11 research outputs found
Usage of LiDAR data for leaf area index estimation
Leaf area index (LAI) can be measured either directly, using destructive methods, or indirectly using optical
methods that are based on the tight relationship between LAI and canopy light transmittance. Third, innovative
approach for LAI measuring is usage of remote sensing data, especially airborne laser scanning (ALS) data shows
itself as a advisable source for purposes of LAI modelling in large areas. Until now there has been very little
research to compare LAI estimated by the two different approaches. Indirect measurements of LAI using
hemispherical photography are based on the transmission of solar radiation through the vegetation. It can thus be
assumed that the same is true for the penetration of LiDAR laser beams through the vegetation canopy. In this
study we use ALS based LiDAR penetration index (LPI) and ground based measurement of LAI obtained from
hemispherical photographs as a reference in-situ method. Several regression models describing the corellation LAI
and LPI were developed with various coefficients of determination ranging up to 0,81. All models were validated
and based on the tests performed, no errors were drawn that would affect their credibility
Comparison of LiDAR-based Models for True Leaf Area Index and Effective Leaf Area Index Estimation in Young Beech Forests
The leaf area index (LAI) is one of the most common leaf area and canopy structure quantifiers. Direct LAI measurement and determination of canopy characteristics in larger areas is unrealistic due to the large number of measurements required to create the distribution model. This study compares the regression models for the ALS-based calculation of LAI, where the effective leaf area index (eLAI) determined by optical methods and the LAI determined by the direct destructive method and developed by allometric equations were used as response variables. LiDAR metrics and the laser penetration index (LPI) were used as predictor variables. The regression models of LPI and eLAI dependency and the LiDAR metrics and eLAI dependency showed coefficients of determination (R2) of 0.75 and 0.92, respectively; the advantage of using LiDAR metrics for more accurate modelling is demonstrated. The model for true LAI estimation reached a R2of 0.88.O
Laser scanning in forests
Peer reviewe
Uncertainty in parameterizing floodplain forest friction for natural flood management, using remote sensing
One potential Natural Flood Management (NFM) option is floodplain reforestation or manage existing riparian forests, with a view to increasing flow resistance and attenuate flood hydrographs. However, the effectiveness of floodplain forests as resistance agents, during different magnitude overbank floods, has yet to be appropriately parameterized for hydraulic models. Remote sensing offers high-resolution datasets capable of characterizing vegetation structure from a variety of platforms, but they contain uncertainty. For the first time, we demonstrate uncertainty propagation in remote sensing derivations of complex vegetation structure through roughness prediction and floodplain flow for extreme flows and different forest types (young and old Poplar plantations, young and old Pine plantations, and an unmanaged riparian forest). The lowest uncertainties resulted from terrestrial and airborne lidar, where airborne lidar is currently best at defining canopy leaf area, but more research is needed to determine wood area. Mean literature uncertainties in stem density, trunk diameter, wood, and leaf area indices (20, 10, 30, 20%, respectively) resulted in a combined Manning’s n uncertainty from 11–13% to 11–17% at 2 m to 8 m flow depths. This equates to 7–8% roughness uncertainty per 10% combined forest structure uncertainty. Individually, stem density and trunk diameter uncertainties resulted in the largest Manning’s n uncertainty at all flow depths, especially for flow though Pine plantations. For deeper flows, leaf and woody areas become much more important, especially for unmanaged riparian forests with low canopy morphology. Forest structure errors propagated to flow depth demonstrate that even small flows can change by a decimeter, while deeper flows can change by 40 cm or more. For flow depth, errors in canopy structure are deemed more severe in flows depths beyond 4–6 m. This study highlights the need for lower uncertainty in all forest structure components using remote sensing, to improve roughness parameterization and flood modeling for NFM
Available and missing data to model impact of climate change on European forests
Climate change is expected to cause major changes in forest ecosystems during the 21st century and beyond. To assess forest impacts from climate change, the existing empirical information must be structured, harmonised and assimilated into a form suitable to develop and test state-of-the-art forest and ecosystem models. The combination of empirical data collected at large spatial and long temporal scales with suitable modelling approaches is key to understand forest dynamics under climate change. To facilitate data and model integration, we identified major climate change impacts observed on European forest functioning and summarised the data available for monitoring and predicting such impacts. Our analysis of c. 120 forest-related databases (including information from remote sensing, vegetation inventories, dendroecology, palaeoecology, eddy-flux sites, common garden experiments and genetic techniques) and 50 databases of environmental drivers highlights a substantial degree