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Using interpreted large scale aerial photo
data to enhance satellite-based mapping and
explore forest land definitions
Tracey S. Frescino 1 , Gretchen G. Moisen 2
Abstract: The Interior-West, Forest Inventory and Analysis (FIA), Nevada Photo-Based
Inventory Pilot (NPIP), launched in 2004, involved acquisition, processing, and
interpretation of large scale aerial photographs on a subset of FIA plots (both forest and
nonforest) throughout the state of Nevada. Two objectives of the pilot were to use the
interpreted photo data to enhance satellite-based mapping capabilities by providing
training information at a moderate scale, and to refine definitions of forest land by
facilitating explorations of forest land definition changes. This study examines the
usefulness of NPIP data for fulfilling these objectives by exploring relationships between
photo-interpreted information and FIA ground data with Moderate Resolution Imaging
Spectroradiometer (MODIS) data using machine learning, Random Forests models.
Photo-interpreted data were determined to be valuable training data for models using
MODIS imagery, even in areas of low tree cover, and were found to be slightly more
effective than using field data.
Keywords: Nevada, photo interpretation, large scale photography (LSP), MODIS
INTRODUCTION
With increasing pressure for natural resource information and concurrent
decreases in available funds, there is a need for exploring alternatives to the timeconsuming, high cost ground data traditionally collected by inventories, such as
the USDA Forest Inventory and Analysis (FIA) program. In 2004, the InteriorWest FIA program launched the Nevada Photo-based Inventory Pilot (NPIP) to
1
United States Forest Service; Rocky Mountain Research Station; Forest Inventory and Analysis
Program; 507 25th Street; Ogden, UT 84404 USA; tfrescino@fs.fed.us
2
United States Forest Service; Rocky Mountain Research Station; Forest Inventory and Analysis
Program; 507 25th Street; Ogden, UT 84404 USA; gmoisen@fs.fed.us
In: McWilliams, Will; Moisen, Gretchen; Czaplewski, Ray, comps. 2009. 2008 Forest Inventory and Analysis (FIA)
Symposium; October 21-23, 2008: Park City, UT. Proc. RMRS-P-56CD. Fort Collins, CO: U.S. Department of
Agriculture, Forest Service, Rocky Mountain Research Station. 1 CD.
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explore the use of GPS-controlled, large-scale aerial photography (LSP) for
enhancing our existing inventory by complementing our field sample, augmenting
our traditional FIA estimation process, providing strategic level information on
nonforest lands not traditionally inventoried by FIA, and increasing efficiencies,
particularly in the extensive, slow-growing woodland forests in the West.
NPIP involved acquisition and processing of LSP on a subset of FIA plots
(both forest and non-forest) throughout the state of Nevada during two
consecutive field seasons. Nevada was a particularly good study area for several
reasons: it was not yet funded for annual inventory (Gillespie 1999); it has the
most incomplete and outdated periodic data in the Interior West; it is
predominantly nonforest federal lands; and of the forested lands, it is mostly
woodland forest types. The specific objectives of the pilot were as follows:
• Exceed information requirements by providing strategic level information on
all cover type groups in a consistent fashion. LSP provides an opportunity to
characterize vegetated and non-vegetated cover types typically not sampled by
FIA, such as shrublands and grasslands.
• Speed up inventory timeline by producing meaningful estimates after 2 years
of data collection, at a lower cost. The data from NPIP can supplement the
sparse annual data collected in the state to produce more precise and timely
estimates of field-based attributes, such as area by forest type, area by owner,
and area by land class, as well as basal area by tree species group.
• Reduce inventory costs in prefield and potentially in future field efforts
particularly on marginal forest lands. IW-FIA’s ground sample is based on a
priori interpretation of aerial digital imagery (e.g. DOQ). LSP provides higher
resolution imagery and improved temporal control for making better decisions
and thus, eliminating unnecessary field visits and reducing costs.
• Enhance mapping capabilities by providing training information at a
moderate scale. In the past, FIA map products have relied on the acre-size, FIA
plot data for training moderate-scale, and satellite-based map products of forest
attributes. NPIP photos offer the opportunity to collect training data at a scale
that is comparable to the satellite imagery which should improve accuracy of
these map products.
• Provide data to evaluate forest land definition changes. FIA needs to define
and account for all forest land in the U.S. in a consistent and efficient manner.
