Biomass Prediction Using Machine Learning Techniques In Google Earth Engine: A Case Study Of The Azrou Forest In The Middle Atlas Mountains, Morocco
https://doi.org/10.24057/2071-9388-2025-3876
Abstract
In the context of climate change, forests are a vital source of ecosystem services for humankind, acting primarily as carbon sinks. The aim of this study is to use the machine learning algorithms available in the Google Earth Engine (GEE) to predict the above-ground biomass of the Azrou forest in the Middle Atlas Mountains of Morocco. After a literature review, the work consisted of characterizing the natural features through Land Use Land Cover analysis (LULC) and forest stand types. The accuracy of the forest stand type classification was assessed at 81.55% using the kappa index. Analysis of vegetation cover time series data, derived from NASA imagery and MODIS, was carried out, focusing on four key indices: NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), LAI (Leaf Area Index), and FPAR (Fraction of Photo synthetically Active Radiation). The study predicted biomass using the Random Forest machine-learning model, implemented in GEE with JavaScript. NASA/ORNL biomass data for 2010 served as the dependent variable, while LULC, elevation, and the four indices were used (selected summer period) as independent explanatory variables. In addition, forest stand types were integrated to calculate total biomass for specific stand types and for the study area as a whole for the years 2015, 2020 and 2024. In 2024, the predicted biomass is 461,587 tons, compared with 501,172 tons in 2010. The biomass median values by species were 29 tons/ha for pure Atlas cedar (Cedrus atlantica Manetti), 24 tons/ha for pure holm oak (Quercus ilex) and 31 tons/ha for a mixture of Atlas cedar and holm oak. The results highlight challenging conditions for the Azrou forest, with a notable decline in biomass over the study period. These results will serve as a basis for future forestry planning in the context of climate change.
Supporting agencies: The authors gratefully acknowledge the support of the entire staff at the Provincial Forest Directorate of Ifrane (Morocco).
About the Authors
Said Laaribya
Ibn Tofail University/Laboratory of Territory planning Geo-Environment and Development
Morocco
Morocco
B.P 242 Kenitra
Assmaa Alaoui
Ibn Tofail University/Laboratory of Plant and Animal Production and Agro-Industry
Morocco
Morocco
B.P 242 Kenitra
References
1. Aguiar, L. J. G., Fischer, G. R., Ladle, R. J., Malhado, A. C. M., Justino, F. B., Aguiar, R. G., & Da Costa, J. M. N. (2012). Modeling the photosynthetically active radiation in South West Amazonia under all sky conditions. Theoretical and Applied Climatology, 108(3–4), 631– 640. https://doi.org/10.1007/s00704-011-0556-z
2. Alaoui, A., Laaribya, S., Sezgin, A., Ghallab, A., & López-Tirado, J. (2021). Modelling spatial distribution of endemic Moroccan fir (Abies marocana Trabut) in Talassemtane National Park, Morocco. 138(2), 73–94.
3. Bagnouls, F., & Gaussen, H. (1953). Dry season and xerothermic index. 193–239.
4. Behera, S. K., Srivastava, P., Pathre, U. V., & Tuli, R. (2010). An indirect method of estimating leaf area index in Jatropha curcas L. using LAI-2000 Plant Canopy Analyzer. Agricultural and Forest Meteorology, 150(2), 307–311. https://doi.org/10.1016/j.agrformet.2009.11.009
5. Boulmane, M., Santa-Regina, M. D. C., Halim, M., Khia, A., Oubrahim, H., Abbassi, H., & Santa-Regina, I. (2015). Organic Carbon Storage in Evergreen Oak Forest Ecosystems of the Middle and High Moroccan Atlas Areas. Open Journal of Forestry, 05(03), 260–273. https://doi.org/10.4236/ojf.2015.53023
6. Breiman, L. (2001). Random Forests. Machine Learning. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
