Close Home
iForest - Biogeosciences and Forestry
vol. 10, pp. 590-596
Copyright © 2017 by the Italian Society of Silviculture and Forest Ecology
doi: 10.3832/ifor1989-010

Research Articles

Estimation of aboveground forest biomass in Galicia (NW Spain) by the combined use of LiDAR, LANDSAT ETM+ and National Forest Inventory data

Enrique Jiménez (1)Corresponding author, José A Vega (1), José M Fernández-Alonso (1), Daniel Vega-Nieva (2), Luis Ortiz (3), Pablito M López-Serrano (2), Carlos A López-Sánchez (2)

Introduction 

Measurement and mapping of aboveground forest biomass at diverse scales is critical for estimating global carbon storage and assessing those ecosystem responses to climate change and anthropogenic disturbance ([38]). Estimation of forest biomass is of great importance because of the increasing value of this renewable resource for energy production ([15]). Furthermore, biomass estimation and mapping are used to quantify fuel loads for fire risk assessment and fire prevention planning purposes ([16]).

The method most commonly used to estimate aboveground forest biomass is the forest inventory based on plot data. However, given the high costs and operational difficulties associated with this method, the use of remotely sensed data in combination with field-work is becoming increasingly popular for forest inventory purposes ([19]). Moreover, remote-sensing methods enable the estimation of stand dendrometric and biomass values at each pixel location, rather than estimation of average or total biomass within a given area ([18]).

Most studies using remote-sensing methods have been based on medium-to-high resolution sensors, especially the Landsat-TM sensor, given the good compromise between spectral and temporal resolution of the data achieved ([19], [29]). Remote-sensing indices have been used as explanatory variables for models of forest biomass and productivity ([30]). LiDAR (Light Detection and Ranging) technology has been recognized as a much more efficient, accurate and cost effective approach to sensing aboveground biomass. This technology is generally regarded as a more accurate method because LiDAR sensors provide information about vertical height of individual pulses returns, which can then be used to predict canopy attributes ([10]). Previous studies have demonstrated the success of LiDAR estimates of aboveground biomass based on the relationship between LiDAR metrics and field measurements of biomass obtained from allometric models ([45]). In this study we estimated aboveground forest biomass in Galicia (NW Spain) by the combined use of LiDAR, LANDSAT ETM+ and field data.

Two main types of statistical approaches are applied to estimate biomass from remotely sensed data: one focused on the data and the other on algorithms ([6]). The first assumes a stochastic model (linear, nonlinear regressions) used both to predict responses of population units not in the sample and future responses ([19], [14], [15]). The second approach (generally called machine-learning models) has been reported for implicitly inferring unknown relationships underlying a given dataset, being versatile enough to uncover complicated non-linear relationships ([11]). In the present study the first approach was chosen.

Forest inventories based on remotely-sensed data can be carried out at two different spatial scales: the individual tree level (associated with spatially dense LiDAR data, > 5 points m-2 - [22]) and the area-based, which can also be used with low density LiDAR data ([45], [14], [15]). In this study, we focused on area-based forest inventories, as we used low density LiDAR data provided by the Spanish National Plan for Aerial Ortophotography (PNOA) for the Spanish territory (0.5 pulse m-2), being higher point densities required for the estimation at individual tree level ([43]). The use of LiDAR for aboveground biomass estimations is still considered a challenging task, especially when using low density scanning systems, due to its lower accuracy in comprehensively reflecting the canopy structure ([27]). However, low density LiDAR has been shown to be quite effective for predicting biomass and other variables in temperate forests ([36], [39], [33]).

For large areas covered by different types of vegetation, models based on remote sensing data are usually developed for each vegetation type, as this kind of models is greatly influenced by species and stand structure ([36], [39], [24]). Implementation of models for mapping stand dendrometric and biomass variables requires an accurate classification of the area ([36], [39], [24]).

Galicia has one of the greatest potential forest production rates in Europe, varying from 6 to 12 m3 ha-1 year-1 depending on the particular species ([8]) and producing 45% of Spain’s timber and 4.5% of Europe’s. The most productive and fast-growing tree species in Galicia are Eucalyptus spp. (Eucalytus globulus Labill. and Eucalyptus nitens Deane et Maiden), Pinus pinaster Ait. and Pinus radiata D. Don, all of which are grown in extensive commercial forest plantations to produce panelboard, sawlog and pulpwood ([34]). Stands dominated by these tree species account for 133.224.356 m3 of standing timber (69.1% of the total tree standing timber volume) in Galicia, and cover an area of 896.342 ha (62.9% of the total tree-covered area) in the region ([31]).

