Assessing sensor effects and effects of leaf-off and leaf-on canopy conditions on biophysical stand properties derived from small-footprint airborne laser data

Remote Sensing of Environment 98 (2005) 356 – 370 www.elsevier.com/locate/rse

Assessing sensor effects and effects of leaf-off and leaf-on canopy conditions on biophysical stand properties derived from small-footprint airborne laser data Erik Næsset * Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 A˚s, Norway Received 26 April 2005; received in revised form 6 July 2005; accepted 8 July 2005

Abstract Canopy height distributions were created from small-footprint airborne laser scanner data collected over 51 georeferenced field sample plots with a size of 232.9 m2 and 27 large test plots with an average size of 3435 m2. Laser data were acquired under leaf-on and leaf-off canopy conditions. The plots covered stand conditions from young forest to mature forest. The plots were divided into two categories, i.e., coniferous forest dominated by spruce and pine, and mixed forest with an average proportion of deciduous species of 31 – 42%. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were computed from the laser-derived canopy height distributions. In the mixed forest, corresponding metrics derived from the two laser data acquisitions were compared. In general, canopy metrics derived from the last returns were more affected by canopy conditions than the first return data. Furthermore, canopy height measures of the lower and intermediate parts of the canopy were more affected than maximum canopy height, and the variability of the height distribution tended to increase from leaf-on to leaf-off conditions. The coniferous plots were used to demonstrate to what extent canopy properties derived from airborne lasers may be affected by sensor-specific characteristics. The same laser system was used during the two acquisitions, but the repetition frequency was upgraded from 10 to 33 kHz in between the two missions. Comparison of the two acquisitions showed that the first return measurements of canopy height tended to be unaffected or shifted somewhat upwards by system upgrade and ground penetration was reduced, whereas the last return data indicated unaffected or downwards shifted canopy heights and increased penetration. D 2005 Elsevier Inc. All rights reserved. Keywords: Forest inventory; Canopy conditions; Canopy density; Canopy height; Laser scanning

1. Introduction Airborne scanning lasers with the ability to collect height measurements of terrain and vegetation from strips with a width of up to several hundred meters on the ground were for the first time tested in forest measurements in the early 1990’s. These sensors offer an opportunity to determine biophysical properties of forest stands such as mean tree height, basal area, and timber volume that could be used for

* Tel.: +47 64948906; fax: +47 64948890. E-mail address: [email protected]. 0034-4257/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2005.07.012

wall-to-wall mapping in forest inventories (e.g. Holmgren et al., 2003; Magnussen & Boudewyn, 1998; Means et al., 2000; Næsset, 1997a,b). Over the last six to seven years, practical procedures for large-area, stand-based forest inventories utilizing data from laser scanning which produce inventory data that complies with the data requirements in forest management and planning have been developed and tested in Norway (Næsset, 2002; Næsset & Bjerknes, 2001) and Sweden (Holmgren, 2004). These tests have been performed in study areas with a size ranging from 1000 ha (Næsset, 2002) up to 6500 ha (Næsset, 2004b). The results of these tests correspond very well with each other. The findings indicate

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a precision of biophysical stand characteristics derived from laser data such as mean height, basal area, and volume on the order of 5%, 10%, and 10%, respectively. These figures indicate a much higher precision than obtained by conventional methods where stereo photogrammetry plays a significant role. Even though laser scanning up to now has tended to increase the total inventory costs in forest inventories, the increased utility of the inventory data due to improved accuracy more than compensates for the increased costs by improving the quality of the management decisions made on the basis of inventory data and thus increases the economic value of the forest (Eid et al., 2004). Recently, a test performed on a 25,000 ha forest area, which is the first among seven laser-based operational forest inventories on commercial terms since 2002 currently under contract in Norway, showed results as indicated above (Næsset, 2004c). As we now see the contours of laser-based inventories covering thousands of hectares of forest land, the methods used must have the ability to provide reliable estimates of biophysical properties for a wide range of forest types. This is not a trivial task. The method used to estimate forest stand properties in Norway (Næsset, 2002; Næsset & Bjerknes, 2001) is based on a two-step procedure. In the first step, sample plots measured in field are used as training data to relate laser-derived measures of canopy height and density to the biophysical properties of interest. In the second step, the estimated relationships (regression equations) are used to predict the biophysical properties in question from the laser data for every stand over the entire landscape. Previous studies have indicated that a careful stratification improves the predictions. Since the laser measures the tree canopies, it is likely that the most efficient stratification criteria are those that best discriminates between crown shapes and can separate forest types with trees of different stem forms. A major challenge in estimating biophysical variables such as basal area and volume from airborne laser data is the many different stem forms that may appear for trees in stands with a given canopy—leading to entirely different relationships between the laser data and the biophysical variables of interest. Unless efficient stratification criteria can be found, the precision may degrade, and the properties for certain forest types will tend to be over-estimated while other will be under-estimated. In the boreal forest dominated by conifers, tree species, stand age, and site quality may be appropriate stratification criteria. Pinus, for example, tend to develop more rounded crown forms than species such as Picea, while some species like Pinus tend to develop more rounded crowns by age and on poor sites. These criteria have therefore been implemented in many studies and seem to work well (Næsset, 2002; 2004b,c). However, a very important distinction is between coniferous and deciduous species. Many authors have been concerned about the different properties of airborne laser data acquired for different crown shapes and structures (Lefsky et al., 1999; Næsset,

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1997a; Nelson, 1997; Nelson et al., 1997), and thus for different species—and coniferous versus deciduous species in particular. Nelson et al. (2004) found that regression models for conifers predicted volume and biomass significantly more accurate than hardwood models. A problem in a boreal forest is that while the biophysical properties of coniferous stands can be estimated accurately by good regression models and efficient stratification, the deciduous species rarely appear in pure stands. Furthermore, the deciduous species often occur on just a small portion of the total area in a landscape, and is thus not economically feasible to isolate as individual strata for which separate regressions can be developed. Models developed for coniferous species will often tend to give very biased predictions for deciduous stands. Næsset (2004b) used models for conifers to estimate mean height, basal area, and volume in stands dominated by birch, and revealed good results for mean height, but an over-estimation of stand density and volume by up to 90%. A similar tendency will tend to appear in mixed stands of coniferous and deciduous species, although probably less pronounced than in pure deciduous stands. The problem with deciduous and mixed stands in the boreal forest seems in part to be that the deciduous and coniferous species tend to develop different crown widths and shapes depending on site properties, competition, and management regimes, and thus affect the relationships between the stand variables and the canopy metrics as derived from the laser data. The severe over-estimation of birch-dominated stands revealed by Næsset (2004b) was mainly due to the fact that the birch crowns tended to fill the gaps in the upper canopy while the conically shaped conifers tended to leave the upper canopy more open even when the stem density was high. Another factor that potentially could contribute to differences in relationships between biophysical parameters and laser data for different tree species is the difference in reflectivity between species—and between deciduous species (leaves) and coniferous species (needles) in particular, which may affect the laser range measurements (Baltsavias, 1999; Wehr & Lohr, 1999). Leaves of many tree species tend to have a higher reflectivity than needles at the wavelengths typically used by airborne lasers (e.g. Clark et al., 2003), even though deciduous species are a heterogeneous group with respect to reflectivity properties. By giving less weight to the laser measurements of the deciduous crowns, the problems associated with prediction of biophysical parameters in mixed stands in particular could be reduced. We therefore hypothesize that laser data acquisition of mixed stands in leaf-off canopy conditions would give fewer laser measurements in the upper parts of the canopy and thus make the signatures of coniferous stands and mixed stands with large proportions of deciduous trees more similar. In the present study, it was analyzed how canopy conditions affected some important laser-derived canopy metrics often used to estimate biophysical stand properties, and the effects of canopy

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conditions on the estimation of some selected biophysical properties were assessed. When laser-based inventories cover extensive areas of forest land a large number of strata and thus a significant number of field sample plots are needed to represent the variety of different forest types in a statistically sound way. However, field inventory is time-consuming and expensive. In the four empirical tests of the outlined inventory procedure carried out so far (Holmgren, 2004; Næsset, 2002, 2004b,c), sample plots have been collected locally in each area. In regions where the climatic conditions are similar and the trees tend to develop similar stem and crown shapes, it might be possible to take advantage of sample plots from adjacent survey areas. In a recent study where sample plot data with corresponding laser data from two different regions were combined to develop common regression models for six selected biophysical properties, the effect of geographical origin of the data was found to be not significant in the statistical sense (Næsset et al., 2005). However, the same sensor was used in both inventories and the flight, system, and processing parameters were very similar on both occasions. Due to rapid technological development, airborne laser systems have a limited lifetime in the market and many instruments operated commercially have also been upgraded to improved performance. If sensor-dependent effects turn out to affect the signatures of forest canopies as measured by airborne lasers, the potential benefit of reusing old field sample plots with corresponding laser data is limited. In this study, an upgraded laser scanner was used to illustrate the effects on coniferous canopy signatures of system upgrade by data acquisition before and after upgrade. The objective of this research was (1) to assess how laser-derived metrics in a mixed forest were affected by canopy conditions (leaf-on versus leaf-off), (2) how the same metrics in a coniferous forest were affected by laser system configurations, and (3) to assess how canopy conditions in a mixed forest affected stand estimates of three biophysical properties, i.e., mean tree height, basal area, and volume.

