**3. Results and discussion**

## **3.1 Forest cover in Colombia**

In 2017, the natural forest area in Colombia was 59'312.369 ha, which represented 51.9% of the continental and insular Colombian territory (**Figure 5**). At a regional level, some provinces show high forest cover like Amazonas (97.3%), Vaupés (96.5%), and Guainía (92.9%). Likewise, other departments like Atlántico (1.4%), Sucre (2.6%), and Cesar (8.7%) have the smallest area of their territory with natural forests.

*Forest Degradation Around the World*

*2.2.3.4 Step 13: error matrix and confidence intervals*

*2.2.4 Phase 4: calculations and reports*

class until after 6 years.

sified samples (**Figure 3**).

available.

During this procedure, we also perform the classification and identify the nonclas-

Thematic accuracy assessment of the forest cover and deforestation data for the reference year is done by constructing a confusion matrix [20], using the data generated in the previous step. Subsequently, from the error matrix, a new matrix is constructed and is expressed in terms of the proportion of the estimated area.

To calculate the deforested area between two analysis periods, only the areas with available data in the two analysis periods are considered, so the associated

Forest losses detected after one or several dates without information were not included in the reports in order to avoid overestimated rates due to different factors (e.g., high cloudiness or sensor failures). After each deforestation monitoring period, an analysis of consistency of the time series is performed, verifying that each pixel marked as deforestation has not been marked in the previous periods (at least 6 years) as deforested. If this is the case, the most recent result is corrected and marked as no forest (NB) or the specific area is reviewed retrospectively. The same procedure is applied for "forest recovering," maintaining the same check process in which a pixel marked as deforested could not be assigned as "forest"

(un)certainty that the event occurred in the period is analyzed.

*Acatama QGIS© tool. Verification window for sampling point interpretation.*

We used the satellite data as reference data for the sampling point interpretation for periods before and after annual composite generated in Step 6. Also, we used Google Earth Engine, Bing images, or other high-resolution image repositories if

**42**

**Figure 3.**
