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Research article
Open Access

Estimating forest aboveground biomass by low density lidar data in mixed broad-leaved forests in the Italian Pre-Alps

  • Antonio Montagnoli1Email author,
  • Sara Fusco1,
  • Mattia Terzaghi1,
  • Alan Kirschbaum3,
  • Dirk Pflugmacher2,
  • Warren B Cohen4,
  • Gabriella S Scippa5 and
  • Donato Chiatante1
Forest Ecosystems20152:10

https://doi.org/10.1186/s40663-015-0035-6

©  Montagnoli et al. 2015

Received: 16 December 2014

Accepted: 10 April 2015

Published: 17 April 2015

Abstract

Background

Estimation of forest biomass on the regional and global scale is of great importance. Many studies have demonstrated that lidar is an accurate tool for estimating forest aboveground biomass. However, results vary with forest types, terrain conditions and the quality of the lidar data.

Methods

In this study, we investigated the utility of low density lidar data (<2 points∙m−2) for estimating forest aboveground biomass in the mountainous forests of northern Italy. As a study site we selected a 4 km2 area in the Valsassina mountains in Lombardy Region. The site is characterized by mixed and broad-leaved forests with variable stand densities and tree species compositions, being representative for the entire Pre-Alps region in terms of type of forest and geomorphology. We measured and determined tree height, DBH and tree species for 27 randomly located circular plots (radius =10 m) in May 2008. We used allometric equations to calculate total aboveground tree biomass and subsequently plot-level aboveground biomass (mg∙ha−1). Lidar data were collected in June 2004.

Results

Our results indicate that low density lidar data can be used to estimate forest aboveground biomass with acceptable accuracies. The best height results show a R 2  =  0.87 from final model and the root mean square error (RMSE) 1.02 m (8.3% of the mean). The best biomass model explained 59% of the variance in the field biomass. Leave-one-out cross validation yielded an RMSE of 30.6 mg∙ha−1 (20.9% of the mean).

Conclusions

Low-density lidar data can be used to develop a forest aboveground biomass model from plot-level lidar height measurements with acceptable accuracies. In order to monitoring the National Forest Inventory, and respond to Kyoto protocol requirements, this analysis might be applied to a larger area.