2. Materials and methods 2.1. Study area This study was based on data from a forest inventory in southeast Norway conducted in the municipality of Krødsherad (60-10VN 9-35VE, 130– 660 m a.s.l). The size of the inventory was approximately 3000 ha. The trial area was surrounded by mountainous terrain with elevations up to about 1200 m a.s.l. The main tree species were Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.), but a large portion of especially younger stands was dominated by deciduous species, mainly birch (Betula pubescens Ehrh.). The selected study area was

located within the northern part of a 6500 ha area used to test laser scanning as an operational method for forest inventory (Næsset, 2004b). Further details about the study area can be found in Næsset (2004b). Stand delineation was accomplished by stereoscopic photointerpretation. Interpretation of aerial photography was used to classify the delineated stands according to criteria such as age class, site index, and tree species. The photointerpretation was used as prior information in designing the inventory. Two different ground reference datasets were acquired; (1) one consisting of small sample plots distributed systematically throughout the entire 3000 ha study area, and (2) one dataset with large plots selected subjectively in all parts of the study area. The small sample plots were denoted as ‘‘training plots’’ and the large plots as ‘‘test plots’’. 2.2. Sample plots A total of 51 circular training plots classified as young or mature forest according to the preliminary photointerpretation were distributed systematically throughout the productive forest of the 3000 ha study area according to regular grids. The training plots had an area of 232.9 m2 each and they were sited independently of the test plots. The plots were divided into three predefined classes according to age and site quality derived by photointerpretation, i.e., (1) young forest, (2) mature forest with poor site quality, and (3) mature forest with good site quality. The distribution of the training plots on classes is shown in Table 1. Ground reference data were collected during summer 2001. On each of the 26 young plots and 25 mature plots all trees with diameter at breast height (d bh) > 4 and >10 cm, respectively, were callipered. Tree heights were measured on sample trees with a Vertex hypsometer. The sample trees were selected with probability proportional to stem basal area. Differential Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) were used to determine the position of the centre of each training plot using dual-frequency survey-grade receivers. Collection of data lasted for about 15– 30 min for each plot with a twosecond logging rate. The accuracy of the planimetric plot coordinates ranged from < 0.1 to 1.2 m with an average of approximately 0.2 m (Næsset, 2001, 2004b). On each plot, ground reference mean height was computed as Lorey’s mean height (h L), i.e., mean height weighted by basal area. Plot basal area ( G) was computed as basal area per hectare from the stem breast height diameter measurements. Volume of each tree was computed by means of volume equations of individual trees (Braastad, 1966; Brantseg, 1967; Vestjordet, 1967), which are based on height and diameter as predictor variables. The heights of trees without height measurements were calculated from diameter – height relationships (Fitje & Vestjordet, 1977). Total plot volume (V) was computed as the sum of the individual tree volumes for trees with d bh > 4 cm and

E. Næsset / Remote Sensing of Environment 98 (2005) 356 – 370 Table 1 Summary of training plot (232.9 m2) reference data according to field measurements, summer 2001 Characteristic

Young forest No. of plots h L (m) G (m2 ha 1) V (m3 ha 1) Tree species distribution by Spruce (%) Pine (%) Deciduous species (%)

Coniferous foresta

Mixed forestb

Range

Range

Mean

Mean

13 13 8.11 – 19.53 13.79 9.66 – 18.18 14.59 7.59 – 62.40 27.25 12.02 – 54.90 27.84 34.4 – 617.6 209.4 60.0 – 475.6 193.7 volume 13 – 100 84 2 – 87 59 0 – 87 13 0 – 12 1 0–8 3 13 – 98 40

Mature forest, poor site quality No. of plots 8 4 h L (m) 11.05 – 18.65 14.65 13.32 – 25.44 18.54 G (m2 ha 1) 9.29 – 42.75 25.05 20.81 – 41.02 31.46 60.7 – 302.9 178.5 127.9 – 446.3 272.4 V (m3 ha 1) Tree species distribution by volume Spruce (%) 0 – 100 32 23 – 88 58 Pine (%) 0 – 100 67 0 – 57 15 Deciduous species (%) 0–5 1 12 – 60 27 Mature forest, good site quality No. of plots 7 6 h L (m) 16.59 – 24.36 20.10 16.50 – 21.36 18.81 G (m2 ha 1) 22.23 – 47.30 36.88 10.97 – 43.24 32.43 V (m3 ha 1) 175.1 – 478.1 342.5 79.1 – 377.3 269.8 Tree species distribution by volume Spruce (%) 21 – 100 76 0 – 71 42 Pine (%) 0 – 79 22 0 – 10 3 Deciduous species (%) 0–5 2 29 – 99 55 h L = Lorey’s mean height, h dom = dominant height, d g = mean diameter by basal area, N = stem number, G = basal area, V = volume. a Volume of coniferous species
90% of total plot volume. b Volume of coniferous species <90% of total plot volume.

d bh > 10 cm, respectively. Further details about the field inventory can be found in Næsset (2004b). Since the laser data were acquired in 2001 and 2003 (see below), the area was revisited in field in September 2003 to verify that none of the 51 plots had been subject to any harvests or serious natural disturbances. h L, G, and V were prorated by 0.2 to 2.0 years using growth models (Blingsmo, 1984; Braastad, 1975, 1980) to correspond to the dates on which the 2001 and 2003 laser data were acquired. These prorated values were used as ground reference. In order to analyze how laser-derived metrics and estimated biophysical properties were affected by canopy conditions in a mixed forest (objectives 1 and 3), and how laser system configurations affected the laser measurements in a coniferous forest (objective 2), the sample plots were divided into two strata regardless of which of the primary forest classes (age and site quality) they belonged to. The strata were defined according to the proportion of deciduous species. Plots with volume of coniferous species
90% of total plot volume were denoted as ‘‘coniferous forest’’ and labelled ‘‘stratum A’’. Plots with volume of coniferous

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species <90% of total plot volume where denoted as ‘‘mixed forest’’ and labelled ‘‘stratum B’’. A summary of the groundtruth plot data is displayed in Table 1. 2.3. Test plots The study comprised a total of 27 large test plots. They were located in stands selected subjectively among the stands delineated by photointerpretation in order to represent different combinations of age classes, site quality classes, and tree species mixtures. The test plots were divided into the three predefined classes according to the classification criteria used for the training plots. Ground reference data for the test plots were collected during summer 2001. Initially, each plot was defined to be a quadrangle with an area of approximately 3700 m2. The first corner of each plot was selected in field in an appropriate place. The remaining three corners were determined by measuring tape and compass. The actual position of each corner was finally determined by differential GPS + GLONASS measurements using the procedure described above. Collection of data lasted for about 20 – 80 min for each corner, and the average accuracy of the planimetric coordinates was approximately 0.1 m (Næsset, 2004b). Due to steep slopes and practical difficulties, the horizontal plot area ranged from 3377 to 3987 m2 (Table 2). Within each test plot, all trees with d bh > 4 cm and d bh > 10 cm were callipered in young and mature stands, respectively. The heights of sample trees selected with probability proportional to stem basal area at breast height were measured by a Vertex hypsometer. The number of sample trees per plot ranged from 36 to 73 with an average of 59. The mean height of each test plot was computed from the height measurements as Lorey’s mean height (h L). Stand basal area ( G) was computed as basal area per hectare of the callipered trees. Total plot volume (V) was calculated as the sum of the total volume of each diameter class according to a stratified sampling scheme. Further details about field sampling schemes and computation of field values can be found in Næsset (2004b). Also the test plots were revisited in the field in September 2003 to eliminate stands that had been subject to harvests and natural disturbances in the period after the field inventory took place in 2001. The variables h L, G, and V were prorated according to the procedure described above to correspond to the dates on which the laser data were acquired. The prorated values were used as ground reference. A summary of the ground-truth data is displayed in Table 2. 2.4. Laser scanner data Laser scanner data for this study were acquired in the period between July 23 and August 1, 2001 under leaf-on

