B31H-01 INVITED
Global Vegetation Structure from NASA's DESDynI Mission: An Overview
How are the Earth's carbon cycle and ecosystems changing, and what are the consequences for the Earth's
carbon budget, ecosystem sustainability, and biodiversity? This is a central science question behind NASA's
DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice) mission. The National Research Council
Committee on Earth Science and Applications from Space recommended DESDynI in 2007 to measure
changes in land, ice and vegetation structure. DESDynI consists of an L-band polarimetric/interferometric Sar
(InSAR) and a multi-beam lidar, together to be used for addressing critical environmental issues related to
natural hazards, ecosystem structure, and climate change. In this presentation we provide an overview of the
DESDynI mission concept from the driving perspective of ecosystem science. We first present the mission
science objectives which are focused around the use of 3-dimensional vegetation structure to estimate
carbon stocks, quantify changes in terrestrial carbon sources and sinks, and characterize habitat structure
for assessments of ecosystem function and biodiversity. We next outline the major science and measurement
requirements for the DESDynI mission, and their translation into possible mission scenarios. Lastly, we
discuss potential algorithms for the fusion of radar and lidar data to derive global vegetation structure and
look ahead to the data products and their application expected from DESDynI
http://desdyni.jpl.nasa.gov
B31H-02
Modeling lidar waveforms over sloped terrain for retrieving canopy structural variables
Lidar waveforms measured by GLAS onboard ICESAT provide a valuable data source of global coverage for mapping canopy structures of forest ecosystems. A waveform is recorded as a collective response of the incident laser to the complex of forest canopy and terrain within the footprint. Non-flat terrain often complicates the analysis of waveform data, and the ignorance of terrain effects greatly degrades the accuracy in estimating canopy structure variables. This study aims to first develop a simple model to simulate lidar waveforms from forest canopies over sloped terrain, and then investigate the usefulness of the model in several schemes for estimating canopy structural variables from waveform data. Specifically, the model is formulated based on the radiative transfer theory with simple parameterization of canopies; the retrieval schemes will be either physically- or statistically- based. The model will be tested using ground-truth data or reference data derived from the airborne laser scanner at several sites across USA, and the retrieval methods will be evaluated using both synthesized data and ground-truth data. Results from this study are expected to help establish practical procedures for improved analysis of waveform data in vegetation studies.
B31H-03
Synergistic Use of Space-borne LiDAR and Optical Imagery for Assessing Fire Disturbance across Alaska
Fire disturbance at high latitudes modifies a broad range of ecosystem properties and processes, and these
changes extend for decades beyond the disturbance event. It is therefore important to monitor the response
of vegetation to fire disturbance, and this can be facilitated by lidar data, which capture information on
vegetation structure. We used Geoscience Laser Altimetry System (GLAS) data to derive canopy structure
information for a wide range of burned areas across Alaska. The GLAS height metrics were augmented with
MODIS reflectance data, which were used to stratify vegetation cover into predominantly deciduous versus
evergreen vegetation types. We also made use Landsat burn severity maps to further stratify the height
metrics. Results indicate that canopy height decreases following fire, as expected, but height is not a good
overall indicator of fire disturbance in the boreal forest ecosystems of Alaska (and probably North America)
because many areas within the burn perimeters were either unburned or burned at different severity levels,
typically leaving many standing dead snags even in severely burned areas. Moreover, because vegetation
recovery following fire is differentially affected by burn severity, greater height growth was documented in
more severely burned areas due to a greater proportion of deciduous vegetation cover. When these factors
were considered, GLAS height metrics were useful for substantially augmenting the information that could be
derived over burned areas, thereby facilitating monitoring and mapping efforts following fire disturbance.
http://www.whrc.org/borealnamerica
B31H-04
Linking Models and Data on Vegetation Structure
Forested ecosystems consist of a dynamic mosaic of patches on the landscape at different stages of recovery from disturbances. Recent studies have addressed this heterogeneity by combining remotely sensed measurements of vegetation structure, and advanced ecological models that track the dynamics of vegetation structure, to produce accurate estimates of both carbon stocks and fluxes at a set of important study sites. Now future satellite missions such as DESDYNI hold the potential to provide key data on vegetation structure needed to reduce uncertainties in terrestrial carbon dynamics globally. Here, we developed and analyzed a set of models to quantify the effects of limited sampling and/or coarse resolution averaging of structure measurements on model predictions. Generally, both limited sampling and coarse resolution averaging caused model initialization error, and led to subsequent prediction uncertainty and error. In cases with representative sampling, sufficient resolution, and linear dynamics, errors in initialization tended to compensate at larger scales. However, with inadequate sampling, overly coarse resolution data, and non-linear dynamics, errors in initialization led to bias. This study provides a generalized framework for assessing the tradeoffs between the quantity and quality of data on vegetation structure, and the science from models which depend on it.