Keywords

LiDARAllometric equationsPlant heightMixed forest

Background

Forest biomass is a key biophysical property that describes the carbon content of vegetation. Quantification at various scales, from root system (Montagnoli et al. 2012a, 2014) to above-ground organs, is critical for understanding the stocks and fluxes associated with forest clearance, degradation, and regeneration, particularly given current concerns regarding global climate change (Barrett et al. 2001; Palombo et al. 2014). Knowledge of carbon dynamics (Montagnoli et al. 2012b) is crucial when addressing issues relating to carbon accounting, including quantifying carbon for credit schemes (Patenaude et al. 2004; Kim et al. 2009). National reporting of carbon sources and sinks is also required to fulfill obligations to international agreements such as the United Nations Framework Convention on Climate Change (Rosenqvist et al. 2003). Despite these requirements, there is still much uncertainty in biomass estimation at a range of scales and in particular on how much carbon is cycled through the Earth’s forests. Scenario development to assess whether this cycling might change as a result of forest alteration (e.g. degradation induced by climate change) is also needed and is becoming increasingly important as a research field (Brack et al. 2006; Lucas et al. 2008). A better knowledge of forest ecosystems is critical to greenhouse gas control and biodiversity conservation. In fact, the Kyoto Protocol accounts for sinks of carbon emissions associated to vegetation growth and expansion (UNFCCC 1997; Rosenqvist et al. 2003). This is the reason why sustainable forest management is assuming an increasing importance and represents the second line of action of every governmental institution to be added to their commitment to reduce CO2 emission. In this context biomass is important to monitor, whether associated with land use change, afforestation, reforestation or deforestation (Schulze et al. 2002). It is not surprising that assessment of vegetated land characteristics by remote sensing is highlighted as a recommended tool in a number of political charters and treaties of different nations (Almeida et al. 2014). A detailed knowledge covering the large areas of variability, which characterize forest biomass and biophysical structure, the properties and the state of evolution of vegetation, is frequently critical given that it requires significant campaign operations in terms of time/operator and cost (Chen et al. 2007; Popescu 2007; Wallerman and Holmgren 2007). Remote sensing in general is an excellent tool for monitoring the environmental state of a vegetation canopy over space and time. It provides spatially continuous and temporally frequent information products over extended areas (Coops et al. 2007; Ota et al. 2014). To date many methods have been used to study the vegetation with remote sensing and different spectral indices have been proposed (e.g. NDVI and EVI) (Glenn et al. 2008; Kouadio et al. 2014; White et al. 2014). Despite recent steady advancements in remote sensing techniques, there still remains an inability to reliably quantify plant diversity and totally eliminate environmental interferences (Wang et al. 2010; Pettorelli et al. 2014). Remote sensing is basically the measurement and interpretation of spatially distributed radiation fluxes reflected or emitted from the Earth surface. The measured radiation fluxes are driven by radiative transfer processes, such as scattering, absorption and emission, intrinsically related to the properties of the observed surface (Campbell 1996; Jensen 2006). However, the variables controlling the radiative transfer and thus also remotely sensed data are not necessarily directly related to the surface properties of ultimate interest, like vegetation cover or bare soil (Verstraete et al. 1996). Due to the indirect and in the case of vegetation, mostly underdetermined character of this relationship, interpretation of remote sensing data should thus rely on as many independent observations as possible. In addition, the knowledge of the physical and biological processes involved needs to be considered in the interpretation of remote sensing data. Considering the limited amount of measurements generally provided by remote sensing and the high number of open variables, the problem of estimating vegetation properties based on remote sensing data is underdetermined (Wang and Sassen 2001; Combal et al. 2003; Kimes et al. 2006). A reliable retrieval is thus only possible if additional assumptions, constraints or further independent observations (e.g. field data, other sensors) are introduced (Verstraete et al. 1996). Assumptions and constraints are often used to simplify the problem, but are also limiting the retrieval in its transferability since they are generally only applicable for a specific problem (Duggin and Robinove 1990; Kötz et al. 2004). In this context, the aerial territorial survey technique, using laser scanning instruments (lidar) is particularly promising and, in some aspects, represents an alternative to satellite or aerial remote sensing because it makes it possible to directly survey the three-dimensional structure of the vegetation (Lindberg et al. 2012; Korpela and Hovi 2013). Lidar systems are an active remote sensing device that measure the time of travel needed for a pulse of laser energy sent from the airborne system to reach the ground and reflect back to the sensor. The time measured is converted into a distance measurement that is used to derive a precise three-dimensional characterization of the reflecting ground surface (Lim et al. 2003). In fact, the active optical remote sensing system, light detection and ranging (lidar), provides direct measurements on the vertical distribution of canopy elements within a vegetation canopy (Næsset and Bjerknes 2001; Lefsky et al. 2002). The measurement principle of lidar relies on laser pulses propagating vertically through the canopy, while scattering events are recorded as function of time. The remote sensing technique lidar is thus particularly suited to derive vegetation properties such as tree elevation, the vertical profile of foliage and terrain height (Harding et al. 2001). Usually the high resolution of small footprint lidar even allows for the three dimensional geometric reconstruction of single trees within a forest (Hyyppä et al. 2001; Morsdorf et al. 2004; Wang et al. 2008). Furthermore, lidar has also been shown to be an innovative tool for the study of vegetation at large scale, in particular for canopy structure, plant height and biomass (Andersen et al. 2005; Lefsky et al. 2002, 2005, 2010; Hansen et al. 2014). In this study, we investigated the utility of low-density lidar data (<2 points∙m−2) whether this type of data are useful for measuring forest attributes, such as height and above ground biomass (AGB) in the mountainous forests of northern Italy. The literature (Bortolot and Wynne 2005; Pilli et al. 2006) shows high correlations between forest AGB and tree height. The objectives of this work were: (i) to develop models of forest AGB from plot-level lidar height metrics and (ii) to understand if low density lidar is accurate enough in such conditions to produce a map of forest AGB for the region.

Methods

Study area

The low density lidar data cover a total area of approximately 80 km2 located in northern Italy in the Lombardy Pre-Alps (Valsassina; small black box in the larger are, Figure 1, left) (46°00′N, 9°23′E centre area coordinates). In the present study, to test if these lidar data are reliable for forest AGB estimation, a sample area of 8 km2 was analysed (gray cell, Figure 1, right). The study area topography is characterised by a mean elevation of 890 m with minimum elevation of 590 m and maximum of 1293 m. The study area includes mixed and broad-leaved forest with variable stand densities and tree species compositions. The site is representative for the entire Pre-Alps region in terms of type of forest and geomorphology. The main forest types are coppice management with plantations of chestnut (Castanea sativa) together with beech (Fagus sylvatica), birch (Betula pendula), linden (Tilia cordata), ash (Fraxinus excelsior), poplar (Populus tremula), field maple (Acer campestre), hazel (Corylus avellana), European Hop Hornbeam (Ostrya carpinifolia), wild cherry (Prunus avium) and natural stands of oak (Quercus spp.).
Figure 1
Figure 1

Map of the Lombardia region (larger area, left) and study area location (small black polygon, left). Grid area (right) indicates the LiDAR data survey. Aerial photos from free access Geoportale Lombardia. Gray cell (right) indicate location of field plots.

Lidar data

Large-footprint discrete first and last returns lidar data were acquired in October 2003 when the canopy first started to change colour in the fall. Acquisition was made by Compagnia Generale Riprese aree SpA, Parma – CGR and property of Regione Lombardia, using an Optech ALTM 3033 scanner Airborne, at flying height of 2000 m with a swath width of 1450 m. Scan angle was 20° with an approximate footprint of 50 cm, and an average pulse spacing of 1.75 m and pulse rate of 33 MHz.

