D. Project Description: Acquiring airborne LiDAR data to study hydrologic, geomorphologic, and
geochemical processes at three Critical Zone Observatories (CZOs)
1. Introduction
The critical zone is defined as the external terrestrial layer extending from the top of the atmospheric
boundary layer down through groundwater. The Critical Zone Observatories (CZOs) provide important
platforms for studying the processes occurring in the Critical Zone. The goal of CZOs is to build a
network to advance interdisciplinary studies of Earth surface processes as well as foster collaboration
among scientists and engineers from different disciplines. In 2007 the NSF has funded three
observatories: Boulder Creek, Southern Sierra, and Shale Hills (Figure 1). Research at the CZOs involves
a growing list of Earth scientists doing integrated science and pursuing both disciplinary and
interdisciplinary research (Anderson et al., 2008). At an all-CZO meeting in fall 2008, it was determined
that having airborne LiDAR (Light Detection and Ranging) for the three CZOs would significantly
advance research, both within and across the CZOs.
This under this project we propose to acquire airborne LiDAR data for the study of hydrologic,
geomorphologic, and geochemical processes at three CZOs. LiDAR is an optical remote sensing
technology that measures properties of scattered light to find range and/or other information of a distant
object. The range to an object is calculated by measuring the time delay between transmission of a laser
pulse and detection of the reflected signal. Due to its ability to generate 3-dimensional data with high
spatial resolution and
accuracy, LiDAR
technology is being
increasingly used in
ecology, geography,
geology,
geomorphology,
seismology, remote
sensing and atmospheric
physics (Brandtberg and
Warner, 2003; Gaveau
and Hill, 2003;
Hopkinson et al, 2004;
Figure. 1. Location of three Critical Zone Observatories. SS: Southern Sierra
CZO; BC: Boulder Creek CZO; SH: Shale Hills CZO.
Lefsky et al, 2002).
2. Existing spatial data and scientific justifications of LiDAR data for three CZO sites:
2.1. Limitations of existing spatial data.
We have partial spatial data for three CZO sites; however, none of the existing data meet the research
needs of our CZO teams. There are four main problems with the existing data.
1. Incomplete existing data. LiDAR data are available for the headwaters of Boulder Creek CZO, but not for
two of three focus subcatchments or for Boulder Canyon and its knick zone. For the Southern Sierra
CZO, the LiDAR data only cover part of the study area.
2. Insufficient spatial resolution. For part of the Shale Hills CZO, we have 1.4-m spatial resolution LiDAR
data, however at this resolution, many features cannot be resolved. For example, small headwater
streams that are smaller than 1 meter in width are numerous in the study area (details see below
session 2.2.1). In this proposal, we propose to acquire LiDAR data with ~10 point per square meter,
which will result in 25-cm spatial resolution DEM. For the Southern Sierra CZO site, we have LiDAR
data with >15-m spatial resolution, which is insufficient for many geomorphologic and hydrologic
research questions that we plan to address.
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3. Outdated spatial data. The partial LiDAR data that are available for Southern Sierra and Boulder Creek
CZOs were acquired in 2001 and 2005 respectively. Hydrologic and vegetation products derived from
these LiDAR data will be outdated and problematic in hydrologic and biogeochemical modeling.
4. Insufficient temporal scale. For the Shale Hills and Southern Sierra CZO sites, we propose to acquire two
LiDAR flights per site (leaf on/off, and snow on/off) to estimate the change of vegetation structure and
snow depth. None of current existing data could meet our objective. Boulder would like snow on/off
as well. This was our request for the 2005 acquisition, but only the snow off flight was done.
2.2 Science questions and hypothesis that rely on the proposed LiDAR acquisition
The following science questions are arranged by CZO, however it should be recognized that
although articulated by groups from each CZO, most apply across the network of CZOs and when
addressed will stimulate cross-CZO research. That is, questions common to all three CZO’s are not
duplicated under each heading.
2.2.1. Boulder Creek CZO
Geomorphic models. Recent models of regolith generation from bedrock and downslope transport predict
that an inverse relationship should exist between regolith thickness and hillslope curvature. Curvature
is highly sensitive to noise in topographic data, and cannot be reliably measured at typical DEM scales
of 10-30 meters, but it can and has been measured reliably using bare-ground LiDAR data. Thus, only
with bare-ground LiDAR data can this prediction be tested.
Geophysics. LiDAR data can speed up the data collection routine especially in areas with steep slopes and
in harsh environments. Collecting the start and end point of each line with a good GPS and adding
topographic data by the LiDAR dataset afterwards could be a real advantage especially at Green
Lakes Valley (short survey times due to thunderstorms etc.) .
Snow. Snowmelt runoff provides up to 80% of all water availability in 11 western states for municipal,
industrial, and agricultural uses. Snow deposition is heterogeneous, a function of redistribution by
wind, avalanches, and sloughing. To understand, quantify, and model runoff, it is essential to account
for the spatial variation in snow accumulation. The primary deterrent to advancing our
understanding of the processes that control the spatial distribution of snow properties is the lack of
spatially distributed measurements of these snow properties. LiDAR data from snow on-snow off
time periods will allow us to evaluate: (i the predictive capability of various topographic parameters
that may control the spatial distribution of snow, (ii the change in relative importance of each of these
parameters over time, (iii the importance of nonlinear interactions between these parameters, and (iv
the use of a single point index of total precipitation (such as SNOTEL) to improve models of the spatial
distribution of snow depth.
