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Within-crown heterogeneity exists in multiple vegetation scenes including forest stands with heterogeneous distribution of damaged foliage. The existing radiative transfer approaches implement the within-crown heterogeneity either using complicated three-dimensional (3D) scenes with excessive amounts of parameters, or simplifying the scenes to homogeneous cases by averaging canopy optical properties. In order to ascertain the optical response to within-crown heterogeneity both efficiently and accurately, we proposed a method for simulating bi-directional reflectance factor (BRF) of forests infected by pests based on the stochastic radiative transfer (SRT) theory. Each damaged tree crown was classified into one of the three types: top-only damage, bottom-only damage and random damage. The statistical properties of such canopy structures were described with extended definitions of the stochastic moments of the SRT model, including the probabilities of finding different foliage classes and the conditional pair-correlation functions between healthy and damaged foliage. Field measurements and remote sensing data from unmanned aerial vehicle (UAV) over infected Yunnan Pine (Pinus yunnanensis) forests, and simulations of two 3D models were utilized to evaluate the performance. Results showed that the extended SRT agreed well with 3D models and field measurements, which means it has the capability to model BRF over forests with heterogeneous foliage within crowns based on one-dimensional (1D) theory. The extended SRT provides a framework to analyze the sensitivity of canopy reflectance to the distribution of damaged foliage with the variation of other key scene factors. For the three types of damaged tree crowns, the thresholds of detectable optical changes were quite different, in which 20% for top-only damage, 80% for bottom-only damage and 50% for random damage. The uncertainties caused by other scene factors were also considered. The extended SRT is promising for crown vertical profile specification, forest damage quantitative inversion, and other applications with coupling models.