Marc Bouvier1,*, Sylvie Durrieu1, Richard A. Fournier2, Nathalie Saint-Geours1, Dominique Guyon3, Eloi Grau1
1
UMR TETIS Irstea-Cirad-AgroParisTech/ENGREF, Maison de la Télédétection en Languedoc-Roussillon, 500, rue J.F. Breton BP 5095, 34196 Montpellier Cedex 05, France
2Centre d’Applications et de Recherches en Télédétection (CARTEL), Département de géomatique appliquée, Université de Sherbrooke, Sherbrooke, Québec, Canada, J1K 2R1 3INRA, UMR1391 ISPA, 33140 Villenave d’Ornon, France
Article soumis à Forestry
2.1 Abstract
This study investigated the influence of sampling design parameters on biomass prediction accuracy obtained from airborne lidar data. A one-factor-at-a-time and a global sensitivity analysis were applied to identify the parameters most impacting model
accuracy. We focused on several lidar and field survey parameters that can be easily controlled by users. In this pine plantations study site, a decrease in pulse density (4 to 0.5 pulse/m2) led to a small decrease in prediction accuracy (-3%). However, variability in the number of field plots, positioning accuracy, and plot size, significantly impacted model performance. To obtain a robust model, a minimum of 40 field plots, along with field plot position accuracy of 5 m or lower, and field plot radius exceeding 13 m are recommended. Minimum DBH threshold and the choice of the allometric biomass equation were found to have a lesser impact on model accuracy. In addition, DBH and H measurement accuracies were respectively shown to have a minor and negligible contribution to the prediction error. Significant field measurement costs will still be needed to ensure good quality models for AGB mapping. However, by reducing pulse density, cost savings can be made on lidar acquisition.
Keywords: Airborne lidar, Area-based approach, Aboveground Biomass, Model accuracy, Sensitivity analysis, Monte Carlo analysis.
2.2 Introduction
Lidar (Light Detection And Ranging) is an active remote sensing technology based on the principles of laser ranging (Young, 2000). Airborne laser scanning (ALS) combines a lidar system, a scanning device and highly accurate navigational and positioning systems and is used to perform spatialized measurements of ground topography and vegetation structure. ALS is used for many forest applications, and, in particular, to support forest inventory.ALS coupled with field measurements is an effective approach that can be used to develop predictive models for assessing forest inventory attributes over large areas at a much lower cost than with traditional inventory practices (Lim et al., 2003a). ALS is now used operationally to enhance existing inventories (Woods et al., 2011). With the increased use of ALS in forest applications, good survey design is increasingly important to enhance information content while maximizing cost-effectiveness.
Stand volume and aboveground biomass (AGB) are key forest attributes required for forest management. The utility of Lidar to estimate volume and AGB is widely acknowledged (Nelson et al., 1988; Næsset, 2004b). Volume and AGB estimations have been computed from statistical relationships between reference measurements taken from field plots and lidar metrics. Volume and AGB are interdependent and strongly correlated (Brown and Lugo, 1984; Fang et al., 1996). Previous lidar studies have reported considerable variability in AGB prediction accuracy (Zolkos et al., 2013). Numerous
parameters may affect the ability to reliably predict forest parameters from ALS data. Prediction accuracy on volume and AGB primarily depends on four groups of parameters: (1) lidar sensor and flight parameters, (2) stand complexity, (3) field protocols and measurements, and (4) methods used to predict stand attributes. These parameters affect prediction quality and consistency. Stand complexity is inherent to the sites under study and cannot be modified; one must cope with it and try to use models that have proven their effectiveness in complex environments. Nevertheless, the three other groups of parameters can be studied and carefully defined in order to maximize the chances of meeting accuracy requirements.
First, regarding the methods used to develop a predictive model, the investigation of the best approach for model development is a relevant research question that is widely addressed in the scientific literature (Hyyppä et al., 2008; Li et al., 2008). In an area-based approach (ABA), lidar metrics are computed from each lidar sub-point clouds corresponding to the extent of each field plot. Statistical relationships are established between stand structural attributes (e.g. volume or AGB) obtained from field measurements at the plot scale and the most explanatory metrics chosen from a multitude of lidar metrics (Lefsky et al., 1999b; Næsset, 2002). Statistical models, i.e. step-wise regression models based on height and point density distribution metrics, have dominated the application of ALS with ABA. However, metric selection from stepwise regression usually leads to models that suffer from over-fitting problems (Chen et al., 2007). The use of a predefined set of four complementary metrics known to be related to stand attributes was recently proposed to avoid model over-fitting (Bouvier et al., 2015).