Hydrology and hydrochemistry. We propose merging ground-based and remotely sensed snow observations
along with LiDAR within a spatially distributed, physically based snowmelt model to estimate the
spatial and temporal distribution of SWE and snowmelt and to explicitly calculate the seasonal water
balance. These estimates are then coupled to the Alpine Hydrochemical Model (AHM) to simulate
discharge and hydrochemical fluxes at the catchment scale. This approach is used to address the
following questions. i) What is the spatial and temporal distribution of snow water equivalent and
snowmelt over different land cover types in the Green Lake 4 catchment? ii) Does explicit
representation of this variability improve simulations of hydrochemical fluxes?
2.2.2. Shale Hills CZO
Geomorphic models. A primary question is: what processes control the downslope transport and erosion of
regolith, and can these processes be functionally related to regolith thickness in a deterministic
fashion? in order to understand the feedbacks between regolith transport, thickness, and weathering,
we need to i) characterize rates of erosion throughout the watershed and ii) relate these to
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distributions of hillslope topography and regolith thickness through a landscape evolution model. The
first task to address this will be to quantify rates of regolith transport and erosion. Central to the
development of a model to predict regolith evolution in the Shale Hills CZO is the quantitative
understanding of the rates and processes of soil transport on hillslopes. To characterize erosion rates
we will exploit a high resolution data set of hillslope topography and soil depth, densifying these
observations where necessary using a combination of hand-auger, soil pits, and ground penetrating
radar. Moreover, understanding how the downslope transport of regolith depends on topographic
characteristics such as hillslope gradient will provide the basis for a more process-based model of
sediment transport. The second task is to develop and test competing models for soil transport. We
propose to develop and test competing models for soil transport (linear creep, depth-dependent creep,
disturbance-driven (tree-throw) transport) against the present-day distribution of soil depth, hillslope
topography and erosion rate. In particular, observations of pit-and-mound topography in the
watershed lead us to hypothesize that disturbance-driven transport (tree-throw -[24]) is a key
component of sediment transport on hillslopes in the Shale Hills CZO. We will incorporate rules that
describe various soil transport processes into a new landscape evolution model and test these against
the distributions of topographic gradients, regolith depth, and lowering rates across the watershed.
Vegetation. A second question is: what is the role of biological processes in the shale weathering engine
and in particular, what roles do rooting and tree-throw play? Our hypothesis is that tree-throw [24]
and the physical and chemical activity of roots strongly influence the evolution of regolith in the SHO
by: i) moving regolith downslope; ii) mixing and chemically affecting bedrock and subsoil; iii)
providing the principal pipes through the regolith mill for flow of water and solutes; and, perhaps, iv)
dictating the microtopography of channelized flow onthe surface. We will deconvolve these effects by
quantifying the role of tree-throw in regolith transport and landscape evolution. We will couple
measurements of root-length density and soil strength as a function of regolith depth to develop a
quantitative model for sediment transport and erosion by tree-throw that will then be incorporated
into a new landscape evolution model. We will link this model to observations of erosion across the
watershed and with historic records of climate (primarily, frequency of strong wind storms) to scale
individual transport events to long-term records of denudation. Moreover, we think it is reasonable to
assume that tree-throw swales impart a roughness to the surface that plays an important role in
focusing overland flow and funneling surface water to the regolith-bedrock interface. We will map
these surface features at the meter scale using a total station and terrestrial LiDAR. We will then
conduct a series of numerical experiments using a new landscape evolution model subject to varying
degrees of roughness based on our surveys. The experiments will test the hypothesis that regions of
simultaneous tree-throw (e.g. during severe storms) determine the location of first-order channels by
providing pathways for flow accumulation and saturation in the near subsurface.
Hydrologic Modeling. A third question is: what are the processes and functional relationships
interconnecting the products and throughput of the regolith mill with topography and local
hydrology, and how have these changed in response to a past climatic perturbation? Here we wish to
define the numerous interactions and feedbacks between the evolving three dimensional ground
surface, the bedrock-regolith interface, regolith thickness, and the hydrologic flow field. Furthermore,
the Shale Hills landscape has experienced repeated climate and vegetation changes from glacial to
interglacial conditions as well as post-colonial land-use changes. We need to understand the extent to
which the present system is a palimpsest. Our approach to obtaining both objectives is to develop a
new landscape evolution model and conduct numerical experiments with it driven by fluctuating
boundary conditions and calibrated against measurements of both short- and long-term rates of
downslope regolith transport and isotopic measurement of long term regolith production rates. The
main task involves coupling sediment transport modules to our hydrologic module. The landscape
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model will be based on PIHM, the Penn State Integrated Hydrologic Model (REF). We will add
functions describing conservation of bedrock and sediment mass, multi-grain size sediment routing,
channel initiation and growth, hillslope sediment flux by three-throw, rilling after fires, gravity slides,
and regolith-generating functions. This model will differ from present models such as SIBERIA [25],
PRECIPTON [26], DRAINAL [27], Detachment-limited model [28], GOLEM [29], CASCADE [30],
CAESAR [31], ZSCAPE [32], CHILD [33], and LAPSUS (LandscApe Process modelling at mUltidimensions and scaleS) [34] by containing full treatments of regolith hydrology and weathering rules,
and will describe the evolution of ground and bedrock surfaces.