The objective of this study was to evaluate the feasibility of the combined use of freely available remote sensing (low density LiDAR and Landsat) and National Forest Inventory data combined with the National Forest Map (based on PNOA aerial ortophoto, with a scale of 1:25.000 and minimum mapping unit of 1 ha) to estimate stand dendrometric and aboveground biomass variables of the most productive tree species in a pilot area in Galicia. This is the first study using this freely available information for these tree species in the NW Spain. The results may also be applicable to similar areas covered by similar sensors.

Material and methods 

The study was conducted in the municipality of Palas de Rei (Lugo), in northwestern Spain (42° 52′ 23″ N; 07° 52′ 08″ W - Fig. 1). The study area consisted of a 60 × 60 km square, centered in Palas de Rei encompassing parts of the provinces of Lugo, Pontevedra and A Coruña. The mean elevation of the area is 525 m (range 147-1179 m) and the mean slope is 11.3%. The climate is Mediterranean, with a continental influence. The average precipitation is 1188 mm year-1, and the mean annual temperature is 12.3 °C, with maximum temperatures in August (19.0 °C) and minimum temperatures in January (6.8 °C). Forests (more than 20% of tree cover) occupy 150.396.6 ha of the total surface area. The main tree species in the area are Pinus pinaster (covering 27.8% of the total forest area), Pinus radiata (22.7%), Quercus robur (30.7%) and Eucalyptus spp. (mainly E. nitens and E. globulus - 13.8%).

Fig. 1 - Location of the study area and National Forest Map polygons dominated by Pinus pinaster, Pinus radiata and Eucalyptus spp.

Field data

The Fourth Spanish National Forest Inventory (SNFI-4) plots were used as the source of field data for this study. Plots within the study area dominated by the three selected tree species, with tree cover > 20% and presence of trees with diameter at breast height (dbh) > 7.5 cm, were selected for the study ([31]). The plots were established at the intersections of a 1 × 1 km grid, totaling 873 plots over the whole study area. In 749 of these, tree cover was > 20%, with presence of trees with dbh > 7.5 cm. In total, 159 plots were dominated by Pinus pinaster, 142 by Pinus radiata and 109 by Eucalyptus spp. The measurements were carried out during 2008 and 2009 (Tab. 1).

Tab. 1 - Mean (± standard error) tree density, basal area (G), dominant height (H0), crown biomass (Wcr), stem biomass (Wst) per sample plot of the National Forest Inventory within the study area for each species.

Sample plots consisted of four concentric circles of radii 5, 10, 15 and 25 m, in which dbh and total height were measured in all trees of dbh > 7.5, 12.5, 22.5 and 42.5 cm, respectively. Diameter at breast height was measured to the nearest 0.1 cm with a graduated caliper, and tree height was measured to the nearest 0.1 m with a hypsometer. The number of stems per hectare, basal area and dominant height (mean height of the 100 thickest trees per hectare) were calculated from the tree variable measurements by the use of expansion factors. These factors express the number of trees per hectare that each measured tree represents in the inventory in relation to the subplot radius.

Aboveground biomass per plot was estimated by applying existing allometric models for each measured tree within the plot. Specific allometric models were used for each species, and models constructed for the same ecoregion (NW Spain) were selected. The explanatory variables included in the models were tree variables measured in the National Forest Inventory (dbh and tree height). The allometric models by the following authors were used for the different species: Jiménez et al. ([20]) and Gómez-Vázquez et al. ([13]), for Pinus pinaster; Balboa-Murias et al. ([2]), for Pinus radiata; Brañas et al. ([5]) for Eucalyptus globulus; and Pérez-Cruzado & Rodríguez-Soalleiro ([40]) for Eucalyptus nitens. For other tree species inside the plots, we used the model by Balboa-Murias ([1]) for Quercus robur and those reported by Montero et al. ([35]) for other species. After applying the models, we estimated the biomass values for following components in each tree measured: leaves, stem, fine branches (diameter < 2 cm) and coarse branches (diameter ≥ 2 cm). To obtain the aboveground biomass at plot level, we used the above-mentioned expansion factor. Aboveground biomass at plot level was subdivided in two components: crown biomass (leaves and branches) and stem biomass (Tab. 1).

