International Journal of Applied Earth Observation and Geoinformation
Stratified aboveground forest biomass estimation by remote sensing data
Introduction
The estimation of aboveground forest biomass from remotely-sensed data is currently of great interest, due to important applications ranging from forest management to environmental and climate policy. Forest biomass is directly linked to carbon stocks, which are crucial for establishing future mitigation scenarios under climate change. The importance of forest biomass in the context of such mitigation strategies is demonstrated by international initiatives such as reducing emissions from deforestation and forest degradation (REDD and REDD+) (e.g., Hill et al., 2013). Furthermore, biomass estimates can support surveys assessing the bioenergy potential of certain landscapes and help to monitor the sustainability of forest resources (e.g., Rosillo Calle et al., 2008).
Metrics from light detection and ranging (LiDAR) data have been frequently reported to provide good estimates of aboveground biomass across different geographical units (e.g., Hall et al., 2005, Næsset and Gobakken, 2008, Bright et al., 2012). A possibility to improve predictive accuracy could be including additional information, for example on species composition, in the estimation process. This could be achieved by various techniques. One is combining LiDAR information with optical data, but results have been mixed. Whereas some improvements could be obtained (e.g., Popescu et al., 2004), these were occasionally reported to be only marginal (Kulawardhana et al., 2014), particularly in case of pure deciduous stands (Tonolli et al., 2011). Previous studies using predictors from LiDAR-based biomass models (Packalén and Maltamo, 2006, Packalén and Maltamo, 2007, Breidenbach et al., 2010a, Breidenbach et al., 2010b) show promising results for predicting biomass on species level. Further refinements have been reported by incorporating hyperspectral metrics (e.g., Sarrazin et al., 2011). However, in many cases (e.g., in highly mixed stands) a realistic biomass prediction at tree species level will be severely restricted by factors such as spectral mixture due to tree crown overlaps. In such cases, a coarser division (i.e., post-stratification) into species groups (or communities) or into major strata of coniferous, deciduous and mixed stands is a compromise to retrieve strata-specific estimates (e.g., Eckert, 2012, Latifi et al., 2012). A practical example under which a similar stratification approach is applied is the Forest Inventory and Analysis program of the US, where remote sensing data are used to stratify sample plots from a nation-wide regular grid to subpopulations. The proportionally-allocated samples of each subpopulation are eventually inventoried in the field (e.g., Reams et al., 2005).
A superiority of species (or strata) – specific biomass models to those predicting the entire units at once has been found in a number of previous reports (Breidenbach et al., 2010a, Breidenbach et al., 2010b, Latifi et al., 2012). In case of LiDAR data, this may be related to the differing interactions of the laser pulse signals with the architecture of broadleaved and coniferous trees, as stated by Heurich and Thoma, (2008) who suggested the stratification into deciduous, coniferous and mixed strata for LiDAR-assisted forest parameter estimation.
There are several examples on comparisons between modeling approaches while predicting area-based biomass (e.g., Breidenbach et al., 2010a, Latifi et al., 2010, Powell et al., 2010, Main-Knorn et al., 2015, Gagliasso et al., 2014). However, studies addressing the general issue of post-stratification of the input data for remote sensing-based estimates are still scarce (see Heurich and Thoma, 2008, Dahlke et al., 2013). It has been suggested that classifying inventory plots information to forest types or districts may improve the precision of forest attribute estimation (Reams et al., 2005, Nelson, 2010, Latifi and Koch, 2012), particularly when the aim is to design a multi-level forest inventory for large area estimations (Katila and Tomppo, 2002, Andersen et al., 2011). However, recent reports also state an existing shortage of statistical analysis on post-stratified estimation of forest attributes to be a function of restriction in the sample size in small scale domains (McRoberts et al., 2012), who also provided examples on regional inferences of standing timber volume (McRoberts et al., 2013). Yet in order to draw reliable conclusions on the effect of stratification on forest biomass estimates, stratification approaches are needed to be examined in interaction with several other parameters which are known to influence remote sensing-based biomass estimates (e.g., sensor type, prediction method, sample size).