2.2.3. Southern Sierra CZO
Snow. A primary Question is: what is the control of vegetation characteristics and topography in the
spatial distribution of snow, and how such control influences melting patterns and runoff generation?
We have two hypotheses: i) In mixed-conifer, Southern Sierra Nevada forests, vegetation exerts a
controlling influence on snow distribution and melt, in patterns that are reproducible across the
landscape. That control comes about mainly due to how vegetation influences patterns of solar and
longwave radiation, and ii) the spatial patterns of snow distribution and melt across the landscape
vary in a consistent manner with vegetation (tree) patterns. Understanding of the interactions
between snow distribution and vegetation, topography, and other meteorological factors has been
limited by the lack of spatially distributed and high-definition data. LiDAR measurements of snow
depth provide information at spatial scales that are unmatched by any other existing technology. The
snow depth exhibits significant spatial variability induced by wind redistribution of snow, and by the
interactions of the snow with topographic features such as ridges and depressions, as well as the
location of trees and tree clusters [Trujillo et al., 2007]. A spatially distributed and high-definition
LIDAR dataset can be combined with the time series of meteorological and snow variables obtained at
the instrument clusters to estimate expected spatial patterns of melting, and consequently runoff
generation. In addition, these data will complement hyperspectral airborne data leveraged through
collaboration with Noah Molotch at the NASA Jet Propulsion Laboratory. The combination of these
two airborne data sets will provide unprecedented information regarding vegetation structure,
phenology and interactions with the distribution of snow and snow-surface microstructure.
Soil. Downscaling or soil properties and processes can be accomplished through the fusion of existing
soil survey observations with high-resolution, raster-based, sources of terrain and micro-climate data
to model near surface processes; resulting in a new raster-based description of soil properties at a finer
scale than that of the existing soil survey, which depicts general soil groupings. Five specific
hypotheses of this approach are as follows. First, soil bodies are not mapped in most traditional soil
surveys, soil assemblages are depicted within map units. Existing soil survey datasets can be downscaled through the identification of geomorphic surfaces, microclimate and terrain attributes most
correlated to the unmapped locations of soil types within a map unit. LiDAR can be used to derived
very precise measures of landform shape, thus leading to much more accurate identification of
geomorphic surfaces (as compared to photogrammetrically-derived elevation data). Second,
continuous maps of specific soil properties (and confidence intervals) can be developed by statistically
modeling landscape-scale pedogenic processes with soil characterization data and appropriate
environmental raster-based data (i.e. LiDAR derived terrain attributes and indices of microclimate).
Third, hydrologic flowpaths can be derived from a combination of downscaled soil survey information
and surface topography. Field studies have shown that flowpaths derived from conventional DEM
data (photogrammetric sources) deviate considerably from reality at the landscape to watershed
scales. Fourth, the size of wetland meadows and the extent of hydric soils within these environments
are a result of the contributing area and connectivity of hydrologic flowpaths to meadows in
surrounding terrain. Field studies have shown topographic features (at the landscape to watershed
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scale) such as low-lying areas or swales are often poorly represented in conventional DEM data, and
connectivity between such features does not match reality. Fifth, the degree of conifer encroachment
into meadows is a function of the extent of hydric soils, and the extent of saturated conditions.
Conversely, pulses of fresh sediment from surrounding terrain raise the elevation of meadows
resulting in microtopographic highs that promote conifer encroachment. Microtopographic features
are not discernible in conventional DEM data. The ground-based characterization data and
distributed measurements needed to address these hypotheses are in place or underway.
2.3. Existing spatial data
2.3.1. Boulder Creek CZO
Our focus is on three subcatchments within the mountainous region of Boulder Creek watershed.
We are also interested in topography of Boulder Canyon in a swath from the mountain front to the
Continental Divide. Currently we have available:
 1/3 arc second (approx 10 m) DEM of the full catchment (from USGS NED)
 11-m LiDAR-based DEM of a 40-km2 area in the Boulder Creek headwaters, including our uppermost
focus subcatchment, Green Lakes Valley. These data were acquired in fall of 2005 and are available in a
“filtered” (bare earth) and “unfiltered” version. The data were collected by NCALM and they did the
processing as well. This data can be downloaded at
http://culter.colorado.edu/exec/Database/gis_layer_query.cgi
 There is some information on vegetation in our uppermost watershed (Green Lakes Valley), available
from the url given above for the LiDAR data. No high-spatial-resolution vegetation data are available
for the rest of the watersheds
2.3.2. Shale Hills CZO
Our focus is on the Shavers Creek watershed and in particular the Shale Hills subcatchment. The
larger Shavers Creek watershed will best suit the nested nature of the project by including 3 watersheds
northwest of Shale Hills that will contribute their 50 year continuous data histories, including National
Atmospheric Deposition Program (NADP) records since 1979. Currently, the entire Shavers Creek
watershed has the following spatial data available
 1/3 arc second (approx 10 m) DEM of the full catchment (from USGS NED)
 1.4 meter average point spacing operational LiDAR, with vertical accuracy of 18.5 cm RMSE. These data
are being collected by the PAMAP program (http://www.dcnr.state.pa.us/topogeo/pamap)
 1-m Digital Ortho Photos (www.pasda.psu.edu)
 An extensive geodatabase of physical land properties has been collated for the entire Susquehanna
River Basin. Examples of data include soils, topography, geology, hydrology, etc. The data are
available upon request.