7. Calvin, K., Dasgupta, D., Krinner, G., Mukherji, A., Thorne, P. W., Trisos, C., Romero, J., Aldunce, P., Barrett, K., Blanco, G., Cheung, W. W. L., Connors, S., Denton, F., Diongue-Niang, A., Dodman, D., Garschagen, M., Geden, O., Hayward, B., Jones, C., … Péan, C. (2023). IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland. (First). Intergovernmental Panel on Climate Change (IPCC). https://doi.org/10.59327/IPCC/AR6-9789291691647
8. Del Río, M., Barbeito, I., Bravo-Oviedo, A., Calama, R., Cañellas, I., Herrero, C., Montero, G., Moreno-Fernández, D., Ruiz-Peinado, R., & Bravo, F. (2017). Mediterranean Pine Forests: Management Effects on Carbon Stocks. In F. Bravo, V. LeMay, & R. Jandl (Eds.), Managing Forest Ecosystems: The Challenge of Climate Change (Vol. 34, pp. 301–327). Springer International Publishing. https://doi.org/10.1007/978-3-319-28250-3_15
9. El Mderssa, M. (2022). Détermination des paramètres d’évaluation du stock de carbone dans les écosystèmes forestiers (Cedrus atlantica Manetti, cèdre de l’Atlas au Maroc): Méthodes spécifiques et génériques. BOIS & FORETS DES TROPIQUES, 351, 67–77. https://doi.org/10.19182/bft2022.351.a36330
10. El Mderssa, M., Belghazi, B., Benjelloun, H., Zennouhi, O., Nassiri, L., & Ibijbijen, J. (2019). Estimation of Carbon Sequestration; Using Allometric Equations; in Azrou Cedar Forests (Cedrus atlantica Manetti) in the Central Middle Atlas of Morocco under Climate Change. Open Journal of Forestry, 09(03), 214–225. https://doi.org/10.4236/ojf.2019.93011
11. Erickson, L. E., & Brase, G. (2020). Reducing Greenhouse Gas Emissions and Improving Air Quality: Two Interrelated Global Challenges. Taylor & Francis.
12. FAO. (2020). Global Forest Resources Assessment 2020. FAO. https://doi.org/10.4060/ca9825en
13. Forsell, N., Turkovska, O., Gusti, M., Obersteiner, M., Elzen, M. D., & Havlik, P. (2016). Assessing the INDCs’ land use, land use change, and forest emission projections. Carbon Balance and Management, 11(1), 26. https://doi.org/10.1186/s13021-016-0068-3
14. Friedlingstein, P., Jones, M. W., O’Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J., Le Quéré, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., … Zeng, J. (2022). Global Carbon Budget 2021. Earth System Science Data, 14(4), 1917–2005. https://doi.org/10.5194/essd-14-1917-2022
15. García, M., Riaño, D., Chuvieco, E., & Danson, F. M. (2010). Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sensing of Environment, 114(4), 816–830. https://doi.org/10.1016/j.rse.2009.11.021
16. García-Rodríguez, A., Granados-López, D., García-Rodríguez, S., Díez-Mediavilla, M., & Alonso-Tristán, C. (2021). Modelling Photosynthetic Active Radiation (PAR) through meteorological indices under all sky conditions. Agricultural and Forest Meteorology, 310, 108627. https://doi.org/10.1016/j.agrformet.2021.108627
17. Gómez, C., Wulder, M. A., White, J. C., Montes, F., & Delgado, J. A. (2012). Characterizing 25 years of change in the area, distribution, and carbon stock of Mediterranean pines in Central Spain. International Journal of Remote Sensing, 33(17), 5546–5573. https://doi.org/10.1080/01431161.2012.663115
18. González‐Alonso, F., Merino‐De‐Miguel, S., Roldán‐Zamarrón, A., García‐Gigorro, S., & Cuevas, J. M. (2006). Forest biomass estimation through NDVI composites. The role of remotely sensed data to assess Spanish forests as carbon sinks. International Journal of Remote Sensing, 27(24), 5409–5415. https://doi.org/10.1080/01431160600830748
19. Huete, A. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59(3), 440–451. https://doi.org/10.1016/S0034-4257(96)00112-5
20. Laaribya, S. (2024). Analysis of the determinants of the regeneration and growth of Cedar Atlas (Cedrus atlantica (Endl.) Manetti ex Carrière), an endangered endemic taxon in Morocco-Case of the Middle Atlas forests. IOP Conference Series: Earth and Environmental Science, 1398(1), 012002. https://doi.org/10.1088/1755-1315/1398/1/012002