Field measurements

In a selected 2 km  ×  2 km area (Figure 1, right) 27 circular plots (radius  =  10 m) were randomly located. During May 2008 tree number, height and diameter at breast height (DBH) were measured for all tree species within each plot (Table 1). Possible lidar data underestimation of tree height due the 5 years growth discrepancy between lidar data collection (2003) and field measurements was considered minimal. Analysed forests were at the mature stage (20–30 years old) therefore minimal growth was assumed (Brassard et al. 2009; Franceschini and Schneider 2014). Trees with diameters smaller than 5 cm were excluded. A total of 1417 trees were measured on these plots. On each plot the tree heights were measured using a Vertex Laser VL-400 - telemeter/hypsometer (Haglöf), and DBH with a Forestry Suppliers Metric Fabric Diameter Tape. Although it can be difficult to clearly distinguish the treetop in dense forests, the measured forests were not dense enough to completely block the treetops. Moreover, effort was taken in the field to move around in the forest until a spot was found that did not obscure the treetop. Geographic coordinates were recorded at the centre of each plot with a Trimble® GeoXM™ GPS with 1–3 meter accuracy. Specific allometric equations (Leonardi et al. 1996; Hamburg et al. 1997; Gasparini et al. 1998; Zianis et al. 2005; Alberti et al. 2006; Tabacchi et al., 2011) were used to calculate total aboveground tree biomass and subsequently plot-level biomass (mg∙ha−1) (see Table 2).
Table 1

Forest tree density and characteristics for each forest plot of the study area

Plot number

Number of stem

Height (m)

DBH* (cm)

1

40.5

9.3 (0.5)

14.3 (1.4)

2

20

19.4 (0.7)

32.5 (2.4)

3

32

13.5 (0.9)

20.4 (1.8)

4

27

13.8 (1.1)

27.0 (2.7)

5

53

11.6 (0.7)

13.3 (0.7)

6

64

11.9 (0.4)

11.0 (0.5)

7

22

18.4 (1.3)

28.7 (3.1)

8

40

11.0 (0.5)

16.6 (1.1)

9

59

9.4 (0.5)

11.6 (0.9)

10

46

13.3 (0.6)

14.6 (0.9)

11

31

13.8 (0.3)

20.0 (1)

12

80

10.5 (0.4)

12.1 (0.8)

13

56

9.6 (0.5)

13.0 (0.8)

14

36

15.5 (0.9)

19.1 (1.4)

15

47

12.1 (0.6)

14.3 (1.3)

16

32

14.8 (0.8)

19.0 (1.3)

17

38

15.9 (0.6)

25.7 (2)

18

42

11.7 (0.8)

15.8 (1.2)

19

59

12.7 (0.4)

12.0 (0.5)

20

25

11.2 (0.6)

18.8 (1.4)

21

55

15.6 (0.6)

15.3 (0.9)

22

92

9.4 (0.2)

10.9 (0.5)

23

93

10.6 (0.3)

11.0 (0.6)

24

90

9.0 (0.2)

10.4 (0.5)

25

119

8.9 (0.2)

10.4 (0.3)

26

41

9.5 (0.4)

14.4 (1.2)

27

79

10.0 (0.2)

10.2 (0.3)

*DBH (diameter at breast height), height and DBH values are mean of the number of stem (±SE).

Table 2

Biomass equations by tree species

Species name

Equation

Parameters

Reference

a

b

c

Acer campestre (L.)

a · (DBH) b

0.05

2.67

 

Alberti et al. 2006

Betula pendula (Roth)

a · [(DBH)2 · H] b

0.5443

0.65270

 

Hamburg et al. 1997

Castanea sativa (Miller)

a · (DBH) b

0.137

2.247

 

Leonardi et al. 1996

Corylus avellana (L.)

a · (H) b

0.0768

1.8329

 

Hamburg et al. 1997

Fagus sylvatica (L.)

a + b · (DBH)2 · H

1.6409

0.030775

 

Tabacchi et al. 2011

Fraxinus excelsior (L.)

a · (DBH) b

0.11

2.49

 

Alberti et al. 2006

Populus tremula (L.)

a · (DBH) b

0.0519

2.545

 

Zianis et al. 2005

Prunus avium (L.)

a · (DBH) b

0.12

2.33

 

Alberti et al. 2006

Quercus petraea (Matt.) Liebl.

a · (DBH) b

0.2176

2.0513

 

Alberti et al. 2006

Tilia cordata (Miller)

ln(ABW) = a + b · ln(DBH)

−2.6788

2.4542

 

Zianis et al. 2005

Ostrya carpinifolia (Scop.)

a + b · DBH · H + c · DBH2

−4.5877

5.2638 · 10−3

4.09 · 10−1

Gasparini et al. 1998

DBH = Diameter at breast height; H = Plant height; ln = natural logarithm; ABW = Total aboveground woody biomass.

The format of the biomass equation is given in the column labelled Equation, and a, b, and c are parameter values. References to the original papers according to authors.

