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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (6): 695-709    DOI: 10.1007/s40333-023-0017-4
Research article     
Estimation of aboveground biomass of arboreal species in the semi-arid region of Brazil using SAR (synthetic aperture radar) images
Janisson B de JESUS1(), Tatiana M KUPLICH2, Íkaro D de C BARRETO3, Fernando L HILLEBRAND4, Cristiano N da ROSA5
1Postgraduate Program in Remote Sensing, Federal University of Rio Grande do Sul, Campus Vale, Porto Alegre 91501970, Brazil
2National Institute for Space Research (INPE), COESU (Southern Spatial Coordination), Santa Maria 97105970, Brazil
3Postgraduate Program Biometry and Applied Statistics, Rural Federal University of Pernambuco, Recife 52171900, Brazil
4Federal Institute of Education, Science and Technology of Rio Grande do Sul (IFRS), Campus Rolante, Rolante 95690000, Brazil
5Postgraduate Program in Remote Sensing, Polar and Climate Center, Federal University of Rio Grande do Sul, Porto Alegre 91501970, Brazil
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Abstract  

The Caatinga biome is an important ecosystem in the semi-arid region of Brazil. It has significantly degraded due to human activities and is currently a region undergoing desertification. Thus, monitoring the variation in the Caatinga biome has become essential for its sustainable development. However, traditional methods for estimating aboveground biomass (AGB) are time-consuming and destructive. Remote sensing, such as optical and radar imaging, can estimate and correlate with vegetation. Nevertheless, radar imaging is still a novelty to be applied in estimating the AGB of this biome, which is an area with little research. Therefore, this study aimed to use Sentinel-1 images to estimate the AGB of the Caatinga biome in Sergipe State (northeastern Brazil) and to verify its influencing factors. Nineteen sample plots (30 m×30 m) were selected, and the stems of individuals with a circumference at breast height (1.3 m above the ground) equal to or greater than 6.0 cm were measured, and the AGB through an allometric equation was estimated. The Sentinel-1 images from 3 different periods (green, intermediate, and dry periods) were used to consider the phenological conditions of the Caatinga biome. All the pre-processing and extraction of attributes (co-polarized VV (vertical transmit and vertical receive), cross-polarized VH (vertical transmit and horizontal receive), and band ratio VH/VV backscatter, radar vegetation index, dual polarization synthetic aperture radar (SAR) vegetation index (DPSVI), entropy (H), and alpha angle (α)) were performed with Sentinel's Application Platform. These attributes were used to estimate the AGB through simple and multiple linear regressions and evaluated by the coefficients of determination (R2), correlation (r), and root mean squared error (RMSE). The results showed that the attributes individually had little ability to estimate the AGB of the Caatinga biome in the three periods. Combined with multiple regression, we found that the intermediate period presented the equation with the best results among the observed and estimated variables (R2=0.73; r=0.85; RMSE=8.33 Mg/hm2), followed by the greenness period (R2=0.72; r=0.85; RMSE=8.40 Mg/hm2). The attributes contributing to these equations were VH/VV, DPSVI, H, α, and co-polarized VV for the green period and cross-polarized VH for the intermediate period. The study showed that the Sentinel-1 images could be used to estimate the AGB of the Caatinga biome in the green and intermediate phenological periods since the SAR attributes highly correlated with the estimated variable (i.e., AGB) through multiple linear equations.

Key words： Caatinga      tropical dry forest      coherent and incoherent attributes      C-band      Sentinel-1     
Received: 04 November 2022      Published: 30 June 2023
Corresponding Authors: * Janisson B de JESUS (E-mail: janisson.eng@gmail.com)
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Janisson B de JESUS
Tatiana M KUPLICH
íkaro D de C BARRETO
Fernando L HILLEBRAND
Cristiano N da ROSA
Cite this article:

Janisson B de JESUS, Tatiana M KUPLICH, Íkaro D de C BARRETO, Fernando L HILLEBRAND, Cristiano N da ROSA. Estimation of aboveground biomass of arboreal species in the semi-arid region of Brazil using SAR (synthetic aperture radar) images. Journal of Arid Land, 2023, 15(6): 695-709.