21. Laaribya, S., & Alaoui, A. (2025). Spatio-Temporal Trends and Climate Change Impacts on Land Cover Dynamics in Talassemtane National Park (Morocco) Using Artificial Intelligence and Google Earth Engine. Journal of Sustainable Forestry, 1–30. https://doi.org/10.1080/10549811.2025.2516072
22. Laaribya, S., Alaoui, A., Ayan, S., Benabou, A., Labbaci, A., Ouhaddou, H., & Bijou, M. (2021). Prediction by maximum entropy of potential habitat of the cork oak (Quercus suber L.) in Maamora Forest, Morocco. Forestist, 71(2), 63–69. https://doi.org/10.5152/forestist.2021.20059
23. Laaribya, S., Alaoui, A., Ayan, S., & Dindaroglu, T. (2024). Changes in the Potential Distribution of Atlas Cedar in Morocco in the Twenty-First Century According to the Emission Scenarios of RCP 4,5 and RCP 8,5. Forestist. https://doi.org/10.5152/forestist.2023.0004
24. Le Clec’h, S., Oszwald, J., Jégou, N., Dufour, S., Cornillon, P. A., Miranda, I. D. S., Gonzaga, L., Grimaldi, M., Gond, V., & Arnauld De Sartre, X. (2013). Cartographier le carbone stocké dans la végétation: Perspectives pour la spatialisation d¿un service écosystémique. BOIS & FORETS DES TROPIQUES, 316(316), 35. https://doi.org/10.19182/bft2013.316.a20529
25. Linares, J. C., Taïqui, L., & Camarero, J. J. (2011). Increasing Drought Sensitivity and Decline of Atlas Cedar (Cedrus atlantica) in the Moroccan Middle Atlas Forests. Forests, 2(3), 777–796. https://doi.org/10.3390/f2030777
26. Merlo, M., & Croitoru, L. (Eds.). (2005). Valuing mediterranean forests: Towards total economic value (1st ed.). CABI Publishing. https://doi.org/10.1079/9780851999975.0000
27. Mutanga, O., & Kumar, L. (2019). Google Earth Engine Applications. Remote Sensing, 11(5), 591. https://doi.org/10.3390/rs11050591
28. Myneni, R. B., Dong, J., Tucker, C. J., Kaufmann, R. K., Kauppi, P. E., Liski, J., Zhou, L., Alexeyev, V., & Hughes, M. K. (2001). A large carbon sink in the woody biomass of Northern forests. Proceedings of the National Academy of Sciences, 98(26), 14784–14789. https://doi.org/10.1073/pnas.261555198
29. Myneni, R., Knyazikhin, Y., & Park, T. (2021). MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V061 [Dataset]. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD15A2H.061
30. Nourelbait, M., Rhoujjati, A., Benkaddour, A., Carré, M., Eynaud, F., Martinez, P., & Cheddadi, R. (2016). Climate change and ecosystems dynamics over the last 6000 years in the Middle Atlas, Morocco. Climate of the Past, 12(4), 1029–1042. https://doi.org/10.5194/cp-12-1029-2016
31. Oubrahim, H., Boulmane, M., Bakker, M., Augusto, L., & Halim, M. (2016). Carbon storage in degraded cork oak (Quercus suber) forests on flat lowlands in Morocco. iForest - Biogeosciences and Forestry, 9(1), 125–137. https://doi.org/10.3832/ifor1364-008
32. Ourbak, T., & Magnan, A. K. (2018). The Paris Agreement and climate change negotiations: Small Islands, big players. Regional Environmental Change, 18(8), 2201–2207. https://doi.org/10.1007/s10113-017-1247-9
33. Pan, Y., Birdsey, R. A., Phillips, O. L., Houghton, R. A., Fang, J., Kauppi, P. E., Keith, H., Kurz, W. A., Ito, A., Lewis, S. L., Nabuurs, G.-J., Shvidenko, A., Hashimoto, S., Lerink, B., Schepaschenko, D., Castanho, A., & Murdiyarso, D. (2024). The enduring world forest carbon sink. Nature, 631(8021), 563–569. https://doi.org/10.1038/s41586-024-07602-x
34. Pandey, A., Arunachalam, K., Thadani, R., & Singh, V. (2020). Forest degradation impacts on carbon stocks, tree density and regeneration status in banj oak forests of Central Himalaya. Ecological Research, 35(1), 208–218. https://doi.org/10.1111/1440-1703.12078
35. Rudel, T. K., Coomes, O. T., Moran, E., Achard, F., Angelsen, A., Xu, J., & Lambin, E. (2005). Forest transitions: Towards a global understanding of land use change. Global Environmental Change, 15(1), 23–31. https://doi.org/10.1016/j.gloenvcha.2004.11.001
