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Multitemporal assessment of aboveground forest biomass using aerial photography and allometric equat

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The photogrammetric process requires the usage of ground control points (GCP's). 12 GCP's were collected using an Ashtech Z surveyor GPS. Stereoscopic models solutions ... – PowerPoint PPT presentation

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Title: Multitemporal assessment of aboveground forest biomass using aerial photography and allometric equat


1
Multi-temporal assessment of above-ground forest
biomass using aerial photography and allometric
equations
  • An M.Sc thesis by
  • Avi Bar-Massada1
  • Academic advisors
  • Dr. Yohay Carmel1, Dr. Gilad Even-Tzur1 and Prof.
    Dan Yakir2
  • 1Faculty of Civil and Environmental Engineering
  • Technion Israel Institute of Technology
  • 2Environmental Sciences and Energy Research
  • Weizmann Institute of Science

2
Motivation
  • Forest biomass dynamics have a major role in the
    study of global warming and land cover change.
  • Traditional biomass dynamics assessment methods
    rely on long-term forest inventory data, that
    exist only in few countries in the world.
  • Thus, it is desirable to develop an alternative
    method for forest biomass dynamics assessment.

3
Objective
  • Development of a method for estimating the
    dynamics of above-ground forest biomass.
  • The method introduces a combination of two
    distinct scientific fields Allometry and
    Photogrammetry.
  • The method will be applied to Yatir forest,
    southern Israel.

4
Background
5
What is Biomass?
  • Biomass represents the amount of organic
    (biological) matter in a system. In plants, the
    amount is defined as dry weight.
  • In terms of ecosystems, Biomass is usually
    defined per unit area (biomass density). Common
    units are Kg dry weight / m2 .

6
Biomass Measurements
  • The accurate measurement of biomass is chop and
    burn
  • Common biomass assessment methods rely on
    allometric equations.
  • These equations quantify relationships between
    various structural components of plants, such as
    biomass, DBH, crown diameter, height etc.
  • The relationships are species-specific, and
    site-specific.

7
For example
(Source Grunzweig, unpublished data)
8
  • Multi-temporal biomass studies usually require
    many years of field sampling.
  • In places where no such field-data exists, it may
    be useful to generate data retroactively, via
    other data sources.
  • Aerial-photographs may provide high resolution
    spatial data for several decades

9
Photogrammetric Measurements
  • Photogrammetry is the science of determining the
    position and shape of objects from photographs.
  • Using a couple of overlapping photos (a stereo
    pair), it is possible to calculate the 3D
    characteristics of objects appearing on the
    photos. image
  • Thus, it is possible to measure tree height and
    crown diameter from aerial photographs.

10
  • It is possible to obtain parameters for the
    allometric equations through photogrammetric
    measurements of aerial photographs.
  • By repeating such a process in different years,
    we can describe the dynamics of the above-ground
    woody components of the forest biomass.

11
The Model
12
Study Area
  • The research will be conducted on Yatir
    Forest, an arid-land forest in southern Israel.
  • This is the largest planted forest in Israel,
    consisting mainly Pinus halepensis (Jerusalem
    Pine) species. Planting started in the mid 60s.

13
Data Acquisition Aerial photographs
  • 113000 panchromatic aerial photographs of Yatir
    forest in previous years 1978, 1987, 1992 and
    1996, were obtained from MAPI.
  • A new set of photos was acquired in a
    photographic flight mission, in summer 2003.
  • Low-scale and panchromatic photos were used since
    this represents the available data in
    photo-archives in Israel.

14
Ground control points collection
  • The photogrammetric process requires the usage of
    ground control points (GCPs).
  • 12 GCPs were collected using an Ashtech Z
    surveyor GPS.

15
Stereoscopic models solutions
  • The stereo-models were solved in ERDAS IMAGINE
    8.6 OrthoBASE software, using the photos, camera
    calibration information and the GCPs.
  • Overall, Five stereo-models were solved one for
    each period 1978, 1987, 1992, 1996 and 2003.
  • In addition to the stereo-models, orthophotos
    were generated for each model.

16
Stereo-models solutions
  • The overall quality of the solutions is
    satisfying

17
Plot locations
18
Tree height measurements
  • Individual tree height was measured in monoscopic
    view.
  • The software measures absolute height (above sea
    level). Thus, tree ground height needs to be
    measured.

19
Tree height measurements
  • Since the canopy blocks the view of tree ground
    height, it needs to be estimated.
  • Tree ground height is estimated as the projection
    of the tree apex on a DSM.

20
Tree height dynamics
21
Tree height dynamics
22
Height measurements
  • The height variability between plots could not be
    explained by the environmental variables.
  • Presumably, the differences are results of local
    plot characteristics micro-hydrology etc.

23
Crown diameter measurements
  • Tree crown diameter was digitized from an
    orthophoto, in ArcGIS software, for all trees
    with a measured height.

24
Allometric equations generation
  • 28 Pinus halepensis trees were selected, cropped,
    measured, and sampled. The samples were later
    oven-dried to assess moisture content.

