57
Derivation of Metrics for Vertical Vegetation Structure
There are numerous statistical measures of the vertical distribution of the point
cloud that have been used within the literature (e.g., Bunting et al. 2013). These
include, the ratio of the number of ground returns to all returns, mean height, median
height, mode height, maximum height, standard deviation of all or returns above a
certain height, percentiles of height, skewness in height, Pearson mode of height,
Pearson median of height and the kurtosis in height. Additionally, by filtering the
returns based on their classification (e.g., ground or not-ground) or return number
(e.g., first returns) etc., there are many variants of metrics which can be calculated.
Your given choice of software tools will enable these metrics to be calculated. For
instance, LAStools provides a command line tool to retrieve forestry metrics (lascanopy) while SPDLib provides a tool called spdmetrics. Once calculated, these
metrics are commonly used within either a classification scheme to retrieve categorical classes for the scene or used within a regression analysis to field data to retrieve
parameters, such as above ground biomass.
Radiometric Correction
There have been a number of attempts to radiometrically correct and/or normalise
the LiDAR intensity/amplitude data (e.g., Donoghue et al. 2007). However, as of
yet, there are few examples within the literature that demonstrate a clear application
for this product. Therefore, for information on these processing stages, the reader is
referred to Wagner (2010) and Coren and Sterzai (2007).
Standard Data Specifications
A number of organisations worldwide (e.g., the Intergovernmental Committee on
Surveying and Mapping’s; http://www.icsm.gov.au/elevation/) have set out standard
specifications for the acquisition of LiDAR data. These specifications are commonly regarded as the minimum specification for the organisation. These specifications help ensure that data acquisitions are fit for purpose and can be used to meet
the wider requirements of the organisation rather than just specific project needs.
Table 8 lists a number of available specifications and, if you are acquiring LIDAR
data, reference to these specifications is recommended.
Pre-processing of Remotely Sensed Imagery
Derivation of Metrics for Vertical Vegetation Structure
There are numerous statistical measures of the vertical distribution of the point
cloud that have been used within the literature (e.g., Bunting et al. 2013). These
include, the ratio of the number of ground returns to all returns, mean height, median
height, mode height, maximum height, standard deviation of all or returns above a
certain height, percentiles of height, skewness in height, Pearson mode of height,
Pearson median of height and the kurtosis in height. Additionally, by filtering the
returns based on their classification (e.g., ground or not-ground) or return number
(e.g., first returns) etc., there are many variants of metrics which can be calculated.
Your given choice of software tools will enable these metrics to be calculated. For
instance, LAStools provides a command line tool to retrieve forestry metrics (lascanopy) while SPDLib provides a tool called spdmetrics. Once calculated, these
metrics are commonly used within either a classification scheme to retrieve categorical classes for the scene or used within a regression analysis to field data to retrieve
parameters, such as above ground biomass.
Radiometric Correction
There have been a number of attempts to radiometrically correct and/or normalise
the LiDAR intensity/amplitude data (e.g., Donoghue et al. 2007). However, as of
yet, there are few examples within the literature that demonstrate a clear application
for this product. Therefore, for information on these processing stages, the reader is
referred to Wagner (2010) and Coren and Sterzai (2007).
Standard Data Specifications
A number of organisations worldwide (e.g., the Intergovernmental Committee on
Surveying and Mapping’s; http://www.icsm.gov.au/elevation/) have set out standard
specifications for the acquisition of LiDAR data. These specifications are commonly regarded as the minimum specification for the organisation. These specifications help ensure that data acquisitions are fit for purpose and can be used to meet
the wider requirements of the organisation rather than just specific project needs.
Table 8 lists a number of available specifications and, if you are acquiring LIDAR
data, reference to these specifications is recommended.
Pre-processing of Remotely Sensed Imagery