College of Behavioral & Social Sciences

Permanent URI for this communityhttp://hdl.handle.net/1903/8

The collections in this community comprise faculty research works, as well as graduate theses and dissertations..

Browse

Author: search.filters.author.Tang, Hao

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar
    (MDPI, 2016-09-12) Tang, Hao; Swatantran, Anu; Barrett, Terence; DeCola, Phil; Dubayah, Ralph
    Airborne single-photon lidar (SPL) is a new technology that holds considerable potential for forest structure and carbon monitoring at large spatial scales because it acquires 3D measurements of vegetation faster and more efficiently than conventional lidar instruments. However, SPL instruments use green wavelength (532 nm) lasers, which are sensitive to background solar noise, and therefore SPL point clouds require more elaborate noise filtering than other lidar instruments to determine canopy heights, particularly in daytime acquisitions. Histogram-based aggregation is a commonly used approach for removing noise from photon counting lidar data, but it reduces the resolution of the dataset. Here we present an alternate voxel-based spatial filtering method that filters noise points efficiently while largely preserving the spatial integrity of SPL data. We develop and test our algorithms on an experimental SPL dataset acquired over Garrett County in Maryland, USA. We then compare canopy attributes retrieved using our new algorithm with those obtained from the conventional histogram binning approach. Our results show that canopy heights derived using the new algorithm have a strong agreement with field-measured heights (r2 = 0.69, bias = 0.42 m, RMSE = 4.85 m) and discrete return lidar heights (r2 = 0.94, bias = 1.07 m, RMSE = 2.42 m). Results are consistently better than height accuracies from the histogram method (field data: r2 = 0.59, bias = 0.00 m, RMSE = 6.25 m; DRL: r2 = 0.78, bias = −0.06 m and RMSE = 4.88 m). Furthermore, we find that the spatial-filtering method retains fine-scale canopy structure detail and has lower errors over steep slopes. We therefore believe that automated spatial filtering algorithms such as the one presented here can support large-scale, canopy structure mapping from airborne SPL data.
  • Thumbnail Image
    Item
    Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling
    (Wiley, 2023-04-04) Ma, Lei; Hurtt, George; Tang, Hao; Lamb, Rachel; Lister, Andrew; Chini, Louise; Dubayah, Ralph; Armston, John; Campbell, Elliott; Duncanson, Laura; Healey, Sean; O'Neil-Dunne, Jarlath; Ott, Lesley; Poulter, Benjamin; Shen, Quan
    Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.
  • Thumbnail Image
    Item
    Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA
    (Springer Nature, 2015-08-16) Huang, Wenli; Swatantran, Anu; Johnson, Kristofer; Duncanson, Laura; Tang, Hao; O’Neil Dunne, Jarlath; Hurtt, George; Dubayah, Ralph
    Continental-scale aboveground biomass maps are increasingly available, but their estimates vary widely, particularly at high resolution. A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making. To this end, we compare four continental-scale maps with a recent high-resolution lidar-derived biomass map over Maryland, USA. We conduct detailed comparisons at pixel-, county-, and state-level. Spatial patterns of biomass are broadly consistent in all maps, but there are large differences at fine scales (RMSD 48.5–92.7 Mg ha−1). Discrepancies reduce with aggregation and the agreement among products improves at the county level. However, continental scale maps exhibit residual negative biases in mean (33.0–54.6 Mg ha−1) and total biomass (3.5–5.8 Tg) when compared to the high-resolution lidar biomass map. Three of the four continental scale maps reach near-perfect agreement at ~4 km and onward but do not converge with the high-resolution biomass map even at county scale. At the State level, these maps underestimate biomass by 30–80 Tg in forested and 40–50 Tg in non-forested areas. Local discrepancies in continental scale biomass maps are caused by factors including data inputs, modeling approaches, forest/non-forest definitions and time lags. There is a net underestimation over high biomass forests and non-forested areas that could impact carbon accounting at all levels. Local, high-resolution lidar-derived biomass maps provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale maps produced in carbon monitoring systems.
  • Thumbnail Image
    Item
    Lidar Remote Sensing of Vertical Foliage Profile and Leaf Area Index
    (2015) Tang, Hao; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Leaf Area Index (LAI) and Vertical Foliage Profile (VFP) are among the most important forest structural parameters, and characterization of those parameters in high biomass forests remains a major challenge in passive remote sensing due to signal saturation problem. Recently an active remote sensing technology, light detection and ranging (lidar), has shown a great promise in this task recognizing its accuracy in measuring aboveground biomass and canopy height. This dissertation further expands current application of lidar on ecosystem monitoring, and explores the capacity of deriving LAI and VFP from lidar data in particular. The overall goal of this study is to derive large scale forest LAI and VFP using data from the Geoscience Laser Altimeter System (GLAS) on board of ICESat, and provide a framework of validating such LAI products from plot level to global scale. To achieve this goal, a physically based Geometry Optical and Radiative Transfer (GORT) model was first developed using high quality airborne waveform lidar data over a tropical rainforest in La Selva, Costa Rica. The excellent agreement between lidar data and field destructively sampled data demonstrated the effectiveness of the Lidar-LAI model and suggested large footprint waveform lidar can provide accurate vertical LAI profile estimates that do not saturate even at the highest possible LAI levels. Next, an intercomparative study of ground-based, airborne and spaceborne retrievals of total LAI was conducted over the conifer-dominated forests of Sierra Nevada in California. Good relationships were discovered in their comparisons, following a scaling-up validation strategy where ground-based LAI observations were related to aircraft observations of LAI, which in turn were used to validate GLAS LAI derived from coincident data. Successful implementation of this strategy can pave the way for the future recovery of vertical LAI profiles globally. LAI and VFP products were then derived over both the entire state of California and Contiguous United States as an efficacy demonstration of the method. These products were the first ever attempts to obtain large scale estimates of LAI and VFP from lidar observations. Such forest structural measurement can be used not only to quantify carbon stock and flux of terrestrial ecosystem, but also to provide spatial information of specie abundance in biodiversity. Results from this study can also greatly help broaden scientific applications of future spaceborne lidar missions (e.g. ICESat-2 and GEDI).