**4.3.3 Cokriging neighborhood definition and application**

To run cokriging, it is also necessary to define a neighborhood containing the data relevant to the local estimation. Given the very different number of data between the variables (AGB with very few data in comparison with covariates), we decided to use a two-part search:


Cokriging was performed with an *ad hoc* MATLAB routine, since no known commercial software is able to perform cokriging with the above specifications and 11 covariates. The results are estimated values and error variances for AGB. The estimates were made for all the study areas, at the nodes of a grid with cells of 16m × 16m or 30m × 30m, depending on the case study, assuming unknown mean values for all the variables (ordinary cokriging).

Since not all the land in each area is covered by vegetation, we subsequently multiplied the estimates and error variances by the fraction of the cells located inside the identified stands, using vector digital layers of their boundaries. Figures 6 and 7 present the field data and identified stands, as well as the cokriging results for the Pantanillos area.

Geostatistical Estimation of Biomass Stock in Chilean Native Forests and Plantations 287

Fig. 6. Field data for Pantanillos area (179 data), measuring above ground biomass (ton/ha)

The estimation approach we developed is a real alternative for assessing AGB at communal and regional scales. It has a relatively low implementation costs when compared to traditional methods (i.e. inventories of land) and can be used to assess and monitor the stock of biomass over time on a country-wide scale. Although it requires field data, it is possible to estimate other areas using the already gained knowledge (in particular, the spatial correlation structure, via the direct and cross variogram models). The idea is based on two

a. The amount of biomass available at any point in the geographical space depends, first, on the productivity of the site and, second, on the *status* of the vegetation that grows on

b. Using satellite data (reflectance in several bands of optical range of the electromagnetic spectrum) and topography attributes, we can estimate both the productivity of the site and the vegetation *status* in an integrated manner. Then we can estimate the spatial variation of biomass concentrations. Using terrain data, the variation is scaled to the actual values for specific areas and subsequently aggregated at any available

(left), and fraction of each grid cell (16m × 16m) contained in stands (right)

**4.4 Conceptualizing and generalizing the method** 

administrative division (stands, sites or districts).

hypotheses:

it.

Fig. 5. Experimental (dots and dashed lines) and modeled (solid lines) variograms along the directions N0°E (black) and N90°E (blue) for AGB (top left), TC1 (top right), TC2 (bottom left) and TC3 (bottom right) in Pantanillos study area.

Fig. 5. Experimental (dots and dashed lines) and modeled (solid lines) variograms along the directions N0°E (black) and N90°E (blue) for AGB (top left), TC1 (top right), TC2 (bottom

left) and TC3 (bottom right) in Pantanillos study area.

Fig. 6. Field data for Pantanillos area (179 data), measuring above ground biomass (ton/ha) (left), and fraction of each grid cell (16m × 16m) contained in stands (right)