36. Schilling, J., Freier, K. P., Hertig, E., & Scheffran, J. (2012). Climate change, vulnerability and adaptation in North Africa with focus on Morocco. Agriculture, Ecosystems & Environment, 156, 12–26. https://doi.org/10.1016/j.agee.2012.04.021
37. Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. The Stata Journal: Promoting Communications on Statistics and Stata, 20(1), 3–29. https://doi.org/10.1177/1536867X20909688
38. Scott, M., Lennon, M., Tubridy, F., Marchman, P., Siders, A. R., Main, K. L., Herrmann, V., Butler, D., Frank, K., Bosomworth, K., Blanchi, R., & Johnson, C. (2020). Climate Disruption and Planning: Resistance or Retreat? Planning Theory & Practice, 21(1), 125–154. https://doi.org/10.1080/14649357.2020.1704130
39. Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall’s Tau. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934
40. Shammi, S. A., & Meng, Q. (2021). Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Ecological Indicators, 121, 107124. https://doi.org/10.1016/j.ecolind.2020.107124
41. Sinha, S. (2022). H/A/α Polarimetric Decomposition Of Dual Polarized Alos Palsar For Efficient Land Feature Detection And Biomass Estimation Over Tropical Deciduous Forest. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY, 15(3), 37–46. https://doi.org/10.24057/2071-9388-2021-095
42. Spawn, S. A., Sullivan, C. C., Lark, T. J., & Gibbs, H. K. (2020). Harmonized global maps of above and belowground biomass carbon density in the year 2010. Scientific Data, 7(1), 112. https://doi.org/10.1038/s41597-020-0444-4
43. Terrab, A., Paun, O., Talavera, S., Tremetsberger, K., Arista, M., & Stuessy, T. F. (2006). Genetic diversity and population structure in natural populations of Moroccan Atlas cedar ( Cedrus atlantica ; Pinaceae) determined with cpSSR markers. American Journal of Botany, 93(9), 1274–1280. https://doi.org/10.3732/ajb.93.9.1274
44. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
45. Vayreda, J., Gracia, M., Canadell, J. G., & Retana, J. (2012). Spatial Patterns and Predictors of Forest Carbon Stocks in Western Mediterranean. Ecosystems, 15(8), 1258–1270. https://doi.org/10.1007/s10021-012-9582-7
46. Watson, D. J. (1958). The Dependence of Net Assimilation Rate on Leaf-area Index. Annals of Botany, 22(1), 37–54. https://doi.org/10.1093/oxfordjournals.aob.a083596
47. Wei, X., Shao, M., Gale, W., & Li, L. (2014). Global pattern of soil carbon losses due to the conversion of forests to agricultural land. Scientific Reports, 4(1), 4062. https://doi.org/10.1038/srep04062
48. Weston, C. J., & Whittaker, K. L. (2004). SOIL BIOLOGY AND TREE GROWTH | Soil Biology. In Encyclopedia of Forest Sciences (pp. 1183–1189). Elsevier. https://doi.org/10.1016/B0-12-145160-7/00248-9
49. Zaher, H., Sabir, M., Benjelloun, H., & Paul-Igor, H. (2020a). Effect of forest land use change on carbohydrates, physical soil quality and carbon stocks in Moroccan cedar area. Journal of Environmental Management, 254, 109544. https://doi.org/10.1016/j.jenvman.2019.109544
50. Zaher, H., Sabir, M., Benjelloun, H., & Paul-Igor, H. (2020b). Effect of forest land use change on carbohydrates, physical soil quality and carbon stocks in Moroccan cedar area. Journal of Environmental Management, 254, 109544. https://doi.org/10.1016/j.jenvman.2019.109544
51. Zhao, M., Cheng, C., Zhou, Y., Li, X., Shen, S., & Song, C. (2021). A global dataset of annual urban extents (1992-2020) from harmonized nighttime lights (p. 358204912 Bytes) [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.16602224.V1
Review
For citations:
Laaribya S., Alaoui A. Biomass Prediction Using Machine Learning Techniques In Google Earth Engine: A Case Study Of The Azrou Forest In The Middle Atlas Mountains, Morocco. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2025;18(3):43-58. https://doi.org/10.24057/2071-9388-2025-3876
Views:
4
Email this article 