NPIP photos can provide data to explore the impact of definitional changes of
forest land on forest estimates, using criteria such as percentage of tree crown
cover.
The focus of this study is based on the last 2 objectives of the pilot: enhancing
mapping capabilities and evaluate definitions of forest land. The study explores
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relationships between photo-interpreted information and Moderate Resolution
Imaging Spectroradiometer (MODIS) data, examining photo data as compared to
FIA field data and analyzing different thresholds of tree cover interpreted from
the photo plots. This paper first describes the data available, photo, field, and
MODIS imagery, and then how these data were used for addressing these 2
objectives.
DATA
Photo Data
GPS-controlled LSP was collected in 2004 and 2005 in the state of Nevada,
consisting of 28,629,728.6 hectares (70,745,600 acres)
(http://dcnr.nv.gov/nrp01/land01.htm), including water but excluding 3,514,290
hectares (8,684,000 acres) of restricted air space. Nevada is less than 14 percent
forested and of these forested lands, the dominant forest type is pinyon-juniper
woodland type, covering approximately 7 million acres (Born et al. 1992). Other
woodland types found in Nevada include Cercocarpus species and Gambel oak
(Quercus gambelii). Timberlands cover less than 1 percent of the total forest land
and include white fir, aspen, and limber pine with bristlecone pine, subalpine fir
and Engelmann spruce (Picea engelmannii) at higher elevations and black
cottonwood (Populus trichocarpa) in the riparian areas (Born et al. 1992).
The sample survey design for NPIP follows the systematic sampling design of
the national FIA program (Reams et al. 2005). The FIA program conducts a
comprehensive inventory of forest lands across all ownerships in the United
States. Permanently-established ground plots are measured annually based on a
systematic sample of regularly spaced hexagons, each representing approximately
6000 acres. The plots are delineated into 5 panels, each panel represents 20
percent of the data and is measured on an annual cycle. In the western region,
panels are divided again into subpanels; each subpanel representing half a panel
or 10 percent of the data. A subpanel is measured every year over a ten year cycle.
The state was pre-stratified into 3 initial strata using a pixel-based, 250-meter
resolution map of predicted timberland forest, woodland forest, and nonforest
areas (Figure 1; Blackard et al. 2004). All FIA locations (i.e., all 10 subpanels)
within the timberland and woodland strata were photo-sampled, totaling 1,455
plots. In addition, one-tenth of the FIA locations (i.e., one subpanel) within the
nonforest stratum were photo-sampled, totaling 877 plots, resulting in an overall
total of 2,332 photo plots.
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Figure 1: Stratification map.
In 2004, 395 locations were acquired by contract with the Remote Sensing
Application Center (RSAC) using a direct-to-digital camera, DCS645C, with a
55-mm lens. Flights were flown 3,000 feet above ground with a 2,002-ft swath
width, resulting in a 0.49-ft ground sample distance (GSD). In 2005, a contract
through the USDA Aerial Photography Field Office (APFO) to Aerial Services,
Inc. (ASI) provided photos for 1,937 locations. The ASI photography used natural
color 9x9-inch film and a 6-inch lens. These plots were flown at a scale of 1:5,000
feet with a 3,750-ft swath width. Stereo triplicate photographs were acquired for
all plots, converted to a digital Tagged Image File Format (TIFF), and geolocated
to each FIA X-Y plot center location.
The NPIP photo-interpreted plot sample design consisted of a dot grid sample
within a 250 meter radius circle covering approximately 20 hectares (48 acres) of
land. There were a total of 49 points per plot each representing about an acre size
(Figure 2) with the center point of each plot straddling the FIA field plot center.
Point generation and photo interpretation were accomplished using the Digital
Mylar Image Sampler tool developed by the Remote Sensing Applications Center
(RSAC) (Clark et al. 2004). Each point was assigned a value of condition class,
defined as an area of homogenous vegetation having similar characteristics
(USDA 2006), and an object class identifying the object in the photograph the
point fell on, such as a tree or shrub (USDA 2006).
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Figure 2: NPIP plot sample design.
The individual point data were summarized to plot level and condition level
attributes. One attribute analyzed in this study was percent cover of live trees,
calculated by summing the points that fell on live trees and dividing by the
number of points per plot or condition, respectively. More details on photo
sampling procedures are documented in Frescino et al., In Press.