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Table 2 Summary of test plot reference data according to field measurements, summer 2001 Characteristic

Coniferous foresta

Mixed forestb

Range

Range

Mean

Mean

Young forest No. of plots 5 6 Plot area (m2) 3644 – 3899 3796 3446 – 3899 3663 14.89 – 18.73 16.53 10.49 – 17.56 13.49 h L (m) G (m2 ha 1) 20.89 – 38.31 33.10 11.97 – 33.65 23.33 V (m3 ha 1) 152.7 – 329.6 276.5 64.0 – 255.7 157.5 Tree species distribution by volume Spruce (%) 0 – 96 57 21 – 86 44 Pine (%) 0 – 99 39 0 – 65 14 Deciduous species (%) 1–8 4 14 – 72 42 Mature forest, poor site quality No. of plots 2 4 Plot area (m2) 3386 – 3976 3681 3377 – 3969 3739 14.16 – 14.57 14.37 12.15 – 19.13 15.49 h L (m) G (m2 ha 1) 20.06 – 22.27 21.16 14.51 – 31.53 22.85 V (m3 ha 1) 140.6 – 155.4 148.0 93.2 – 288.2 179.3 Tree species distribution by volume Spruce (%) 2 – 41 21 25 – 86 65 Pine (%) 56 – 94 75 0 – 35 12 Deciduous species (%) 3–5 4 11 – 40 23 Mature forest, good site quality No. of plots 6 Plot area (m2) 3431 – 3987 3719 18.17 – 22.86 20.14 h L (m) G (m2 ha 1) 23.18 – 37.73 31.52 V (m3 ha 1) 234.8 – 376.7 297.7 Tree species distribution by volume Spruce (%) 1 – 99 63 Pine (%) 0 – 95 33 Deciduous species (%) 0 – 10 4

4 3572 – 3705 3646 15.73 – 21.71 18.97 21.92 – 36.16 28.23 186.0 – 366.6 253.3 30 – 81 0 – 43 16 – 27

66 14 20

h L = Lorey’s mean height, h dom = dominant height, d g = mean diameter by basal area, N = stem number, G = basal area, V = volume. a Volume of coniferous species
90% of total plot volume. b Volume of coniferous species <90% of total plot volume.

canopy conditions (Næsset, 2004b) and on April 16, 2003 under leaf-off conditions. When data were collected in 2003, there was still some snow on the ground, but the depth was less than 1 m. No snow was left in three crowns due to warm weather from around April 10 with little precipitation in the first half of April (Anon., 2003). As long as the depth was less than 2 m, the snow did not influence on the laser-derived canopy metrics, since a threshold value of 2 m was used to define the tree canopy, see below. On both occasions, a fixed-wing Piper PA31-310 aircraft carried the Optech ALTM 1210 laser scanning system. The same instrument was used in 2001 and 2003, but in the meanwhile it was upgraded from a pulse repetition frequency of 10 to 33 kHz. The major components of the ALTM 1210 are the near-infrared laser (1064 nm), the scanner transmitting the laser pulse and receiving the first and last echoes of each pulse, the time interval meter measuring the elapsed time between transmittance and receipt, the GPS airborne and ground receivers, and the

inertial reference system reporting the aircraft’s roll, pitch, and heading. Before upgrade, the ALTM1210 had a pulse width, defined by the time during which the laser output pulse power remains continuously above half its maximum, of 7 ns. The corresponding value after upgrading was 11 ns. Pulse energy was 138 and 84 AJ, respectively, and corresponding values of peak power was 20.0 and 7.6 kW (Table 3). The average flight altitudes over the test plots were 822 and 896 m a.g.l. in 2001 and 2003, respectively, and the pulse repetition frequencies were 10 and 33 kHz. First and last returns were recorded. Maximum scan angles were 16and 15-, but pulses transmitted at scan angles that exceeded 15- and 14-, respectively, were excluded from the final datasets. Average footprint diameters at the ground were 24 and 27 cm and the average pulse densities were 0.8 and 1.0 m 2 (Table 3). The initial processing of the laser data was accomplished by the contractor (Blom Geomatics, Norway). Planimetric coordinates (x and y) and ellipsoidic height values were computed for all first and last returns. A matching between swaths was performed in order to remove orientation errors. The last return data acquired in 2001 were used to model the ground surface. In a filtering operation undertaken by the contractor using a proprietary routine (Anon., 2004), local maxima assumed to represent vegetation hits were discarded. A triangulated irregular network (TIN) was generated from the planimetric coordinates and corresponding height values of the individual terrain ground points retained in the last return dataset. The ellipsoidic height accuracy of the TIN model was expected to be around 25 cm (Kraus & Pfeifer, 1998; Reutebuch et al., 2003). The height values of both first and last return data from 2001 as well as 2003 were co-register to the same terrain model to eliminate effects of systematic shifts in the heights (the z Table 3 Summary of laser scanner data and flight parameters for the 2001 and 2003 laser data acquisitions Parameter

2001

System Pulse width Pulse energy Peak power Wavelength Repetition frequency Scan frequency Date Min. flying altitudea Max flying altitudea Mean flying altitudea Max. scan angle Max. processing angle Mean footprint diameterb Mean pulse densityb

ALTM 1210 ALTM 1210 7 ns 11 ns 138 AJ 84 AJ 20.0 kW 7.6 kW 1064 nm 1064 nm 10 kHz 33 kHz 30 Hz 37 Hz 23 July to 1 August (leaf-on) 16 April (leaf-off) 567 m a.g.l. 719 m a.g.l. 991 m a.g.l. 1117 m a.g.l. 822 m a.g.l. 896 m a.g.l. 1615151424 cm 27 cm 0.8 m 2 1.0 m 2

a b

2003

Refers to flying altitude above the 27 selected test plots. Refers to the 27 selected test plots.

E. Næsset / Remote Sensing of Environment 98 (2005) 356 – 370

coordinates). Since the TIN model was based on the 2001 data, any potential problems associated with the snow-cover in the 2003 data were avoided. Four different datasets were derived from the laser data for further analysis, i.e., first and last returns from 2001 and 2003. All first and last return observations (points) were spatially registered to the TIN. Terrain surface height values were computed for each point by linear interpolation from the TIN. The relative height of each point was computed as the difference between the height of the first or last return and the terrain surface height. These datasets were spatially registered to the sample plots measured in field. To calibrate the height values of the first return data from 2001 and the first and last return data from 2003 according to the TIN model derived from the 2001 last return data, a public road that goes through the entire study area was identified. Six paved road segments along flat parts of the road were selected. Within each segment, a square with an approximate size of 3  3 m was laid out in the middle of the road. Within each square we identified the points that were first returns in the 2001 laser data and first and last returns in the 2003 data. The height values of the observations selected for these three datasets were compared with the height values interpolated from the TIN model for the same positions. For the first return data from 2001, no systematic shift was found. For the first and last return data from 2003, the computed mean differences were  1.7 cm (SD = 4.6 cm) and  1.6 cm (SD = 5.0 cm), respectively, i.e., the 2003 laser data were shifted downwards as compared to the TIN surface. All laser pulses of the 2003 datasets were corrected according to the estimated differences. 2.5. Computations The most commonly used canopy height-related metrics are the percentiles of the height distributions of laser pulses classified as canopy hits. In this study, height distributions were created from laser canopy heights for each training and test plot. The lower limit of the canopy was defined by a threshold value of 2 m (Nilsson, 1996). Separate distributions were created for the first and last return data, respectively, and percentiles for the canopy height for 10% (h 10), 50% (h 50), and 90% (h 90) were computed. In addition, also the maximum (h max) and mean values (h mean), and the coefficient of variation (h cv) of the height distributions were computed. Furthermore, several measures of canopy density were derived. The range between the lowest laser canopy height (> 2 m) and the maximum canopy height was divided into 10 fractions of equal length. Canopy densities were then computed as the proportions of laser hits above fraction # 0 (> 2 m), 1, . . . , 9 to total number of pulses. The densities for fraction # 1 (d 1), # 5 (d 5), and # 9 (d 9) were selected for further studies. To assess how canopy conditions affected the laserderived metrics in a mixed forest and how system

