Wenge Ni-Meister

Modeling lidar waveforms in heterogeneous and discrete canopies

This study explores the relationship between laser waveforms and canopy structure parameters and the effects of the spatial arrangement of canopy structure on this relationship through a geometric optical model. Studying laser waveforms for such plant canopies is needed for the advanced retrieval of three-dimensional (3D) canopy structure parameters from the vegetation canopy lidar (VCL) mission. For discontinuous plant canopies, a hybrid geometric optical and radiative transfer (GORT) model describing the effects of 3D canopy structure parameters of discrete canopies on the radiation environment has been modified for use with lidar. The GORT model is first used to describe the canopy lidar waveforms as a function of canopy structure parameters and then validated using scanning lidar imager of canopies by echo recovery (SLICER) data collected in conifer forests in central Canada during the boreal ecosystem-atmosphere study (BOREAS). Model simulations show that the clumping in natural vegetation, such as leaves clustering into tree crowns causes larger gap probability and smaller waveforms for discontinuous plant canopies than for horizontally homogeneous plant canopies. Ignoring the clumping effect can result in significantly lower values for the estimated foliage amount in the profile and in turn lower estimated biomass. Because of clumping, only the gap probability and apparent vertical projected foliage profile can be directly retrieved from the canopy lidar data. The retrieval is sensitive to the ratio of the volume backscattering coefficients of the vegetation and background, and this ratio depends on canopy architecture as well as foliage spectral characteristics.

Recent Advances On Soil Moisture Data Assimilation

This study reviews recent progress on soil moisture data assimilation. Data assimilation is a process of merging observations with a system dynamic model to provide an improved estimate of the states of the environment. The application of data assimilation in hydrology is relatively new, however, rapid progress has been made in the last decade or so with the available remotely sensed soil moisture data. After briefing the history of soil moisture data assimilation, the review focuses on the most common data assimilation methods and recent progress made in soil moisture data assimilation through a case study of the soil moisture initialization activities for NASAs seasonal and interannual climate prediction. The example demonstrates that soil moisture data assimilation has made great progress in the last decade, however is still in its infancy. Good quality remotely sensed soil moisture data with accurate uncertainty information at continental and global scale are needed to ensure the success of the operational use of soil moisture data assimilation technique. Further advancement on the current soil moisture data assimilation methods is necessary to be able to assimilate multisource hydrological remote sensing data into land surface models for the best use of various remote sensing data sources at continental and global scales.

Modeling the hemispherical scanning, below-canopy lidar and vegetation structure characteristics with a geometric-optical and radiative-transfer model

This study applied a hybrid canopy geometric optical and radiative transfer (GORT) model to study the vegetation structure characteristics and lidar signals from a terrestrial below-canopy lidar instrument, Echidna Validation Instrument (EVI), developed by CSIRO Australia. Off-nadir scans from the below-canopy lidar show strong laser energy returns from both leaves and tree trunks. The GORT model was modified to include the effect of both leaves and trunks on below-canopy lidar energy returns by treating the trunks as simple uniform cylinders extending to the middle of each tree crown. GORT was also extended to allow multiple canopy layers by convolution of the canopy gap probability profiles for individual canopy layers. The extended leaf-and-trunk GORT model was evaluated by comparing the modeled and EVI-derived gap probability profiles in a single-layer pine plantation and a two-layer eucalypt forest at the Tumbarumba flux tower site in southeastern New South Wales, Australia. Results show that the new leaf-and-trunk GORT model improves estimates of EVI-derived gap probability profiles. This study demonstrates the potential use of terrestrial upward-scanning hemispherical lidar to retrieve forest canopy structural information. A future goal is to link these terrestrial hemispherical lidar measurements to downward-looking airborne lidar, such as the Laser Vegetation Imaging Sensor (LVIS), and spaceborne lidar, such as the Geoscience Laser Altimeter System (GLAS) on ICESat, through a common model to provide large-area mapping of vegetation structural properties and biomass.