 Spatial vegetation information currently being used includes the National Land Cover Data NLCD,
2001, for the greater Shavers Creek area and initial evaluation of the operational LiDAR (not expected
to produce high resolution information of canopy structure). On a smaller scale, an extensive tree
survey was conducted at the 20-acre Shale Hills catchment, in which all trees over 18 cm diameter at
breast height (DBH) were identified, inventoried and recorded geospatially (~2100 trees).
2.3.3. Southern Sierra CZO
Our main focus is on the Kings River Experimental Watershed (KREW) catchments, which cross the
rain-snow transition elevations in the southern Sierra Nevada. LiDAR data are needed for the nested
Providence catchments, which are the focus of our ground instrumentation, and for comparison with the
higher-elevation snow zone, the Bull, Teakettle and Wolverton catchments. All of the KREW catchments,
which are on the order of 1 km2 (5-10 km2 for integrating catchments), are instrumented and have ground
data on: stream profiles and cross sections, ii) stream conditions, iii) geology and soils, and iv) litter,
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riparian and upland vegetation. In addition, we have:
 LiDAR data acquired in 2001 that cover part of
our study area, with >15-m spatial resolution. For
the rest of watersheds, we have a 30-m spatial
resolution DEM from USGS NED. We have
developed a DEM from existing LIDAR data, but
numerous problems limit its use (Figure 2).

The vegetation data are from the USFS standard
vegetation product, which were based on 2001
satellite images. The spatial resolution of the
vegetation data is 100 m.

Only selected areas are covered by high-resolution
aerial photos at this site.

An extensive database of physical land properties
has been collated for the entire Sierra National
Forest and Sequoia-Kings Canyon National Park.
Examples of data include soil surveys,
topography, geology, hydrology, habitat, etc.
2.4. Science justifications for LiDAR data
2.4.1. Boulder Creek CZO
Figure 2. Hillshade of Southern Sierra CZO
site, which is 10m x 10m and created from the
existing LIDAR data. Watershed boundaries
are overlain on the hillshade. The LIDAR data
points are in a very obvious pattern as NE-SW
strips, which are also generally noticeable in
the hillshade. Those areas in the red circle are
especially apparent.
Understanding geomorphic processes. High-resolution
topographic data are needed to characterize
landscapes and test hypotheses about the
geomorphic processes that have generated these
landscapes. Three elements of the landscapes of interest are i) bedrock tors (knobs) that range in scale
from a few m to 10s of m in width and height, ii) colluvial deposits that swamp bedrock topography in
some locations, and should be identifiable by a smooth bare-ground surface, and iii) gullies cut into
colluvium. A LiDAR-generated bare-ground 1-m DEM would have the spatial resolution needed to
identify bedrock tors and gullies and to test models of sediment transport based on topographic
gradient, curvature, and upslope drainage area. Some models of hillslope creep depend non-linearly
on slope (topographic gradient), and hence require accurate characterization of slopes. Similarly, since
curvature is a second derivative, the topographic data must be high resolution to produce meaningful
results.
Slope aspect controls on the critical zone. A specific question that has emerged from inspection of Boulder
Creek CZO catchments is the role of aspect in geomorphic and hydrologic behavior. We can see
qualitatively that north- and south-facing slopes differ strongly in tree density, and suspect that these
differences carry over into hillslope morphology and hydrologic function. Testing hypotheses about
the role of aspect will require first characterizing differences in bare-earth topography and roughness
as a function of aspect. Understanding the role of vegetation could be approached by measuring
canopy height and its variance, as well as tree density, as a function of aspect. From this, one could
perhaps estimate canopy interception losses and maybe even root density (if one did some field
studies of root density and sought correlation between this and canopy properties). Finally, one could
quantify the frequency / extent of bare bedrock as a function of aspect.
Geomorphic process rates. High-resolution topography will be useful for interpreting alluvial cosmogenic
nuclide concentrations, particularly in comparing results between catchments. Significant differences
in apparent erosion rate determined from cosmogenic nuclides could be interpreted from detailed
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measures of slope, aspect, vegetation cover, channel slope, etc. Standard 10-m DEMs are so noisy that
it would be hard to resolve differences in slope, stream power and other measures.