FIA Field Data
As mentioned previously, the FIA sampling design is based on a nationally
consistent and uniform spatial distribution of field plots divided into annual
panels across the United States. In the Interior-West, where and when funding is
available, a subpanel of data is measured each year, representing 10 percent of the
total plots. In Nevada, 2 subpanels (20% of the entire sampling grid) of data were
collected in years 2004 and 2005, totalling 1216 plots; of these, 381 plots sampled
forest land. Live tree crown cover was measured using four 25-ft transects and
included all live trees 1.0 inch and greater (USDA 2006). These data were
collected at the condition level and summed to plot level.
MODIS Data
We used 16-day, cloud-free, composites of MODIS imagery for spring,
summer, and fall of 2005. These included visible-red (RED) and near-infrared
(NIR) bands and 2 vegetation indices, normalized difference vegetation index
(NDVI) and enhanced vegetation index (EVI). NDVI measures energy absorption
of chlorophyll and cell wall reflectance in vegetation cover and is defined as (NIR
– RED) / (NIR + RED) (Tucker 1979). NDVI is powerful in its reduction of
multiplicative noise among bands but is sensitive to canopy background changes
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(Huete et al. 2002). EVI incorporates the blue visible spectral band, helping to
reduce this sensitivity in background noise while improving sensitivity in high
biomass regions. EVI is defined as G*(NIR – RED) / (NIR + C1*RED –
C2*BLUE+ L), where BLUE is visible-blue band, C1 and C2 are aerosol resistance
coefficients, G is a gain factor, and L is the canopy background adjustment factor
(Huete et al 2002).
METHODS
Random Forests
In meeting both our objectives of enhancing mapping capabilities and
evaluating definitions of forest land, we used a machine learning algorithm,
Random Forests (Breiman, 2001), to model relationships between response
variables collected on photos or field plots, and the MODIS data. Random Forests
is a classifier that generates multiple classification predictive models referred to
as trees, and outputs the class that occurs most frequently. For each tree a random
subset of the training data (approximately two thirds) is used to construct the tree,
with the remaining data points used to construct out-of-bag (OOB) error
estimates. For categorical responses, the OOB error is the average number of
times the class was not correctly classified, yielding OOB estimates of percent
correctly classified (PCC) and Kappa, an index that compares agreement against
that which might be expected by chance. For a continuous response, the output
includes the total percent variance explained, root mean square error (RMSE), and
Pearson’s correlation (Cor). At each node of each tree, a random selection of
predictors is chosen to determine the split. Random Forests will not overfit data,
therefore the only penalty of increasing the number of trees is computation time.
For this study, we used 500 trees to build the models and relied on the OOB error
estimates to compare different models.
Enhance Mapping Capabilities
The first objective addressed in this paper was enhancing mapping capabilities
by providing training information at a moderate scale, compatible with the
MODIS data. Recent mapping efforts, regionally (Blackard et al. 2004;
Ruefenacht et al. 2004) and nationally (Blackard, et al. 2008; Ruefenacht et al.
2008), have coupled FIA data with MODIS imagery with the assumption that the
acre-size (4047 sq meter) FIA sample plot is representative of the 15-acre (250-sq
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meter) pixel size of MODIS. This mismatched scale has not previously been
analyzed, thus the impact on the models and map products are unknown. The
NPIP sample plot is 500 meters in diameter and therefore provides data for a
sampled area that is thought to better match the area of the MODIS pixel. To
understand this impact of scale better, we explored relationships between MODIS
data and the compiled photo-interpreted data at both the plot and condition levels
using Random Forests and compared the output from these models to output from
models generated using FIA ground plot data.
We focused on 2 different questions:
1. How does using photo plots as training data differ from using field plots as
training data for generating models with MODIS imagery?
2. How does the cost of photos and field training data affect models with
MODIS imagery?