Impacts of vegetation and cold season processes on soil moisture and climate relationships over Eurasia

[1] A number of modeling studies have addressed soil moisture persistence and its effects on the atmosphere. Such analyses are particularly valuable for seasonal to interannual prediction. In this study, we perform an observation-based study to further investigate the impacts of vegetation and cold season processes on soil moisture persistence and climate feedbacks. The joint analysis of independent meteorological, soil moisture and land cover measurements, without the use of a model, in the former Soviet Union provides a unique look at soil moisture–climate relationships at seasonal to interannual timescales. Averaged data over the growing season show a strong consistency between soil moisture and precipitation over grassland dominant regions, suggesting that precipitation anomalies are a dominant control of soil moisture at interannual timescales. Investigation of soil moisture persistence at the seasonal timescale shows a strong correlation between soil moisture in spring and the subsequent precipitation in summer over forest dominant regions and between cold season precipitation accumulation in winter and soil moisture in the following spring. Our findings can be explained by the theory proposed by Koster and Suarez (2001) and are consistent with the results from other modeling studies. Although it is hard to obtain the statistical meaningful conclusions because of the short data records, our results show the potential role of vegetation and cold season processes in land-atmosphere interactions. Further modeling studies and analyses using long in situ data records are necessary to fully verify our results.

Remote Sensing of Forest Biomass

Forest biomass reflects sequestration or release of carbon between terrestrial ecosystems and the atmosphere. Measuring the size and complexity of forest biomass over large areas can enable us to better understand the environmental processes, availability of renewable energy, and global carbon cycle. This chapter reviews recent progress in measuring forest biomass from remote sensing. In quantifying forest biomass, forest properties are often characterized from three types of remote sensing data. Passive optical spectral reflectances are sensitive to vegetation structure (leaf area index, crown size and tree density), texture and shadow. Radar data measure dielectric and geometrical properties of forests. Lidar data characterize vegetation vertical structure and height. Because these instruments have their advantages and disadvantages in reflecting forest properties, data fusion techniques can combine data from multiple sensors and related information from associated databases to achieve improved accuracy in biomass estimation. The remote sensing data or derived forest attributes are commonly correlated to forest biomass using empirical regression models, non-parametric methods, and physically-based allometric models. Although forest biomass is widely estimated at various scales from remote sensing data, models tend to underestimate large biomass densities and overestimate small ones because of saturation issues. Finally, the assessment and validation of forest biomass obtained from remote sensing is critical because current biomass estimates at large area are of large uncertainties.

3D vegetation structure extraction from lidar remote sensing

Large footprint (15m-25m diameter) lidar records the full lidar energy return (lidar waveform), when laser energy penetrates into vegetation canopy. The full lidar waveforms are directly linked with the three-dimensional characterization of vegetation structure. Recently studies on vegetation structure parameter retrievals from large foot-print lidar found direct relationships between vegetation structure parameters such as tree height, stem diameter, above ground biomass and full lidar waveforms. But these studies are mainly limited to empirical studies and these relationships vary for different sites. To better understand the link between large foot print lidar waveforms and vegetation structure parameters, we applied a vegetation Geometric-Optical and Radiative Transfer (GORT) model to simulate vegetation lidar waveforms with 3-D vegetation structure parameters as inputs. We evaluated the performance of the GORT model in conifer forests using the data collected by Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) and found that GORT simulates large-footprint vegetation lidar waveforms well. To better retrieve 3-D vegetation structure parameters, we investigate the sensitivity of waveforms to GORT input parameters. Our analysis shows that lidar waveforms is most sensitive to the tree density, then to the foliage density and the least to the tree size. A stochastic inversion method will be implemented for inversion.

Recirculation over complex terrain

This study generated eddy covariance data to investigate atmospheric dynamics leeward of a small, forested hillside in upstate New York. The causes and effects of recirculation eddies were examined to support the larger goal of improving measurement of the exchange of energy, moisture and trace gases between the terrestrial biosphere and the atmosphere over complex terrain. Sensors operated at five different altitudes on two separate towers – one at the top of the hill and one down the slope to the east – for approximately eight weeks in the spring of 2013. During the experiment, the vertical potential temperature gradient was found to be the primary factor for determining whether winds interacting with the terrain features caused a recirculating eddy leeward of the hill. The study found evidence that the recirculation influenced carbon dioxide flux and caused the air column to be vertically well-mixed.

Fusion of LiDAR and radar for vegetation structure and biomass retrieval

The objective of this study is to develop a fusion scheme to fuse LiDAR and radar data to retrieve vegetation structure and biomass using Bayesian inversion. The common Geometric Optical and Radiative Transfer model developed for discontinuous plant canopy are used to simulate LiDAR and radar backscattering. Preliminary inversion results show tha tree density can be retrieved with most accuracy and then to crown size and the least to foliage area volume density. Ongoing work is to apply this fusion scheme to real LiDAR and radar data collected in New England region.