Information about species mapping is an essential approach for the management of forests and sustainable practices of conservation. Remote sensing data have proven to be an asset for the assessment of the spatial distribution of species over time. We used time-series data from a single sensor (PlanetScope) and dual-sensor (PlanetScope with Sentinel-2) to map Dalbergia sissoo, a timber species, found on both sides of the Jakhan river in Dehradun district of Lesser Himalaya. The dimensionality of the temporal images was reduced by normalized difference vegetation index (NDVI) and class-based sensor independent NDVI (CBSI-NDVI). Separability analysis was conducted to find optimal dates of the data set using three measures of separability, and Euclidean distance was found to be the best measure of separability for both the indices. Due to limitations of the classifiers in handling mixed pixels, a fuzzy-based modified possibilistic c-means (MPCM) algorithm was tested to extract a single class, i.e., Shisham (Dalbergia sissoo) tree. We used the conventional mean and individual sample as mean (ISM) as training parameter concepts in the MPCM supervised classification approach. We found that the ISM approach was able to handle the heterogeneity within the class for both vegetation indices. It was seen that PlanetScope data were able to spatially map the target class in a better way, whereas combined data of PlanetScope and Sentinel-2 helped to reduce the spectral overlap between target and nontarget classes. An accuracy assessment was performed using mean membership difference, variance, and entropy where variance and entropy showed that NDVI-based ISM approach outperformed the CBSI-NDVI-based approach. Both single-and dual-sensor time-series data showed good classification results for single species extraction.