## 6 Role of the PhenoCam Network in Supporting Forest Management in India  
Long-term datasets on ecosystem dynamics are crucial for effective forest management and biodiversity conservation. Unfortunately, in the case of India, there is currently a limited number of long-term observation sites, and access to such datasets is restricted [83]. Introducing a PhenoCam network across various forest types in India could be a valuable addition to forest management efforts. PhenoCams are widely used globally to understand and predict changes in vegetation patterns and phenology, which have significant implications for ecosystem health and climate research. Gross primary productivity is an important indicator of forest health as well as an indicator of climate change [84]. [PERSON] et al. (2020) showed that GPP derived from PhenoCam shows a higher correlation (R\({}^{2}\) varied from 0.80 to 0.87) with GPP derived from an eddy covariance tower with high statistical significance (\(p<\) 0.01) [25]. Similarly, PhenoCam-based analysis (Excess green index and GCC) on plant health shows a significant relation to the ground-based metrics of stand health, which includes vigor, mortality, foliar disease occurrence, and root disease symptoms at both the plot and tree level [85]. Several studies have also indicated that camera-derived LAI and chlorophyll concentration correlate with ground-based observations [40; 86; 87]. Similarly, PhenoCam-derived GCC demonstrates a significant correlation with aboveground green biomass (AGB) in grassland ecosystems [53]. PhenoCam data have proven valuable in tracking global climate events. For instance, [PERSON] et al. (2017) effectively utilized time series PhenoCam data from grasslands in the Western European Alps to monitor the impact of heat waves [7]. Similarly, [PERSON] et al. (2020) examined the response of forests to an unexplained drought triggered by El Nino in 2015-2016 and observed post-drought fluctuations in leaf demography using multi-year PhenoCam data [88]. In light of the changing climatic conditions, the introduction of PhenoCam in India's forests holds great promise for enhancing our understanding of ecosystem dynamics, facilitating informed decision-making, and supporting proactive conservation strategies. In addition to direct monitoring, PhenoCam data can be also used to validate satellite-based products, which are highly used for forest management and decision making in the current scenario. Validating satellite-based phenological products is important for understanding variations in seasonal and inter-annual biome responses due to climatic variability [89]. The following are some of the most widely used satellite-based vegetation products to understand forest dynamics: (a) the Normalized Difference Vegetation Index (NDVI), (b) the Enhanced Vegetation Index (EVI), (c) MODIS LAI, and (d) MODIS and AVHHR Fraction of Absorbed Photosynthetic Active Radiation (fPAR). Currently, data from the eddy covariance tower (EC) [87; 90], ground-based measurements of LAI, time-lapse digital photographs, and tower radiation sensors (PAR) are being used to calibrate these products. However, in India, the number of active eddy covariance sites is very limited. Therefore, the establishment of the Indian PhenoCam Network could significantly increase the number of calibration points for satellite-based products, addressing the current gap in phenological monitoring in the South Asian region. Additionally, this could help in validating satellite-derived phenological matrices (e.g., MCD12Q2) and proceed one step closer to qualifying phenological matrices with products from satellites to become one of the essential climate variables (ECVs). Indian forest calendar for management. Presently, available information about the Indian forests is limited to broad and sporadic measurements (monthly observations in field plots). Such data are inadequate for generating spatial maps on phenology, health, biomass, and other essential datasets.