Second, we will consider the impact of the data used to build the models, i.e. lidar data and field measurements. Deciding which lidar sensor and flight parameters are the most suitable when planning and designing a lidar survey involves a trade-off between acquisition cost and result accuracy. The first parameter that needs to be set is pulse density (in pulses/m2). The pulse density chosen can be obtained by adjusting the scanning angle, flight altitude and speed. For a given speed, increasing swath width by increasing the scanning angle or the flight altitude reduces pulse density at ground level. Korhonen et al. (2011) have assessed the scanning angle impact. They found that lidar metrics were only slightly impacted by low scanning angle, i.e. angles smaller than 32°. Regarding the effects of pulse density, Næsset (2009) found only small differences in results when comparing stand volume predictions from data acquired at different flight altitudes, and leading to pulse densities ranging from 0.8 to 3.0 pulses/m². However, he also found that relevant lidar metrics selected to build predictive models differed significantly with pulse
density. The impact of pulse density has also been investigated by Gobakken and Næsset (2008). They reported a slight decrease in volume prediction accuracy with a decrease in pulse density in the 0.06 to 1.13 pulses/m² range; although Maltamo et al. (2006) and Treitz et al. (2012) reported that pulse density does not affect the accuracy of stand attribute prediction for pulse densities varying from 0.13 to 12.7 pulses/m² and 0.5 to 3.2 pulses/m² respectively. However, in both studies, the decrease in point density was obtained by decimating lidar data acquired during a single flight, Indeed, considering that flying over the same area with different flight configurations often poses practical and financial challenges, simulations have also been used, as an alternative to real surveys, to evaluate the impact of pulse density (e.g. Maltamo et al., 2006; Magnusson et al., 2007). However, simulation-based results must be interpreted cautiously as simulations resulting from lidar data decimation cannot take into account all the changes in data quality associated with a change in pulse density driven by flight and sensor parameter setting. This might explain the difference in the conclusions obtained by Gobakken and Næsset (2008) and Maltamo et al. (2006) or Treitz et al. (2012). Pulse density is a key parameter in ABA and a major cost driver dictated by ALS system setting and flight configuration (Hyyppä et al., 2008). It is therefore important to investigate how, and to what extent, pulse density affects stand attribute prediction accuracy.
Third, field protocols and measurements involve other parameters affecting stand attribute prediction accuracy that need to be optimized as field surveys are time consuming and costly. The cost associated with fieldwork may be reduced by reducing the number of measured plots and by simplifying field measurement protocols or by relaxing measurement quality constraints and by accepting a decrease in measurement accuracy. Field protocol design requires setting many rules regarding the choice of the number and the size of field plots, their spatial distribution, the threshold of the diameter at breast height (DBH, trunk diameter measured at 1.3 m above ground) defining trees to be inventoried, to name but a few. Field plot number is a major parameter affecting field data quality. The set of field plots must represent the variability of the stands surveyed. Junttila et al. (2008) only found a slight decrease in volume prediction accuracy following a significant reduction in plot number from 465 to 63 plots using either sparse Bayesian regressions (from 19.9% to 22.4%) or ordinary least square regressions (from 20.3% to 22.6%). Maltamo et al. (2011) compared different field plot selection strategies in Norway forests dominated by Norway spruce. The study concluded that the accuracy of stand attribute predictions tended to degrade significantly when less than 50 field plots were used. Gobakken and Næsset (2008) examined both the number and size of field plots. They
forest structure. Plot size was also shown to influence predictions of AGB as larger plots reduce edge effects (Frazer et al., 2011; Strunk et al., 2012). Moreover, higher prediction accuracy is expected with larger plots due to spatial averaging of errors (Goetz and Dubayah, 2011). Zolkos et al. (2013) found a moderate but significant positive correlation between AGB prediction accuracy and plot size amongst 48 lidar studies. Accuracy of volume and AGB reference measurements used for both calibration and validation of models is typically based on the measurements of only few variables, namely tree height (H) and DBH, but also on plot position, usually measured with a Global Positioning System (GPS), and on allometric equations used to predict reference volume and AGB. Gobakken and Næsset (2009) have shown that GPS position errors have a significant impact on prediction accuracy in Norway forests; volume prediction accuracy was decreased by 15.8% for position errors up to 5 m. Frazer et al. (2011) investigated how plot size and co-registration errors interact to influence AGB prediction. They found that an increase in circular plot radius from 10 to 25 m reduces the impact of co-registration error and improves AGB prediction accuracy by 13.3%. Thus, volume and AGB predictions from ALS data are highly dependent on the way field surveys were conducted as well as on the way lidar data were acquired. Both these groups of parameters can be, to some extent, set in order to optimize assessment of forest attributes from ALS data.
