIS) and models (carbon consumption model, emission factors model, and radiative forcing model) to calculate the carbon consumption, GHGs and aerosol emissions, and their radiative forcing of 2001–2010 fires at 30 m resolution in the Yukon River Basin of Alaska. Total carbon consumption showed significant spatial variation, with a mean of 2,615 g C m −2 and a standard deviation of 2,589 g C m −2 . The carbon consumption led to different amounts of GHGs and aerosol emissions, ranging from 593.26 Tg (CO 2 ) to 0.16 Tg (N 2 O). When converted to equivalent CO 2 based on global warming potential metric, the maximum 20 years equivalent CO 2 was black carbon (713.77 Tg), and the lowest 20 years equivalent CO 2 was organic carbon (−583.13 Tg). The resulting radiative forcing also showed significant spatial variation: CO 2 , CH 4 , and N 2 O can cause a 20-year mean radiative forcing of 7.41 W m −2 with a standard deviation of 2.87 W m −2 . This emission forcing heterogeneity indicates that different boreal fires have different climatic impacts. When considering the spatial variation of other forcings, such as surface shortwave forcing, we may conclude that some boreal fires, especially boreal deciduous fires, can warm the climate.

Alaska↗

USGS2015

Spatiotemporal variation of surface shortwave forcing from fire-induced albedo change in interior Alaska

The albedo change caused by both fires and subsequent succession is spatially heterogeneous, leading to the need to assess the spatiotemporal variation of surface shortwave forcing (SSF) as a component to quantify the climate impacts of high-latitude fires. We used an image reconstruction approach to compare postfire albedo with the albedo assuming fires had not occurred. Combining the fire-caused albedo change from the 2001-2010 fires in interior Alaska and the monthly surface incoming solar radiation, we examined the spatiotemporal variation of SSF in the early successional stage of around 10 years. Our results showed that while postfire albedo generally increased in fall, winter, and spring, some burned areas could show an albedo decrease during these seasons. In summer, the albedo increased for several years and then declined again. The spring SSF distribution did not show a latitudinal decrease from south to north as previously reported. The results also indicated that although the SSF is usually largely negative in the early successional years, it may not be significant during the first postfire year. The annual 2005-2010 SSF for the 2004 fire scars was -1.30, -4.40, -3.31, -4.00, -3.42, and -2.47 Wm-2. The integrated annual SSF map showed significant spatial variation with a mean of -3.15 Wm-2 and a standard deviation of 3.26 Wm-2, 16% of burned areas having positive SSF. Our results suggest that boreal deciduous fires would be less positive for climate change than boreal evergreen fires. Future research is needed to comprehensively investigate the spatiotemporal radiative and non-radiative forcings to determine the effect of boreal fires on climate.

Alaska↗

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

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

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↗