Showing papers by "Qi Chen published in 2013"

Online Rainfall Atlas of Hawai‘i

Abstract: nteraction among trade winds,terrain, land thermal effects, andthe trade-wind inversion give theHawaiian Islands one of the mostvaried rainfall patterns on Earth.Distinct, persistent patterns of upliftlead to dramatic rainfall gradientsand, together with elevation-relatedtemperature differences, producenearly the full range of climate types.This microcosm of global environ-mental diversity provides a uniquenatural laboratory for world-classresearch on topics such as terres-trial ecosystem carbon dynamics, soilgeochemistry, and the mechanics ofspecies invasion. Knowledge of meanrainfall patterns in Hawai'i is criticallyimportant in support of these researchendeavors as well as for managing andprotecting groundwater and surfacewater resources, controlling and eradicating invasivespecies, protecting and restoring native ecosystems,and planning for the effects of global climate change.The Rainfall Atlas of Hawai'i is a set of digitalmaps of the spatial patterns of 1978-2007 meanmonthly and annual rainfall for the major Hawaiian

Biotic and Human Vulnerability to Projected Changes in Ocean Biogeochemistry over the 21st Century

Abstract: Ongoing greenhouse gas emissions can modify climate processes and induce shifts in ocean temperature, pH, oxygen concentration, and productivity, which in turn could alter biological and social systems. Here, we provide a synoptic global assessment of the simultaneous changes in future ocean biogeochemical variables over marine biota and their broader implications for people. We analyzed modern Earth System Models forced by greenhouse gas concentration pathways until 2100 and showed that the entire world's ocean surface will be simultaneously impacted by varying intensities of ocean warming, acidification, oxygen depletion, or shortfalls in productivity. In contrast, only a small fraction of the world's ocean surface, mostly in polar regions, will experience increased oxygenation and productivity, while almost nowhere will there be ocean cooling or pH elevation. We compiled the global distribution of 32 marine habitats and biodiversity hotspots and found that they would all experience simultaneous exposure to changes in multiple biogeochemical variables. This superposition highlights the high risk for synergistic ecosystem responses, the suite of physiological adaptations needed to cope with future climate change, and the potential for reorganization of global biodiversity patterns. If co-occurring biogeochemical changes influence the delivery of ocean goods and services, then they could also have a considerable effect on human welfare. Approximately 470 to 870 million of the poorest people in the world rely heavily on the ocean for food, jobs, and revenues and live in countries that will be most affected by simultaneous changes in ocean biogeochemistry. These results highlight the high risk of degradation of marine ecosystems and associated human hardship expected in a future following current trends in anthropogenic greenhouse gas emissions.

Sea-level rise vulnerability mapping for adaptation decisions using LiDAR DEMs

Abstract: Global sea-level rise (SLR) is projected to accelerate over the next century, with research indicating that global meansealevelmayrise18-48cmby2050,and50-140cmby2100.Decision-makers,facedwiththeproblemof adapting to SLR, utilize elevation data to identify assets that are vulnerable to inundation. This paper reviews techniquesandchallengesstemmingfromtheuseofLightDetectionandRanging(LiDAR)digitalelevationmod- els (DEMs) in support of SLR decision-making. A significant shortcoming in the methodology is the lack of com- prehensive standards for estimating LiDAR error, which causes inconsistent and sometimes misleading calculations of uncertainty. Workers typically aim to reduce uncertainty by analyzing the difference between LiDAR error and the target SLR chosen for decision-making. The practice of mapping vulnerability to SLR is based on the assumption that LiDAR errors follow a normal distribution with zero bias, which is intermittently violated. Approaches to correcting discrepancies between vertical reference systems for land and tidal datums may incorporate tidal benchmarks and a vertical datum transformation tool provided by the National Ocean Service (VDatum). Mapping a minimum statistically significant SLR increment of 32 cm is difficult to achieve based on current LiDAR and VDatum errors. LiDAR DEMs derived from 'ground' returns are essential, yet LiDAR providers may not remove returns over vegetated areas successfully. LiDAR DEMs integrated into a GIS can be used to identify areas that are vulnerable to direct marine inundation and groundwater inundation (reduced drainage coupled with higher water tables). Spatial analysis can identify potentially vulnerable ecosys- tems as well as developed assets. A standardized mapping uncertainty needs to be developed given that SLR vulnerability mapping requires absolute precision for use as a decision-making tool.