The National Forest Map, associated with the National Forest Inventory was obtained from the PNOA aerial ortophoto (scale 1:25.000, minimum mapping unit of 1 ha) and used to classify vegetation types and spatially define the polygons dominated by each of the species under study (Fig. 1).

LiDAR data

The LiDAR data were provided by the PNOA. The study area was surveyed at two different times: once in 2009 (province of Lugo) and then in 2011 (provinces of Pontevedra and A Coruña). Data were delivered in 2 × 2 km tiles of points in LAS binary files. The resulting LiDAR point density for the study area was 0.5 pulse m-2, with a vertical accuracy greater than 0.2 m. A total of 961 LAS files were required in order to cover the study area.

The LiDAR data were processed using the software FUSION LDV 3.50 ([32]). After eliminating the noise from the point cloud, the Digital Elevation Model (DEM) of the study area was obtained. This was done by first filtering the ground returns (using the “GroundFilter” tool) by implementing a filtering algorithm, adapted from Kraus & Pfeiffer ([23]). The DEM (1 m spatial resolution) was then generated using these returns through the “GridSurfaceCreate” command and used to normalize the heights of the point cloud. The LiDAR height and intensity statistics for each sample plot were obtained using the “ClipData” and “CloudMetrics” commands and the plot boundaries (25 m radius). A predefined threshold of 2 m above the ground was applied in order to exclude returns not corresponding to crowns (e.g., understory, rocks, shrubs).

Landsat ETM+ data

We obtained a cloud-free Landsat ETM+ scene corresponding to a date close to the National Forest Inventory and LiDAR survey (1 June 2009). Digital numbers were converted to radiometric values by using the specific gain and offset of the sensor. Reflectance was obtained using the method of NASA ([37]). The images (SLC-off) were fused using the method described by Scaramuzza et al. ([42]). Reflectance values were used to obtain several vegetation indices (Tab. 2) for each plot sampled.

Tab. 2 - Landsat vegetation spectral indices employed in the study. (ρNIR): Near infrared; (ρR): Red; (ρB): Blue.

Regression models

Linear, power function and exponential models were used to estimate how plot values (stand variables: basal area and dominant height; aboveground biomass: crown and stem biomass) were related to LiDAR variables (height and intensity metrics) and Landsat vegetation indices. Models were obtained for each dominant species (Pinus pinaster, Pinus radiata and Eucalyptus spp.) and for each LiDAR survey area (2009 and 2011). Separate models were constructed for different species because they have different structural characteristics (e.g., tree architecture, canopy stratification, canopy density, etc.) that generate differences in the models ([10], [11]), and because the accuracy of prediction depends on the type of forest ([10]). The LiDAR surveys were separated because the quality of laser-derived data depends on flight height, scan angle, point density and footprint size, among other factors. In the case of Eucalyptus spp., we only used the LiDAR_2011, as only a few of the National Forest Inventory plots dominated by this species were surveyed in 2009. The model expressions are as follows (eqn. 1, eqn. 2, eqn. 3):

\begin{equation} \text{Linear:}\;\; Y = \alpha +\sum_{i=1}^{m} \beta_{i}X_{i} + \varepsilon \end{equation}
\begin{equation} \text{Power:} \;\;Y = \alpha + \prod_{i=1}^{m} X_{i}^{ \beta i} + \varepsilon \end{equation}
\begin{equation} \text{Exponential:} \;\;Y = exp ( \alpha +\sum_{i=1}^{m} \beta_{i}X_{i}) + \varepsilon \end{equation}

where Y represents the field values (stand dendrometric and aboveground biomass variables), Xi represents a set of m independent variables (height and intensity LiDAR variables and Landsat vegetation indices), α and βi (i = 1, …, m) are parameters to be estimated, and ε is the error term.

Linear models were fitted by stepwise regression. Power and exponential models were fitted by nonlinear regression after prior linearization (taking natural logarithms) to select (by linear regression) the most significant subset of independent variables to include in the model. To correct for bias in log-transformed allometric equations, we adopted a correction factor ([44]). Heterocedasticity and multicollinearity among explanatory variables were checked. The presence of multicollinearity among variables was evaluated by the condition number ([4]), selecting the models with condition number smaller than 10 (collinearity is not a major problem). For each dominant tree species and LiDAR survey, the selected model included the combination of independent variables with the largest coefficient of determination (R2, defined as the square correlation coefficient between the measured and estimated values), the smallest values of the Akaike’s information criterion (AIC - [7]) and the root mean squared error of the estimate (RMSE).

