**2. Methods**

#### **2.1 Forest and deforestation definitions**

We defined a forest as a piece of land mainly covered by trees that might contain shrubs, palms, guaduas, grass, and vines, in which tree cover predominates with a minimum canopy density of 30%, a minimum canopy height (in situ) of 5 m at the time of identification, and a minimum area of 1.0 ha. Commercial forest plantations, palm crops, and planted trees for agricultural production are excluded. This definition is in line with the criteria defined by the UNFCCC in decision 11/CP.7 [9], the definition adopted by Colombia under the Kyoto Protocol [10], and the definition of forest cover used by the Colombian National Greenhouse Gas Inventory [11].

Deforestation is defined as the direct and/or induced conversion of forest cover to another type of land cover in a given time [12].

#### **2.2 Forest and deforestation monitoring**

The SMByC developed a digital image processing protocol to assess the distribution, extension, and changes in forest cover in Colombia [6–8]. This protocol is implemented through the use of LANDSAT images [13]. The LANDSAT program of satellite images has several advantages for the monitoring of forest cover, such as a good record of images through time (historical availability), high temporal and spatial resolution, easy access to data, and possibilities of program permanence. The image processing protocol applied for the forest monitoring includes the following four major phases: (i) the digital preprocessing of satellite images, (ii) digital image processing, (iii) data validation, and (iv) monitoring data reporting. These phases are summarized in 12 methodological steps [14], described below in each technical phase.

**37**

*Colombian Forest Monitoring System: Assessing Deforestation in an Environmental Complex…*

In this phase, corrections, calibrations, radiometric normalizations, exact coregistration, and reduction in atmospheric effects are applied, to assure the comparability of images and to ensure that the detection of changes is not due to this type of factors. The specific steps followed during image preprocessing are as the following:

For each reference year, the SMBYC downloads the entire Landsat satellite program (7 ETM+ and OLI 8), image catalog with L1T level (reflectance surface), and selects all those images with less than 90% cloud cover available between January 1 and December 31 of the reference year. We give special emphasis for the images of the last quarter of the year that compiles the highest deforestation effect, mainly at

Images affected since 2003 by the failure in the Scan Line Corrector of Landsat

In order to archive the complete images, each image is reconstructed by stacking all bands, excluding the thermal infrared wavelength bands. In case of Landsat 8

Given the large number of images used (for example, more than 1400 images were used in 2015), specific algorithms have been developed to facilitate handling

• Algorithm to rename the images according to the SMByC structure [17].

For the construction of the annual image composite, it is necessary to have an exact co-registration at the pixel level among all the images acquired for each scene. The L1T products supplied by the Earth Resources Observation and Science Center (EROS) usually have an exact correspondence of the pixels. However, before performing the interpretation, a review of each image is made and those that do not

This allows masking and eliminates areas of clouds, banding, shadows, or haze. Before performing a change analysis, a semiautomated procedure is executed to integrate the results of the masks produced with different filtering tools implemented by the IDEAM with a QGIS Plugin named cloud masking [15], (see **Figure 1**). For additional documentation on the operation of the Cloud masking tool, refer [19].

• Algorithms to extract information from Landsat metadata files [18].

7 ETM+ are processed applying a masking. To optimize this task, the SMByC developed a specific tool implemented in QGIS® [15]. When Landsat data does not provide sufficient cloud-free coverage, images from the CBERS, RapidEye, ASTER,

*DOI: http://dx.doi.org/10.5772/intechopen.86143*

*2.2.1.1 Step 1: images select and download*

the Amazon region in Colombia.

*2.2.1.2 Step 2: stacking bands*

and Sentinel 2 satellites programs are used.

• Algorithm to stack the bands [16].

*2.2.1.3 Step 3: geometric correction*

meet this condition are adjusted.

*2.2.1.4 Step 4: cloud masking and shadow*

OLI, the aerosols and cirrus layers are also excluded.

and processing, all of which are available for download:

*2.2.1 Phase 1: digital preprocessing of satellite images*

*Colombian Forest Monitoring System: Assessing Deforestation in an Environmental Complex… DOI: http://dx.doi.org/10.5772/intechopen.86143*

#### *2.2.1 Phase 1: digital preprocessing of satellite images*

In this phase, corrections, calibrations, radiometric normalizations, exact coregistration, and reduction in atmospheric effects are applied, to assure the comparability of images and to ensure that the detection of changes is not due to this type of factors. The specific steps followed during image preprocessing are as the following:

#### *2.2.1.1 Step 1: images select and download*

*Forest Degradation Around the World*

use, at link [2].

**2. Methods**

tors can be found in [3].

