Hongqing Wang

Spatial dependence and the relationship of soil organic carbon and soil moisture in the Luquillo Experimental Forest, Puerto Rico

We used geo-spatial statistical techniques to examine the spatial variation and relationship of soil organic carbon (SOC) and soil moisture (SM) in the Luquillo Experimental Forest (LEF), Puerto Rico, in order to test the hypothesis that mountainous terrain introduces spatial autocorrelation and crosscorrelation in ecosystem and soil properties. Soil samples (n = 100) were collected from the LEF in the summer of 1998 and analyzed for SOC, SM, and bulk density (BD). A global positioning system was used to georeference the location of each sampling site. At each site, elevation, slope and aspect were recorded. We calculated the isotropic and anisotropic semivariograms of soil and topographic properties, as well as the cross-variograms between SOC and SM, and between SOC and elevation. Then we used four models (random, linear, spherical and wave/hole) to test the semi-variances of SOC, SM, BD, elevation, slope and aspect for spatial dependence. Our results indicate that all the studied properties except slope angle exhibit spatial dependence within the scale of sampling (200 – 1000 m sampling interval). The spatially structured variance (the variance due to the location of sampling sites) accounted for a large proportion of the sample variance for elevation (99%), BD (90%), SOC (68%), aspect (56%) and SM (44%). The ranges of spatial dependence (the distances within which parameters are spatially dependent) for aspect, SOC, elevation, SM, and BD were 9810 m, 3070 m, 1120 m, 930 m and 430 m, respectively. Cross correlograms indicate that SOC varies closely with elevation and SM depending on the distances between samples. The correlation can shift from positive to negative as the separation distance increases. Larger ranges of spatial dependence of SOC, aspect and elevation indicate that the distribution of SOC in the LEF is determined by a combination of biotic (e.g., litterfall) and abiotic factors (e.g., microclimate and topographic features) related to elevation and aspect. This demonstrates the importance of both elevation and topographic gradients in controlling climate, vegetation distribution and soil properties as well as the associated biogeochemical processes in the LEF.

Image Misregistration Error in Change Measurements

Planimetric positional error limits the accuracy of landscape change measurements based on features interpreted from high spatial resolution imagery (� 1 m), and this limitation depends on the magnitude of the positional error, the spatial heterogeneity of landscapes, and the spatial extent of the change detection window (the change detection resolution). For this reason, accuracy assessments of change measurements from feature-based approaches require careful evaluation of the impacts of positional errors across landscapes differing in spatial heterogeneity at different change detection resolutions. We quantified such impacts by computing the false changes produced by spatially shifting and comparing high-resolution ecological maps derived by feature interpretation and ground interpretation of 1 m resolution Ikonos imagery of rural China and 0.3 m resolution aerial photographs of suburban United States. Change detection error increased significantly as positional errors increased, as landscape heterogeneity increased, and as the change detection resolution became finer. Regression-derived relationships between change estimation error and positional error, change detection resolution, and landscape heterogeneity allow calculation of the minimum change detection window size at which it is possible to obtain change measurements of a specified accuracy given any set of featurebased ecological maps and their positional error. Prediction of this “optimal change detection resolution” is critical in producing reliable high-resolution change measurements from feature-based ecological maps.

Forecasting the Effects of Coastal Protection and Restoration Projects on Wetland Morphology in Coastal Louisiana under Multiple Environmental Uncertainty Scenarios