Here, we explore the question of whether stratification of sampling units into major forest types can influence the predictive quality of area-based forest biomass modeling. We based the models on a number of pre-selected predictors from sets of LiDAR and hyperspectral data. We based the models on a number of pre-selected predictors from sets of LiDAR and hyperspectral data. We did not consider building models based on combined LiDAR and hyperspectral predictors due to the previously-available reports on the fairly similar performance of LiDAR and combined LiDAR + Hyperspectral data for the examined dataset (e.g., Latifi et al., 2012, Fassnacht et al., 2014).
Commonly applied parametric and non-parametric prediction methods were used on bootstrapped subsamples of the data to obtain a relative accuracy measure (RMSE) as well as the degree of variance explained by the models (r2) under cross-validation. Two subsequent analyses of Variance (ANOVA) were used to compare the differences in RMSE and r2 (a) between the strata (b) between the stratified and the non-stratified case with differences in predictive accuracy from other factors (prediction method, input data type and sample size). This allows us to systematical assess the importance of factors which typically occur when modeling stratified forest biomass by means of remote sensing data.
Section snippets
Study site
The study site consists of nearly 900 ha of managed pure and mixed stands located in the vicinity of the southwestern German city of Karlsruhe (8°24′09′′E, 49°03′37′′N to 8°25′49′′E, 49°01′15′′N). The dominant tree species is scots pine (Pinus sylvestris L., with 56.3% of the total timber volume), occurring with other species such as European Beech (Fagus sylvatica L., with 17.8% of the total volume), Sessile Oak (Quercus petraea Liebl.) and Pedunculate Oak (Quercus robur L.) (jointly 14.9% of
Model performances
Fig. 3, Fig. 4, Fig. 5, Fig. 6 summarize the results of model performances. For the strata-specific models, the median r2 values varied between 0.17 (kNN for broadleaves) and 0.48 (RF for coniferous) for the hyperspectral predictors. For the LiDAR predictors they ranged between 0.42 (SVM for coniferous) and 0.6 (RF for coniferous). No explicit trend was observed among the applied modeling approaches. When applying hyperspectral metrics, lower r2 rates were returned by the broadleaved stratum
Discussion
Regional forest inventories are often based on systematic sampling grids, and thus are constrained to previously-defined sampling designs and intensities (McRoberts et al., 2012). Since a stratified sampling can often not be integrated into forest inventory plans prior to the inventory, a post-stratification of the previously- recorded plots into major forest types have been stated to partially enable more precise estimations of forest attributes (Westfall et al., 2011, McRoberts et al., 2012).
Conclusion
In the context of remote sensing-assisted estimation of forest biomass, we examined the importance of sensor type, statistical prediction method, sample size as well as the influence of stratification of the sample units in two experiments. The results lead us to the conclusion that the sensor type (hyperspectral/LiDAR) showed to be the essential source of impact on the yielded predictive performance of the models. This was followed by the effect of prediction method, while sample size turned
Acknowledgements
This study was partly funded by the German Aerospace Center (DLR) and the German Federal Ministry of Economy and Technology based on the Bundestag resolution50EE1025 and 50EE1265-66. The authors would like to acknowledge the valuable suggestions of Prof. Dr. Carsten Dormann concerning the applied methodology and the visualization of the results.
References (64)
- et al.
Prediction of species specific forest inventory attributes using a nonparametric semi-individual treecrown approach based on fused airborne laser scanning and multispectral data
Remote Sens. Environ.
(2010) - et al.
Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors
Remote Sens. Environ.
(2011) - et al.
Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves
J. Plant Physiol.
(2003) - et al.
Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests
For. Ecol. Manage.
(2005) Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment ISPRS
J. Photogramm. Remote Sens.
(2010)- et al.
A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland
For. Ecol. Manage.
(2006) - et al.
Forest structure modelling with combined airborne hyperspectral and LiDAR data
Remote Sens. Environ.
(2012) - et al.
Post-stratified estimation of forest area and growing stock volume using lidar-based stratifications
Remote Sens. Environ.
(2012) - et al.
Inference for lidar-assisted estimation of forest growing stock volume
Remote Sens. Environ.
(2013) - et al.
Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser
Remote Sens. Environ.
(2008)