Raster files were obtained for each explanatory variable (for LiDAR variables through the “CSV2Grid” FUSION command) with a spatial resolution similar to the Landsat image (30 m). The constructed models were used for spatial extrapolation of stand dendrometric and aboveground biomass variables to the study area, and the Spanish Forest Map was used to identify polygons dominated by the species considered. To avoid the inclusion of bias in the spatial extrapolation using model predictions, model-assisted estimators were employed to include a correction for estimated bias ([33]). This estimate is adjusted for deviations between the model predictions and the observed values in the sample (eqn. 4):

\begin{equation} MA\;mean = \frac{1}{N}\sum_{i=1}^{N} \hat{y}_i - \frac{1}{n}\sum_{i=1}^{n} (\hat{y}_i -y_{i}) \end{equation}

where MA mean is the model-assisted regression estimator of means, N is the population size, hat{y}i is obtained from the models using the model parameters estimates and ε = 0. The first term in the equation is the mean of the model predictions (hat{y}i) for all population units, and the second term is an estimate of bias calculated over the sample units and compensates for systematic model prediction errors.

Results 

The models obtained for each dominant species and LiDAR survey, the goodness-of-fit-statistics for the most significant model constructed and the stand dendrometric and aboveground biomass variables are shown in Tab. 3. Linear and exponential models were selected on the basis of their performance. Eucalyptus spp. stands produced the least quality of fit in terms of R2, with models providing values of 49% for basal area, 65% for dominant height, 48% for crown biomass and 55% for stem biomass. By contrast, Pinus radiata stands (LiDAR_2011 data) produced the greatest quality of fit for basal area (65%), crown biomass (68%) and stem biomass (82%) (Fig. 2). The greatest quality of fit for dominant height was obtained for Pinus pinaster (LiDAR_2009 data), with a value of 95%.

Tab. 3 - Results of basal area (G), dominant height (H0), crown biomass (Wcr), stem biomass (Wst) modelling. (RMSE): root of mean squared error; (AIC): Akaike’s information criterion; (LIN): linear models; (EXP): exponential models; (Elmax): Maximum height; (1st Ret above 2): Number of first return above 2 m; (IntL3): L3 moments of intensity; (Int L4): L4 moments of intensity; (Elp90): Height 90th pertencile value; (Elp95): Height 95th pertencile value; (Elskewness): Height skewness; (Perc Ret above mode): Percentage of all returns above the mode height; (Ret above mean): number of returns above the mean value; (Ret above 2): number of returns above 2 m; (Elp10): height 10th percentile value; (ElCURTmenaCUBE): Cubic mean; (Ret2): Number of second returns; (IntLskewness): Moment ratio of intensity skewness; (ElCV): Coefficient of variation of height; (Ret3): Number of third returns; (Elp30): height 30th pertencile value; (Elp40): Height 40th pertencile value; (Intmode): Intensity mode; (Elp75): Height 75th pertencile value; (Elp01): height 1st pertencile value; (Int01): Intensity 1st pertencile value; (SR): Landsat Simple Ratio; (SAVI): Landsat SAVI Index; (ElL4): Moment L4 of height.
Fig. 2 - Field vs. predicted measured values of basal area (a), dominant height (b), crown biomass (c) and stem biomass (d) for Pinus radiata.

Independent variables related to the height distribution metrics were included in all models (Tab. 3). Intensity metrics appeared as explanatory variables in Eucalyptus spp. (basal area and dominant height), Pinus pinaster (LiDAR_2011 data: basal area, crown biomass and stem biomass) and Pinus radiata (LiDAR_2009 data: basal area and crown biomass - Tab. 3). Landsat derived vegetation indices were explanatory variables in Pinus radiata (LiDAR_2011 data), with Simple Ratio (SR) in basal area, dominant height and crown biomass models, and Soil-Adjusted Vegetation Index (SAVI) in the stem biomass model. The inclusion of Landsat resulted in an increase in the R2 values (from 0.64 to 0.65 for basal area, from 0.64 to 0.68 for dominant height, from 0.66 to 0.68 for crown biomass and from 0.60 to 0.82 for stem biomass), and reductions in RMSE (from 5.6 to 5.5 m2 ha-1 for basal area, from 3.9 to 3.7 m for dominant height, from 5.4 to 5.2 Mg ha-1 for crown biomass and from 25.5 to 17.0 Mg ha-1 for stem biomass) and AIC values (from 161 to 158 for basal area, from 126 to 123 for dominant height, from 767 to 761 for crown biomass and from 899 to 869 for stem biomass).