B31H-05
Simulation of Large Footprint Lidar Waveforms from Forests: Analysis of the Sensitivity of Height Estimates to Footprint Characteristics
A spaceborne lidar mission would serve multiple scientific purposes including remote sensing of ecosystem structure and carbon storage, terrestrial and sea ice topography and monitoring of ice sheets. Some spaceborne lidar mission designs include the possibility that a lidar sensor would share a platform with another sensor. To reconcile multiple mission goals and sensor requirements, detailed knowledge of the sensitivity of sensor performance to aspects of mission design is required. Two important aspects of sensor design are footprint size and off-nadir pointing angle. This research uses radiative transfer and waveform synthesis models to investigate the sensitivity of forest height estimates to footprint size and off-nadir pointing and their interaction, over a range of forest canopy properties. An individual-based forest model was used to simulate stands of mixed conifer forest in the Tahoe National Forest (Northern California, USA) and stands of deciduous forests in the Bartlett Experimental Forest (New Hampshire, USA); waveforms were simulated from the forest model's output. A waveform synthesis method was used to create waveforms using airborne lidar data collected at these sites and a site in Dayekou Experimental Forest (Gansu, China). Data in the Tahoe and Bartlett study areas were collected conventionally; at the Dayekou test site, airborne lidar data were collected from five overlapping flight lines with different observation angles. Off-nadir angles varied from 0 to 16 degrees with a 25 m diameter footprint size. Preliminary results show that as the off-nadir angle increases, the intensity of the waveform ground return decreases and the vegetation return intensity increases. Over flat terrain, good linear relationships between waveform shape indices and maximum and mean tree height were found with different off-nadir angles. As terrain slope increases, our ability to retrieve canopy height decreases, and each off-nadir angle must be considered separately. Keywords: waveform lidar, simulation, synthesis, off-nadir pointing, forest height
B31H-06
Evaluating large scale forest disturbance resulting from Hurricane Katrina using the Geoscience Laser Altimeter System (GLAS) LiDAR
Scientists are actively trying to understand the role of forest disturbance and recovery in the global carbon cycle and budget using a variety of methods. Hurricane Katrina has created an opportunity for scientist to further understand hurricanes impacts on forest systems and the carbon cycle. Recent estimates of forest damage resulting from hurricane Katrina have relied primarily on optical remote sensing. GLAS lidar aboard the NASA satellite ICESat have been used to measure forest structure in diverse landscapes over large areas and to assess forest regrowth after large magnitude disturbances. This study uses GLAS, in combination with other data, to improve the detection and modeling of both the impacts and recovery of ecosystems from hurricanes by providing more direct measurements of changes in structure. Three GLAS campaigns were chosen representing fall, winter and spring for the year preceding and following Katrina. GLAS waveforms were compared to wind speed, forest cover, and damage maps to analyze sampling, and structure patterns. Preliminary results show an average decrease in mean canopy height of ~4m in forests experiencing category two winds, a ~2meter decrease in areas experiencing category one winds, and less than a meter change in areas hit by tropical storm winds. Changes in structure were converted into carbon estimates using the Ecosystem Demography (ED) model and compared to prior independent estimates based on field and optical remote sensing. Results are synthesized to help inform the needed characteristics (e.g. accuracy, sampling) for future data on vegetation structure from space.
B31H-07
Forest Biomass retrieval strategies from Lidar and Radar modeling
Estimates of regional and global forest biomass and forest structure are essential for understanding and monitoring ecosystem responses to human activities and climate change. Lidars with capabilities of recording the time-varying return signals provide vegetation height, ground surface height, and vertical distribution of vegetated surfaces intercepted by laser pulses. Large footprint lidar has been shown to be an effective technique for measuring forest canopy height, and biomass from space. Essentially, radar responds to the amount of water in a forest canopy, as well as its spatial structure. Data from these sensors contain information relevant to different aspects of the biophysical properties of the vegetation canopy including above ground biomass. The planned NASA new mission DESDynI will provide global systematic lidar sampling data and complete global coverage of L-band high resolution SAR and InSAR data for vegetation 3D structure mapping. By combining lidar and high-resolution SAR data, our quantitative knowledge of global carbon dynamics and ecosystem structure and function can be improved. This requires some new data processing and fusion technologies. What is the proper lidar sampling design and how to expand the vegetation spatial structural parameters estimated at lidar footprints to global spatial coverage in high resolution need to be resolved. Current configuration of DESDynI may also require lidar observations with variable looking angles, which creates a new challenge in lidar data processing. Models designed to simulate lidar and radar response from a variety of forest canopies can help answer these questions. In this paper we present an overview of our spatially explicit lidar and radar models and their use for examining the questions above. Specifically we will discuss sensitivities of large-footprint lidar and L-band polarimetric and interferometric radar to forest
B31H-08 INVITED
Vegetation Structure and Radar Remote Sensing in Models Predicting Global Change
The inclusion of vegetation structure in Dynamic Global Vegetation Models (DGVM's) is an important next step in improving the ability of these models to incorporate climate and biogeochemical change. This is particularly the case in forest ecosystems where forests of equivalent biomass or leaf area values can have very different dynamics depending upon their structure. In this paper, we initially review model- and observation-based structural influences on forest biomass dynamics. Some of these influential structure aspects of forests can be measured or indexed by using microwave remote sensing. Examples of these measures and methods for their inclusion in ecological models will be provided and discussed.