# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 4 Remote Sensing Applications
### Dendrometric Parameters Using Light Detection and Ranging and Drones  
The advent of LiDAR (light detection and ranging) technology has enabled the creation of precise 3D maps of landscapes. Leveraging laser-based measurements, LiDAR offers profound insights into terrain structure, enhancing our ability to assess flood risks [56; 57; 58; 59], study forest canopy height [60; 61; 62; 63; 64], and plan urban landscapes [65; 66; 67; 64]. It has evolved into a critical tool for managing natural resources and addressing the impacts of climate change.  
Drones, also known as unmanned aerial vehicles (UAVs), are compact, unmanned aircraft equipped with advanced cameras and sensors. These versatile devices play a pivotal role in collecting localized, high-resolution data. Whether its monitoring plant health [9; 68; 69; 70]; assessing the aftermath of disasters [43; 71; 72; 73; 74]; conducting density assessments [75]; measuring 3D canopy height [60; 61; 62; 64; 76; 77; 78], crown diameter [79; 80; 81], diameter at breast height (DBH) [8; 82; 83; 84; 85], and leaf area index (LAI) [86; 87; 88; 23; 89; 90; 91; 92; 93]; estimating evapotranspiration (ET) and water stress [9; 94; 95; 96; 97; 98; 99; 100]; or inspecting critical infrastructure, drones have emerged as indispensable tools for a wide range of applications, owing to their agility and ability to access hard-to-reach areas. UAVs offer a practical alternative for collecting high-resolution forest inventory data due to their cost-effective features [6; 77; 78; 79; 101]. [PERSON] et al. [102] surveyed 30 test sites, each covering 1 hectare, in Argane woodlands of different degradation stages using a UAV equipped with an RGB optical camera. They collected information on tree height and crown size from canopy height models (CHMs) and then analyzed various 3D point-cloud characteristics, including point profile shape, density, and layer structure. Various methods are used for individual tree identification and tree height extraction from CHM, including deep learning [103; 104], local maxima [6; 78; 105], and extended-maxima transformation [106]. Among these, the local maxima algorithm is the most commonly used method for identifying treetops [78; 107]. This analysis was conducted within a geographical information system (GIS).  
Improving research and conservation efforts in ecosystems like Argane stands and other species involves exploring the integration of LiDAR technology and UAVs equipped with advanced sensors. LiDAR's capability to create precise 3D maps enables detailed assessments of terrain structure and supports the management of natural resources, including monitoring forest canopy height, crown diameter, and diameter at breast height (DBH) and planning urban landscapes. Additionally, UAVs equipped with sensors facilitate the collection of high-resolution data essential for tasks such as monitoring plant health, conducting density assessments, and estimating ecological parameters like leaf area index and evapotranspiration. Machine learning algorithms, such as random forests (RF), artificial neural networks (ANN), and support vector regression (SVR), can be employed to predict dendrometric measurements from LiDAR and UAV data, enhancing the accuracy and efficiency of ecological assessments. Integrating these technologies can provide comprehensive insights into ecosystem dynamics and support effective conservation strategies tailored to specific environmental challenges and management needs.