of data availability and accessibility. However, some critical variables relevant to predicting European forest responses to climate change are only available at relatively short time frames (up to 10-20 years), including intra-specific trait variability, defoliation patterns, tree mortality and recruitment. Moreover, we identified data gaps or lack of data integration particularly in variables related to local adaptation and phenotypic plasticity, dispersal capabilities and physiological responses. Overall, we conclude that forest data availability across Europe is improving, but further efforts are needed to integrate, harmonise and interpret this data (i.e. making data useable for non-experts). Continuation of existing monitoring and networks schemes together with the establishments of new networks to address data gaps is crucial to rigorously predict climate change impacts on European forests. © 2019 The Author(s
Available and missing data to model impact of climate change on European forests
Climate change is expected to cause major changes in forest ecosystems during the 21st century and beyond. To assess forest impacts from climate change, the existing empirical information must be structured, harmonised and assimilated into a form suitable to develop and test state-of-the-art forest and ecosystem models. The combination of empirical data collected at large spatial and long temporal scales with suitable modelling approaches is key to understand forest dynamics under climate change. To facilitate data and model integration, we identified major climate change impacts observed on European forest functioning and summarised the data available for monitoring and predicting such impacts. Our analysis of c. 120 forest-related databases (including information from remote sensing, vegetation inventories, dendroecology, palaeoecology, eddy-flux sites, common garden experiments and genetic techniques) and 50 databases of environmental drivers highlights a substantial degree of data availability and accessibility. However, some critical variables relevant to predicting European forest responses to climate change are only available at relatively short time frames (up to 10-20 years), including intra-specific trait variability, defoliation patterns, tree mortality and recruitment. Moreover, we identified data gaps or lack of data integration particularly in variables related to local adaptation and phenotypic plasticity, dispersal capabilities and physiological responses. Overall, we conclude that forest data availability across Europe is improving, but further efforts are needed to integrate, harmonise and interpret this data (i.e. making data useable for non-experts). Continuation of existing monitoring and networks schemes together with the establishments of new networks to address data gaps is crucial to rigorously predict climate change impacts on European forests.Peer reviewe
Mapping vegetation density in a heterogeneous river floodplain ecosystem using pointable CHRIS/PROBA data
River floodplains in the Netherlands serve as water storage areas, while they also have the function of nature rehabilitation areas. Floodplain vegetation is therefore subject to natural processes of vegetation succession. At the same time, vegetation encroachment obstructs the water flow into the floodplains and increases the flood risk for the hinterland. Spaceborne pointable imaging spectroscopy has the potential to quantify vegetation density on the basis of leaf area index (LAI) from a desired view zenith angle. In this respect, hyperspectral pointable CHRIS data were linked to the ray tracing canopy reflectance model FLIGHT to retrieve vegetation density estimates over a heterogeneous river floodplain. FLIGHT enables simulating top-of-canopy reflectance of vegetated surfaces either in turbid (e.g., grasslands) or in 3D (e.g., forests) mode. By inverting FLIGHT against CHRIS data, LAI was computed for three main classified vegetation types, ‘herbaceous’, ‘shrubs’ and ‘forest’, and for the CHRIS view zenith angles in nadir, backward (-36°) and forward (+36°) scatter direction. The -36° direction showed most LAI variability within the vegetation types and was best validated, closely followed by the nadir direction. The +36° direction led to poorest LAI retrievals. The class-based inversion process has been implemented into a GUI toolbox which would enable the river manager to generate LAI maps in a semiautomatic way
Comparing synthetic aperture radar and LiDAR for above-ground biomass estimation in Glen Affric, Scotland
Quantifying above-ground biomass (AGB) and carbon sequestration has been a
significant focus of attention within the UNFCCC and Kyoto Protocol for improvement
of national carbon accounting systems (IPCC, 2007; UNFCCC, 2011). A multitude of
research has been carried out in relatively flat and homogeneous forests (Ranson & Sun,
1994; Beaudoin et al.,1994; Kurvonen et al., 1999; Austin et al., 2003; Dimitris et al.,
2005), yet forests in the highlands, which generally form heterogeneous forest cover and
sparse woodlands with mountainous terrain have been largely neglected in AGB studies
(Cloude et al., 2001; 2008; Lumsdon et al., 2005; 2008; Erxue et al., 2009, Tan et al.,
2010; 2011a; 2011b; 2011c; 2011d). Since mountain forests constitute approximately
28% of the total global forest area (Price and Butt, 2000), a better understanding of the
slope effects is of primary importance in AGB estimation. The main objective of this
research is to estimate AGB in the aforementioned forest in Glen Affric, Scotland using
both SAR and LiDAR data.