Hydrologic simulation. The best high-resolution topography is needed for physics-based simulation of
surface/near-surface hydrologic response. Using lower resolution data will be of much less use in
concept development (understanding how the system works). Data on canopy structure may be
useful for constraining the evapotranspiration (ET) fluxes in such a simulation.
LiDAR data to simplify interpretation of geophysical data. Topography must be accounted for in
interpretation of GPR, seismic refraction, and resistivity. Figure 3 shows tomographic output at two of
the Boulder Creek CZO sites based on seismic refraction with and without LiDAR DEM data.
Topographic variations across the entire watershed. We have requested a 10-km wide swath of LiDAR along
Boulder Canyon from the mountain front to the headwaters at the Continental Divide. This will allow
testing of any contrasts in vegetation structure, hillslope topography, north slope vs. south slope
differences, and channel topography across a prominent knickpoint. We can begin to develop models
of hillslope response to baselevel lowering.
Vegetation. We have very strong differences in tree density on north and south facing slopes. There is
interest in understanding the influence of slope aspect on soil moisture, sediment movement, hillslope
morphology (slope, roughness, curvature, tor frequency). with the high resolution vegetation structure
and DEM data derived from LiDAR, we can address many interesting ecological questions: i) to study
how higher tree density on N facing slopes affects moisture and recharge, ii) to pinpoint a priori sties
that have more or less light reaching the stream, and study the impact of lighting on stream ecology,
and iii) to provide the baseline forest canopy before the pine beetle infection, and understand what
happens in the aftermath.
Figure. 3. Tomographic output at two of the Boulder Creek CZO sites based on seismic refraction with
and without LiDAR DEM data. Left (without LiDAR): 10-m DEM. Right (with LiDAR): 1-m DEM.
2.3.2. Shale Hills CZO
Geomorphology. The current product available for the area is above mentioned PAMAP LiDAR. At the
estimated 1-m resolution, many features cannot be resolved. For example, small headwater streams
which are smaller than 1-m in width and numerous in the study area (e.g. the Shale Hills
subcatchment stream that is ephemeral in the upper locations of the watershed, especially during the
current drought). Hillslope evolution can occur on areas as small as sub-meter, due to acute events
related to voids created by tree throw, a major activity in the basin. The void scars change in shape
and sediment migrates during large rain events and drought, for that matter. Small landforms such
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as concave and convex slopes can be from sub-meter to 10s of meter in size, necessitating the highest
resolution as possible. Further, the connection of hillslope curvature and soil depth and how that
relates to the occurrence of treethrow will lead to a better model of hillslope erosion from this
phenomenon. With regard to cosmogenics, the measurements of soil depth (already collected),
coupled with higher resolution topography will lead to better estimate of soil flux rates. Comparison
of soil flux rates among different subcatchments will aid in evaluating this technique at a larger scale
Vegetation. Treethrow is an important physical phenomenon in the subcatchments. Large amounts of
sediment and fractured bedrock are displaced during these events. Coupling the high resolution
LiDAR with the existing tree survey will aid in developing a model for species-specific effect on
erosion rate related to this phenomenon. Additionally, we would be able to assess LiDAR as a
technique for tree inventory at subcatchment and even watershed scale. Current ground-based DHB
measurements compared with LiDAR estimates of DBH will inform that technique.
Hydrologic modeling: High-resolution topography is needed for physically based simulation of coupled
surface/ground water. Previous studies using PIHM indicate that patterns evolving at the sub-meter
scale are important for hydraulic connections across the hillslope that lead to increased macropore
flow. Current LiDAR may illuminate these processes, however, the better sub-meter resolution will
continue to advance the realization of these processes at the smallest possible scale. Estimates of
streambed geometry and channel roughness in the ephemeral channel will aid in better estimates of
the timing and magnitude of streamflow (Cobby et al., 2001). Currently those parameters are
estimated in the model and not measured. Due to the prevalence of small headwater streams in this
area, the higher resolution LiDAR will be more adequate in producing these measurements. Further,
sediment transport processes associated with treethrow will be better resolved (mentioned above).
Evapotranspiration is largely unmeasured in hydrologic simulations and is estimated. Canopy
structure data correlated with species-specific DBH sapflow and root density (both currently being
instrumented) data will be useful in estimating ET or at least in constraining the simulated values. The
current PAMAP LiDAR does not provide research grade information on canopy structure
Geophysics. Ground-penetrating radar, gravity and electrical resistance tomography studies have been
conducted in the Shale Hills catchment and are planned at the larger scale. LiDAR data are expected
to reveal micro-topography that will aid in interpretation of these studies.
Hydropedology. Similar to the discussion in the geomorphology section, high-resolution estimates of bare
earth topography and roughness on hillslopes will aid in developing models for soil depth and
hydrologic behavior related to slope, aspect and landscape position. The current data set for soil
depth, type, and moisture will be used in concert with the LiDAR to inform these models.
2.3.3. Southern Sierra CZO
Geomorphology. High-resolution topographic data are needed to help test hypotheses about the
geomorphic and geochemical processes responsible for shaping slopes and stream profiles within the
Southern Sierra Nevada CZO. Of particular interest is the so-called "stepped topography," which is
manifested at multiple scales in the CZO area and adjacent catchments in the Southern Sierra Nevada.