The SMByC complies with the provisions of the relevant decisions of the United Nations Framework Convention on Climate Change—UNFCCC and the Intergovernmental Panel on Climate Change (IPCC) in its guidelines of good practices, and operates under the principles of transparency, completeness, comparability, consistency and precision. Likewise, it complies with the requirements established by National Statistics Agency (DANE, Spanish acronym) to be considered as official information. For the generation of this type of information, the SMByC developed a specific methodology through a publicly available digital satellite image processing protocol [1, 4, 25] and updated in [23]. All the information generated by the SMByC is available on the Web page of the system, with the exception of the one that has restrictions on

The information generated by the SMByC allows the IDEAM to carry out its mission activities to monitor the biophysical resources of the nation, especially those related to forest resources, generating statistics, reports, maps and official reports that give an account of their status and dynamics. More information on the indica-

The main components of the SMByC are: (i) monitoring of forest/deforestation, (ii) monitoring of biomass in natural forests, and (iii) causes and agents of deforestation. The SMByC is an instrument that generates crucial information to design

We defined a forest as a piece of land mainly covered by trees that might contain shrubs, palms, guaduas, grass, and vines, in which tree cover predominates with a minimum canopy density of 30%, a minimum canopy height (in situ) of 5 m at the time of identification, and a minimum area of 1.0 ha. Commercial forest plantations, palm crops, and planted trees for agricultural production are excluded. This definition is in line with the criteria defined by the UNFCCC in decision 11/CP.7 [9], the definition adopted by Colombia under the Kyoto Protocol [10], and the definition of forest cover used by the Colombian National

Deforestation is defined as the direct and/or induced conversion of forest cover

The SMByC developed a digital image processing protocol to assess the distribu-

tion, extension, and changes in forest cover in Colombia [6–8]. This protocol is implemented through the use of LANDSAT images [13]. The LANDSAT program of satellite images has several advantages for the monitoring of forest cover, such as a good record of images through time (historical availability), high temporal and spatial resolution, easy access to data, and possibilities of program permanence. The image processing protocol applied for the forest monitoring includes the following four major phases: (i) the digital preprocessing of satellite images, (ii) digital image processing, (iii) data validation, and (iv) monitoring data reporting. These phases are summarized in 12 methodological steps [14], described below in each technical

and implement national policies on climate change and forests.

**2.1 Forest and deforestation definitions**

Greenhouse Gas Inventory [11].

to another type of land cover in a given time [12].

**2.2 Forest and deforestation monitoring**

**36**

phase.

For each reference year, the SMBYC downloads the entire Landsat satellite program (7 ETM+ and OLI 8), image catalog with L1T level (reflectance surface), and selects all those images with less than 90% cloud cover available between January 1 and December 31 of the reference year. We give special emphasis for the images of the last quarter of the year that compiles the highest deforestation effect, mainly at the Amazon region in Colombia.

Images affected since 2003 by the failure in the Scan Line Corrector of Landsat 7 ETM+ are processed applying a masking. To optimize this task, the SMByC developed a specific tool implemented in QGIS® [15]. When Landsat data does not provide sufficient cloud-free coverage, images from the CBERS, RapidEye, ASTER, and Sentinel 2 satellites programs are used.

#### *2.2.1.2 Step 2: stacking bands*

In order to archive the complete images, each image is reconstructed by stacking all bands, excluding the thermal infrared wavelength bands. In case of Landsat 8 OLI, the aerosols and cirrus layers are also excluded.

Given the large number of images used (for example, more than 1400 images were used in 2015), specific algorithms have been developed to facilitate handling and processing, all of which are available for download:


#### *2.2.1.3 Step 3: geometric correction*

For the construction of the annual image composite, it is necessary to have an exact co-registration at the pixel level among all the images acquired for each scene. The L1T products supplied by the Earth Resources Observation and Science Center (EROS) usually have an exact correspondence of the pixels. However, before performing the interpretation, a review of each image is made and those that do not meet this condition are adjusted.

#### *2.2.1.4 Step 4: cloud masking and shadow*

This allows masking and eliminates areas of clouds, banding, shadows, or haze. Before performing a change analysis, a semiautomated procedure is executed to integrate the results of the masks produced with different filtering tools implemented by the IDEAM with a QGIS Plugin named cloud masking [15], (see **Figure 1**). For additional documentation on the operation of the Cloud masking tool, refer [19].

**Figure 1.** *Example of cloud masking filters available in tool developed by IDEAM.*

## *2.2.1.5 Step 5: radiometric normalization*

We used relative radiometric normalization of the images to adjust the radiometric values in order to reduce the variability between the images due to atmospheric differences, lighting, sensor calibration, and geometric distortions, among others. This step allows the images of different years to be comparable and ensures that the changes detected are not due to this type of factors [20, 21]. Python scripts are available to execute this procedure—ARRNorm [22].