ABSTRACT Couvillion, B.R.; Steyer, G.D.; Wang, H.; Beck, H.J., and Rybczyk, J.M., 2013. Forecasting the effects of coastal protection and restoration projects on wetland morphology in coastal Louisiana under multiple environmental uncertainty scenarios. Few landscape scale models have assessed the effects of coastal protection and restoration projects on wetland morphology while taking into account important uncertainties in environmental factors such as sea-level rise (SLR) and subsidence. In support of Louisianas 2012 Coastal Master Plan, we developed a spatially explicit wetland morphology model and coupled it with other predictive models. The model is capable of predicting effects of protection and restoration projects on wetland area, landscape configuration, surface elevation, and soil organic carbon (SOC) storage under multiple environmental uncertainty scenarios. These uncertainty scenarios included variability in parameters such as eustatic SLR (ESLR), subsidence rate, and Mississippi River discharge. Models were run for a 2010–2060 simulation period. Model results suggest that under a “future-without-action” condition (FWOA), coastal Louisiana is at risk of losing between 2118 and 4677 km2 of land over the next 50 years, but with protection and restoration projects proposed in the Master Plan, between 40% and 75% of that loss could be mitigated. Moreover, model results indicate that under a FWOA condition, SOC storage (to a depth of 1 m) could decrease by between 108 and 250 million metric tons, a loss of 12% to 30% of the total coastwide SOC, but with the Master Plan implemented, between 35% and 74% of the SOC loss could be offset. Long-term maintenance of project effects was best attained in areas of low SLR and subsidence, with a sediment source to support marsh accretion. Our findings suggest that despite the efficacy of restoration projects in mitigating losses in certain areas, net loss of wetlands in coastal Louisiana is likely to continue. Model results suggest certain areas may eventually be lost regardless of proposed restoration investment, and, as such, other techniques and strategies of adaptation may have to be utilized in these areas.

Land Use and Soil Organic Carbon in China’s Village Landscapes

Abstract Village landscapes, which integrate small-scale agriculture with housing, forestry, and a host of other land use practices, cover more than 2 million square kilometers across China. Village lands tend to be managed at very fine spatial scales (≤ 30 m), with managers both adapting their practices to existing variation in soils and terrain (e.g., fertile plains vs. infertile slopes) and also altering soil fertility and even terrain by terracing, irrigation, fertilizing, and other land use practices. Relationships between fine-scale land management patterns and soil organic carbon (SOC) in the top 30 cm of village soils were studied by sampling soils within fine-scale landscape features using a regionally weighted landscape sampling design across five environmentally distinct sites in China. SOC stocks across Chinas village regions (5 Pg C in the top 30 cm of 2 × 106 km2) represent roughly 4% of the total SOC stocks in global croplands. Although macroclimate varied from temperate to tropical in this study, SOC density did not vary significantly with climate, though it was negatively correlated with regional mean elevation. The highest SOC densities within landscapes were found in agricultural lands, especially paddy, the lowest SOC densities were found in nonproductive lands, and forest lands tended toward moderate SOC densities. Due to the high SOC densities of agricultural lands and their predominance in village landscapes, most village SOC was found in agricultural land, except in the tropical hilly region, where forestry accounted for about 45% of the SOC stocks. A surprisingly large portion of village SOC was associated with built structures and with the disturbed lands surrounding these structures, ranging from > 18% in the North China Plain to about 9% in the tropical hilly region. These results confirmed that local land use practices, combined with local and regional variation in terrain, were associated with most of the SOC variation within and across Chinas village landscapes and may be an important cause of regional variation in SOC.

Spatial accuracy of orthorectified IKONOS imagery and historical aerial photographs across five sites in China

High‐resolution (⩽1 m) satellite imagery and archival World War II era (WW2) aerial photographs are currently available to support high‐resolution long‐term change measurements at sites across China. A major limitation to these measurements is the spatial accuracy with which this imagery can be orthorectified and co‐registered. We orthorectified IKONOS 1 m resolution GEO‐format imagery and WW2 aerial photographs across five 100 km2 rural sites in China with terrain ranging from flat to hilly to mountainous. Ground control points (GCPs) were collected uniformly across 100 km2 IKONOS scenes using a differential Global Positioning Systems (GPS) field campaign. WW2 aerial photos were co‐registered to orthorectified IKONOS imagery using bundle block adjustment and rational function models. GCP precision, terrain relief and the number and distribution of GCPs significantly influenced image orthorectification accuracy. Root mean square errors (RMSEs) at GCPs for IKONOS imagery were <2.0 m (0.9–2.0 m) for all sites except the most heterogeneous site (Sichuan Province, 2.6 m), meeting 1:12 000 to 1:4800 US National Map Accuracy Standards and equalling IKONOS Precision and Pro format accuracy standards. RMSEs for WW2 aerial photos ranged from 0.2 to 3.5 m at GCPs and from 4.4 to 6.2 m at independent checkpoints (ICPs), meeting minimum requirements for high‐resolution change detection.