25
Allometric equations
  • Linear regressions of these biomass data are
    invalid since data exhibits heteroscedasticity.
  • Therefore, selection was made between three
    log-transformed non-linear equations

26
Biomass calculations
  • The selected allometric equation, with the height
    and crown diameter corrected data for all years,
    were used to calculate the biomass for all of the
    trees in the study (2127 trees).
  • Spatial and temporal analysis was done to
    describe the biomass dynamics (spatial
    variability, accumulation rates).

27
Mean tree biomass dynamics
28
Plot biomass dynamics
29
Mean tree biomass leap following a thinning
treatment
30
Stand density measurement
  • To assess the entire study area density, 16457
    trees were digitized from the 2003 orthophoto.

31
Error Analysis
32
Measurements error assessment
  • The tree measurements in this method are exposed
    to two types of errors
  • Photogrammetric solution error.
  • Operator errors (false apex, wrong
    correspondence).

33
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34
Measurements error assessment
  • The empirical estimation of the model error
    involves a comparison between computer
    measurements and field data.
  • The validation data set includes 60 trees. Each
    one of them was measured for height and crown
    diameter in the computer and in the field.
  • Tree field height was measured with the
    clinometer and tape method. Crown diameter was
    measured with a tape, according to the direction
    of the photo.

35
Measurements error assessment
  • The measurement errors were statistically
    analyzed. Mean errors, RMSE and error
    distributions were determined. T-test was
    conducted to check whether measurement error is
    related to tree size.
  • The resulting mean errors were used to calibrate
    all tree measurements in this study.

36
Measurement error assessment
  • Height and crown diameter errors are computed as
    model height minus real height.

Height error
Crown diameter error
37
Height errors
38
Crown diameter errors
39
Measurement error assessment
  • There was no significant relation between error
    and tree height (p0.3), and between error and
    crown diameter (p0.72).
  • Therefore, the correction factors are valid for
    all years
  • Height correction -0.762m
  • Crown diameter correction 0.474m

40
Error analysis the reliability of mean biomass
  • One might wonder whether the mean biomass value
    is reliable, considering the measurement errors.
  • Since error data is not normally distributed,
    standard error propagation methods can not be
    used.
  • An alternative approach is using a randomization
    test, with mean biomass as the test statistic.

41
Error analysis the reliability of mean biomass
  • The randomization test checks the following null
    hypothesis
  • The test statistic is compared to a synthetic
    distribution of biomass means, generated by
    resampling combinations of tree data and error
    data.

42
Reliability of the mean biomass
  • The 1000 randomization iterations yielded the
    following distribution of means
    BmeanN(93.58,2.34)

Corrected estimate
Uncorrected biomass estimate
  • The result can also be described as the 95
    confidence interval 89.09,98.03

43
The Model General Aspects
44
Model performance
  • In order to fulfill the research objective, the
    model has to meet three requirements
  • Accurate measurement of tree height and crown
    diameter per tree.
  • Accurate transformation of the height and crown
    diameter data to tree biomass.
  • The selected study plots are representative of
    the biomass in the study area.

45
1. Accurate measurement of tree height and crown
diameter per tree
  • This requirement is validated through the
    error-assessment step.
  • The mean absolute error of tree height was lower
    than the value reported by Gong et al (2002),
    although that research used a better measurement
    technique and better data.

46
2. Accurate transformation of the height and
crown diameter data to tree biomass
  • This requirement is validated by the quality of
    the allometric equation.
  • The allometric equation is highly significant.
  • The trees selected for harvesting are
    representative of the entire forest, since they
    follow an average height/dbh ratio.

47
3. The selected study plots are representative
of the biomass in the study area
  • This requirement is validated by the density
    representation of the study plots.
  • Since all trees in are in the same age, and the
    environmental variables in their area are
    similar, stand density was assumed to be the
    major factor controlling biomass growth.
  • The mean density of the plots is close to that of
    the study area, thus they represent the density
    variability well.

48
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49
Conclusion
  • A new approach for the assessment of forest
    above-ground tree biomass dynamics was presented.
  • The method is a combination of two distinct
    scientific fields photogrammetry and allometry.
  • The method performed well in a case study of
    Yatir forest.

50
Thank you!
  • And, thanks to the following people
  • Grad students from Geodesy, Technion, for the
    lessons in photogrammetry undergrad students
    from Rehovot, for their hard work in the harvest
    and its post-processing Dr. Ofer Zilberstain and
    Dr. Yuri Rizmann for their valuable comments
    regarding the method, Dr. Debbie Hemming and Dr.
    Jose Grunzweig for directing the harvest, Dr.
    Grunzweig also contributed much to the
    understanding of the results My academic
    advisors Dr. Gilad Even-Tsur and Prof. Dan Yakir
    for all of their help and especially, for all
    the time, support and respect, my main advisor
    Dr. Yohay Carmel.

51
Height/DBH rate for 258 inventory trees
52
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55
Stereopair
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