Assessing fire impacts on a landscape scale can be a powerful tool to guide forest management in a changing climate. However, there are many limitations to current approaches to assessment. This research explores emerging new technologies for quantifying forest structure change in the context of management objectives, particularly at the patch scale. Live tree patches are identified in the Dixie fire footprint in Lassen Volcanic National Park by using a supervised object-based classification with Support Vector machine learning from multi-spectral data acquired from the 3-meter resolution Planet Labs SuperDove constellation. Patch size distributions pre- and post-fire were compared to target distributions detailed in the park’s Fire Management Plan (FMP). Overall model classification performance and accuracy was generally good, with overall accuracy at 96% and 98% for the pre-fire and post-fire models, respectively; accuracy for the live canopy classes were 52% and 80%, respectively, and were affected by factors of scene complexity. Live tree patch sizes ranged widely in size, from <0.1ha to >600ha and exhibited a statistically significant decrease in median between the pre-fire to the post-fire landscapes. Patch size distributions did not meet FMP targets pre-fire nor post-fire, though our results indicate Dixie Fire effects did help achieve management targets in some areas. Further, the novel use of such high-resolution, regular acquisition data illustrates the potential to utilize a more robust, repeatable methodology by land managers assessing landscape-level fire effects.