For the first question, we considered only those plots that met the following
two conditions: 1) both field and photo data were available (hereafter referred to
as collocated), and 2) both field and photo plots had only one condition. For the
latter we assumed the homogeneity within the plots would minimize noise or
model error and therefore the error we were seeing would be related to differences
in scale. We modeled 4 different responses, 3 binary and 1 continuous: presence
of forestland (F/NF); presence of pinyon-juniper forest type (PJ Type); presence
of pinyon-juniper species (PJ Spp); and percent live tree cover. Forestland was
defined as lands having 10 percent or greater cover of live trees; pinyon-juniper
forest type included plots with a condition classified as pinyon-juniper; pinyonjuniper species included plots having at least one pinyon or juniper tree; and
percent live tree cover ranged from 0 to 100 percent of live trees on the plot.
The second question examined how the number of field plots and photo plots
affected model error, where the number of plots was based on fixed cost
scenarios. Here, we looked at percent live tree cover at plot- and condition- levels
from both field and photo plots and modeled this data as a function of MODIS
spectral information using Random Forests. The cost, per plot, of the NPIP
inventory was approximately $300 (Table 1) compared to an estimated $1500 per
FIA field plot. We analyzed 4 different cost scenarios based on these estimates,
starting as high as $432,000 and halving the cost each time down to $54,000
(Figure 3). From the total number of available plots in each cost scenario, we
examined plot level information of those plots having only one condition and
condition-level information of conditions that covered 40 percent or greater area
of a plot. These data were randomly selected with replacement for each cost
scenario. We generated Random Forests models of live tree percent cover using
plot and condition data for each scenario and compared the results of each model.
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Table 1: Cost and time constraints for the photo sampling components of NPIP. The costs of per
plot estimates.
Description
Photo acquisition
Photo-to-digital
Hard copy printing
(8x8)
Geolocation
Interpretation
Quality control
Cost
$120 per triplicate
(direct to digital)
$20 (scanning)
$5 (per image)
Time constraints
Photo acquisition should be initiated
before the desired flight month.
Local, professional photo shop.
$5
$80 (includes field training)
(average > one plot per hour)
$30
(includes data compilation and
editing and minimal field
validation)
Difficult to interpret more than 6
hours per day.
Sharing tasks led to timing and
feedback issues.
General cost
1 Tb of computer
storage space
$2,000 (per each; two were
purchased, one for back-up)
Total
$260 per plot + $4,000 storage space
Figure 3: Number of plots used for each cost scenario. Cost1: $432,000; Cost2:
$216,000; Cost3: $108,000, Cost4: $54,000
Evaluate Definitions of Forest Land
The second objective addressed in this paper was evaluating definitions of
forest land by facilitating explorations of forest land definition changes. Frequent
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discussions within FIA review the definition of forest land, including whether to
base the definition on stocking or crown cover and what percent crown cover is
appropriate for all users. For this objective, we explored what the potential would
be to capture different percentages of crown cover using moderate scale imagery.
Specifically, we looked at classification accuracies of percent live tree cover
interpreted from LSP and MODIS spectral data at different thresholds of cover,
5%, 10%, 15%, 20%, 25%, and 30%. We again used the Random Forests to
generate models of each threshold as a binary response and compared the percent
correctly classified (PCC) and Kappa values calculated from the OOB error.
RESULTS/DISCUSSION
Enhancing Mapping Capabilities
There were a total of 682 photo and field collocated plots having only one
condition. For the photo plots, 118 of these were forested with 115 plots classified
as pinyon-juniper forest type and the remaining 564 plots non-forested. Of the
field plots, 114 were forested with 112 classified as pinyon-juniper forest type and
the remaining 568 plots non-forested. The density, or probability distributions of
percent live tree cover for the field and photo collocated, forested plots were very
similar as shown in figure 4, with the photo tree cover generally a little higher
than the field tree cover, overall. As expected, paired t-tests revealed similar
results.
a.
b.
Figure 4: Percent live tree cover for the field and photo collocated, forested plots. a. Density, or
probability distributions. b. Scatterplot of photo versus field with 1:1 and regression lines.
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The OOB error rates for the field and photo predictions with binary responses
are displayed in figure 5. In all cases, the models using photo data had a slightly
lower error rate than the models using field data. The continuous response,
percent live tree cover, showed similar results for the photo models with greater
percent variance explained, a lower RMSE, and a higher correlation. The percent
variance explained, RMSE, and correlation for the field model was 77.14, 6.30,
and 0.88, relatively, whereas the photo model was 84.67, 5.99, and 0.92,
relatively. Figure 6 shows the relationship between the predicted and observed
values resulting from the field and photo models, where the observations were
greater than zero. Although observed values equal to zero were included in model
development, they were excluded from the graphs to get a better picture of the
spread for the non-zero values. These results suggest an advantage using photo
information at a similar scale as the MODIS pixel over using the acre-size field
plot.