Data analysis

The result of a laser scan is a cloud composed by geographically located points (raw data) corresponding to all the elements composing the scanned surface. The first step in data processing was to identify and exclude (filtering) all outliers due to their distance from the mean surface. The TerraScan™ of Terrasolid software was used for this automated process (Axelsson 1999). Afterwards analysis parameters were manually corrected according to the different geo-morphology characteristics and an additional filtering was applied (Barilotti et al. 2007). Interpolating lidar point elevation to a regular grid with a 5 m resolution created a Digital Surface Model (DSM) and Digital Terrain Model (DTM; 6–9 pulses per 5 m2). The characterization of the top canopy surface (DSM) included only the highest laser reflection points, while lowest laser reflection points were used to estimate the ground level (DTM) (Lefsky et al. 1999; Holmgren and Persson 2004; Popescu et al. 2004; Popescu 2007).

The low density point clouds may miss the tree tops, because naturally tree tops have fewer hits compared to the tree crowns. The same problem exists in identifying the ground (Suárez et al. 2005), especially in a steep terrain (Estornell et al. 2011) like the Alps. Therefore, the elevation recorded with GPS units was compared with those identified in the lidar point cloud. Lidar returns were extracted for each plot using the geographic coordinates taken in the field. Returns above a threshold of 2 m were considered vegetation returns, and returns below that threshold were considered ground returns. Height threshold was chosen according to plot analysis where all trees below the 2 m cut off were smaller than 3 cm diameter and not considered for measurement. Then data were normalized to height-above-ground using the DTM (spatial resolution 5 meter; Figure 2b) supplied by the lidar vendor. Furthermore, points below two meters were eliminated in the plot to omit returns from understory or falsely classified ground returns (Figure 2c). The software used for these analyses was the free FUSION/LDV developed by Robert J. McGaughey (U.S. Forest Service Pacific Northwest Research Station, Oregon).
Figure 2
Figure 2

Examples of vertical plot LiDAR return distributions: (a) Raw LiDAR data plot; (b) data normalized by digital terrain model; (c) data points above 2 m from soil surface.

Regression analysis

To investigate the relationship of low density returns and AGB, regression models were used to develop equations relating lidar-derived tree height with field inventory tree height and field-based estimates of aboveground biomass for individual plots. In particular, the first relationship analysed was between lidar height metrics and field-measured height. Secondly the relationships between lidar height metrics and AGB from field data was considered at the plot scale (Pflugmacher et al. 2012). Analysis of variances for linear regression was carried out on results of each relationship to test for significance of the slopes at 95% significance level.

Results and discussion

Plant height

We report the best regression model developed to explain the relationship between lidar height metrics and field-measured height at the plot level. Linear regression indicated a multiple R 2 of 0.87 (Figure 3a; slope test p < 0.001) and a root mean square error (RMSE) of 1.02 m (BIAS of 0; Figure 3b), which is approximately 8.3% of the average plant height of all measured trees. Cross validation showed a RMSE of 2.02 m (16.4% of the mean) and a BIAS of 0.02. The final model derived from height distribution data was based on the following multiple percentile of tree height distribution: Height 25%; Height 20%; Height 30%; Height 10%; Height 80%. Our results are in line with previous studies (Coops et al. 2007; Popescu 2007; Stepper et al. 2014) in which a good correlation between tree height lidar-estimated and field-measured for both needle and broad leaved trees has been reported.
Figure 3
Figure 3

The best regression model developed to explain: (a) Relationship between LiDAR data and mean plant height measured in the field; (b) plant height BIAS against fitted height data LiDAR derived; (c) relationship between LiDAR data and mean biomass measured in the field; (d) plant biomass BIAS against fitted height data LiDAR derived.

Plant biomass

The best model for the relationship between field AGB and lidar height explained 76% (multiple R 2) of the variance in the field biomass (Figure 3c; slope test p < 0.001) with a RMSE of 30.56 mg∙ha−1 (20.9% of the mean) and a BIAS of 0 (Figure 3d). Leave-one-out cross validation yielded an RMSE of 53.7 mg∙ha−1 (36.8% of the mean) and a BIAS of 3.7. The final model selected was based on the following multiple percentile of tree height distribution: Height 0; Height 5%; Height 25%; Height 55%; Height 70%; Height 80%; Height 95%; Height 100%.