There is a scarcity of studies assessing the relative significance of all the parameters that can be easily set and monitored (namely those from lidar survey configuration and field inventory). Indeed, most studies have addressed error evaluation using different parameter combinations but do not characterise the impact of each parameter, thus making it difficult to rank parameter significance. Another explanation lies in the way sensitivity analyses are conducted. Indeed, in most studies, the authors adjust the parameters manually, only considering a small number of alternative values for each of them, in a one- factor-at-a-time (OAT) approach. Independent parameters can be investigated and optimized individually using an OAT approach. But, the OAT approach is known to have one serious drawback: it does not properly account for possible interactions between model parameters (Saltelli et al., 2008). In addition, the use of a small set of alternative values for each parameter is less exhaustive than adopting a probabilistic setting and exploring the space of input uncertainties within a Monte Carlo framework based on a large number of model evaluations. This makes it more difficult to provide the practical recommendations needed to optimize forest resource assessment from ALS data.
There is a need for more comprehensive approaches that can quantify the specific impacts of different lidar acquisition parameters, field protocols and measurements on the
accuracy of the resulting model. Global sensitivity analysis (GSA) may help to overcome these limitations by providing the capacity to study the impact of the variability of all model input variables on the output variables. GSA aims to study how the uncertainty of a model output, i.e. forest attributes, can be apportioned to different sources of uncertainty in its inputs, i.e. survey parameters (Saltelli et al., 2008). It allows for a ranking of the input variables according to their contribution to the output variability. GSA thus helps to identify the key parameters on which further research should be carried out. GSA methods have been widely adopted by modellers in different disciplinary fields (Tarantola et al., 2002; Cariboni et al., 2007; Ascough Ii et al., 2008), and are now recognized as an essential step for rigorous model development (CREM, 2009; European Commission, 2009).
Among the numerous parameters influencing AGB predictive model accuracy, only a few of them can be easily controlled, i.e. lidar pulse density and both field sampling protocols and measurements. Therefore, the main goal of this study was to provide - partly based on GSA results - technical guidelines to optimize lidar pulse density and field survey protocols in order to implement predictive models of AGB from ALS data. To achieve this, three specific tasks were performed. First, we assessed the influence of lidar pulse density on the performance of the model by comparing results obtained with several lidar data sets acquired on the same study site. Second, we identified field measurements most impacting model accuracy by comparing predictions obtained when parameters are changed one at a time (OAT approach) and all simultaneously in a GSA. Third, we defined a range of recommended values for the parameters that can be controlled and, where appropriate, provided recommendations to adopt practices that will enhance the use of lidar data to predict AGB.
2.3 Materials
2.3.1 Study sites
Two study sites were selected to investigate different parameters by introducing uncertainty related to forest attribute estimates derived from ALS data. Both forest sites are located in the Landes region in southwestern France (Figure II.1). Site 1 (44.69° N, 0.90° W) and Site 2 (44.40° N, 0.50° W) had surface areas of 80 km² and 60 km², respectively. The Landes forest is characterized by nutrient-poor, sandy soil with a flat topography. Climate of the region is oceanic (Joly et al., 2010). The area is dominated by mono-specific stands of Maritime pine (Pinus pinaster Aiton) in even aged plantations.
Some Pedunculate oaks (Quercus robur L.) are marginally present (~1%). Both sites were highly representative of Landes forest diversity in terms of forest structure, and management practices.
Figure II.1. Location of the two study sites in the Landes region, in southwestern France.
2.3.2 Field plot data
Field measurements were collected on a series of sample plots for each study site. A different field protocol was used for each site. 100 square plots (400 m2) were measured at Site 1 during the summer of 2012 where all the trees were identified and their species recorded in order to assess the total tree number per plot. In addition, the DBH for the ten trees closest to plot center and H of the five trees closest to plot center were measured. 39 circular plots were measured at Site 2 during spring 2011. For plots having at least one tree with a DBH ≥ 17.5 cm, plot radius was set at 15 m (~707 m², 31 plots). For plots with trees with all DBH under 17.5 cm, plot radius was set to 6 m (~113 m², 8 plots). In these plots, all trees with DBH ≥ 7.5 cm were measured and, for each tree information on their: (1) position in the plot, (2) species, (3) DBH, and (4) H were collected.
AGB is the dry mass (Mg/ha) of all the tree compartments that are above ground, including stems, branches, and leaves. AGB was estimated for each field plot. For trees with both H (m) and DBH (cm) measurements, individual tree AGB was derived from H and DBH using species-specific allometric equations. In Site 1, for trees without height
measurements, H was estimated from DBH using plot- or site-specific allometric relationships between H and DBH. In Site 1 plots, for trees with neither H nor DBH measurements, AGB was extrapolated from the mean tree AGB of the measured trees. Four allometric equations were available to estimate AGB for Maritime pine; the dominant species (see section 3.3.1-7) and, the allometric equation from Hounzandji et al. (2014) was used for Pedunculate oak. Estimations at plot level were then rescaled to per-hectare values. Using the allometric equation from Shaiek et al. (2011) for Maritime pine, mean plots AGB were estimated at 71.8 Mg/ha in Site 1 and 77.5 Mg/ha in Site 2.
