Co-registration of remote senting and field plots

The predictor variables for AGB used in this study were spectral bands, vegetation indexes (VI) and Gray Level Co-occurrence Matrix (GLCM) based texture, calculated from the Landsat imagery for the two data sets used in 2007 and 2013. The spectral bands and VI have been used as predictors of Landsat images to estimate AGB in pine forest (Günlü, et al., 2014), AGB in state inventory for New England, USA (Zheng, Heath, & Ducey, 2008), among other studies. Lu, 2006 and Rodríguez-Veiga et al. (2017) emphasized the importance of including other variables to avoid saturation in the AGB estimation when VI are used. Using GLCMs as predictor variables, the AGB estimates have been above the saturation value that is estimated with the use of VI (Kelsey & Neff, 2014; Wu et al., 2016; Zhao et al., 2016).

III.4.5.1 Spectral Bands

The spectral bands used in this study were the visible spectrum, NIR and SWIR. The MNFI field manual states that the location of the plots was recorded with an accuracy of up to 15 m, and the co-registration of the Landsat images had an RMSE of less than 6 m, which meant that the values of the estimated AGB could converge on different adjacent pixels of the Landsat image. Therefore, to solve the location issue, a window of 3 X 3 pixels was applied to calculate the mean value by pixel of the spectral bands to be used as predictor variables of AGB (A. Günlü et al., 2014; Wu et al., 2016).

III.4.5.2 Vegetation indexes

Spectral indexes are combinations of spectral reflectance of two or more wavelengths (spectral bands) indicating the relative abundance or accumulation in satellite imagery that can be associated

with a feature of interest (Bramhe et al., 2018), such as the AGB in this study. Vegetation indexes (VI) are the most popular type that detects the photosynthetic activity of vegetation and are sensitive to AGB estimation (Rodríguez-Veiga et al., 2017).

The NDVI is the most commonly used index for vegetation studies because it is sensitive to the photosynthetically active biomass (Bannari et al., 1995). EVI, WDRVI, and NDMI were calculated as they are used as an alternative to NDVI because they are more sensitive in areas with high AGB and AGB content in tree crowns (Glenn et al., 2008; Henebry et al., 2004; USGS, 2017). SR and SRG were also calculated due they are sensitive to the amount of vegetation and reduce the effect of atmosphere and topography (Glenn et al., 2008). Other indexes such as SAVI, MSAVI, and SATVI have been calculated because they incorporate a correction factor for areas with spaces between vegetation or senescent vegetation, reducing the effect of the soil and dead wood on the collected vegetation information (Marsett et al., 2006; Qi et al., 1994). The calculated VIs are shown in Table III-10, they were calculated using the Grass module of the QGIS program.

Index Calculation Range Reference

Atmospherically

Table III-10. Vegetation indexes evaluated in this study based on spectral bands information of Landsat imagery.

III.4.5.3 Textures

The texture is a metric of pixel variability across neighboring pixels for a defined processing window (Kelsey & Neff, 2014). GLCM-based texture measurements provides the basis for calculating multiple first or second order statistical quantities and were defined by Haralick et al.

(1973), currently the common procedure for obtaining texture from images (Zhao et al., 2016).

Sohrabi, 2016; Wu et al., 2016; Zhao et al., 2016) the textures to be extracted from Landsat images were selected (Table III-11).

Texture has been calculated for spectral bands (Fuchs et al., 2009; Kelsey & Neff, 2014; Safari &

Sohrabi, 2016; Wu et al., 2016) and for vegetation indexes (Lopez-Serrano et al., 2015). In this study, a Pearson correlation was performed between the response variables (AGB and basal area) and the predictor variables (spectral bands and vegetation indexes). From the two periods of MNFI were selected the predictor variables with the highest correlation coefficient Table III-12. In these response variables were calculated the Haralick textures.

Feature extracted Calculation Feature extracted Calculation

Mean (MN) ^{∑ 𝑖𝑃}𝑖,𝑗

Table III-11. Texture variables used. P (i, j) is the normalized co-occurrence matrix such that sum (i, j = 0, N-1) (P (i, j)) = 1 (Haralick et al., 1973).

Landsat 5 (2007) Landsat 8 (2013)

G W G W

ARVI 0.77*** 0.72 *** ARVI 0.75*** 0.76 ***

NDVI 0.79*** 0.73 *** NDVI 0.75*** 0.76 ***

SRG 0.79*** 0.74 *** SR 0.72*** 0.76 ***

WDRVI 0.79*** 0.74 *** WDRVI 0.74*** 0.77 ***

Note: *** Significant at a 0.001 level.

Table III-12. Vegetation indexes with the highest Pearson correlation coefficient, selected to apply on them the texture calculation.

To estimate the texture it was necessary to define the size of the window to calculate the GLCM (Bramhe et al., 2018). The window size should be appropriate so that the variation will not be exaggerated neither there will be an excess of smoothing in the variation, small and large window size, respectively (Dengsheng Lu, 2006).

In subtropical forest, (Wu et al., 2016) using a window of 3 x 3 pixels for Landsat imagery, detected changes in AGB storage in a 10 years period study. Attarchi and Gloaguen (2014), found a higher correlation in AGB estimation with textures in window size of 11X11 pixel for temperate forest with Landsat images, in comparison of AGB estimation to vegetation indeces. (Kelsey & Neff, 2014), implementing different window sizes (3X3, 5X5, 7X7 and 9X9), found that 3X3 is the optimal size for estimating AGB in temperate forest. Similarly, (Lopez-Serrano et al., 2015) tested

three window sizes (3X3, 5X5 and 7X7) in temperate forest, finding that the combination of the texture variables and window size are important to optimize mixed models for estimating AGB in Landsat images, not concluding in an optimal window size using texture for AGB estimation. In this study, and according to previous estudies, the textures were calculated for three window sizes 3X3 (Kelsey & Neff, 2014), 7X7 (P. López-Serrano et al., 2015) and 11X11(Attarchi & Gloaguen, 2014). The extraction of the textures from the satellite imagery was made using the Orfeo Tool Box (OTB) module implemented in QGis.