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configuration affected the same kind of metrics in a coniferous forest, differences between corresponding metrics derived from the leaf-on data collected with the 2001 configuration and leaf-off data collected with the 2003 configuration were computed for each training and test plot. The standard deviations of the differences were also computed to assess the stability of the respective metrics. Separate comparisons were carried out for first and last return data, respectively. The experimental design used in this study was imperfect in the sense that three undesired effects were inherent in the data. These effects were (1) the growth that had taken place between 2001 and 2003, (2) the difference in flying altitude between the 2001 and 2003 missions, and (3) the fact that a direct comparison of the 2001 and 2003 laser data in the mixed forest specifically would give an estimate of the combined effect of canopy conditions and system configuration—the two primary effects we wanted to isolate and study separately in this experiment. To compensate for these three undesired effects we did as follows: 1. The laser-derived metrics from the 2003 data were corrected for forest growth according to the effects of growth on the laser-derived metrics observed by Næsset and Gobakken (2005) using the same sensor (ALTM 1210). They observed the growth over a two-year period for three different forest types identical to the definition of age and site quality employed in the present study. Separate corrections were accomplished for the first and last return data (Table 4). 2. The laser-derived metrics from the 2003 data were corrected for a 9% higher average flying altitude as compared to the 2001 mission (Table 3) according to the effects of flying altitude on the first and last return metrics observed by Næsset (2004a). Næsset (2004a) analyzed the effects of a 60% increase in flying altitude using the ALTM 1210 sensor and the same definition of forest types. In the present study, we assumed a linear relationship between change in flying altitude and effects on the laser metrics (Table 4). 3. To separate the effects of canopy conditions in the mixed forest from the system configuration effect, we used the coniferous forest as contrast. By assuming an identical effect of system configuration for all forest types, a comparison of differences between metrics derived for the 2001 and 2003 laser data for the coniferous forest with corresponding differences for the mixed forest would balance out the system configuration effect. The effects of canopy conditions on the estimation and prediction of mean tree height, basal area, and volume in a mixed forest (objective 3) were assessed using a two-step procedure applicable to laser scanner based forest inventory proposed by Næsset and Bjerknes (2001) and Næsset (2002). Prior to the first step of this procedure, the

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Table 4 Adjustment factors for laser-derived canopy properties of the 2003 first and last return laser data due to forest growtha and different flying altitudesb in comparison with the 2001 laser data Metricsc

First return

Last return

Growth

Altitude

Growth

Altitude

0.34 0.56 0.52 0.22 0.49 1.08 0.04 0.04 0.00

0.01 0.01 0.00 0.02 0.00 0.03 0.07 0.15 0.01

0.05 0.40 0.48 0.20 0.28 0.96 0.02 0.02 0.00

 0.03  0.04 0.00 0.00  0.03 0.27  0.28  0.21 0.00

Mature forest, poor site quality h 10 (m) 0.15 0.22 h 50 (m) h 90 (m) 0.22 h max (m) 0.14 h mean (m) 0.20 0.41 h cv (%) d 1 (%) 0.03 d 5 (%) 0.02 d 9 (%) 0.00

0.00 0.00 0.00 0.01 0.01 0.00 0.02 0.02 0.00

0.14 0.08 0.17 0.15 0.07 1.78 0.02 0.00 0.00

 0.01  0.07  0.01 0.01  0.04 0.24  0.21  0.18 0.00

Mature forest, good site quality h 10 (m) 0.33 0.45 h 50 (m) h 90 (m) 0.42 h max (m) 0.23 h mean (m) 0.42 0.77 h cv (%) d 1 (%) 0.03 d 5 (%) 0.04 d 9 (%) 0.00

0.01 0.01 0.01 0.00 0.01 0.04 0.03 0.08 0.01

0.19 0.18 0.33 0.29 0.12 1.16 0.02 0.00 0.00

 0.04  0.08  0.02 0.00  0.06 0.33  0.27  0.29  0.01

Young forest h 10 (m) h 50 (m) h 90 (m) h max (m) h mean (m) h cv (%) d 1 (%) d 5 (%) d 9 (%)

a Changes in laser-derived properties over one growth season computed as 50% of the average change observed by Næsset and Gobakken (2005) for a growth period of two growth seasons. b Changes in laser-derived properties due to a different flying altitude in 2003 as compared to 2001. The changes are computed proportional to the average effects of different flying altitudes as observed by Næsset (2004a). c h 10, h 50, and h 90 = percentiles of the laser canopy heights for 10%, 50%, and 90%; h max = maximum laser canopy height; h mean = arithmetic mean laser canopy height; h cv = coefficient of variation of laser canopy heights; d 1, d 5, and d 9 = canopy densities corresponding to the proportions of laser hits above fraction # 1, 5, and 9, respectively, to total number of pulses (see text).

correlations between the three biophysical properties of interest and the most important laser-derived height- and density-related metrics were assessed for the training plots in the mixed forest (stratum B). In the first step of the proposed procedure, the training plots are used to relate the three biophysical properties of interest to the laser data. In the second step, these relationships are used to predict corresponding values of the test plots. Thus, in step 1, multiple regression analysis was used to create relationships between the three biophysical properties and the laser-derived metrics for the 51 training plots. The

51 plots representing a wide range of forest types — from pure conifer stands to mixed stands dominated by deciduous species — were pooled into one estimation dataset because a sufficient number of plots in mixed and deciduous forests are seldom available to form separate strata for such forest types in stand inventories in the boreal forest. Separate equations were fitted for the 2001 and 2003 laser datasets. The estimation of regression models was based on the height- and density-related metrics derived from the first and last return height distributions as candidate explanatory variables. However, the maximum values of the height distributions were not included as candidates since a higher variability seems to be associated with these variables than the other height-related metrics (Næsset, 2004a). In the regression analysis, multiplicative models were estimated as linear regressions in the logarithmic variables. The linear form used in the estimation was lnY ¼ lnb0 þ b1 lnh10f þ b1 lnh50f þ b3 lnh90f þ b4 lnh101 þ b5 lnh501 þ b6 lnh901 þ b7 lnhmeanf þ b8 lnhmeanl þ b9 lnhcvf þ b10 lnhcvl þ b11 lnd1f þ b12 lnd5f þ b13 lnd9f þ b14 lnd11 þ b15 lnd51 þ b16 lnd91

ð1Þ

where Y = field values of h L (m), G (m2 ha 1), or V (m3 ha 1); h 10f, h 50f, h 90f = percentiles of the first return laser canopy heights for 10%, 50%, and 90% (m); h 10l, h 50l, h 90l = percentiles of the last return laser canopy heights for 10%, 50%, and 90% (m); h meanf, h meanl = mean of the first and last return laser canopy heights (m); h cvf, h cvl = coefficient of variation of the first and last return laser canopy heights (%); d 1f, d 5f, d 9f = canopy densities corresponding to the proportions of first return laser hits above fraction 1, 5, 9 to total number of first returns; d 1l, d 5l, d 9l = canopy densities corresponding to the proportions of last return laser hits above fraction # 1, 5, 9 to total number of last returns. Stepwise selection was performed to select variables to be included in these models. No predictor variable was left in the models with a partial F statistic with a significance level greater than 0.05. The standard least-squares method was used (Anon., 1989). In step 2, the estimated regression models were used to predict the three biophysical properties of interest in each of the 27 test plots. Separate predictions were made for the laser data acquired in 2001 and 2003, respectively. This was done by dividing each plot into regular grid cells with a cell size of 232.9 m2, corresponding to the size of the training plots. Laser canopy height distributions were created for each cell from the assigned first and last return laser data, and the biophysical properties were predicted at cell level using the estimated equations and the derived laser metrics. Predicted values at test plot level were computed as mean values of the individual cell estimates. Finally, the differences between predicted values of the biophysical properties and ground-truth values were computed. The standard deviations of the differences were also estimated.