Surface water sulfate dynamics in the northern Florida Everglades.

Sulfate contamination has been identified as a serious environmental issue in the Everglades ecosystem. However, it has received less attention compared to P enrichment. Sulfate enters the Arthur R. Marshall Loxahatchee National Wildlife Refuge (Refuge), a remnant of the historic Everglades, in pumped stormwater discharges with a mean concentration of approximately 50 mg L(-1), and marsh interior concentrations at times fall below a detection limit of 0.1 mg L(-1). In this research, we developed a sulfate mass balance model to examine the response of surface water sulfate in the Refuge to changes in sulfate loading and hydrological processes. Meanwhile, sulfate removal resulting from microbial sulfate reduction in the underlying sediments of the marsh was estimated from the apparent settling coefficients incorporated in the model. The model has been calibrated and validated using long-term monitoring data (1995-2006). Statistical analysis indicated that our model is capable of capturing the spatial and temporal variations in surface water sulfate concentrations across the Refuge. This modeling work emphasizes the fact that sulfate from canal discharge is impacting even the interior portions of the Refuge, supporting work by other researchers. In addition, model simulations suggest a condition of sulfate in excess of requirement for microbial sulfate reduction in the Refuge.

Landscape-Level Estimation of Nitrogen Removal in Coastal Louisiana Wetlands: Potential Sinks under Different Restoration Scenarios

ABSTRACT Rivera-Monroy, V.H.; Branoff, B.; Meselhe, E.; McCorquodale, A.; Dortch, M.; Steyer, G.D.; Visser, J., and Wang, H., 2013. Landscape-level estimation of nitrogen removal in coastal Louisiana wetlands: potential sinks under different restoration scenarios. Coastal eutrophication in the northern Gulf of Mexico (GOM) is the primary anthropogenic contributor to the largest zone of hypoxic bottom waters in North America. Although biologically mediated processes such as denitrification (Dn) are known to act as sinks for inorganic nitrogen, it is unknown what contribution denitrification makes to landscape-scale nitrogen budgets along the coast. As the State of Louisiana plans the implementation of a 2012 Coastal Master Plan (MP) to help restore its wetlands and protect its coast, it is critical to understand what effect potential restoration projects may have in altering nutrient budgets. As part of the MP, a spatial statistical approach was developed to estimate nitrogen removal under varying scenarios of future conditions and coastal restoration project implementation. In every scenario of future conditions under which MP implementation was modeled, more nitrogen () was removed from coastal waters when compared with conditions under which no action is taken. Overall, the MP increased coast-wide average nitrogen removal capacity (NRC) rates by up to 0.55 g N m−2 y−1 compared with the “future without action” (FWOA) scenario, resulting in a conservative estimate of up to 25% removal of the annual + load of the Mississippi-Atchafalaya rivers (956,480 t y−1). These results are spatially correlated, with the lower Mississippi River and Chenier Plain exhibiting the greatest change in NRC. Since the implementation of the MP can maintain, and in some regions increase the NRC, our results show the need to preserve the functionality of wetland habitats and use this ecosystem service (i.e. Dn) to decrease eutrophication of the GOM.