Assessing vulnerability due to sea-level rise in Maui, Hawai ‘ i using LiDAR remote sensing and GIS

Abstract: Sea-level rise (SLR) threatens islands and coastal communities due to vulnerable infrastructure and populations concentrated in low-lying areas. LiDAR (Light Detection and Ranging) data were used to produce high-resolution DEMs (Digital Elevation Model) for Kahului and Lahaina, Maui, to assess the potential impacts of future SLR. Two existing LiDAR datasets from USACE (U.S. Army Corps of Engineers) and NOAA (National Oceanic and Atmospheric Administration) were compared and calibrated using the Kahului Harbor tide station. Using tidal benchmarks is a valuable approach for referencing LiDAR in areas lacking an established vertical datum, such as in Hawai‘i and other Pacific Islands. Exploratory analysis of the USACE LiDAR ground returns (point data classified as ground after the removal of vegetation and buildings) indicated that another round of filtering could reduce commission errors. Two SLR scenarios of 0.75 (best-case) to 1.9 m (worst-case) (Vermeer and Rahmstorf Proc Natl Acad Sci 106:21527–21532, 2009) were considered, and the DEMs were used to identify areas vulnerable to flooding. Our results indicate that if no adaptive strategies are taken, a loss ranging from $18.7 million under the best-case SLR scenario to $296 million under the worst-case SLR scenario for Hydrologically Connected (HC; marine inundation) and Hydrologically Disconnected (HD; drainage problems due to a higher water table) areas combined is possible for Kahului; a loss ranging from $57.5 million under the best-case SLR scenario to $394 million under the worst-case SLR scenario for HC and HD areas combined is possible for Lahaina towards the end of the century. This loss would be attributable to inundation between 0.55 km2 to 2.13 km2 of area for Kahului, and 0.04 km2 to 0.37 km2 of area for Lahaina.

LiDAR Remote Sensing of Vegetation Biomass

Abstract: Accurate estimates of vegetation biomass are critical for calibrating and validating biogeochemical models (Hurtt et al. 2010), quantifying carbon fluxes from land use and land cover change (Shukla et al. 1990; Houghton et al. 2001), and supporting the United Nations Framework Convention on Climate Change (UNFCCC) program to reduce deforestation and forest degradation (Reducing Emissions from Deforestation and Forest Degradation) (Asner 2009). For instance, it was argued that at least half of the uncertainty in the estimates of emissions of carbon from land use change results from uncertain estimates of biomass density (Houghton 2005; Houghton et al. 2009). CONTENTS

Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment: A case study in Kahului, Maui

Abstract: Accurate sea-level rise (SLR) vulnerability assessments are essential in developing effective management strategies for coastal systems at risk. In this study, we evaluate the effect of combining vertical uncertainties in Light Detection and Ranging (LiDAR) elevation data, datum transformation and future SLR estimates on estimating potential land area and land cover loss, and whether including uncertainty in future SLR estimates has implications for adaptation decisions in Kahului, Maui. Monte Carlo simulation is used to propagate probability distributions through our inundation model, and the output probability surfaces are generalized as areas of high and low probability of inundation. Our results show that considering uncertainty in just LiDAR and transformation overestimates vulnerable land area by about 3 % for the high probability threshold, resulting in conservative adaptation decisions, and underestimates vulnerable land area by about 14 % for the low probability threshold, resulting in less reliable adaptation decisions for Kahului. Not considering uncertainty in future SLR estimates in addition to LiDAR and transformation has variable effect on SLR adaptation decisions depending on the land cover category and how the high and low probability thresholds are defined. Monte Carlo simulation is a valuable approach to SLR vulnerability assessments because errors are not required to follow a Gaussian distribution.