Potential source of errors can include statistical error associated with estimating coefficients and form of selected equation. Moreover, errors may occur from both field measurement and data processing as well as errors associated with developing wide scale equation by compiling species- and site-specific equation that may be biased in favour of species for which published equations exist (Sileshi 2014). With the present approach, part of the unexplained variance when estimating aboveground biomass is associated with error of estimating biomass with field measurements of DBH and height, error associated with lidar-measured height and GPS misregistration errors. Selected variables for lidar height with measured field AGB (Figure 4a) were applied to the entire sample area of 8 km2 in order to obtain a biomass map (Figure 4b). Previous studies have successfully estimated AGB or tree volume from lidar-derived vegetation-height statistical metrics in different boreal and temperate forests (Næsset 1997; Magnussen and Boudewyn 1998; Means et al. 2000; Popescu et al. 2004; Hall et al. 2005; Popescu 2007; Alberti et al. 2012). The average AGB and plant height estimated by lidar data of our forests were 146 mg∙ha−1 and 12.28 m respectively. As demonstrated in previous works (Raber et al. 2002; Clark et al. 2004; Estornell et al. 2011) for relatively dense and structural deciduous forest on steep slope conditions, data characteristics such as scan angle most often causes DTM inaccuracy that effect derived trees canopy height (Raber et al. 2002; Clark et al. 2004). In the present study, plant height normalization performed with a low spatial resolution DTM, might have been another cause of error in lidar metrics calculation. Although these possible effects were not analysed, our results were similar to values of above ground biomass (123.7 mg∙ha−1) found in a Pre-alpine beech forest (Montagnoli et al. 2012b). Even though these forests have different species composition, these values are comparable, since our study sites are in the transition zone of lowland and montane forests. Forest estimates (of height, volume and biomass), using laser data, are often based on linear regression models of forest canopy height (Nilsson 1994, 1996) and statistical measures derived from the distribution of laser point data (Lefsky et al. 1999; Næsset and Gobakken 2005). Several studies have noted that measures of canopy characteristics obtained from the laser height distribution, together with selected laser height percentiles, have proven useful for estimating timber volume (Means et al. 1999; Næsset & Økland 2002). Our results also show that mean height was the most reliable estimator of AGB (RMSE = 53.7 mg∙ha−1, corresponding to 36.8% of the mean) in single regression analysis. Finally, in our case we also demonstrated a good fitting model even with a difference in time between lidar data collection and field measurements.
Figure 4
Figure 4

Example of application for plant biomass model by plant height measured by LiDAR data. (a) Digital orthophoto, in the lower part showed plots for field plant measurements. (b) Example of biomass map of the same area. Plant biomass values, represented by a multiple colours legend, increase from pink (lower value) to red (higher value). Pixel (1 m2).

Conclusion

Our results, in mixed broad leaved forests growing on patchy slope conditions, indicate that low-density lidar data can be used to develop a forest AGB model from plot-level lidar height measurements in the study area with acceptable accuracies. Moreover, these results highlight the opportunity to apply this analysis to a larger area, with the aim of monitoring the National Forest Inventory, and create a database of the forest carbon content in order to respond to requirements of the Kyoto protocol. The biomass map derived from the selected regression model and the potential for integrating lidar with co-registered multi and hyperspectral digital imagery, make lidar a realistic alternative to traditional forest measurements.

Declarations

Acknowledgements

This work was supported in part by a grant awarded by MIUR (PRIN 2008 n. 223), grant by University of Insubria (FAR) and EC FP7 Project ZEPHYR-308313. We are grateful to ERSAF and Regione Lombardia for providing lidar data. The authors are also in debt with Dr. Antonino Di Iorio and Dr. Barbara Baesso for helping with biomass equations, Carlo Fraquelli for helping in field measurements.

Authors’ Affiliations

(1)
Department of Biotechnology and Life Science, University of Insubria, Varese, Italy
(2)
Department of Forest Ecosystems and Society, Oregon State University, Corvallis, USA
(3)
National Park Service, Great Lakes Inventory and Monitoring Network, Ashland, USA
(4)
USDA Forest Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, Corvallis, USA
(5)
Department of Biosciences and Territory, University of Molise, Pesche, Italy