Tab. 4 displays the model-assisted estimates for each species of the mean values of basal area and dominant height, crown and stem biomass which were obtained by applying the models achieved to the polygons dominated by each analyzed species in the whole study area. The standard errors for estimates of the means were small, ranging from 0.5 to 0.8 m2 ha-1 for basal area, from 0.3 to 0.7 m for dominant height, from 0.6 to 0.8 Mg ha-1 for crown biomass and from 2.5 to 5.2 Mg ha-1 for stem biomass. Bias estimates for the model assisted estimator were also small, from -0.08 to 0.08 m2 ha-1 for basal area, from -0.01 to 1.40 m for dominant height, from -0.01 to 0.01 Mg ha-1 for crown biomass and from -0.01 to 0.03 Mg ha-1 for stem biomass.

Tab. 4 - Estimates mean values (± standard error) of basal area (G) and dominant height (H0), crown biomass (Wcr) and stem biomass (Wst) for each species for the whole study area.

Discussion 

The findings of this study demonstrate that stand dendrometric and biomass variables can be predicted with reasonable precision by using low density LiDAR variables in combination with Landsat data. The importance of linear and exponential models has previously been reported in studies relating LiDAR metrics (combined or not with spectral data) and stand dendrometric and biomass variables ([14], [15]), even for shrub vegetation ([12]). The R2 values obtained in the present study were of the same order of magnitude or slightly smaller than those previously reported for the same species ([16], [14], [15]), probably as a consequence of the use of lower density LiDAR data, or (in the present study), the use of field data from the National Forest Inventory rather than specifically measured field data.

Previous studies have found that height metrics are closely correlated with stand dendrometric and biomass variables for the same species ([14], [15]) and others ([25]). However, these studies were carried out with higher density LiDAR data and specifically measured field data. The most frequent height metric variables observed in our models are maximum height, the cubic mean of the height and diverse percentiles of height distribution. This is consistent with previously reported correlations between stand dendrometric and/or biomass variables and maximum height ([26]) and also between the former and height percentiles ([14], [15], [25]). The most appropriate height metrics reported in the literature widely differ as a likely consequence of differences in the vegetation structure and data processing procedures used ([10]). The close correlation between aboveground biomass values and maximum height and the higher LiDAR percentiles observed in our study may be a result of the regular structure of the forest plantations surveyed ([15]).

Although intensity metrics may not always selected as explanatory variables ([15]), some studies on pine stands have also reported that the inclusion of intensity variables may improve the predictive power of the models, or may even be decisive if used in combination with density or height LiDAR values ([16], [14]).

In this study the least quality of fit observed for Eucalyptus spp. compared to Pinus species is consistent with previous findings ([11]) and is explained by the sparser canopies of Eucalyptus trees, which result in less accurate digital canopy models.

Although there were not previous studies combining LiDAR and satellite data for these species in NW Spain, an improvement in model accuracy by combining both types of data has been previously observed in other tree species ([9], [11]). Some authors only used optical imagery for the first step in vegetation classification in order to account for the dependence of stand dendrometric and biomass estimation on vegetation types ([9]). In the present study, this classification was implemented by using the Spanish Forest Map based on the analysis of aerial photographs. We used Landsat derived vegetation indices as explanatory variables in combination with LiDAR variables to improve the stand dendrometric and biomass models ([11]). In the present study Landsat derived vegetation indices contributed to significantly improve the quality of fit of the model to the data for Pinus radiata (LiDAR_2011 data). The spectral data alone had a small explanatory power, as previously observed for other tree species ([12], [25]). However, the improvement in model performance for Pinus radiata (LiDAR_2011 data) by the inclusion of Landsat derived vegetation indices is consistent with previous studies in which forest stand structure metrics were predicted using a combination of LiDAR and remote sensing imagery ([11], [25]), although the improvement was relatively small in our case.

The standard error of the model-assisted regression estimates were smaller than those obtained by field sampling (simple random sampling estimates - Tab. 1), confirming that remotely sense data made substantial contributions with greater precision, as compared with estimates based only on plot observations ([33]). Mean values of the model-assisted regression values were smaller than those of the plot observations (Tab. 1), likely because field plots were characterized by tree cover > 20% and the presence of trees with dbh > 7.5 cm, while the area covered by remotely sensed data also included locations with smaller tree cover and/or without the presence of trees with dbh > 7.5 cm.