E. Næsset / Remote Sensing of Environment 98 (2005) 356 – 370

3. Results 3.1. Effects of canopy conditions on laser metrics in mixed forest (objective 1) 3.1.1. First return data First, differences between laser-derived leaf-on and leafoff canopy height and density metrics were computed for the 23 training plots and 14 test plots in mixed forest. For this forest type, the average proportions of deciduous species were 42% and 31% at training and test plots, respectively (Tables 1 and 2). For the first return data, the height metrics tended to be somewhat higher under leaf-on canopy conditions. The lower height percentiles (h 10, h 50) were from 0.31 to 1.25 m higher under leaf-on conditions at the training and test plots (Tables 5 and 6). The differences seemed to be largest at the training plots, which is as expected since they had higher proportions of deciduous species. By adjusting for effects of growth and flying altitude the differences increased by 0.3 –0.5 m. However, when we adjusted for effects of system configuration by contrasting with the height percentiles derived for the coniferous forest, the results indicated a 0.33 – 0.97 m increase in height percentiles from off-leaf to on-leaf conditions (Table 7). For h 90 and maximum (h max) and mean (h mean) canopy height the height values were between 0.05 and 1.08 m

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(Tables 5– 7) higher under leaf-on canopy conditions after adjustment for growth, flying altitude, and system effects. However, one important exception should be noted. The unadjusted maximum canopy height for the large test plots were 0.34 m higher ( p < 0.05) under leaf-off conditions (Table 6). The variability of the canopy height distributions expressed by the coefficient of variation of the laser canopy hits (h cv) was significantly higher in leaf-off conditions. The adjusted differences in h cv ranged from 2.93% to 5.81% across all plots (Tables 5 –7). Thus, the laser pulses that actually hit the canopy on the two occasions were more spread in the leaf-off season. The cumulative canopy densities accounting for the laser hits above the lower (d 1) and intermediate (d 5) parts of the canopy were significantly higher in leaf-on conditions. They were between 1.91% and 12.42% higher in the leaf-on season (Tables 5 –7). The canopy density of the upper canopy layer (d 9) did not differ significantly between the two occasions. 3.1.2. Last return data In general, the effects of canopy conditions tended to be somewhat more pronounced for the last return data than for first return. The unadjusted height percentiles (h 10, h 50, h 90) of the training and test plots were 0.41 to 2.44 m higher in leaf-on conditions (Tables 5 and 6). By adjusting for effects

Table 5 Differences (D) between leaf-on (2001) and leaf-off (2003) laser data expressed by laser-derived metrics for the training plots and standard deviation (S.D.) for the differences for first and last return measurement, respectively Metricsa

Unadjusted

Adjusted for growth and flying altitude

D, first return Mean Coniferous forest – stratum A (n = 28 training h 10 (m) 0.28 * h 50 (m) 0.06 NS h 90 (m) 0.04 NS h max (m) 0.24 NS 0.09 NS h mean (m) h cv (%) 1.47 * d 1 (%) 3.72 *** 3.74 *** d 5 (%) d 9 (%) 0.34 NS

D, last return S.D. plots) 0.67 0.51 0.53 1.11 0.31 2.98 4.14 5.11 3.15

Mixed forest – stratum B (n = 23 training plots) h 10 (m) 1.25 ** 2.02 h 50 (m) 0.76 ** 1.02 h 90 (m) 0.18 NS 0.71 h max (m) 0.04 NS 1.49 0.66 ** 0.97 h mean (m) 0.66 ** h cv (%) 4.90 *** 6.08 d 1 (%) 12.34 *** 6.14 12.01 *** 9.17 d 5 (%) d 9 (%) 0.94 NS 2.88

Mean

S.D.

0.59 ** 0.10 NS 0.11 NS 0.31 NS 0.26 * 2.59 ** 6.09 *** – 3.69 *** 0.47 NS

1.16 2.44 1.68 0.63 2.04 10.22 14.46 14.85 1.58

D, first return

** *** * NS *** ** *** *** **

Mean

D, last return S.D.

Mean

S.D.

1.12 0.75 0.63 1.14 0.55 4.90 6.39 4.85 2.83

0.57 0.50 0.37 0.05 0.48 2.31 3.79 3.87 0.33

*** *** *** NS *** *** *** *** NS

0.68 0.46 0.53 1.12 0.30 2.89 4.14 5.09 3.15

0.67 ** 0.17 NS 0.25 * 0.11 NS 0.08 NS 3.56 *** 5.85 *** 3.46 *** 0.46 NS

1.46 2.75 3.84 2.17 2.21 16.74 16.09 13.86 2.61

1.56 1.24 0.62 0.23 1.08 5.81 12.42 12.16 0.96

** *** *** NS *** *** *** *** NS

2.02 1.11 0.67 1.48 0.96 6.07 6.13 9.17 2.88

1.08 2.75 2.07 0.84 2.26 9.35 14.71 15.09 1.58

** *** * NS *** * *** *** **

1.14 0.75 0.61 1.15 0.59 4.99 6.40 4.85 2.83

1.46 2.75 3.84 2.18 2.21 16.78 16.10 13.87 2.62

Differences computed with and without adjustment for forest growth and different flying altitudes according to Table 4. Level of significance: NS = not significant (>0.05). *<0.05. **<0.01. ***<0.001. a Symbols explained in Table 4.

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E. Næsset / Remote Sensing of Environment 98 (2005) 356 – 370

Table 6 Differences (D) between leaf-on (2001) and leaf-off (2003) laser data expressed by laser-derived metrics for the large test plots and standard deviation (S.D.) for the differences for first and last return measurement, respectively Metricsa

Unadjusted

Adjusted for growth and flying altitude

D, first return

D, last return

Mean Coniferous forest – stratum h 10 (m) 0.06 h 50 (m) 0.02 h 90 (m) 0.09 0.39 h max (m) h mean (m) 0.05 h cv (%) 0.01 d 1 (%) 5.84 5.09 d 5 (%) d 9 (%) 0.22

S.D. A (n = 13 test plots) NS 0.35 NS 0.31 NS 0.31 * 0.62 NS 0.26 NS 1.27 *** 2.81 *** 2.28 * 0.31

Mixed forest – stratum B (n = 14 test plots) h 10 (m) 0.77 *** 0.61 h 50 (m) 0.31 ** 0.37 h 90 (m) 0.01 NS 0.25 0.34 * 0.56 h max (m) h mean (m) 0.33 ** 0.33 h cv (%) 2.92 *** 1.61 d 1 (%) 9.60 *** 2.51 7.00 *** 3.82 d 5 (%) d 9 (%) 0.21 NS 0.52

D, first return

Mean

S.D.

Mean

D, last return S.D.

Mean

S.D.

1.22 0.42 0.12 0.44 0.50 3.96 3.98 2.02 0.10

** NS NS NS ** *** ** * NS

1.15 0.79 0.38 0.82 0.51 2.92 4.28 3.09 0.40

0.25 0.45 0.35  0.18 0.36  0.86 5.92 5.22 0.23

* *** *** NS *** * *** *** *

0.39 0.32 0.28 0.62 0.28 1.40 2.82 2.29 0.31

1.32 0.13 0.26 0.20 0.30 4.84 3.73 1.77 0.10

** NS * NS NS *** ** NS NS

1.13 0.88 0.37 0.82 0.55 3.07 4.28 3.09 0.41

0.43 1.71 0.41 0.20 1.03 5.69 9.04 8.81 0.21

NS *** * NS ** * * ** NS

0.82 1.46 0.52 1.28 0.99 7.75 15.06 9.06 0.42

1.06 0.75 0.41  0.15 0.72  3.74 9.67 7.12 0.22

*** *** *** NS *** *** *** *** NS

0.65 0.36 0.21 0.56 0.35 1.69 2.52 3.83 0.53

0.35 1.97 0.76 0.01 1.21 4.72 9.28 9.04 0.21

NS *** *** NS *** * * ** NS

0.84 1.53 0.52 1.26 1.04 7.92 15.08 9.07 0.42

Differences computed with and without adjustment for forest growth and different flying altitudes according to Table 4. Level of significance: NS = not significant (>0.05). *<0.05. **<0.01. ***<0.001. a Symbols explained in Table 4.

