An Integrating Framework for Biomass and Carbon Stock Spatialization and Dynamics Assessment Using Unmanned Aerial Vehicle LiDAR (LiDAR UAV) Data, Landsat Imagery, and Forest Survey Data in the Mediterranean Cork Oak Forest of Maamora

Sanaa Fadil (), Imane Sebari, Moulay Mohamed Ajerame, Rayhana Ajeddour, Ibtihal El Maghraoui, Kenza Ait El Kadi, Yahya Zefri and Mouad Jabrane
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Sanaa Fadil: Cartography and Photogrammetry Department, School of Geomatics and Surveying Engineering (Hassan II Institute of Agronomy and Veterinary Medicine), Rabat 10000, Morocco
Imane Sebari: Cartography and Photogrammetry Department, School of Geomatics and Surveying Engineering (Hassan II Institute of Agronomy and Veterinary Medicine), Rabat 10000, Morocco
Moulay Mohamed Ajerame: Department of Statistics and Applied Computer Science, IAV Hassan II, Rabat 10000, Morocco
Rayhana Ajeddour: Cartography and Photogrammetry Department, School of Geomatics and Surveying Engineering (Hassan II Institute of Agronomy and Veterinary Medicine), Rabat 10000, Morocco
Ibtihal El Maghraoui: Cartography and Photogrammetry Department, School of Geomatics and Surveying Engineering (Hassan II Institute of Agronomy and Veterinary Medicine), Rabat 10000, Morocco
Kenza Ait El Kadi: Cartography and Photogrammetry Department, School of Geomatics and Surveying Engineering (Hassan II Institute of Agronomy and Veterinary Medicine), Rabat 10000, Morocco
Yahya Zefri: Cartography and Photogrammetry Department, School of Geomatics and Surveying Engineering (Hassan II Institute of Agronomy and Veterinary Medicine), Rabat 10000, Morocco
Mouad Jabrane: Cartography and Photogrammetry Department, School of Geomatics and Surveying Engineering (Hassan II Institute of Agronomy and Veterinary Medicine), Rabat 10000, Morocco

Land, 2024, vol. 13, issue 5, 1-21

Abstract: Spatialization of biomass and carbon stocks is essential for a good understanding of the forest stand and its characteristics, especially in degraded Mediterranean cork oak forests. Furthermore, the analysis of biomass and carbon stock changes and dynamics is essential for understanding the carbon cycle, in particular carbon emissions and stocks, in order to make projections, especially in the context of climate change. In this research, we use a multidimensional framework integrating forest survey data, LiDAR UAV data, and extracted vegetation indices from Landsat imagery (NDVI, ARVI, CIG, etc.) to model and spatialize cork oak biomass and carbon stocks on a large scale. For this purpose, we explore the use of univariate and multivariate regression modeling and examine several types of regression, namely, multiple linear regression, stepwise linear regression, random forest regression, simple linear regression, logarithmic regression, and quadratic and cubic regression. The results show that for multivariate regression, stepwise regression gives good results, with R 2 equal to 80% and 65% and RMSE equal to 2.59 and 1.52 Mg/ha for biomass and carbon stock, respectively. Random forest regression, chosen as the ML algorithm, gives acceptable results, explaining 80% and 60% of the variation in biomass and carbon stock, respectively, and an RMSE of 2.74 and 1.72 Mg/ha for biomass and carbon stock, respectively. For the univariate regression, the simple linear regression is chosen because it gives satisfactory results, close to those of the quadratic and cubic regressions, but with a simpler equation. The vegetation index chosen is ARVI, which shows good performance indices, close to those of the NDVI and CIG. The assessment of biomass and carbon stock changes in the study area over 35 years (1985–2020) showed a slight increase of less than 10 Mg/ha and a decrease in biomass and carbon stock over a large area.

Keywords: biomass; carbon stock; dynamics; modeling regression; spatialization; random forest; LiDAR UAV; Landsat images; cork oak; Maamora forest (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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