The stepped topography, characterized by sequences of steep "steps" and gently sloping "treads," has
previously been described in only general, qualitative terms (e.g. Wahrhaftig, 1965; Clark et al., 2005;
Chartrand and Whiting, 2000). It is thought to reflect hydrologic control of weathering and erosion in
the critical zone. To test this hypothesis, it will be crucial to first quantify the extent and scale of the
steps; a bare-ground 1-m DEM from LIDAR would have ideal spatial resolution for these purposes. A
LIDAR-based DEM would also help characterize the geometry of slopes (gradient, curvature, and
aspect) and channels (width, gradient, and area) at scales that are appropriate to analysis of functional
controls on rates of erosion an weathering---which are being quantified at a range of spatial and
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temporal scales across the site as part of the CZO study. By quantifying functional controls on rates of
erosion and weathering, the LIDAR data would contribute to improved understanding of how (and
why) sediment and solutes move across (and through) the landscape. This type of analysis is crucial
to the development of geomorphic transport laws, which in turn are fundamental to understanding
landscape evolution across a range of scales. A 1-m DEM from LIDAR would also be useful for
identifying individual erosional processes and their pervasiveness across the landscape. For example,
stream bank and headcut erosion are thought to be important contributors to sediment delivery in the
catchments, and the LIDAR data will help in quantitatively mapping it. This should help in
developing realistic budgets for decadal-scale sediment supply and delivery from the catchments
Hydrologic modeling. High-resolution surface topographic data are also needed to i) delineate channel
geometry and ii) quantify fine-scale heterogeneity in surface form for the high-resolution hydrologic
modeling that we are setting up for the CZO catchments. Channel geometry will be used in-stream flow
routing in models. Sub-catchments within the CZO differ in channel form and improved characterization
of these differences will contribute to hydrologic model estimates of snowmelt and winter-precipitation
peak flows. Fine-scale surface heterogeneity contributes to surface detention storage and hydrologic
controls on soil biogeochemical cycling. LiDAR estimates of fine-scale (sub-meter to meter) topography
will be used to parameterize within-unit heterogeneity in a spatially distributed hydrologic model
(Kenward et al., 2000) and support model sensitivity analysis that is designed to explore the role of
critical-zone heterogeneity in watershed-scale responses (e.g. French, 2003). Estimates of vegetation
parameters can also be linked to LiDAR measurements (Entekhabi et al., 1999; Schmugge et al., 2002).
Biogeochemical cycling. Micro-topography gives rise to “hot” and “cold” spots of soil biogeochemical
cycling generated by preferential flow of nutrient-rich litter leachate into mineral soils. LiDAR based
estimates of micro-topography will help identify potential sampling locations along gradients of water
and biological availability, guiding locations for sampling litter and soil nutrient concentrations.
Biogeochemical hot spots may also be created from greater snow retention, resulting in locales of
elevated soil water content during the growing season. Hence, identification of spatial variation in
snow depth using LiDAR (see below) should also enhance our ability to track spatial patterns in
biogeochemical fluxes resulting from snow-melt patterns.
Snow hydrology. Winter LiDAR data are needed to provide high-resolution estimates of spatial variation
in snow depth (Fassnacht and Deems, 2006). One of the central questions in the Southern Sierra CZO is
the role of forest canopy as a control on snow processes. Snapshots of spatial co-variation in snow
depth, surface topography and forest cover will complement plot level studies of snow accumulation
and melt. Plot level instrumentation of snow accumulation and melt along transects from under
canopy to open can be compared with LiDAR-based estimates of spatial patterns of snow depth and
LiDAR used to extend the spatial coverage of these patterns. Methods for estimating the distributions
of snow depth have been developed at another Sierra Nevada site, and can be applied to the CZO.
Forest structure. Summer LiDAR data are needed to fully characterize spatial patterns of open vs. forest
cover and canopy structure. Significant portions of the KREW site have relatively low canopy density,
while others are very dense. LiDAR scenes will contribute to estimates of this vegetation structure that
are used in coupled hydro-ecologic model analysis and scaling of evapotranspiration and carbon flux.
Coupled eco-hydrologic model estimates of evapotranspiration and carbon flux will be compared at
the plot level with eddy-flux tower data and sap-flow measurements. LiDAR scenes support
parameterization of this model at the watershed scale and contribute to spatially distributed model
estimates of ET and carbon flux under different land cover and climate warming scenarios.