References

  1. Alberti G, Marelli A, Piovesana D, Peressotti A, Zerbi G, Gottardo E, Bidese F (2006) Carbon stocks and productivity in forest plantations (Kyoto forests) in Friuli Venezia Giulia (Italy). Forest@ 3:488–495View ArticleGoogle Scholar
  2. Alberti G, Boscutti F, Pirotti F, Bertacco C, De Simon G, Sigura M, Cazorzi F, Bonfanti P (2012) A LiDAR-based approach for a multi-purpose characterization of Alpine forests: an Italian case study.” iForest – Biogeosciences and Forestry 6: 156–168. http://www.sisef.it/iforest/contents/?id=ifor0876-006doi:10.3832/ifor0876-006
  3. Almeida P, Altobelli A, D'Aietti L, Feoli E, Ganis P, Giordano F, Napolitano R, Simonetti C (2014) The role of vegetation analysis by remote sensing and GIS technology for planning sustainable development: A case study for the Santos estuary drainage basin (Brazil). Plant Biosyst 148:540–546View ArticleGoogle Scholar
  4. Andersen H-E, McGaughey RJ, Reutebuch SE (2005) Estimating forest canopy fuel parameters using LIDAR data. Remote Sens Environ 94:441–449View ArticleGoogle Scholar
  5. Axelsson P (1999) Processing of laser scanner data – algorithms and applications. ISPRS J Photogramm Remote Sens 54:138–147View ArticleGoogle Scholar
  6. Barilotti A, Sepic F, Abramo E, Crosilla F (2007) Improving the morphological analysis for tree extraction: a dynamic approach to lidar data. In: Proceedings of the ISPRS Workshop on ‘Laser Scanning 2007 and SilviLaser 2007’ Espoo, Finland, 12–14 September 2007. Volume XXXVI, part 3/W52. Published by ISPRS Working Groups, ASPRS Lidar Committee, Finnish Geodetic Institute Institute of Photogrammetry and Remote Sensing, Helsinki University of Technology (TKK).Google Scholar
  7. Barrett DJ, Galbally IE, Graetz RD (2001) Quantifying uncertainty in estimates of C emissions from above-ground biomass due to historic land-use change to cropping in Australia. Glob Change Biol 7:883–902View ArticleGoogle Scholar
  8. Bortolot ZJ, Wynne RH (2005) Estimating forest biomass using footprint LiDAR data: An individual tree-based approach that incorporates training data. J Phot & Remote Sens 59:342–360View ArticleGoogle Scholar
  9. Brack CL, Richards G, Waterworth R (2006) Integrated and comprehensive estimation of greenhouse gas emissions from land systems. Sustain Sci 1:91–106View ArticleGoogle Scholar
  10. Brassard BW, Chen HYH, Bergeron Y (2009) Influence of environmental variability on root dynamics in northern forests. Cr Rev Plant Sci 28:179–197View ArticleGoogle Scholar
  11. Campbell JB (1996) Introduction to Remote Sensing (2nd Ed). Taylor and Francis, LondonGoogle Scholar
  12. Chen Q, Gong P, Baldocchi D, Tian YQ (2007) Estimating basal area and stem volume for individual trees from lidar data. Photogramm Eng Remote Sensing 73:1355–1365View ArticleGoogle Scholar
  13. Clark ML, Clark DB, Roberts DA (2004) Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape. Remote Sens Environment 91:68–89View ArticleGoogle Scholar
  14. Combal B, Baret F, Weiss M, Trubuil A, Mace D, Pragnere A, Myneni R, Knyazikhin Y, Wang L (2003) Retrieval of canopy biophysical variables from bidirectional reflectance — Using prior information to solve the ill-posed inverse problem. Remote Sens Environ 84:1–15View ArticleGoogle Scholar
  15. Coops NC, Thomas H, Wulder MA, St-Onge B, Newnham G, Siggins A, Trofymow JAT (2007) Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR. Trees 21:295–310View ArticleGoogle Scholar
  16. Duggin MJ, Robinove CJ (1990) Assumptions implicit in remote-sensing data acquisition and analysis. Int J Remote Sens 11:1669–1694View ArticleGoogle Scholar
  17. Estornell J, Ruiz LA, Velázquez-Martí HT (2011) Analysis of the factors affecting LiDAR DTM accuracy in a steep shrub area. Int J Digital Earth 4:521–538View ArticleGoogle Scholar
  18. Franceschini T, Schneider R (2014) Influence of shade tolerance and development stage on the allometry of ten temperate tree species. Oecologia 176:739–749PubMedView ArticleGoogle Scholar
  19. Gasparini P, Nocetti M, Tabacchi G, Tosi V (1998) Biomass equations and data for forest stands and shrublands of the Eastern Alps. Forest and Range Management Research Institute. I.S.A.F.A. - C.R.A.-, Villazzano, Trento, ItalyGoogle Scholar
  20. Glenn EP, Huete AR, Nagler PL, Nelson SG (2008) Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors 8:2136–2160PubMed CentralView ArticleGoogle Scholar
  21. Hall SA, Burke IC, Box DO, Kaufmann MR, Stoker JM (2005) Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. For Ecol Manage 208:189–209View ArticleGoogle Scholar
  22. Hamburg SP, Zamolodchikov DG, Korovin GN, Nefedjev V, Utkin AI, Gulbe T (1997) Estimating the carbon content of Russian forests; a comparison of phytomass/volume and allometric projections. Mitig adapt strategies glob chang 2:247–265View ArticleGoogle Scholar
  23. Hansen AJ, Phillips LB, Dubayah R, Goetz S, Hofton M (2014) Regional-scale application of lidar: Variation in forest canopy structure across the southeastern US. Forest Ecol Manag 329:214–226View ArticleGoogle Scholar
  24. Harding DJ, Lefsky MA, Parker GG, Blair JB (2001) Laser altimeter canopy height profiles — Methods and validation for closed-canopy, broadleaf forests. Remote Sens Environ 76:283–297View ArticleGoogle Scholar