of system configuration, the results indicated a 0.53 –2.54 m increase in height percentiles from leaf-off to leaf-on conditions (Table 7). Table 7 ¯ AD ¯ B) for Comparisons between mean differences of strata A and B (D laser-derived metrics (unadjusted) from 2001 (leaf-on) and 2003 (leaf-off) Metricsa First return data h 10 (m) h 50 (m) h 90 (m) h max (m) h mean (m) h cv (%) d 1 (%) d 5 (%) d 9 (%) Last return data h 10 (m) h 50 (m) h 90 (m) h max (m) h mean (m) h cv (%) d 1 (%) d 5 (%) d 9 (%)

Training plots 0.97 0.70 0.22 0.28 0.58 3.43 8.62 8.27 1.28

* ** NS NS * * *** *** NS

1.75 *** 2.54 *** 1.79 * 0.94 *** 2.30 *** 12.81 *** 20.55 *** 18.54 *** 1.11 *

Test plots 0.83 0.33 0.10 0.05 0.38 2.93 3.76 1.91 0.01

*** * NS NS ** *** ** NS NS

1.65 *** 2.13 *** 0.53 ** 0.24 NS 1.53 *** 9.65 *** 13.02 ** 10.83 *** 0.11 NS

Level of significance (t tests): NS = not significant (> 0.05). *< 0.05. **<0.01. ***<0.001. a Symbols explained in Table 4.

For h max and h mean the height values were between 0.01 and 2.30 m (Tables 5 –7) higher under leaf-on canopy conditions after adjustment for growth, flying altitude, and system effects. However, for the large test plots the maximum value did not differ significantly between the two seasons. The variability of the canopy height distributions (h cv) was significantly higher under leaf-off conditions and the variability tended to be much higher for the last return than for the first return data. The adjusted differences in h cv ranged from 2.72% to 12.81% across all plots (Tables 5 –7). The cumulative canopy densities (d 1, d 5, d 9) were significantly higher under leaf-on conditions. They were between 0.11% and 20.55% higher in the leaf-on season (Tables 5– 7) when accounting for growth, flying altitude, and system effects. 3.2. Effects of laser system configuration on laser metrics in coniferous forest (objective 2) 3.2.1. First return data The differences between canopy height and density metrics derived from laser data acquired before and after system upgrade were computed for the 28 training plots and 13 test plots in coniferous forest. The average proportions of deciduous species were 2% and 4% at training and test plots, respectively (Tables 1 and 2). For the first return data, the height metrics seemed to be more or less unaffected by

E. Næsset / Remote Sensing of Environment 98 (2005) 356 – 370

system upgrade. The difference between height percentiles of the vegetation hits (h 10, h 50, h 90) and the maximum (h max) and mean (h mean) values derived from data acquired in 2001 and 2003 were less than 0.39 m, and most of the differences were not significant in the statistical sense (Tables 5 and 6). By adjusting for effects of forest growth and flying altitude, the canopy height metrics tended to be a few decimetres higher on the first occasion. After adjustment, the height metrics were up to 0.59 m higher. The variability of the canopy height distributions was either not significantly different on the two occasions or slightly higher in 2003. The adjusted values for canopy variability were 0.86% to 2.89% greater in 2003 than in 2001 (Tables 5 and 6).

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The canopy densities accounting for the laser hits above the lower (d 1) and intermediate (d 5) parts of the canopy were significantly higher on the first occasion, indicating that a lower proportion of the laser pulses penetrated the canopy. The adjusted values were 3.79 – 5.92% higher in 2001. The canopy density of the upper canopy layer (d 9) was slightly higher (0.22 – 0.31%, Table 6) on the first occasion or did not differ significantly between the two acquisitions (Table 5). 3.2.2. Last return data As opposed to laser-derived metrics for the first return data, the last return data indicated an increase from 2001 to 2003 in most of the height and canopy density-related

1

0,9

0,9

Pearson correlation coefficient

Pearson correlation coefficient

Lorey's mean height 1

0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0

h10

h50 Canopy height metrics

Leaf-on, first pulse Leaf-off, first pulse

0,8

Leaf-on, last pulse

0,7

Leaf-off, last pulse

0,6 0,5 0,4 0,3 0,2 0,1 0

h90

d1

d5 Canopy density metrics

d9

d1

d5 Canopy density metrics

d9

d1

d5 Canopy density metrics

d9

1

0,9

0,9

Pearson correlation coefficient

Pearson correlation coefficient

Basal area 1

0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0

h10

h50 Canopy height metrics

0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0

h90

1

0,9

0,9

Pearson correlation coefficient

Pearson correlation coefficient

Volume 1

0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0

h10

h50 Canopy height metrics

h90

0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0

Fig. 1. Pearson correlation coefficient between laser-derived canopy height and density metrics (h 10, h 50, h 90, d 1, d 5, d 9) and biophysical properties (Lorey’s mean height, basal area, volume) for first and last return data acquired under leaf-on and leaf-off conditions.

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variables, and a decline in the canopy height variability. The height percentiles and mean and maximum values increased by 0.08– 1.32 m (Tables 5 and 6) after adjustment for forest growth and flying altitude effects. One exception was the upper height percentile (h 90), where the height value declined by 0.25 –0.26 m ( p < 0.05). The change in canopy density was either not significant or indicated an increase from 2001 to 2003 (0.46 –5.85%). The canopy height variability was 3.56 – 4.84% higher on the first occasion. 3.3. Effects of canopy conditions on biophysical properties in mixed forest (objective 3) First, correlations between laser-derived metrics of canopy height and density and biophysical properties (mean height, basal area, and volume) were estimated for the mixed forest (stratum B). It was revealed that metrics derived from either the first or last return leaf-off data tended to be more correlated with the biophysical properties than the metrics derived from the leaf-on data (Fig. 1). The first and last return leaf-on data showed more similar correlation patterns than did the leaf-off data. Especially the last return data acquired under leaf-off conditions showed quite different correlations patterns over the canopy height and density gradients as compared to the other laser-derived datasets. In general, mean height tended to be more correlated with the canopy height metrics than basal area and volume. The intermediate canopy density metrics (d 1 and d 5) tended to be more correlated with basal area and volume than with mean height. The selected regression models estimated for mean height, basal area, and volume on basis of all the 51 training plots revealed a proportion of explained variability of 88% and 92% for mean height using leaf-on and leaf-off laser data, respectively (Table 8). The R 2 values for basal area and volume tended to be of a similar magnitude or slightly higher using the leaf-off data (R 2 = 0.66– 0.81) as compared to the leaf-on data (R 2 = 0.62– 0.73). The RMSE values ranged from 0.09 to 0.34 under leaf-on conditions and from 0.07 to 0.29 under leaf-off conditions. None of the selected models comprised more than four explanatory variables. Multicollinearity issues were addressed by calculating and monitoring the size of the condition number. None of the selected models had a condition number greater than 3.6,

Fig. 2. Mean differences (top) between predicted and ground reference values in percent of average ground values for the test plots of Lorey’s mean height, basal area, and volume, and standard deviations (bottom) for the differences. Predictions (n = 27) according to regression equations presented in Table 8.

indicating that there was no serious collinearity inherent in the selected models (Weisberg, 1985). When the selected regression models were used to predict the three biophysical variables on the 27 large test plots, no significant differences between predicted values and ground-truth were found for mean height (Fig. 2). However, for basal area and volume, the ground-truth was overestimated by 12.6 –14.5% and 12.9 –13.3%, respectively. The bias for the two latter variables tended to be of a similar magnitude. None of the difference between the two laser data acquisitions was significantly greater than what could be expected due to randomness ( p > 0.05).