2.3.3 Lidar data
ALS data were collected at both study sites using a small footprint lidar. Site 1 was surveyed in February 2013 using an ALTM 3100 (Optech, Canada) system. Four different acquisitions were carried out with different flight parameters and system configurations in order to produce ALS data at different pulse density: (flight A) 0.5 pulse/m², (flight B) 1 pulse/m², (flight C) 2 pulses/m², and (flight D) 4 pulses/m². Site 2 was surveyed using a LMS Q560 (Riegl, Austria) system in April 2011 with a pulse density of 8 pulses/m². Additional data specifications for ALS data sets are given in Table II.2. For both sites, lidar surveys were conducted at the same growing stage as field surveys.
Table II.2. Technical specifications of Lidar surveys on both study sites
Site 1
Site 2
A B C D
Date of survey February 2013 April 2011 ALS sensor Optech ALTM 3100 Riegl LMS Q560
Wavelength (nm) 1064 1550
Ground speed (m/s) 80 50
Beam divergence (mrad) 0.8 0.5
Scan angle (°) 25 14 16 16 29.5 Repetition rate (Hz) 70 50 70 70 100 Flight altitude (m) 1530 2250 1530 1530 550 Pulse density (pulses/m²) 0.5 1.0 2.0 4.0 8.0
Data pre-processing was performed by the data providers, i.e. IGN and Sintegra for sites 1 and 2, respectively. Ground points were classified using the TIN-iterative algorithm (Axelsson, 2000) in order to produce a digital terrain model (DTM). The DTM was then converted into a 1 m resolution grid. For each acquisition, aboveground heights were calculated by subtracting from each lidar point elevation the corresponding ground elevation given by the DTM, thereby removing topographic effects from lidar point clouds. From the resulting lidar point clouds, sub-point clouds corresponding to the spatial extent of each field plot were extracted.
2.4 Methods
We adopted an ABA and developed regression models for AGB predictions from lidar data according to a new method developed by Bouvier et al. (2015). We decided to choose just one methodology from among all those available as we wished to focus on the parameters explaining AGB prediction accuracy variability rather than model selection. We chose parameters that were easy to control when implementing an ABA approach, i.e. lidar pulse density and both field sampling protocols and measurements. Considering the available data sets, the following eight parameters were studied: lidar pulse density, field plot number and size, minimum DBH threshold defining trees to be inventoried (DBHmin), H and DBH measurement errors, position accuracy of plot centers, and the
choice of allometric equation. Two sensitivity analysis approaches were used to investigate the role of these parameters in the prediction quality of AGB models. The first was a standard OFAT approach. When this approach was applied, regression analyses were carried out to assess the influence of key parameters individually on AGB accuracy. Each parameter was tested using a wide range of values in order to assess its specific impact on AGB prediction accuracy. The parameter value ranges were defined by gradually degrading the characteristics of the available data sets except in the cases of the choice of allometric equations and pulse density. The second approach used was a GSA based on Monte Carlo simulations. This approach aimed at identifying the parameters related to field protocols and measurements that explain most AGB prediction variance and also interactions between parameters. Data processing was performed in the R statistical environment (http://www.r-project.org/).
2.4.1 Modelling approach
We used the method described by Bouvier et al. (2015), which was then used to produce AGB models from four categories of lidar metrics. This approach was tested and validated in several forest environments. The model is developed using four metrics to describe complementary 3D structural aspects of a stand unlike conventional statistical models that are based on height and point density distribution metrics (Næsset, 2002). The four metrics were estimated from ALS data: mean canopy height (𝜇𝐶𝐻); height
heterogeneity (𝜎𝐶𝐻2 ); horizontal canopy distribution (P); and a metric that was estimated
from leaf area density profiles to provide information on stand vertical structure (𝐶𝑣𝐿𝐴𝐷). 𝜇𝐶𝐻 was calculated as the average of first return heights. 𝜎𝐶𝐻2 was calculated as the variance of first return heights. P was calculated from the lidar data as the ratio of the number of first returns below 2 m to the total number of first returns. Lastly 𝐶𝑣𝐿𝐴𝐷 was calculated as the ratio of the standard deviation to the mean of the leaf area density profile. The density profile was computed by assessing a transmittance profile and then using the