Conclusions 

Our findings confirmed the potential of the combined use of freely available remote sensing and regional forest inventories to establish relationships that allow to determine the spatial distribution of both stand dendrometric and aboveground biomass variables of stands dominated by different species in a large area (60 × 60 km) in Galicia. This is the first study combining this freely available information for these species in this area. This information is critical for calibrating and validating biogeochemical models, quantifying carbon fluxes and supporting the United Nations Framework Convention on Climate Change program ([10]).

The future periodicity and availability of this information will enable spatial estimation of stand, biomass and carbon temporal evolution in different vegetation types. The findings of this study support the use of this approach to reduce the cost of forest inventories, thus providing a useful tool enabling stakeholders to map forest stand variables and biomass stocks.

Acknowledgements 

This research was funded by Projects of the Conselleria de Medio Rural e do Mar Xunta de Galicia (FEADER 2013/38) and INIA RTA 2014-00011-C06 (FEDER). This work was also co-financed by the INIA and European Social Fund (via a grant awarded to E. Jiménez). We also thank the anonymous reviewer for the useful comments and suggestions, which helped to improve an earlier version of the manuscript.

References

(1)
Balboa-Murias MA (2005). Biomasa arbórea y estabilidad nutricional de los sistemas forestales de Pinus pinaster Ait., Eucalyptus globulus Labill. y Quercus robur L. en Galicia [Aboveground biomass and nutritional stability of Pinus pinaster Ait., Eucalyptus globulus Labill. and Quercus robur L. forest systems in Galicia]. PhD Thesis, Universidad de Santiago de Compostela, Spain, pp. 256. [in Spanish]
::Google Scholar::
(2)
Balboa-Murias M, Rodríguez-Soalleiro R, Merino A, Alvarez-González JG (2006). Temporal variations and distribution of carbon stocks in aboveground of radiata pine and maritime pine pure stands under different silvicultural alternatives. Forest Ecology and Management 237: 29-38.
::CrossRef::Google Scholar::
(3)
Baret F, Guyot G (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment 35: 161-173.
::CrossRef::Google Scholar::
(4)
Belsley DA (1991). Conditioning diagnostics, collinearity and weak data regression. Wiley Series in Probability, John Wiley and Sons, New York, USA, pp. 396.
::Google Scholar::
(5)
Brañas J, González-Río F, Merino A (2000). Contenido y distribución de nutrientes en plantaciones de Eucalyptus globulus del Nordeste de la Península Ibérica [Nutrients content and distribution in Eucalyptus globulus plantations in northwestern Iberian peninsula]. Investigación Agraria: Sistemas y Recursos Forestales 9: 316-335. [in Spanish]
::Google Scholar::
(6)
Breiman L (2001). Statistical modeling: the two cultures. Statistical Science 16: 199-301.
::CrossRef::Google Scholar::
(7)
Burnham KP, Anderson DR (1998). Model selection and inference. Springer-Verlag, New York, USA, pp. 515.
::Google Scholar::
(8)
Chas-Amil ML (2007). Forest fires in Galicia (Spain): threats and challenges for the future. Journal of Forest Economics 13: 1-5.
::CrossRef::Google Scholar::
(9)
Chen Q, Laurin GV, Battles JJ, Saah D (2012). Integration of airborne LiDAR and vegetation types derived from aerial photography for mapping aboveground live biomass. Remote Sensing of Environment 121: 108-117.
::CrossRef::Google Scholar::
(10)
Chen Q (2013). Lidar remote sensing of vegetation biomass. In: “Remote Sensing of Natural Resources” (Weng Q, Wang G eds). CRC Press, Taylor and Francis Group, Boca Raton, FL, USA, pp. 399-420.
::Online::Google Scholar::
(11)
Cortés L, Hernández J, Valencia D, Corvalán P (2014). Estimation of above-ground biomass using Landsat ETM+, Aster GDEM and LiDAR. Forest Research 3: 117.
::Google Scholar::
(12)
Estornell J, Ruiz LA, Velázquez B, Hermosilla T (2012). Estimation of biomass and volume of shrub vegetation using LiDAR and spectral data in a Mediterranean environment. Biomass and Bioenergy 46: 710-721.