Table 8 Selected models for biophysical properties (response variables) from stepwise multiple regression analysis of the training plots using metrics derived from the 2001 (leaf-on) and 2003 (leaf-off) laser data as explanatory variables (n = 51) Response variablea

lnh L lnG lnV a b

Leaf-on

Leaf-off

Expl. variablesb

R2

RMSE

Expl. variablesb

R2

RMSE

lnh 90f lnh meanl, lnd 1f lnh meanl, lnd 9l

0.88 0.62 0.73

0.09 0.30 0.34

lnh 90f, lnh 90l, lnd 9f, lnd 1l lnh 10f, lnh 90l, lnd 1f lnh 90l, lnh meanf, lnd 1f

0.92 0.66 0.81

0.07 0.28 0.29

h L = Lorey’s mean height (m), G = basal area (m2 ha 1), V = volume (m3 ha 1). Symbols explained in Table 4.

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The standard deviation for the differences between predicted and ground-truth values of mean height was 4.6% and 5.2% of mean ground-truth value under leaf-off and leaf-on canopy conditions, respectively (Fig. 2). Standard deviation for the differences for basal area was 14.5% and for volume it ranged between 15.4% and 25.4%. According to F-tests of equality of variance, the precision of predicted volume differed significantly between the two occasions ( p < 0.05).

4. Discussion and conclusions The major findings of this study indicate that: 1. In mixed forest, a. last-return pulse measurements are in general more affected by canopy conditions than the first return data, b. canopy height measurements of the lower and intermediate parts of the canopy (e.g., h 10 and h 50) are more affected by canopy conditions than the maximum canopy height (h max), c. the variability of the laser-measured canopy height (e.g., h cv) is significantly higher under leaf-off conditions, and d. accuracy of estimated biophysical properties in mixed forest is unaffected or slightly improved under leaf-off conditions. 2. Use of an upgraded airborne laser where the instrument configuration was changed between the two acquisitions has demonstrated that both first and last return measurements can be significantly affected by system configuration. In this particular study, the first return measurements of canopy height tended to be unaffected or shifted somewhat upwards by system upgrade and ground penetration was reduced, whereas the last return data indicated unaffected or downwards shifted canopy heights and increased penetration. It should be noted that even though the system configuration effect was confounded with the seasonal effect (2 –4% deciduous trees), the first and last return metrics were affected differently by system upgrade. The three undesired effects that were confounded with the primary effects we wanted to isolate and analyze in this study seem to have limited influence on the general trends indicated by these major findings. According to Næsset (2004a), it is estimated that the effect of different flying altitudes, i.e., 822 versus 896 m a.g.l. (Table 3), was almost neglectable (Table 4). The effects of canopy conditions on the laser-derived height and canopy metrics seem to be of a magnitude on an order of 10– 50 times the effects of flying altitude. Based on the experience gained by Næsset and Gobakken (2005), the effects of forest growth in the period between the two laser data acquisitions are likely to account

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for less than 10% of the observed effects of canopy conditions for most of the studied variables, although for a few variables that where hardly affected by canopy conditions, like maximum canopy height (h max), the growth effects are probably more pronounced than the effects of canopy conditions (Tables 5 and 6). By contrasting with the coniferous forest it seems that even the effects of system configuration accounted for less than 20 – 30% of the observed differences between the two acquisitions for most of the variables (Table 7). It is an interesting find that the maximum canopy height as measured by the laser seems to be little influenced by canopy conditions even in the mixed forest. The mixed stands contained at least 1% conifers (Tables 1 and 2) and on the average the proportions of conifers on the training and test plots were 58% and 69%, respectively. The high degree of correspondence between the maximum height measurements on the two occasions may therefore to some extent be attributed to laser pulses reflected from the coniferous trees. However, when Brandtberg et al. (2003) detected individual leaf-off tree crowns they did not find any significant tendency of underestimation of the true tree heights by comparison with the maximum laser-derived heights. Therefore, it is likely that a significant portion of the laser beams have been reflected from the upper canopy of the deciduous trees even under leaf-off conditions. Measuring the mixed canopies under leaf-off conditions has reduced the values of the laser-derived metrics of canopy height and canopy density, as could be expected. The major reason is obviously the reduced amount of biological matter under leaf-off conditions. However, bark and branches have a lower reflectivity at 1064 nm than leaves, and the different values of the laser-derived metrics under various canopy conditions are probably due to the different reflectivity properties as well. Based on this study it is not possible to quantify the effects of each of the various range-affecting factors more specifically. The fact that maximum canopy height is little influenced by canopy conditions whereas the canopy density is reduced under leaf-off conditions explains why the variability of the laser canopy heights is higher in leaf-off conditions. The more compact leaf-on canopies in terms of biological matter will tend to give a sample of reflected pulses that are more concentrated in the upper part of the canopy whereas leafoff canopies will tend to give reflected pulses more evenly scattered through the entire canopy. A hypothesis in this study was that by reducing the values of the laser-derived metrics, the relationships between the laser-derived variables and the biophysical properties (mean height, basal area, and volume) would be more similar in mixed and coniferous stands and thus improve the accuracy when estimating these properties in mixed forests. It is evident that either of the canopy metrics derived from the leaf-off data often tends to be more correlated with the biophysical properties than metrics derived from the leaf-on data (Fig. 1). The tendency of

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being more correlated with the biophysical properties is also indicated by the regression analysis. For each of the biophysical properties, the models estimated under leaf-off

conditions tended to yield higher proportions of explained variability and lower residual sum of squares (Table 8). The quality of the laser data acquired after system upgrade was

Fig. 3. Illustrations from a young birch-dominated plot with a total volume of 231 m3 ha 1 and 66% deciduous trees (test plot #216). Top: First (left) and last (right) return laser height distributions from summer (leaf-on). Middle: First (left) and last (right) return laser height distributions from winter (leaf-off). Bottom: Photography (left) of test plot #216 taken in the summer season, and residuals (right) in percent of field-measured values for mean height, basal area, and volume predicted according to regression equations presented in Table 8.

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probably poorer than before upgrade (see discussion below). In spite of that, the testing of the selected regression models against the independent test plots revealed a precision of the leaf-off models that was similar to or better than the corresponding leaf-on predictions (Fig. 2). The testing revealed a significant bias of up to 15% for basal area and volume when predicted values of the test plots were compared with the ground-truth (Fig. 2). This bias was most likely caused by a poor representation of the forest area in question. The training plots used to estimate the regression models covered a wide range of forest types and they were distributed systematically across the entire study area. With a plot size of 200 – 250 m2, the variability between plots spanning a range of forest types typically appearing in southeast Norway is around 50% (Nersten, 1987; Næsset, 1991). Thus, due to the large sampling error, it is not surprising that a limited sample of 51 plots has resulted in a biased prediction. Likewise, the sample of 27 test plots was selected independently of the training plots and subjectively among more than two thousand stands in the area in question. In previous tests of stand-based predictions of biophysical properties from laser data, around 120 training plots or more have been used to estimate stratum-specific regressions that relate the laser data to the biophysical properties (Næsset, 2002, 2004b,c). These tests have revealed that the bias often is neglectable. Næsset (2004b) demonstrated that serious errors may occur in density-related variables such as basal area and stand volume when regression models developed for coniferous forest are used for prediction purposes in deciduous and mixed stands. In Fig. 3, the height distributions for leaf-on and leaf-off data in a young mixed stand illustrate how the penetration rate is increased and canopy heights are changed under leaf-off conditions. For this specific plot (test plot #216), basal area and volume were underestimated by 57% and 86%, respectively, when a coniferous model was used to predict the biophysical properties (Næsset, 2004b). Using a mixed model (Table 8) based on leaf-on data reduced the errors to less than 50% and the leaf-off data improved the predictions even more (Fig. 3). This study has demonstrated that the vegetation signatures derived from airborne laser data may be affected by system-specific properties. In this study, systematic shifts in canopy heights and canopy penetration were revealed. The system specifications clearly indicate that important properties such as pulse energy and peak power were reduced by 40% and 60%, respectively, by system upgrade (Table 3). Components associated with signal reception were also replaced or adjusted. An important aspect, which this study did not address specifically, is the effects of pulse width on the vegetation signatures. The pulse width was 7 and 11 ns, respectively. The pulse width affects the range accuracy (Wehr & Lohr, 1999). It is straightforward to obtain empirically based estimates of the effects of pulse width on range accuracy for solid surfaces, but far more complicated to quantify such effects for semi-

