Opportunities for the use of drone technology in forest ecosystems

Au cours de la dernière décennie, la technologie drone a suscité un grand intérêt et a été largement utilisée pour des applications civiles. Ainsi, les drones ont rapidement prouvé leurs efficiences dans les ressources naturelles, l’environnement, l’agriculture et la foresterie. Étant une plate-forme de télédétection, les drones ont le potentiel d'augmenter l'efficacité d'acquisition des données forestières en ayant des résolutions spatiale et temporelle beaucoup plus importantes que celles des autres techniques de télédétection. Dans cet article, nous présentons une synthèse des travaux de recherche portant sur l’utilisation de la technologie drone dans diverses applications forestières, dont la modélisation de la canopée forestière, l’évaluation des paramètres de l’inventaire forestier, le suivi de la santé des forêts et la discrimination des essences forestières. L’analyse de ces travaux a montré que l’utilisation de la technologie drone a concerné plusieurs aspects diversifiés, tandis que d’autres thématiques de recherche sont encore peu étudiées, notamment l’évaluation de la régénération naturelle, le suivi des projets de réhabilitation des écosystèmes naturels, l’étude des impacts des changements climatiques et des impacts anthropogènes sur un écosystème forestier. Le drone offre des opportunités d’utilisation de la technologie drone dans la gestion du domaine forestier Marocain, afin de remédier aux limites des techniques de télédétection utilisées actuellement en termes de résolution spatiale, de flexibilité du choix du temps d’acquisition des données et en terme du coût. Mots clés: Drone, Écosystème forestier, Inventaire forestier, Modélisation de la canopée forestière, Photogrammétrie, LidarOver the past decade, UAV technology has attracted a great deal of interest and has been widely used for civilian applications. UAVs have rapidly proven their efficiency in natural resources, the environment, agriculture and forestry. As a remote sensing platform, UAVs have the potential to increase the efficiency of forest data acquisition, having much higher spatial and temporal resolutions than other remote sensing techniques. In this paper, we present a synthesis of research on the use of UAV technology in various forestry applications, such as forest canopy modelling, forest inventory parameter assessment, forest health monitoring and forest species discrimination. The analysis of this research has shown that the use of UAV technology has concerned several diversified aspects, while other research themes are less studied, notably the assessment of natural regeneration, the monitoring of natural ecosystem rehabilitation projects, the study of climate and anthropogenic change impacts forest ecosystems. UAV technology is an opportunities for management of the Moroccan forest ecosystem, in order to overcome the limitations of the remote sensing techniques currently used, in terms of spatial resolution, flexibility in the choice of data acquisition time and in terms of cost. Keywords: UAV, Forest ecosystem, Forest inventory, Forest canopy modelling, Photogrammetry, Lida

Biomass and Carbon Stock Quantification in Cork Oak Forest of Maamora Using a New Approach Based on the Combination of Aerial Laser Scanning Carried by Unmanned Aerial Vehicle and Terrestrial Laser Scanning Data

The Mediterranean forests, particularly Cork oak (Quercus suber L., 1927), make a major contribution to the fight against climate change through Carbon sequestration. Hence, there is a great interest in the accurate quantification of biomass and carbon stock. In this context, this study aims at assessing the performance of a new approach, based on the combination of Unmanned aerial vehicle airborne Aerial laser scanning (ALS-UAV) and Terrestrial laser scanning (TLS) data, in the determination of dendrometric parameters (Circumference at 1.30 m and Tree Height), and consequently the estimation of biomass and carbon stock, considering field data as reference. This study takes the Maamora forest in Morocco as an example of a Mediterranean Cork oak forest. The methodology consists of collecting data at three levels: the entire area level for an ALS-UAV scan, the plot and tree levels for TLS surveys, as well as field data collection. Afterwards, dendrometric parameters (Circumference at 1.30 m and the Tree height) were estimated using individual tree segmentation and biomass; the carbon stock (aboveground, belowground, and total) was estimated using allometric equations. The comparison of the estimated dendrometric parameters with those measured in the field shows a strong relationship, with a Pearson coefficient of 0.86 and 0.83, a correlation coefficient (R2) of 0.81 and 0.71, and a Root mean square error (RMSE) of 1.84 cm and 0.47 m, respectively. Concerning the biomass and carbon stock estimation, the proposed approach gives a satisfactory accuracy, with a Pearson coefficient of 0.77, an R2 of 0.83, and an RMSE of 36.40 kg for biomass and 20.24 kg for carbon stock

Biomass and Carbon Stock Quantification in Cork Oak Forest of Maamora Using a New Approach Based on the Combination of Aerial Laser Scanning Carried by Unmanned Aerial Vehicle and Terrestrial Laser Scanning Data

The Mediterranean forests, particularly Cork oak (Quercus suber L., 1927), make a major contribution to the fight against climate change through Carbon sequestration. Hence, there is a great interest in the accurate quantification of biomass and carbon stock. In this context, this study aims at assessing the performance of a new approach, based on the combination of Unmanned aerial vehicle airborne Aerial laser scanning (ALS-UAV) and Terrestrial laser scanning (TLS) data, in the determination of dendrometric parameters (Circumference at 1.30 m and Tree Height), and consequently the estimation of biomass and carbon stock, considering field data as reference. This study takes the Maamora forest in Morocco as an example of a Mediterranean Cork oak forest. The methodology consists of collecting data at three levels: the entire area level for an ALS-UAV scan, the plot and tree levels for TLS surveys, as well as field data collection. Afterwards, dendrometric parameters (Circumference at 1.30 m and the Tree height) were estimated using individual tree segmentation and biomass; the carbon stock (aboveground, belowground, and total) was estimated using allometric equations. The comparison of the estimated dendrometric parameters with those measured in the field shows a strong relationship, with a Pearson coefficient of 0.86 and 0.83, a correlation coefficient (R2) of 0.81 and 0.71, and a Root mean square error (RMSE) of 1.84 cm and 0.47 m, respectively. Concerning the biomass and carbon stock estimation, the proposed approach gives a satisfactory accuracy, with a Pearson coefficient of 0.77, an R2 of 0.83, and an RMSE of 36.40 kg for biomass and 20.24 kg for carbon stock

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

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 R2 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