Figure 5: The OOB error rate for Random Forests binary models for field and photo collocated
single condition plots.
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Figure 6: The OOB error for Random Forests model of percent live tree cover for field and photo
collocated single condition plots, where observed values are greater than zero.
Table 2 shows the total number of plots used in each cost scenario, the
number of single condition plots, and the number of conditions that covered
greater that 40 percent of the plot area. Here, it is interesting to note the much
higher number of training data available when using condition-level information,
especially with the photo plots. The larger size of the photo plots is conducive to
sampling more conditions.
The results of the Random Forests models for percent live tree cover under all
cost scenarios are displayed in figure 7. This figure shows the OOB variance
explained from the photo plot-level (PHOTO-c1) and condition-level (PHOTOcnd) models along with the field plot-level (FIELD-c1) and condition-level
(FIELD-cnd) models.
Table 2: Number of plots used for comparing different cost scenarios.
Cost
Scenario
1
2
3
4
Cost
$432,000
$216,000
$108,000
$ 54,000
Total
288
144
72
36
Field
Single
Condition
Conditions
>=40%
Total
248
123
62
34
285
142
71
36
11
1440
720
360
180
Photo
Single
Conditions
Condition >= 40%
613
306
154
70
1520
757
375
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Figure 7: OOB variance explained for percent live tree cover for four cost scenarios, and four plot
selection criteria
Overall, the models using photo data explained more variance than the models
using field data at the both plot and condition levels. Cost scenario 1, having the
highest cost ($432,000) had the highest percent variance explained in all cases, as
would be expected. As the scenarios decreased in cost, or in the number of plots
used, the variance explained in the models decreased as well. Using the conditionlevel information seemed to be more effective in each cost scenario with slightly
higher percent variance explained in each case. Again, we see the photo data
adding valuable information to our models, and at a much lower cost. We also see
the positive affect of using condition-level data for training.
Refine Definitions of Forest Land
We used a total of 2328 photo plots from NPIP. Figure 8 shows the division of
plots for each forest definition threshold. The OOB PCC and Kappa values
resulting from fitting each of these five datasets in a binary forest/nonforest
Random Forests model are shown in figure 9. The PCC and Kappa values are
fairly high for cover thresholds at 5 percent up to 15 percent but then begin to
decline at 20 percent. This result shows that there is potential to successfully
model forestland at cover less than 20 percent. Further testing is needed to
determine if this is a true effect or an artifact of the training data.
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Figure 8: The number of forest (blue) and nonforest (purple) plots by
threshold used in the binary Random Forests models.
Figure 9: OOB PCC and Kappa values from models of percent live tree cover at different
thresholds.
CONCLUSION
Large scale aerial photography can provide valuable information as training
data for mid-scale modeling and mapping efforts as a link between MODIS
imagery and ground data and as auxiliary information for tree cover estimations,
even at the lower extremes. Using the photo data to establish relationships with
MODIS imagery was found to be slightly more effective than using field data
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according to the OOB error estimates from Random Forests models. This was true
for the binary responses of forest presence, pinyon-juniper presence, and pinyonjuniper presence, as well as percent live tree cover.
The cost scenario analysis showed the affect of using photo data versus field
data for modeling percent tree cover at different costs. The photo interpretation
data look promising as an alternative or ancillary data source for providing
information at lower costs. It was also determined that the addition of conditionlevel information was valuable for increasing the variance explained in the
models.
LSP is also a valuable data source for generating spatial products of not only
forest, but rangeland conditions including presence of shrub type and, more
specifically, sage (Figure 10).
Figure 10: Spatially-explicit maps generated from Random Forests models. a. Presence
of shrub type. b. Presence of sage species.
This paper was a start at analyzing the valuable data available from NPIP and
the relationships with MODIS imagery. Further research is needed to investigate
this relationship with MODIS (and other predictors) in more detail, as well as to
continue exploring the use of condition-level information as training data. Also,
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looking at cost scenarios including both photo and field data could provide more
insight into cost savings and efficiencies of large scale inventories.
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