  25. Holmgren J, Persson Å (2004) Identifying species of individual trees using airborne laser scanner. Remote Sens Environ 90:415–423View ArticleGoogle Scholar
  26. Hyyppä J, Kelle O, Lehikoinen M, Inkinen M (2001) A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE T Geosci Remote 39:969–975View ArticleGoogle Scholar
  27. Jensen JR (2006) Remote sensing of the environment: an earth resource perspective, 2nd edn. Prentice Hall, New JerseyGoogle Scholar
  28. Kim Y, Yang Z, Cohen WB, Pflugmacher D, Lauver CL, Vankat JL (2009) Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data. Remote Sens Environ 113:2499–2510View ArticleGoogle Scholar
  29. Kimes DS, Ranson KJ, Sun G, Blair JB (2006) Predicting lidar measured forest vertical structure from multi-angle spectral data. Remote Sens Environ 100:503–511View ArticleGoogle Scholar
  30. Korpela I, Hovi A (2013) Korhonen L (2013) Backscattering of individual LiDAR pulses from forest canopies explained by photogrammetrically derived vegetation structure. Isprs J Photogramm 83:81–93View ArticleGoogle Scholar
  31. Kötz B, Schaepman M, Morsdorf F, Bowyer P, Itten K, Allgöwerd B (2004) Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties. Remote Sens Environ 92:332–344View ArticleGoogle Scholar
  32. Kouadio L, Newlands NK, Davidson A, Zhang Y, Chipanshi A (2014) Assessing the performance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale. Remote Sens 6:10193–10214View ArticleGoogle Scholar
  33. Lefsky MA (2010) A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system. Geophys Res Lett 37:L15401, http://dx.doi.org/10.1029/2010GL043622 View ArticleGoogle Scholar
  34. Lefsky MA, Harding D, Cohen WB, Parker G, Shugart HH (1999) Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern maryland, USA. Remote Sens Environ 67:83–98View ArticleGoogle Scholar
  35. Lefsky MA, Cohen WB, PARKER GG, Harding DJ (2002) Lidar Remote Sensing for Ecosystem Studies. Bioscience 52:19–30View ArticleGoogle Scholar
  36. Lefsky M, Harding D, Keller M, Cohen W, Carabajal C, Espirito-Santo F, Hunter M, de Oliveira R (2005). Estimates of forest canopy height and aboveground biomass using ICESat. Geophys Res Lett 32:L22S02. http://dx.doi.org/10.1029/2005GL023971
  37. Leonardi S, Santa Regina I, Rapp M, Gallego HA, Rico M (1996) Biomass, litterfall and nutrient content in Castanea sativa coppice stands of southern Europe. Ann For Sci 53:1071–1081View ArticleGoogle Scholar
  38. Lim K, Treitz P, Wulder M, St-Onge B, Flood M (2003) Lidar remote sensing of forest structure. Prog Phys Geog 27:88–106View ArticleGoogle Scholar
  39. Lindberg E, Olofsson K, Holmgren J, Olsson H (2012) Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data. Remote Sens Environ 118:151–161View ArticleGoogle Scholar
  40. Lucas R, Lee A, Armston J, Breyer J, Bunting P, Carreiras J (2008) Advances in forest characterisation, mapping and monitoring through integration of LiDAR and other remote sensing datasets. SilviLaser, Edinburgh, UKGoogle Scholar
  41. Magnussen S, Boudewyn P (1998) Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators. Can J For Res 28:1016–1031View ArticleGoogle Scholar
  42. Means JE, Acker SA, Harding DJ, Blair JB, Lefsky MA, Cohen WB, Harmon ME, McKee WA (1999) Use of large-footprint scanning airborne lidar to estimate forest stand characteristics in the western cascades of Oregon. Remote Sens Environ 67:298–308View ArticleGoogle Scholar
  43. Means JE, Acker SA, Brandon JF, Renslow M, Emerson L, Hendrix CJ (2000) Predicting forest stand characteristics with airborne scanning lidar. Photogramm Eng Remote Sens 66:1367–1371Google Scholar
  44. Montagnoli A, Terzaghi M, Di Iorio A, Scippa GS, Chiatante D (2012a) Fine-root morphological and growth traits in a Turkey-oak stand in relation to seasonal changes in soil moisture in the southern Apennines, Italy. Ecol Res 27:725–733View ArticleGoogle Scholar
  45. Montagnoli A, Terzaghi M, Di Iorio A, Scippa GS, Chiatante D (2012b) Fine-root seasonal pattern, production and turnover rate of European beech (Fagus sylvatica L.) stands in Italy Prealps: Possible implications of coppice conversion to high forest. Plant Biosyst 146:1012–1022View ArticleGoogle Scholar
  46. Montagnoli A, Di Iorio A, Terzaghi M, Trupiano D, Scippa GS, Chiatante D (2014) Influence of soil temperature and water content on fine-root seasonal growth of European beech natural forest in Southern Alps, Italy. Eur J Forest Res, doi:10.1007/s10342-014-0814-6Google Scholar
  47. Morsdorf F, Meier E, Kötz B, Itten KI, Dobbertin M, Allgöwer B (2004) LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wild land fire management. Remote Sens Environ 92:353–362View ArticleGoogle Scholar
  48. Næsset E (1997) Estimating timber volume of forest stands using airborne laser scanner data. Remote Sens Environ 61:246–253View ArticleGoogle Scholar
  49. Næsset E, Bjerknes KO (2001) Estimating tree heights and number of stems in young forest stands using airborne laser scanner. Remote Sens Environ 78:328–340View ArticleGoogle Scholar