::CrossRef::Google Scholar::
(13)
Gómez-Vázquez I, Crecente-Campo F, Diéguez-Aranda U, Castedo-Dorado F (2013). Modelling canopy fuel variables in Pinus pinaster Ait. and Pinus radiata D. Don stands in Northwestern Spain. Annals of Forest Science 70: 161-172.
::CrossRef::Google Scholar::
(14)
González-Ferreiro E, Dieguez-Aranda U, Miranda D (2012). Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities. Forestry 85: 281-292.
::CrossRef::Google Scholar::
(15)
González-Ferreiro E, Miranda D, Barreiro-Fernández L, Bujan S, García-Gutierrez J, Dieguez-Aranda U (2013). Modelling stand biomass fractions in Galician Eucalyptus globulus plantations by use of different LiDAR pulse densities. Forest Systems 22: 510-525.
::CrossRef::Google Scholar::
(16)
González-Olabarria JR, Rodríguez F, Fernández-Landa A, Mola-Yudego B (2012). Mapping fire risk in the model forest of Urbión (Spain) based on airborne LiDAR measurements. Forest Ecology and Management 282: 149-156.
::CrossRef::Google Scholar::
(17)
Huete AR (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25: 259-309.
::CrossRef::Google Scholar::
(18)
Jenkins JC, Birdsey RA, Pan Y (2001). Biomass and NPP estimation for the mid-Atlantic Region (USA) using plot level forest inventory data. Ecological Applications 11: 1174-1193.
::CrossRef::Google Scholar::
(19)
Ji L, Wylie BK, Nossov DR, Peterson B, Waldrop MP, McFarland JW, Rover J, Hollingsworth TN (2012). Estimating aboveground biomass in interior Alaska with Landsat data and field measurements. International Journal of Applied Earth Observation and Geoinformation 18: 451-461.
::CrossRef::Google Scholar::
(20)
Jiménez E, Vega JA, Fernández-Alonso JM, Vega-Nieva D, González JG, Ruiz-González AD (2013). Allometric equations for estimating canopy fuel load and distribution of pole-size maritime pine trees in five Iberian provenances. Canadian Journal of Forest Research 43: 149-158.
::CrossRef::Google Scholar::
(21)
Jordan CF (1969). Derivation of leaf area index from quality of light in the forest floor. Ecology 50: 663-666.
::CrossRef::Google Scholar::
(22)
Kaartinen H, Hyyppa J, Yu X, Vastaranta M, Hyyppa H, Kukko A, Holopainen M, Heipke C, Hirschmugl M, Morsdorf F (2012). An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sensing 4: 950-974.
::CrossRef::Google Scholar::
(23)
Kraus K, Pfeiffer N (1998). Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing 53: 193-203.
::CrossRef::Google Scholar::
(24)
Lafiti H, Fassnacht FE, Hartig F, Berger C, Hernández J, Corvalán P, Koch B (2015). Stratified aboveground forest biomass estimation by remote sensing data. International Journal of Applied Earth Observation and Geoinformation 38: 229-241.
::CrossRef::Google Scholar::
(25)
Laurin GV, Chen Q, Lindsell JA, Coomes DA, Frate F, Guerriero L, Pirotti F, Valentini R (2014). Above ground biomass estimation in an Africa tropical forest with LiDAR and hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing 89: 49-58.
::CrossRef::Google Scholar::
(26)
Lim K, Treitz P, Wulder MA, St-Onge B, Flood M (2003). LiDAR remote sensing of forest structure. Progress in Physical Geography 27: 88-106.
::CrossRef::Google Scholar::
(27)
Lin Y, Jaakkola A, Hyyppä J, Kaartinen H (2010). From TLS to VLS: biomass estimation at individual tree level. Remote Sensing 2: 1864-1879.
::CrossRef::Google Scholar::
(28)
Liu HQ, Huete AR (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing 33: 457-465.
::CrossRef::Google Scholar::
(29)
López-Serrano PM, López-Sánchez CA, Díaz-Varela RA, Corral-Rivas JJ, Solís-Moreno R, Vargas-Larreta B, González JG (2015). Estimating biomass of mixed and unevenaged forests using spectral data and a hybrid model combining regression trees and linear models. iForest - Biogeosciences and Forestry 9 (2): 226-234.
::CrossRef::Google Scholar::
(30)
Manes F, Ricotta C, Salvatori E, Bajocco S, Blasi C (2010). A multiscale analysis of canopy structure in Fagus sylvatica L. and Quercus cerris L. old-growth forests in the Cilento and Vallo di Diano National Park. Plant Biosystems 144 (1): 202-210.