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transparent objects with irregular shapes like tree canopies. This aspect is, however, becoming more and more important also in laser-based studies of vegetation and forests. The state of the art proprietary airborne laser scanners can be operated with different pulse repetition frequencies. For example, the latest Optech ALTM 3100 can be operated in four different modes from 33 to 100 kHz covering a much wider range of system parameters than in the present study, like pulse widths and pulse energy in the ranges of 9 – 16 ns and 66 – 160 AJ, respectively, which obviously will influence on the vegetation characteristics derived from the data. To conclude, the present study has indicated that lastreturn pulse measurements of mixed tree canopies are more affected by canopy conditions than first return data. Furthermore, data acquisition in leaf-off conditions can be a means to improve the accuracy of biophysical properties estimated and predicted in a mixed forest. However, a careful stratification of the forest according to criteria reflecting different canopy structures is also a realistic alternative to improve accuracy and reduce the risk of extreme prediction errors that might occur when discrimination between coniferous, deciduous, and mixed stands is neglected. This study has demonstrated that system-specific effects may affect canopy signatures as derived from airborne laser data. Further studies are required to assess systematically how and to what extent different system characteristics affect the canopy properties derived for different canopy types. The outcome of such analysis will have an essential influence on the way future multi-temporal forest resource assessments and carbon monitoring systems have to be designed, for example, whether there is a need for systemspecific ground calibration to follow every repeated acquisition by airborne laser.

Acknowledgments This research has been funded by the Research Council of Norway (research project no. 166482/I10) and Borregaard Research Fund. Thanks to Blom Geomatics, Norway, for collection and processing of the airborne laser scanner data. References Anon. (1989) (4th ed.). SAS/STAT user’s guide, version 6, Vol. 2. Cary, NC’ SAS Institute, 846 pp. Anon. (2003). Norwegian meteorological institute. Klimatisk hurtigoversikt, April 2003. www.met.no/observasjoner/maned/2003/2003-04.pdf (in Norwegian). Anon. (2004). TerraScan users guide. Helsinki, Finland’ Terrasolid Ltd. 156 pp. www.terrasolid.fi Baltsavias, E. P. (1999). Airborne laser scanning: basic relations and formulas. ISPRS Journal of Photogrammetry and Remote Sensing, 54, 199 – 214.

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E. Næsset / Remote Sensing of Environment 98 (2005) 356 – 370

Blingsmo, K. R. (1984). Diameter increment functions for stands of birch, Scots pine and Norway spruce. Research paper vol 7/84. Norw. For. Res. Inst. 22 pp. (in Norwegian with English summary). Braastad, H. (1966). Volume tables for birch. Meddelelser fra det Norske Skogforsøksvesen, 21, 265 – 365. Braastad, H. (1975). Yield tables and growth models for Picea abies. Reports of the Norwegian Institute for Forest Research, 31, 357 – 540. Braastad, H. (1980). Growth model computer program for Pinus sylvestris. Reports of the Norwegian Institute for Forest Research, 35, 265 – 360. Brandtberg, T., Warner, T. A., Landenberger, R. E., & McGraw, J. B. (2003). Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America. Remote Sensing of Environment, 85, 290 – 303. Brantseg, A. (1967). Volume functions and tables for Scots pine. South Norway. Meddelelser fra det Norske Skogforsøksvesen, 22, 695 – 739 (in Norwegian with English summary). Clark, R. N., Swayze, G. A., Wise, R., Livo, K. E., Hoefen, T. M., & Kokaly, R. F., et al. (2003). USGS digital spectral library splib05a. U.S. Geological Survey Open File Report 03-395 http://pubs.usgs.gov/of/ 2003/ofr-03-395/ofr-03-395.html Eid, T., Gobakken, T., & Næsset, E. (2004). Comparing stand inventories based on photo-interpretation and laser scanning by means of cost-plusloss analyses. Scandinavian Journal of Forest Research, 19, 512 – 523. Fitje, A., & Vestjordet, E. (1977). Stand height curves and new tariff tables for Norway spruce. Reports of the Norwegian Intstitute for Forest Research, 34, 23 – 62 (in Norwegian with English summary). Holmgren, J. (2004). Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning. Scandinavian Journal of Forest Research, 19, 543 – 553. Holmgren, J., Nilsson, M., & Olsson, H. (2003). Estimation of tree height and stem volume on plots using airborne laser scanning. Forest Science, 49, 419 – 428. Kraus, K., & Pfeifer, N. (1998). Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, 53, 193 – 203. Lefsky, M. A., Cohen, W. B., Acker, S. A., Parker, G. G., Spies, T. A., & Harding, D. (1999). Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests. Remote Sensing of Environment, 70, 339 – 361. Magnussen, S., & Boudewyn, P. (1998). Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators. Canadian Journal of Forest Research, 28, 1016 – 1031. Means, J. E., Acker, S. A., Brandon, J. F., Renslow, M., Emerson, L., & Hendrix, C. J. (2000). Predicting forest stand characteristics with airborne scanning lidar. Photogrammetric Engineering and Remote Sensing, 66, 1367 – 1371. Næsset, E. (1991). Accuracy of stand volume estimation by means of strata mean values. Comm. Norw. For. Res. Inst., vol. 44(2) (pp. 1 – 24) (in Norwegian with English summary). Næsset, E. (1997a). Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, 52, 49 – 56.

Næsset, E. (1997b). Estimating timber volume of forest stands using airborne laser scanner data. Remote Sensing of Environment, 61, 246 – 253. Næsset, E. (2001). Effects of differential single-and dual-frequency GPS and GLONASS observations on point accuracy under forest canopies. Photogrammetric Engineering and Remote Sensing, 67, 1021 – 1026. Næsset, E. (2002). Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment, 80, 88 – 99. Næsset, E. (2004a). Effects of different flying altitudes on biophysical stand properties estimated from canopy height and density measured with a small-footprint airborne scanning laser. Remote Sensing of Environment, 91, 243 – 255. Næsset, E. (2004b). Practical large-scale forest stand inventory using smallfootprint airborne scanning laser. Scandinavian Journal of Forest Research, 19, 164 – 179. Næsset, E. (2004c). Accuracy of forest inventory using airborne laserscanning: evaluating the first Nordic full-scale operational project. Scandinavian Journal of Forest Research, 19, 554 – 557. Næsset, E., & Bjerknes, K.-O. (2001). Estimating tree heights and number of stems in young forest stands using airborne laser scanner data. Remote Sensing of Environment, 78, 328 – 340. Næsset, E., Bollandsa˚s, O. M., & Gobakken, T. (2005). Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data. Remote Sensing of Environment, 94, 541 – 553. Næsset, E., & Gobakken, T. (2005). Estimating forest growth using canopy metrics derived from airborne laser scanner data. Remote Sensing of Environment, 96, 453 – 465. Nelson, R. (1997). Modeling forest canopy heights: the effects of canopy shape. Remote Sensing of Environment, 60, 327 – 334. Nelson, R., Oderwald, R., & Gregoire, G. (1997). Separating the ground and airborne laser sampling phases to estimate tropical forest basal area, volume, and biomass. Remote Sensing of Environment, 60, 311 – 326. Nelson, R., Short, A., & Valenti, M. (2004). Measuring biomass and carbon in Delaware using an airborne profiling lidar. Scandinavian Journal of Forest Research, 19, 500 – 511. Nersten, S. (1987). Coefficients of variation in sample plot inventory. Research Paper vol 1/87. Norw. For. Res. Inst. 12 pp. (in Norwegian with English summary). Nilsson, M. (1996). Estimation of tree heights and stand volume using an airborne lidar system. Remote Sensing of Environment, 56, 1 – 7. Reutebuch, S. E., McGaughey, R. J., Andersen, H.-E., & Carson, W. W. (2003). Accuracy of a high-resolution lidar terrain model under a conifer forest canopy. Canadian Journal of Remote Sensing, 29, 527 – 535. Vestjordet, E. (1967). Functions and tables for volume of standing trees. Norway Spruce. Meddelelser fra det Norske Skogforsøksvesen, 22, 543 – 574 (in Norwegian with English summary). Wehr, A., & Lohr, U. (1999). Airborne laser scanning—An introduction and overview. ISPRS Journal of Photogrammetry and Remote Sensing, 54, 68 – 82. Weisberg, S. (1985). Applied linear regression (2nd edR). New York’ Wiley. 324 pp.