Accurate estimation of forest carbon stocks from remote-sensing imagery is critical for climate monitoring and ecosystem management. We propose HAF R-CNN (Height-perceptual Attention Fusion R-CNN), a Faster R-CNN extension that fuses RGB (red-green-blue) and canopy height model (CHM) features via multi-level cross-attention to improve tree crown detection. Unlike previously proposed RGB-CHM fusion approaches that rely mainly on simple concatenation or late fusion, our cross-attention design enables richer bidirectional structural-spectral interactions. We further develop a tree-level carbon estimation pipeline combining crown structural descriptors and vegetation indices with a Random Forest regressor. On the NEON dataset, HAF R-CNN outperforms baselines in detection, and the pipeline achieves reliable carbon estimation at the OSBS site ( \(R^2\) = 0.67, MAE = 59.6 kg), though performance decreases in denser forests (MLBS). These results highlight both the promise and current limitations of multimodal detection for scalable carbon monitoring.