Two types of Synthetic Aperture Radar (SAR) data were used in this research:
TerraSAR-X, operating at X-band and ALOS PALSAR, operating at L-band, both are
fully polarimetric. The former data was acquired on 13 April 2010 and of the latter, two
scenes were acquired on 17 April 2007 and 08 June 2009. Airborne LiDAR data were
acquired on 09 June 2007. Two field measurement campaigns were carried out, one of
which was done from winter 2006 to spring 2007 where physical parameters of trees in
170 circular plots were measured by the Forestry Commission team. Another intensive
fieldwork was organised by myself with the help of my fellow colleagues and it
comprised of tree measurement in two transects of 200m x 50m at a relatively flat and
dense plantation forest and 400m x 50m at hilly and sparse semi-natural forest. AGB is
estimated for both the transects to investigate the effectiveness of the proposed method
at plot-level. This thesis evaluates the capability of polarimetric Synthetic Aperture Radar data
for AGB estimation by investigating the relationship between the SAR backscattering
coefficient and AGB and also the relationship between the decomposed scattering
mechanisms and AGB. Due to the terrain and heterogeneous nature of the forests, the
result from the backscatter-AGB analysis show that these forests present a challenge for
simple AGB estimation. As an alternative, polarimetric techniques were applied to the
problem by decomposing the backscattering information into scattering mechanisms
based on the approach by Yamaguchi (2005; 2006), which are then regressed to the field
measured AGB. Of the two data sets, ALOS PALSAR demonstrates a better estimation
capacity for AGB estimation than TerraSAR-X. The AGB estimated results from SAR
data are compared with AGB derived from LiDAR data. Since tree height is often
correlated with AGB (Onge et al., 2008; Gang et al., 2010), the effectiveness of the tree
height retrieval from LiDAR is evaluated as an indicator of AGB. Tree delineation was
performed before AGB of individual trees were calculated allometrically. Results were
validated by comparison to the fieldwork data. The amount of overestimation varies
across the different canopy conditions. These results give some indication of when to
use LiDAR or SAR to retrieve forest AGB. LiDAR is able to estimate AGB with good
accuracy and the R2 value obtained is 0.97 with RMSE of 14.81 ton/ha. The R2 and
RMSE obtained for TerraSAR-X are 0.41 and 28.5 ton/ha, respectively while for ALOS
PALSAR data are 0.70 and 23.6 ton/ha, respectively. While airborne LiDAR data with
very accurate height measurement and consequent three-dimensional (3D) stand profiles
which allows investigation into the relationship between height, number density and
AGB, it's limited to small coverage area, or large areas but at large cost. ALOS
PALSAR, on the other hand, can cover big coverage area but it provide a lower
resolution, hence, lower estimation accuracy
Novel insights into Mediterranean forest structure using high-resolution remote sensing.
PhD Theses.Tree crown morphology and arrangement in three-dimensional space is a key driver of forest dynamics,
determining not only the competitiveness of an individual but also the competitive effect exerted on
neighbouring trees. Many theoretical frameworks aim to predict crown morphology from first principles and
assumptions of Euclidean form and ultimately infer whole forest stand structure and dynamics but paucity in
data has limited vigorous testing. Tree crowns are also not rigid in form and due to their sessile nature, must
morphologically adapt to immediate abiotic and biotic surroundings to enhance survival.
The characterisation of tree structure has been limited by the simplicity and associated error of traditional
crown measurements. This project uses Terrestrial Laser Scanning data collected from a water limited
Mediterranean forest community in Spain to highlight methodological opportunities presented by TLS in
understanding forest structure and also the various developments required to extract ecologically meaningful
metrics from these data. It then applies these novel metrics to answer questions about how tree crowns scale
with size, the effects of competition and how plasticity in shape and arrangement interacts with light capture
at the individual and plot scales.
Modification to existing code as well as bespoke development were required to segment and calculate
individual metrics from trees in this forest type. Accurate measures of crown morphology highlighted
allometric scaling deviations from theoretical predictions and intra-specific differences in response to
competition, calculated using more representative neighbourhood metrics. Inter-specific differences in crown
plasticity and significant effects of size (height) were also evident, along with trade-offs between
morphological plasticity and crown size. Light capture was positively affected by plasticity with inter-specific
differences highlighting various biomass allocations strategies species undertake to acquire light. At the plot
scale, mixed-genus plots intercepted less direct light and were structurally more complex rather than more
volume filling