#### *2.2.1.6 Step 6: generation of the annual composite of images*

This step uses all satellite images available for Colombia of the corresponding year, allowing for each pixel a series of annual time values that include the reflectance surface data valid for that year. The main metric used is the annual median of each spectral band, which has shown good results for the detection of changes. Thus, for each observation unit, a single radiometric value of annual reflectance surface is obtained in each one of the radiometric bands (Red, NIR, and SWIR-1 and SWIR-2). The creation of these annual composites for the whole country is done through specific tools developed in the Python language [23]. **Figure 2** shows the final result of the phase 1 using an RGB combination 453 that remarks the healthy vegetation distribution (brown tones), related mainly to forest distribution.

Although the use of the annual median values of reflectance reduces the areas without information, the error by omission could also increase if the forest cover changes occur during the last year's months. To avoid this problem, after the process described above, a visual verification and manual adjustment of the results is performed using the data of the last pixel of the year (last pixel) and the last available image of the last year's quarter.

#### *2.2.2 Phase 2: digital image processing*

This involves the automated detection of changes in forest areas using algorithms, the visual verification of detected changes, and the execution of a quality control protocol.

**39**

**Figure 2.**

*2.2.2.1 Step 7: detection of changes*

*from January 1 to December 31).*

To identify forest cover change, a direct and automated method is used applying the principal component analysis (PCA), over the image annual composite generated in the previous step, to then make a reclassification of the values of the pixels to the value of the corresponding class. The legend and the values assigned in the

*Annual image composite of surface reflectance for the year 2017 (Landsat ETM and Landsat 8 OLI images* 

*Colombian Forest Monitoring System: Assessing Deforestation in an Environmental Complex…*

*DOI: http://dx.doi.org/10.5772/intechopen.86143*

*Colombian Forest Monitoring System: Assessing Deforestation in an Environmental Complex… DOI: http://dx.doi.org/10.5772/intechopen.86143*

#### **Figure 2.**

*Forest Degradation Around the World*

*2.2.1.5 Step 5: radiometric normalization*

**Figure 1.**

available image of the last year's quarter.

*2.2.2 Phase 2: digital image processing*

are available to execute this procedure—ARRNorm [22].

*Example of cloud masking filters available in tool developed by IDEAM.*

*2.2.1.6 Step 6: generation of the annual composite of images*

We used relative radiometric normalization of the images to adjust the radiometric values in order to reduce the variability between the images due to atmospheric differences, lighting, sensor calibration, and geometric distortions, among others. This step allows the images of different years to be comparable and ensures that the changes detected are not due to this type of factors [20, 21]. Python scripts

This step uses all satellite images available for Colombia of the corresponding year, allowing for each pixel a series of annual time values that include the reflectance surface data valid for that year. The main metric used is the annual median of each spectral band, which has shown good results for the detection of changes. Thus, for each observation unit, a single radiometric value of annual reflectance surface is obtained in each one of the radiometric bands (Red, NIR, and SWIR-1 and SWIR-2). The creation of these annual composites for the whole country is done through specific tools developed in the Python language [23]. **Figure 2** shows the final result of the phase 1 using an RGB combination 453 that remarks the healthy vegetation distribution (brown tones), related mainly to forest distribution. Although the use of the annual median values of reflectance reduces the areas without information, the error by omission could also increase if the forest cover changes occur during the last year's months. To avoid this problem, after the process described above, a visual verification and manual adjustment of the results is performed using the data of the last pixel of the year (last pixel) and the last

This involves the automated detection of changes in forest areas using algorithms, the visual verification of detected changes, and the execution of a quality

**38**

control protocol.

*Annual image composite of surface reflectance for the year 2017 (Landsat ETM and Landsat 8 OLI images from January 1 to December 31).*

#### *2.2.2.1 Step 7: detection of changes*

To identify forest cover change, a direct and automated method is used applying the principal component analysis (PCA), over the image annual composite generated in the previous step, to then make a reclassification of the values of the pixels to the value of the corresponding class. The legend and the values assigned in the

reclassification for each class are as follows: (1) stable forest, (2) stable nonforest, (3) deforestation, (4) regeneration, and (5) without information (corresponding to masked data). To adjust the areas without information detected for each reporting period, a time series analysis is applied to verify the temporal consistency. For this process, the information from the most recent reporting period is considered, and the areas "without information" are adjusted compared with the other reporting periods.