::CrossRef::Google Scholar::
(31)
MARM (2011). Cuarto Inventario Forestal Nacional. Comunidad Autónoma de Galicia [Fourth National Forest Inventory. Galicia]. Dirección General del Medio Natural y Política Forestal, Galicia, Madrid, Spain, pp. 52. [in Spanish]
::Google Scholar::
(32)
McGaughey R (2009). FUSION/LDV: software for LIDAR data analysis and visualization. USDA Forest Service, Pacific Northwest Research Station, Seattle, WA, USA, pp. 123.
::Google Scholar::
(33)
McRoberts RE, Naesset E, Gobakken T (2013). Inference for lidar-assisted estimation of forest growing stock volume. Remote Sensing of Environment 128: 268-275.
::CrossRef::Google Scholar::
(34)
Merino A, Balboa MA, Rodríguez-Soalleiro R, Alvarez-González JG (2005). Nutrient exports under different harvesting regimes in fast-growing forest plantations in southern Europe. Forest Ecology and Management 207: 325-339.
::CrossRef::Google Scholar::
(35)
Montero G, Ruiz-Peinado R, Muñoz M (2005). Producción de biomasa y fijación de CO2 por los bosques españoles [Spanish forest biomass production and CO2 fixing]. Monografías INIA, Serie Forestal no. 13, Madrid, Spain, pp. 265. [in Spanish]
::Google Scholar::
(36)
Naesset E, Gobakken T, Solberg S, Gregoire TG, Nelson R, Stahl G, Weydahl D (2011). Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: a case study from a boreal forest area. Remote Sensing of Environment 115 (12): 3599-3614.
::CrossRef::Google Scholar::
(37)
NASA (2011). Landsat 7 science data users handbook. Landsat Project Science Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA, pp. 186.
::Google Scholar::
(38)
Ni-Meister W, Lee S, Strahler AH, Woodcock AH, Schaaf C, Ranson J, Sun G, Blair JB (2010). Assessing general relationships between aboveground biomass and vegetation structure parameters for improved carbon estimate from vegetation lidar. Journal of Geophysical Research 115 (G2): 2156-2202.
::CrossRef::Google Scholar::
(39)
Nord-Larsen T, Schumacher J (2012). Estimation of forest resources from a country wide laser scanning survey and national inventory data. Remote Sensing of Environment 119: 148-157.
::CrossRef::Google Scholar::
(40)
Pérez-Cruzado C, Rodríguez-Soalleiro R (2011). Improvement in accuracy of aboveground biomass estimation in Eucalyptus nitens plantations: effect of bole sampling intensity and explanatory variables. Forest Ecology and Management 261: 2016-2028.
::CrossRef::Google Scholar::
(41)
Rouse J, Haas R, Schell J, Deering D (1973). Monitoring vegetation system in the great plains with ERTS. In: Proceedings of the “3rd ERTS Symposium”. NASA SP-351, NASA, Washington, DC, USA, pp. 309-317.
::Google Scholar::
(42)
Scaramuzza P, Micijevic E, Chander G (2004). SLC gap-filled products, phase one methodology. Landsat Technical Notes, pp. 5.
::Online::Google Scholar::
(43)
Shendryk I, Hellström M, Klemedtsson L, Kljun N (2014). Low-density LiDAR and optical imagery for biomass estimation over boreal forest in Sweden. Forests 5: 992-1010.
::Google Scholar::
(44)
Sprugel DG (1983). Correcting for bias in log-transformed allometric equations. Ecology 64: 209-210.
::CrossRef::Google Scholar::
(45)
Zhao K, Popescu S, Nelson R (2009). LiDAR remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers. Remote Sensing of Environment 113: 182-196.
::CrossRef::Google Scholar::

Download

Paper Contents

Paper Sections

Paper Figures

Paper Tables

 
 
Close

 

Jiménez E, Vega JA, Fernández-Alonso JM, Vega-Nieva D, Ortiz L, López-Serrano PM, López-Sánchez CA (2017).
Estimation of aboveground forest biomass in Galicia (NW Spain) by the combined use of LiDAR, LANDSAT ETM+ and National Forest Inventory data
iForest - Biogeosciences and Forestry 10: 590-596. - doi: 10.3832/ifor1989-010
Close
First Previous Next Last
 
Close
© iForest

Download Reference

Paper ID# ifor1989-010
Title Estimation of aboveground forest biomass in Galicia (NW Spain) by the combined use of LiDAR, LANDSAT ETM+ and National Forest Inventory data
Authors Jiménez E, Vega JA, Fernández-Alonso JM, Vega-Nieva D, Ortiz L, López-Serrano PM, López-Sánchez CA
Format
Close Download