| dc.description.abstracteng | The impact of changing the scale of observation on information derived from forest inventories
is the basis of scale-related research in forest inventory and analysis (FIA). Interactions between
the scale of observation and observed heterogeneity in studied variables highlight a dependence
on scale that affects measurements, estimates, and relationships between inventory data from
terrestrial and remote sensing surveys. This doctoral research defines "scale" as the divisions
of continuous space over which measurements are made, or hierarchies of discrete units of
study/analysis in space. Therefore, the "scale of observation" (also known as support) refers
to that integral of space over which statistics are computed and forest inventory variables
regionalized.
Given the ubiquitous nature of scale issues, a case study approach was undertaken in
this research (Articles I-IV) with the goal to provide fundamental understanding of responses
to the scale of observation for specific FIA variables. The studied forest inventory variables
are; forest stand structural heterogeneity, forest cover proportion and tree species identities.
Forest cover proportion (or simply forest area) and tree species are traditional and fundamental
forest inventory variables commonly assessed over large areas using both terrestrial samples
and remote sensing data whereas, forest stand structural heterogeneity is a contemporary FIA
variable that is increasingly demanded in multi-resource inventories to inform management
and conservation efforts as it is linked to biodiversity, productivity, ecosystem functioning and
productivity, and used as auxiliary data in forest inventory.
This research has two overall aims:
1. To improve the understanding of the association between the scale of observation and
observed heterogeneity in inventory of forest stand structural heterogeneity, forest-cover
proportions, and identification of tree species from a combination of terrestrial samples
and remote sensing data.
2. To contribute knowledge to the estimation of scale-dependence in inventory of forest
stand structural heterogeneity, forest-cover proportions, and identification of tree species
from a combination of terrestrial samples and remote sensing data.
Different scales of observation were considered across the four case studies encompassing
individual leaf, crown-part or branch, single-tree crown, forest stand, landscape and global levels
of analysis. Terrestrial and remote sensing data sets from a variety of temperate forests in
Germany and France were utilized across case studies. In cases where no inventory data were
available, synthetic data was simulated at different scales of observation. Heterogeneity in FIA
variable estimates was monitored across scales of observation using estimators of variance and
associated precision. As too much heterogeneity is hardly interpreted due to a low signal to noise
ratio, object-based image analysis (OBIA) methods were used to manage heterogeneity in high resolution
remote sensing data before evaluating scale dependence or scaling across observed
scales. Similarly, ensemble classification techniques were applied to address methodological
heterogeneity across classifiers in a case study on classification of two physically and spectrally
similar Pinus species. Across case studies, a dependence on the scale of observation was
determined by linking estimates of heterogeneity to their respective scales of observation using
linear regression and a combination of geo-statistics and Monte-Carlo approaches. In order to
address scale-dependence, thresholds to scale domains were identified so as to enable efficient
observation of studied FIA variables and scaling approaches proposed to bridge observations
across scales. For scaling, this research evaluated the potential of different regression techniques
to map forest stand structural heterogeneity and tree species wall-to-wall from remote sensing
data. In addition, radiative transfer modelling was evaluated in the transfer between leaf and
crown hyperspectra, and a global sampling grid framework proposed to efficiently link different
stages of survey sampling.
This research shows that the scale of observation affected all studied FIA variables albeit
to varying degrees, conditioned on the spatial structure and aggregation properties of the
assessed FIA variable (i.e. whether the variable is extensive, intensive or scale-specific) and
the method used in aggregation on support (e.g. mean, variance, quantile etc.). The scale
of observation affected measurements or estimates of the studied FIA variables as well as
relationships between spatially structured FIA variables. The scale of observation determined
observed heterogeneity in FIA variables, affected parameter retrieval from radiative transfer
models, and affected variable selection and performance of models linking terrestrial and remote
sensing data. On the other hand, this research shows that it is possible to determine domains
of scale dependence within which to efficiently observe the studied FIA variables and to bridge
between scales of observation using various scaling methods.
The findings of this doctoral research are relevant for the general understanding of scale
issues in FIA. Research in Article I, for example, informs optimization of plot sizes for efficient
inventory and mapping of forest structural heterogeneity, as well as for the design of natural
resource inventories. Similarly, research in Article II is applicable in large area forest (or general
land) cover monitoring from sampling by both visual interpretation of high resolution remote
sensing imagery and terrestrial surveys. This research is also useful to determine observation
design for efficient inventory of land cover. Research in Article III contributes in many contexts
of remote sensing assisted inventory of forests especially in management and conservation
planning, pest and diseases control and in the estimation of biomass. Lastly, research in Article IV
highlights scale-related effects in passive optical remote sensing of forests currently understudied
and can ultimately contribute to sensor calibration and modelling approaches. | de |