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

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 preprocessing steps as detailed below:


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


Geostatistical Estimation of Biomass Stock in Chilean Native Forests and Plantations 281

Fig. 3. LANDSAT ETM+ SLC-off Correction. Left: before, with the uncorrected data, right:

The integration of all layers of information was carried out in GIS environment (ArcGIS

c. Topographic information derived from SRTM data: elevation, slope, and orientation

d. Vector files with property boundaries, stands, and base cartography support, which are

For each of the four study areas a database was generated, which was used subsequently in the geostatistical analysis. Data were treated as point processes or count variables in the bidimensional space. Each set of field data is associated with a specific geographical area, for which all values to a resolution of 16 or 30 meters were considered in a comprehensive manner. Figure 4 shows the general methodological approach and the generic structure of

From here and for the remaining sections, we considered biomass and all covariates mentioned above as regionalized variables to be able to follow the formality of geostatistical analysis. First, we performed a variogram analysis with the aim of studying the spatial autocorrelation of biomass and its spatial dependence with covariates. The result is a set of spatial models called variograms for the variable of interest (biomass) and the covariates. In all cases, we analyzed the anisotropy of the models, incorporating it explicitly in subsequent interpolations whenever possible. The variograms were used in the spatial estimation of biomass via cokriging. The exploratory analysis and modeling process were performed

a. Field data, which contain the observed AGB (target variable) and its coordinates. b. ETM spectral bands, excluding the thermal band, plus their derivatives: NDVI and

useful in the stratification of the results and administrative aggregation.

using IsatisRM geostatistical software and MatlabRM environment.

after with the multidate correction.

9.3RM) and grouped into four classes:

Tasseled Cap components (covariates).

**4.2.2 Data integration** 

(covariates).

the databases for each area.

Fig. 2. Forest resources in Chile and geographical location of the study areas.

Fig. 3. LANDSAT ETM+ SLC-off Correction. Left: before, with the uncorrected data, right: after with the multidate correction.