3. Field work and LiDAR data-processing methods
9
We propose to have two LiDAR flights per site. Snow on/off for the Boulder Site and Southern Sierra
Site, and leaf on/off for the Shale Hills site. We will use physiographic data already available from the
CZO science teams and from other sources to derive the LiDAR products. In addition, we will visit each
CZO site to collect ground-truth data for evaluate the products, i.e. collect accurate references points,
elevation, tree heights, leaf area index, DBH; and systematically validate and calibrate the LiDAR derived
products. A team of 3 people will visit each site to conduct the field work. The field data will be used to
evaluate the LiDAR product or correlate with LiDAR product to upscale site measurements to the
landscape level. LiDAR data processing methods on DEM, vegetation parameters and snow depth are
described as below:
3.1. DEM
Although LiDAR data can generate a DEM with high accuracy and resolution, appropriate
interpolation methods with suitable parameters should be chosen based on the characteristics of data
sets. There are a variety of methods to derive DEM from point data, and each method has its own
advantages and disadvantages, depending on the characteristics of the data sets. One of our current
researches focuses on identifying suitable approaches for deriving DEM from LiDAR data in different
situations, and providing guidance in choosing the appropriate interpolation method that best suits the
datasets. Factors that are used to describe the characteristics of different data sets include surface
roughness, elevation slope, and LiDAR point density. For example, Inverse distance weighted (IDW)
interpolation methods have been commonly used to generate DEM from LiDAR data (Hopkinson, 2004;
Liu, 2007), but our preliminary results show that IDW performs poorly in high slope areas. Figure 4
shows the Root Mean Square Error (RMSE) of generating DEM by different interpolation methods. We
plan to evaluate different interpolation methods with consideration of the influencing factors mentioned
above, and produce the optimal DEM data with the suitable parameters based on the characteristics of
datasets.
3.2. Vegetation parameters
RMSE(m)
Compared to traditional methods for assessing forest structure like field inventories and aerial photo
interpretation, which are limited in their capacity to make detailed, spatially explicit measurements (Lang
et al., 2006), LiDAR has
demonstrated the ability to
0.25
generate 3-D data with high spatial
resolution and accuracy in forest
0.20
measurements (Figure 5). Methods
to extract some vegetation
parameters are described as
0.15
follows:
IDW
Vegetation height. Appropriate
0.10
Neares Neighbor
classification procedure should
Spline
be applied and pre-classify the
Ordinary Kriging
0.05
LiDAR data into ground and
Universal Kriging
non-ground returns. The
0.00
ground returns and canopy
1
2
3
4
5
6
7
8
9
returns can be used to generate
Tile number
a DEM and digital surface
model (DSM,) respectively, with
Figure 4. Root Mean Square Error (RMSE) of DEM by different
interpolation methods with increasing slope from Tile 1 (5 degree)
appropriate interpolation
to Tile 9 (25 degree) (IDW: Inverse Distance Weighted, Nearest
methods. Subtracting DEM
Neighbor, Spline, Ordinary Kriging, and Universal Kriging)
10
from DSM thereby creates
a digital canopy model
(DCM), a canopy surface
with height values
recorded in square pixels
or cells (Clark et al., 2004).
With fine spatial
resolution DCMs,
individual tree crowns
can be detected by
appropriate segmentation
technique (Brandtberg et
al., 2003; Persson et al.,
2002). The height and
structure of individual
crowns can then be
estimated by applying
metrics to either DCM
cells or footprint heights
from within crown
segments (Brandtberg et
al., 2003; Gaveau & Hill,
2003; Persson et al., 2002).
Figure 5. 3D vegetation structure visualization from LiDAR data for Fish
Camp, CA (a study area from the PI's Sierra Nevada Adaptive
Management Project; acquisition date: Sept 2007; spatial resolution:
average 9 points/meter.)
Crown cover (CC). Crown cover is defined as the percentage of an area within the vertical projection of the
periphery of crowns (McDonald et al., 1998). To generate LiDAR-derived equivalent CC estimates,
returns greater than a height threshold are considered as tree crown elements which will be
interpolated using the highest returns within a proper search radius into a top of canopy surface with
appropriate pixel size (Lucas et. al, 2006). The height threshold should be chosen to conform to
definitions of forest cover. Therefore, all pixels above the threshold would be coded as 1 or 0
otherwise. As a result, canopy cover percent (CC%) could then be estimated as the sum of all cells with
a value of 1 as a percentage of the total (Lucas et. al, 2006).
Leaf Area Index (LAI). Leaf Area Index, defined as the single-sided, green leaf area per unit area of ground
surface, is a critical variable in ecosystem modeling because of its influence on bio-geochemical cycles
(Houldcroft et al., 2005; Gower et al., 1999). LAI can be estimated by measuring both incident (I0) and
below-canopy radiation (I) with following equation (Monsi and Saeki, 1953):
LAI = −1/ k ln (I / I0 )
where k is the extinction coefficient. The total emitted laser point from an aircraft could be considered
as the total amount of sunlight, and the total intercepted or penetrated laser point through the canopy
could be regarded as the total amount of blocked or incident sunlight(Kwak et al, 2007). To generate
LiDAR-derived LAI, raw LiDAR points are classified into four groups: Ground Return (GR), Low
Vegetation Return (LVR), Medium Vegetation Return (MVR) and High Vegetation Return (HVR) (Lim
et al., 2001). The Laser Penetration Index (LPI) can be estimated by the HVR and GR, and the Laser
Intercept Index (LII) can be calculated from the GR and all point data. Thus, instead of the radiation
which reaches from the sun to the ground and vegetation, LAI can be analyzed with the number of
ground returns and vegetation returns which reaches from an aircraft (Kwak et al, 2007). In addition,
tree Diameter at Breast Height (DBH) and individual tree distribution will also be retrieved from the
LiDAR. Detailed methods could refer to (Chen et al, 2006; Popescu, 2007)
11
Snow depth. Two LiDAR datasets are required for this study—both snow on and off for the Boulder Site
and Southern Sierra Site. The raw LiDAR data are processed to generate XYZ data files of first and last
returns. The last return data from the snow-off survey provides ground surface elevations, and the last
return data from the snow-on survey provides snow surface elevations. The bare earth point data
would be interpolated into DEM with appropriate pixel size using appropriate interpolation method.
