Estimating forest biophysical and biochemical parameters in Behali Reserve Forest (Assam) using proximal and remote sensing techniques

Forest biophysical and biochemical parameters are critical for assessing forest health. The integration of proximal and remote sensing approaches is becoming more prevalent for plant characterization because of the benefits associated with multi-dimensional data collection and interpretation. This study aims to deduce the biophysical and biochemical parameters of forests in the Behali Reserve Forest (BRF) located in the Eastern Himalayas. Specifically, the red-edge spectral bands of the Sentinel-2A sensor were deployed to derive the Leaf Area Index (LAI), Enhanced Vegetation Index (EVI), and Normalized Difference Red-Edge (NDRE). Furthermore, the Normalized Area Over Reflectance Curve (NAOC) is used to deduce leaf chlorophyll content and leaf nitrogen content. The biophysical parameters analysis showed that the LAI ranged from 0 to 5.5 m²/m². The healthy dense forests showed an LAI of more than 4.5 that comprised 37.5% of the area. The satellite-derived NDRE has a significant positive association with measured leaf chlorophyll and nitrogen contents that exhibited coefficient of determination (R²) of 0.88 and 0.89, respectively. The NAOC-based empirical model leaf chlorophyll content of dense forests ranges between 30 and 45 μg/cm². The leaf nitrogen content of dense forest as demonstrated by the Nitrogen Balance Index (NBI) was estimated between 40 and 70 (unitless). The synergy of near-proximal and remote sensing data has demonstrated a robust and efficient method of monitoring the health of forests in reserve forests. The retrieved biophysical and biochemical parameters have supplied crucial information on forest health which is vital for forest conservation, plantation, monitoring and management.