Modeling the effects of Hurricane Hugo on spatial and temporal variation in primary productivity and soil carbon and nitrogen in the Luquillo Experimental Forest, Puerto Rico

Hurricanes account for much of the spatial and temporal variation in forest productivity and soil organic matter pools in many forest ecosystems. In this study, we used an ecosystem level model, TOPOECO, to simulate the effects of Hurricane Hugo (18 September 1989) on spatial and temporal patterns of gross primary productivity (GPP), net primary productivity (NPP), soil organic carbon (SOC) and nitrogen over the entire Luquillo Experimental Forest (LEF), Puerto Rico, a tropical rainforest. Our simulation results indicated that simulated annual GPP increased by an average of 30% five years after Hugo in the Tabonuco forest at low elevations where there was a fast recovery of the canopy, whereas simulated GPP decreased by an average of 20% in the Palm and Dwarf forests at high elevations as a result of the slow recovery of the canopy. Simulated annual NPP in the Palm and Dwarf forests also did not recover to pre-Hugo levels within 5 years. Simulated storages of SOC, CO2 emission from decomposition of SOC and total soil nitrogen increased slightly but N mineralization rate increased significantly in all four vegetation types due to the massive input of plant materials from Hugo at low elevations and the slow decomposition at high elevations.

Estimating area errors for fine‐scale feature‐based ecological mapping

High spatial resolution feature‐based approaches are especially useful for ecological mapping in densely populated landscapes. This paper evaluates errors in estimating ecological map class areas from fine‐scale current (∼2002) and historical (∼1945) feature‐based ecological mapping by a set of trained interpreters across densely populated rural sites in China based on field‐validated interpretation of high spatial resolution (⩽1 m) imagery. Median overall map accuracy, corrected for chance, was greater than 85% for mapping by trained interpreters, with greater accuracy for current versus historical mapping. An error model based on feature perimeter proved as reliable in predicting 90% confidence intervals for map class areas as did models derived from the conventional error matrix. A conservative error model combining these approaches was developed and tested for statistical reliability in predicting confidence intervals for ecological map class areas from fine‐scale feature‐based mapping by a set of trained interpreters across rural China, providing a practical basis for statistically reliable ecological change detection in densely populated landscapes.

Detecting marine intrusion into rivers using EO-1 ALI satellite imagery: Modaomen Waterway, Pearl River Estuary, China

Because of increasing marine intrusion into the Pearl River Estuary (PRE) in China, salinity has become one of the important and necessary hydrological and water quality monitoring parameters. In this research, we examined the relationships between the reflectance from Earth Observing-1 (EO-1) Advanced Land Imager (ALI) satellite imagery and total suspended solids (TSS) based on the synchronous in situ spectra analysis of the river water, in an attempt to detect salinity using remote sensing technique. The study site was the Modaomen Waterway in the PRE, Guangdong Province, China. We found a strong negative linear relationship between in situ reflectance at 549 nm and TSS concentrations (R 2 = 0.91, p < 0.001) when the salinity of the river was less than 1.46‰. It indicates that the TSS near Pinggang and Nanzhen in Modaomen Waterway of PRE tends to be dominated by organic mater carried by the particles and this is one major reason for the inverse relation between reflectance and TSS. Meanwhile, a strong correlation was observed between salinity and TSS (R 2 = 0.70, p < 0.001). The salinity-TSS model accounted for 70% of variation in salinity and allowed the estimation of salinity with a root mean square error (RMSE) of less than 0.036‰ when the TSS concentrations were between 7.4 and 28 mg l−1. Therefore, we were able to develop a new method of detecting surface salinity of the river estuary from the calibrated EO-1 ALI reflectance data. The EO-1 ALI derived surface salinity and TSS concentrations were validated using in situ data that were collected on 18 December 2005, synchronous with EO-1 ALI satellite imagery acquisition. The results showed that the semi-empirical relationships are capable of deducing the TSS concentrations and then salinity from EO-1 ALI imagery in the PRE under low salinity. The methodology of detecting salinity from ALI imagery provides potential to monitor coastal saltwater intrusion and provides the water supply and conservancy authorities with useful spatial information to spatially understand and manage the marine intrusion.