# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 4 Remote Sensing Applications
### Health Monitoring
Many studies have used machine learning methods for LAI prediction, such as those by [87, 88, 118, 119, 120, 121, 122], indicating a trend towards integrating remote sensing data  
Figure 3: Theoretical model of the difference in reflectance between near-infrared and red light. with machine learning for improved vegetation monitoring. Chlorophyll indices, like the Chlorophyll Index (CI) [123, 124] and Transformed Chlorophyll Absorption in Reflectance Index (TCARI) [125], focus on chlorophyll content, a key indicator of plant biomass [126] and plant health. The Chlorophyll Index green (CIg) [124] evaluates chlorophyll concentration and, consequently, vegetation health, using the green spectral region, valuable for detecting nutrient deficiencies related to chlorophyll production and photosynthetic activity. The Plant Senescence Reflectance Index (PSRI) captures chlorophyll and carotenoid levels to detect plant stress and senescence, while the Photochemical Reflectance Index (PRI) proxy of ecophysiological parameters monitor photosynthetic activity by assessing changes in xanthophyll cycle pigments [127], valuable for monitoring plant responses to environmental stressors like drought and disease. In drought-prone regions, the Water Stress Index (WSI) developed by [128], is instrumental in identifying water stress, enabling the assessment of drought impacts [129]. The Vegetation Health Index (VHI) proposed by [130], a composite index, integrates multiple parameters, including NDVI and surface temperature, to provide a holistic evaluation of vegetation health widely used for drought monitoring. These indices collectively constitute a comprehensive toolkit for researchers and environmental scientists engaged in the critical task of vegetation health assessment and management, providing comprehensive assessments that consider multiple aspects of vegetation health, as summarized in Table 1.  
Effective implementation of these indices requires a nuanced understanding of the specific context and conditions of the study area. For instance, while NDVI is broadly useful, its accuracy can be compromised in areas with high soil brightness or sparse vegetation, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Name** & **Formulation** & **Reference** \\ \hline NDVI & Normalized Difference Vegetation Index & \(\dfrac{NIR-Red}{NIR+Red}\) & [108] \\ \hline EVI & Enhanced Vegetation Index & \(2*\left(\dfrac{NIR-Red}{NIR+6*Red-7.5*Blue+1}\right)\) & [113] \\ \hline NDWI & Normalized Difference Water Index & \(\dfrac{NIR-SWIR}{NIR+SWIR}\) & [115] \\ \hline SAVI & Soil Adjusted Vegetation Index & \(1.5*\left(\dfrac{NIR-Red}{NIR+Red+0.5}\right)\) & [116] \\ \hline MSAVI & Modified Soil Adjusted Vegetation Index & \(2*NIR+1-\sqrt{\left(2*NIR+1\right)^{2}-8*\left(NIR-Red\right)}\) & [117] \\ \cline{3-4} LAI\({}_{\text{NDVILog}}\) & Leaf Area Index from Log NDVI & \(\dfrac{1}{2}*log_{e}\left(\dfrac{NDVI+0.1}{0.2}\right)\) & [131, 132, 133] \\ \hline CI\({}_{\text{green}}\) & Chlorophyll Index green & \(\dfrac{NIR}{\text{Green}}-1\) & [123, 124] \\ \hline CI\({}_{\text{red-edge}}\) & Chlorophyll Index red-edge & \(\dfrac{NIR}{\text{RE}}-1\) & [123, 124] \\ \hline TCARI &
\begin{tabular}{c} Transformed Chlorophyll Absorption in \\ Reflectance Index \\ \end{tabular} & \(3*\left[\left(RE1-Red\right)-0.2*\left(RE1-Green\right)*\left(RE1/Red\right)\right]\) & [125] \\ \hline PSRI & Plant Senescence Reflectance Index & \(\dfrac{Red-Green}{RE2}\) & [134] \\ \hline VHI & Vegetation Health Index & \(VHI=a\times VCI+(1-a)\times TCI\) & [130, 135] \\ \hline VCI & Vegetation Condition Index & \(\dfrac{\left(NDVI\right)_{current}-\left(NDVI\right)_{min}}{\left(NDVI\right)_{ max}-\left(NDVI\right)_{min}}\) & [136] \\ \hline TCI & Temperature Condition Index & \(\dfrac{\left(LST\right)_{max}-\left(LST\right)_{current}}{\left(LST\right)_{ max}-\left(LST\right)_{min}}\) & [137] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of vegetation indices used for health and drought monitoring. necessitating the use of SAVI or MSAVI.