Synergistic Use of Satellite Laser Altimetry and Shuttle Radar Topography Mission DEM for Estimating Ground Elevation Over Mountainous Vegetated Areas

Abstract: Topography is fundamental to numerous environmental studies for understanding Earth surface processes. With a near global coverage, satellite lidar Geoscience Laser Altimeter System (GLAS) onboard Ice, Cloud, and land Elevation Satellite (ICESat) provides valuable terrain elevation information via Gaussian decomposition of its waveforms. It is commonly assumed that the lowest decomposed Gaussian peak corresponds to terrain surface. Although this assumption is valid over flat areas with sparse canopy, it might be problematic over sloped areas with surface objects. This letter proposes a new algorithm to estimate ground elevation over mountainous vegetated areas using GLAS data in conjunction with Shuttle Radar Topographic Mission Digital Elevation Models (SRTM DEMs). It was found that incorporating SRTM DEM can reduce the bias of the mean ground elevation estimates by up to 1-3 m. This letter also suggests that more research is needed for improving ground elevation estimates over mountainous areas with relatively open canopy.

Above ground biomass estimation from lidar and hyperspectral airbone data in West African moist forests.

Abstract: The development of sound methods for the estimation of forest parameters such as Above Ground Biomass (AGB) and the need of data for different world regions and ecosystems, are widely recognized issues due to their relevance for both carbon cycle modeling and conservation and policy initiatives, such as the UN REDD+ program (Gibbs et al., 2007). The moist forests of the Upper Guinean Belt are poorly studied ecosystems (Vaglio Laurin et al. 2013) but their role is important due to the drier condition expected along the West African coasts according to future climate change scenarios (Gonzales, 2001). Remote sensing has proven to be an effective tool for AGB retrieval when coupled with field data. Lidar, with its ability to penetrate the canopy provides 3D information and best results. Nevertheless very limited research has been conducted in Africa tropical forests with lidar and none to our knowledge in West Africa. Hyperspectral sensors also offer promising data, being able to evidence very fine radiometric differences in vegetation reflectance. Their usefulness in estimating forest parameters is still under evaluation with contrasting findings (Andersen et al. 2008, Latifi et al. 2012), and additional studies are especially relevant in view of forthcoming satellite hyperspectral missions. In the framework of the EU ERC Africa GHG grant #247349, an airborne campaign collecting lidar and hyperspectral data has been conducted in March 2012 over forests reserves in Sierra Leone and Ghana, characterized by different logging histories and rainfall patterns, and including Gola Rainforest National Park, Ankasa National Park, Bia and Boin Forest Reserves. An Optech Gemini sensor collected the lidar dataset, while an AISA Eagle sensor collected hyperspectral data over 244 VIS-NIR bands. The lidar dataset, with a point density >10 ppm was processed using the TIFFS software (Toolbox for LiDAR Data Filtering and Forest Studies)(Chen 2007). The hyperspectral dataset, geo-referenced with lidar DEM, was processed to remove noise and for feature extraction with Minimum Noise Fraction and/or Principal Component Analysis. Orthophotos data were also gathered. For corresponding areas of ground truth, lidar metrics and hyperspectral pixel values were calculated and extracted. The ground truth was collected in forest plots during different field campaigns (Lindsell and Klop 2013; CMCC field unpublished data) conducted between 2007 and 2012, and provided information on forest structure and species composition. This presentation illustrates the first results from this massive data collection in West Africa tropical forests. Preliminary findings indicate that estimating biomass with lidar in these areas is a more difficult task with respect to other tropical forests: in Sierra Leone the best results (R2 = 0.65) was obtained with a with power model based on lidar mean plot height. This is possibly due to forest complexity, lack of specific allometric relationships, and field plots geo-location inaccuracies. The preliminary analysis of hyperspectral data and its fusion with lidar is challenging, with different results obtained according to the considered area. Interesting spectral profiles showing green-up of specific trees crowns in the dry season were highlighted with hyperspectral data analysis.