  50. Næsset E, Gobakken T (2005) Estimating forest growth using canopy metrics derived from airborne laser scanner data. Remote Sens Environ 96:453–465View ArticleGoogle Scholar
  51. Næsset E, Økland T (2002) Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sens Environ 79:105–115View ArticleGoogle Scholar
  52. Nilsson M (1994) Estimation of tree heights and stand volume using airborne lidar system. In: Report 57, Dept of Forest Survey. Swedish Univ of Agric Sciences, Umeå, p 59Google Scholar
  53. Nilsson M (1996) Estimation of tree heights and stand volume using an airborne lidar system. Remote Sens Environ 56:1–7View ArticleGoogle Scholar
  54. Ota T, Ahmed OS, Franklin SE, Wulder MA, Kajisa T, Mizoue N, Yoshida S, Takao G, Hirata Y, Furuya N, Sano T, Heng S, Vuthy M (2014) Estimation of airborne lidar-derived tropical forest canopy height using landsat time series in Cambodia. Remote Sens 6:10751–10772View ArticleGoogle Scholar
  55. Palombo C, Marchetti M, Tognetti R (2014) Mountain vegetation at risk: Current perspectives and research reeds. Plant Biosys 148:35–41View ArticleGoogle Scholar
  56. Patenaude G, Hill R, Milne R, Gaveau D, Briggs B, Dawson T (2004) Quantifying forest above ground carbon content using lidar remote sensing. Remote Sens Environ 93:368–380View ArticleGoogle Scholar
  57. Pettorelli N, Safi K, Turner W (2014) Satellite remote sensing, biodiversity research and conservation of the future. Philos Trans R Soc Lond B Biol Sci 369:20130190, doi:10.1098/rstb.2013.0190PubMed CentralPubMedView ArticleGoogle Scholar
  58. Pflugmacher D, Cohen WB, Kennedy RE, Yang Z (2012) Using Landsat-derived disturbance history (1972–2010) to predict current forest structure. Remote Sens Environ 122:146–165View ArticleGoogle Scholar
  59. Pilli R, Anfodillo R, Carrer M (2006) Toward a functional and simplified allometry for estimating forest biomass. Forest Ecol Manag 237:583–593View ArticleGoogle Scholar
  60. Popescu SC (2007) Estimating biomass of individual pine trees using airborne LiDAR. Biomass Bioenerg 31:646–655View ArticleGoogle Scholar
  61. Popescu SC, Wynne RH, Scrivani JA (2004) Fusion of small foot print LiDAR and multispectral data to estimate plot-level volume and biomass in deciduous and pine forests in Virginia, USA. Forest Sci 50:551–565Google Scholar
  62. Raber GT, Jensen JR, Schill SR, Schuckman K (2002) Creation of digital terrain models using an adaptive lidar vegetation point removal process. Photogramm Eng Remote Sens 68:1307–1315Google Scholar
  63. Rosenqvist Å, Milne A, Lucas R, Imhoff M, Dobson C (2003) A review of remote sensing technology in support of the Kyoto Protocol. Environ Sci Policy 6:441–455View ArticleGoogle Scholar
  64. Schulze ED, Valentini R, Sanz MJ (2002) The long way from Kyoto to Marrakesh: Implications of the Kyoto Protocol negotiations for global ecology. Glob Change Biol 8:505–518View ArticleGoogle Scholar
  65. Sileshi GW (2014) A critical review of forest biomass estimation models, common mistakes and corrective measures. For Ecol Manag 329:237–254View ArticleGoogle Scholar
  66. Stepper C, Straub C, Pretzsch H (2014) Assessing height changes in a highly structured forest using regularly acquired aerial image data. Forestry 0: 1–13, doi:10.1093/forestry/cpu050Google Scholar
  67. Suárez JC, Ontiveros C, Smith S, Snape S (2005) Use of airborne LiDAR and aerial photography in the estimation of individual tree heights in forestry. Computers & Geosciences 31:253–262View ArticleGoogle Scholar
  68. Tabacchi G, Di Cosmo L, Gasparini P (2011) Aboveground tree volume and phytomass prediction equations for forest species in Italy. Eur J Forest Res 130:911–934View ArticleGoogle Scholar
  69. UNFCCC (1997) Kyoto Protocol to the United Nations Framework Convention on Climate Change adopted at COP3 in Kyoto, Japan, on 11 December 1997Google Scholar
  70. Verstraete MM, Pinty B, Myneni R (1996) Potential and limitations of information extraction on the terrestrial biosphere from satellite remote sensing. Remote Sens Environ 58:201–214View ArticleGoogle Scholar
  71. Wallerman J, Holmgren J (2007) Estimating field-plot data forest stands using airborne laser scanning and SPOT HRG data. Remote Sens Environ 110:501–508View ArticleGoogle Scholar
  72. Wang Z, Sassen K (2001) Cloud type and property retrieval using multiple remote sensors. J Appl Meteorol 40:1665–1682View ArticleGoogle Scholar
  73. Wang Y, Weinacker H, Koch B (2008) A lidar point cloud based procedure for vertical canopy structure analysis and 3D single tree modelling in forest. Sensors 8:3938–3951PubMed CentralView ArticleGoogle Scholar
  74. Wang K, Franklin SE, Guo X, Cattet M (2010) Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists. Sensors 10:9647–9667, doi:10.3390/s101109647PubMed CentralPubMedView ArticleGoogle Scholar
  75. White K, Pontius J, Schaberg P (2014) Remote sensing of spring phenology in northeastern forests: a comparison of methods, field metrics and sources of uncertainty. Remote Sens Environ 148:97–107View ArticleGoogle Scholar
  76. Zianis D, Muukkonen P, Mäkipääand R, Mencuccini M (2005) Biomass and stem volume equations for tree species in Europe. Silva Fenn Monogr 4(1–2):5–63Google Scholar

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