The DEM elevations were then subtracted from the snow surface elevation points, producing a dataset
of snow depth point estimates. Both survey datasets should be pre-classified into ground or vegetation
returns in order to remove the influence of vegetation so that the snow-on ground surface could be
compared directly with the snow-off surface (Hopkinson et al., 2004).
4. Broader impacts
There are two main, direct broader impacts of this project. First, making LIDAR data available for
the three CZOs will enhance their potential and use as community platforms for research. Both
investigators currently engaged at the CZOs and others who would like to work at one or more CZO sites
after LiDAR data are made available contributed to this proposal.
A second broader impact of this proposal will be to make our LiDAR product easily understood and
widely used by researchers working at CZOs and other similar study areas. We plan to organize a LiDAR
data-education workshop to introduce our LiDAR products and methods we use to derive our products.
We will take advantage of the annual CZO meeting at each CZO site, and schedule the workshop during
the annual meeting to discuss with the CZO scientists on LiDAR data collection and processing as well as
synergies among scientists to use these data. Meanwhile, we plan to work with other organizations (e.g.
CUAHSI) to co-sponsor the LiDAR workshop and make the data more broadly accessible to researchers
who are interested in working at the CZO sites. In terms of the data distribution plan, we will make the
raw LiDAR data as well as the derived products available through our UC Merced digital library, which
has already hosted the spatial and non-spatial data for the Southern Sierra CZO Site. We will also explore
the opportunity to make the data more accessible via sharing the data with other organizations (e.g.
CUAHSI).
Another educational impact of this project is to help to build both graduate and undergraduate
research at UC Merced, the PI’s home institution. UC Merced has a special commitment to diversity, and
we will take advantage of emerging efforts that will help recruit under-represented groups, particularly
in California’s San Joaquin Valley. Based on the undergraduate class, UC Merced is provisionally
classified as a minority-serving institution. Of the undergraduate students (Fall 2006) 50% identified
themselves as first-generation college students and 43.1% as first-generation college students who come
from families earning less than $30,000 annually. Two undergraduate students and three graduate
students are currently working in Q. Guo’s research group: both undergraduate students are of Hispanic
origin from the San Joaquin Valley; one graduate student is Hispanic. Q. Guo will use results from this
work in his classes (spatial analysis and modeling, and remote sensing of environment) at UC Merced,
and will be involved in an REU program, which will reach out to undergraduate students from the
Central Valley, California.
12
5. Schedule
Time
July - Sept 2009
Oct - Dec 2009
Jan - May 2010
June - Sep 2010
Oct - Dec 2010
Jan - May 2011
June 2011
Sept 2011
Task
Contract with NCALM for LiDAR flight at the Shale Hill site (1st flight)
Conduct the field work to collect high accurate localities and forest parameters
(LAI, tree height, Crown cover, etc)
Contract with NCALM for LiDAR flight at the Boulder Creek and Southern Sierra
site (1st flight), and LiDAR flight for Shale Hill (2nd flight)
Georeference field locality data & collecting relevant field data and geospatial data
from each CZO site.
Process LiDAR data
 evaluate different interpolation methods to generate the bare earth and digital
surface models
 generate digital crown model and extracting forest parameters
Do LiDAR flight at Boulder Creek and Southern Sierra sites (2nd flight)
Conduct field work on these two sites
Present the preliminary LiDAR results during the annual CZO meeting, and solicit
comments and feedback
Process LiDAR data
Generate bare earth and digital surface models for Bounder Creak and Southern
Sierra Sites
Analyze snow off/on LiDAR data for these two sites
Up-scaling field measurements to the landscape level
Assess the accuracy of the LiDAR derived products (topographic and vegetation
variables)
Document the project and create all necessary metadata for the spatial data derived
from this project
Upload and share the data in the digital library
Prepare the LiDAR workshop
Invite scientists working at CZOs and other similar study areas
Present the final results at the annual CZO meeting
6 Management team
Q.Guo (PI) will have responsibility, management, and oversight of the project. The PI will contract
with NCALM to acquire airborne LiDAR data, and design the overall sampling strategy to collect ground
truth for accuracy assessment . A research scientist will conduct field work, and develop algorithms to
process and upscale LiDAR data from the plot level to the landscape level. Two undergraduate assistants
will help to collect field data & document the project. The PI and his team will work closely with the CZO
PIs (Drs. Suzanne Anderson, Chris Duffy, and Roger Bales, see support letters) and other CZO scientists
to acquire additional field data for accuracy assessment and upscalling. Meanwhile, the CZO PIs will also
provide expert knowledge on earth science to review and interpret the LiDAR products.
13
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