44 research outputs found
Individual tree detection and modelling aboveground biomass and forest parameters using discrete return airborne LiDAR data
Individual tree detection and modelling forest parameters using Airborne Laser
Scanner data (Light Detection and Ranging (LiDAR) is becoming increasingly
important for the monitoring and sustainable management of forests. Remote sensing
has been a useful tool for individual tree analysis in the past decade, although
inadequate spatial resolution from satellites means that only airborne systems have
sufficient spatial resolution to conduct individual tree analysis. Moreover, recent
advances in airborne LiDAR now provide high horizontal resolution as well as
information in the vertical dimension. However, it is challenging to fully exploit and
utilize small-footprint LiDAR data for detailed tree analysis. Procedures for forest
biomass quantification and forest attributes measurement using LiDAR data have
improved at a rapid pace as more robust and sophisticated modelling used to improve
the studies.
This thesis contains an evaluation of three approaches of utilizing LiDAR data for
individual tree forest measurement. The first explores the relationship between
LiDAR metrics and field reference to assess the correlation between LiDAR and field
data at the individual-tree level. The intention was not to detect trees automatically,
but to develop a LiDAR-AGB model based on trees that were mapped in the field so
as to evaluate the relationships between LiDAR-type metrics under controlled
conditions for the study sites, and field-derived AGB. A non-linear AGB model based
on field data and LiDAR data was developed and LiDAR height percentile h80 and
crown width measurement (CW) was found to best fit the data as evidenced by and
Adj-R2 value of 0.63, the root mean squared error of the model of 14.8% and analysis
of the residuals. This paper provides the foundation for a predictive LiDAR-AGB
model at tree level over two study sites, Pasoh Forest Reserve and FRIM Forest
Reserve.
The second part of the thesis then takes this AGB-LiDAR relationship and combines
it with individual tree crown delineation. This chapter shows the contribution of
performing an automatic individual tree crown delineation over the wider forest areas.
The individual tree crown delineation is composed of a five-step framework, which is
unique in its automated determination of dominant crown sizes in a forest area and its
adaption of the LiDAR-AGB model developed for the purpose of validation the
method. This framework correctly delineated 84% and 88% of the tree crowns in the
two forest study areas which is mostly dominated with lowland dipterocarp trees.
Thirdly, parametric and non-parametric modelling approaches are proposed for
modelling forest structural attributes. Selected modelling methods are compared for
predicting 4 forest attributes, volume (V), basal area (BA), height (Ht) and
aboveground biomass (AGB) at the species level. The AGB modelling in this paper is
extracted using the LiDAR derived variables from the automated individual tree crown
delineation, in contrast to the earlier AGB modelling where it is derived based on the
trees that were mapped in the field. The selected non-parametric method included, k-nearest
neighbour (k-NN) imputation methods: Most Similar Neighbour (MSN) and
Gradient Nearest Neighbour (GNN), Random Forest (RF) and parametric approach:
Ordinary Least Square (OLS) regression. To compare and evaluate these approaches
a scaled root mean squared error (RMSE) between observed and predicted forest
attribute sampled from both forest site was computed. The best method varied
according to response variable and performance measure. OLS regression was to found
to be the best performance method overall evidenced by RMSE after cross validation
for BA (1.40 m2), V (1.03 m3), Ht (2.22 m) and AGB (96 Kg/tree) respectively, showed
its applicability to wider conditions, while RF produced best overall results among the
non-parametric methods tested.
This thesis concludes with a discussion of the potential of LiDAR data as an
independent source of important forest inventory data source when combined with
appropriate designed sample plots in the field, and with appropriate modelling tools