**3.1 Native forest description**

276 Remote Sensing of Biomass – Principles and Applications

b. Estimation of AGB for areal units. To this end, one has to use sampling plots within the target units and then their statistics as the estimates. In order for this to work properly, it is necessary to measure DBH, species and some health variable of the tree (usually subjective). In natural forests, due to the high variability for trees having the same DBH range, it is better make measurements of *H* and *S* in order to improve the precision of the results. In plantations (more homogeneous forests) the same can be made by applying a subsample for H and then to estimate the rest using a regression model for

c. Estimation of biomass at the stand, local, regional or national levels. The scales of interest for estimating AGB ranges from stand up to national levels according to the scale of the project. From stand level estimations the other aggregation levels can easily be archived by simply adding other stands estimation into the calculation. The biomass

The use of optical satellite and topography data as auxiliary variables (covariables) allow the accuracy of AGB estimations to improve because they are based on their spatial covariation to field data by applying geostatistical interpolation. Using topographic data, the AGB variation is scaled to the actual values for that area, and then, AGB can be obtained by

(1)

(2)

(3)

<sup>i</sup> S is an estimator of the sound/dead biomass proportion of the *ith* tree

<sup>c</sup> B f(DBH ,H ,S ) i i ii i

where:

i 

> nj c c j ij j i 1 B BF

<sup>m</sup> c c <sup>1</sup> r k <sup>m</sup> k 1 BaB 

where:

where:

Hi is the total height for the *ith* tree

*H* as a function of the DBH.

n is the number of trees in the j *jth* plot

a is the total area of the stand (ha) m is the number of plots within a the stand

F is the hectare expansion factor for the j *jth* plot

is the regression error of the *ith* tree

<sup>c</sup> Bi is the biomass of component *c* in the *ith* tree DBHi is the diameter at breast height of the *ith* tree

The estimated AGB at plot level has the following generic form:

<sup>c</sup> B is the biomass of the component j *c* for the *jth* plot (ton/ha)

estimate at stand level has the following generic form:

<sup>c</sup> B is the biomass of the component r *c* for the *rth* stand (ton)

overlaying any available administrative division (stands, sites or districts).

Chilean forests cover an area of 15.6 million hectares (20.7% of the national territory), of which 13.4 million hectares are natural forests (85.9% of the forested area). Currently 3.6 million hectares of forest are secondary forests (CONAF et al., 1999). Mixed forest of *Nothofagus obliqua*, *Nothofagus alpina* and *Nothofagus dombeyi* are one of the most important forest types in the country and cover 1.5 million hectares (12.2% of the total native forest). The genus *Nothofagus* has ten species that have a high economic value because of the quality of their wood. These *Nothofagus* forests area concentrated between 36° 30' S and 40° 30' S and between 100 and 1,000 m a.m.s.l. in Central Chile. They are present in both mountain ranges, costal and the Andes, where *N. obliqua* occupies the lowest areas (between 100 and 600 m, approximately), *N. alpina* intermediate ones (between 600 and 900 m) and *N. dombeyi* the highest (between 900 and 1,000 m), resulting in overlap ecotones with pure and mixed formations (CONAF et al., 1999; Donoso, 1981; Gajardo 1994). The main secondary species in these mixed *Nothogafus* second growth forest are *Aextoxicon punctatum*, *Genuine avellana*, *Laurelia sempervirens*, *Persea lingue* and *Eucryphia cordifolia* (Donoso, 1981). Today, a major part of these forests exhibit some state of degradation or have some form of human perturbation (Donoso, 1981; Gajardo, 1994). Nevertheless, they have a high productive potential and they need to be managed to improve their current condition. Usually, the quantification of these resources is done by applying volume tables or biomass functions, but these biomass functions rarely exist for native species and have only local applications

Geostatistical Estimation of Biomass Stock in Chilean Native Forests and Plantations 279

We used a random sampling design and concentric circular sampling units (8m radius). All the data were collected in summer 2010 and the total size of the sampling was 348 plots, with an average sampling intensity of one sampling units every 63.2 hectares. Each selected tree was measured for DBH and one out of three for total height (H). For young plantations where understory foliage did not allow seeing trees at DBH we used circular plots with a radius of 5.65 m. In plantations stands where individuals grew in clusters - coppice type - we counted the number of individuals per unit and DBH and H was measured for the smaller and bigger

LANDSAT ETM + images were obtained from the Earth Explorer Web site of the United States Geological Survey (USGS). Additionally, the SRTM digital elevation model (90 m) was freely downloaded from the site Earth Explorer. Bands ETM+ 1, 2, 3, 4, 5 and 7 were grouped into a single file and then projected to WGS84 UTM 18 South or 19 South, according to the area to which they correspond. Subsequently, there were a series of

a. SLC-off Correction: The images acquired after 2003 have missing data due to malfunction of an instrument called Scan Line Corrector (Figure 3). This so-called SLCoff error was corrected using ENVIRM software application, which fits the flaw by using two images with the error in different areas, i.e. non-overlapping. Interpolation is

b. Geometric Correction: The images were rectified using Chilean regular cartography 1:50.000 with at least 30 control points per image and a root mean square error less than

c. Radiometric corrections: Standard radiometric corrections were applied on all images to reduce the atmospheric effect following the method proposed by Chavez (1996), and for

b. Tasseled Cap bands: Brightness (TC1), Greenness (TC2) and Wetness (TC3). The other three components (TC4, TC5 and TC6) do not have a direct biophysical interpretation

trees. Biomass calculations were performed using the methods presented in Section 3.

Pantanillos 400 Mainly *Pinus radiata* and few *Eucaliptus* 

with intensive management. Quivolgo 20 000

*globulus* stands, both at different ages

Mainly *N. obliqua* with several secondary tolerant species and informal management.

Mixed of *N. obliqua*, *N.Alpina*, *N. dombeyi* at different ages and without management.

Forest type Study area Ha Description

Monte Oscuro 1 600

Curacautín 20 000

**4.2.1 ETM+ and SRTM data acquisition and preprocessing** 

performed considering the local histogram of the two images.

the topographic the one proposed by Riaño et al. (2003).

a. Normalized Difference Vegetation Index or NDVI (Tucker, 1979).

c. Slope, Aspect and Altitude were directly calculated from SRTM data.

Once ETM+ and SRTM were co-registered, several variables were then derived:

but were also calculated to take into account the complete data variation.

Table 3. Description of the study areas

preprocessing steps as detailed below:

30 m was achieved.

Plantations

Native, secondary forest

**4.2 Methods** 

(Caldentey, 1995; Donoso et al., 2010; Garfias, 1994; Gayoso et al., 2002; Herrera and Waisberg, 2002; Sáez, 1991; Schlegel, 2001, Schmidt et al., 2009) (see Table 2). For native species, the biomass estimation methods most used in Chile are regression or allometry for the average tree (see section 3.0). In recent years, further attention has been paid in less expensive methods to assess AGB in native forests, based on remote sensing data and spatially explicit methods (Peña, 2007; Valdez et al., 2006).