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http://jal.xjegi.com/10.1007/s40333-023-0017-4     OR     http://jal.xjegi.com/Y2023/V15/I6/695

Fig. 1 Location of the study area and distribution of the sample plots (a). Variation in the leaf cover of the Caatinga Biome considering three phenological periods: green (b), intermediate (c), and dry (d) periods from Sentinel-2 images (RGB (red-green-blue): 4/3/2) dated 2018/12/27 (yyyy/mm/dd), 2019/09/28, and 2019/12/02, respectively (ESA, 2020a). The delimitation of the semi-arid region and the occurrence of the Caatinga biome in Brazil are also illustrated.
Fig. 2 Methodological flowchart of the study. SAR, synthetic aperture radar.
Fig. 3 Distribution of monthly precipitation and NDVI (normalized difference vegetation index), emphasizing NDVI for periods close to Sentinel-1 image acquisition (green: 0.72 and 0.71; intermediate: 0.50 and 0.47; and dry: 0.32 and 0.35).
Fig. 4 Distribution of aboveground biomass of individuals (plot 19 had no boxplot because there was no individual, a) and total biomass of each plot (b). In Figure 4a, boxes indicate the IQR (interquartile range, 75th to 25th of the data). The median value is shown as a line within the box. Whiskers extend to the most extreme value within 1.5×IQR. Outlier is shown as circle.
Index Green period Intermediate period Dry period
B SE t P value B SE t P value B SE t P value
VH 4.97 2.74 1.809 0.088 4.10 1.73 2.366 0.030* 3.73 1.61 2.306 0.034*
VV 3.35 3.19 1.051 0.307 3.71 2.61 1.423 0.172 6.61 2.28 2.896 0.010*
VH/VV -4.88 19.74 -0.248 0.807 -19.00 23.79 -0.798 0.436 22.03 29.68 0.742 0.468
DPSVI -13.12 29.03 -0.452 0.657 11.78 26.98 0.437 0.667 -1.00 29.71 -0.034 0.973
RVI 12.95 19.63 0.660 0.518 26.02 17.13 1.518 0.147 21.42 14.52 1.475 0.158
H 46.77 62.40 0.749 0.464 50.02 40.97 1.221 0.239 34.58 41.48 0.834 0.416
α -0.27 0.95 -0.285 0.779 1.47 0.59 2.500 0.022* 0.81 0.66 1.218 0.240
Table 1 Values for the simple linear regression hypothesis test for each SAR (synthetic aperture radar) attribute for each analyzed period
Fig. 5 R2 (coefficient of determination) and RMSE (root mean squared error) of the analyzed SAR (synthetic aperture radar) attributes for estimating the AGB (aboveground biomass) of arboreal Caatinga biome for each period evaluated. (a and b), green period; (c and d), intermediate period; (e and f), dry period.
Period Biomass equation R2/r RMSE (Mg/hm2)
Green 343.119+17.72×VV-115.896×VH/VV-84.557×DPSVI+233.448×H-4.428×α 0.720/0.85 8.40
Intermediate 342.552+9.920×VH-45.972×VH/VV-126.629×DPSVI-129.917×H+2.021×α 0.730/0.85 8.33
Dry 195.06+8.52×VH-102.93×DPSVI 0.550/0.74 10.65
Table 2 Multiple linear regression model for estimating the AGB (aboveground biomass) of arboreal Caatinga biome for each period evaluated
Fig. 6 Relationship between the observed AGB (aboveground biomass) and the estimated AGB by best multiple linear regression model (a, green period; b, intermediate period; c, dry period) and their respective residuals per sample plot for each analyzed value (d-f)
Reference Type of forest, location SAR attribute R2
Present study Caatinga, Brazil Coherent and incoherent 0.730
Jesus et al. (2023) Caatinga, Brazil H and α 0.320
David et al. (2022) Savanna, Southern Africa Backscatter coefficients 0.580
Forkuor et al. (2020) Sudanian savanna, West Africa Backscatter coefficients 0.760
Bao et al. (2019) Hulun Buir grassland, China Backscatter coefficients and texture 0.500
Pötzschner et al. (2022) Dry Chaco ecoregion, South America Backscatter coefficients 0.690
Table 3 Examples of the AGB estimation in drylands using the Sentinel-1 SAR (synthetic aperture radar) data
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