Although ABAs are commonly applied in operational forestry applications [38], they show several limitations (i.e., they are dependent on extensive in situ data [36; 37], they are site-specific [39], and they are limited in sensitivity to TD [40]). In contrast, ITD-based approaches utilize either ALS point cloud data or canopy height models (CHM) to identify individual trees [35; 38; 41]. Plot or stand estimates are obtained afterwards by summing up identified trees. [PERSON] et al. [42] indicate that ITD approaches provide true stem distribution series and require less in situ data compared to ABAs. ITD approaches are, however, currently under development and have not yet been widely applied in practice, or across heterogeneous forests in terms of structural complexity, species composition, and silivcultural treatments [38; 42]. FP can be approximated with GPP, which itself can stem from various approaches ranging from empirical to process-based modeling approaches. Empirical approaches based on optical EO data are simple to implement, but rely on extensive in situ data and are thus site- and ecosystem-specific. A frequently-used approach to mapping FP exploits optical EO data in combination with [PERSON]'s light use efficiency model [43; 21; 44]. Both terms in [PERSON]'s equation, i.e., absorbed photosynthetic active radiation (APAR) and light use efficiency (LUE), are typically parameterized with greenness-based indices and semi-empirical modeling. Assumptions inherent to this approach (i.e.,approximation of potential rather than actual photosynthesis), however, limit the representativeness of obtained GPP values [45] across ecosystems (cf. [PERSON] et al. [46] for an assessment of the MODIS-GPP product (MOD17), or [PERSON] et al. [47] for an evaluation of other satellite-based approaches). A recent achievement in optical remote sensing is the measurement of sun-induced chlorophyll fluorescence (SIF). SIF is the most direct measurement of plant photosynthesis, and opens new perspectives on constraining estimates of GPP across ecosystems [48; 49]. Although this new EO approach is already well matured, certain components (e.g., the integration of SIF in photosynthesis models) remain to be fully developed in order to operationally apply SIF measurements. The use of process-based models provides the most mechanistic approach, but requires precise and detailed information on soil, climate, and additional forest properties that are often lacking or are inaccurate [44]. The above argumentation yields two research needs that are addressed in this study. This includes (i) the evaluation of pragmatic and less data driven ALS and imaging spectroscopy (IS)-based approaches for obtaining reliable estimates of TD and FP in heterogeneous forest environments, and (ii) the advancement of knowledge about the mechanistic interrelation of TD and FP with surface elevation. In particular, we estimate TD in a heterogeneous mountain forest using a combination of an ITD approach and ALS data, and validate results with in situ data. Further, we estimate FP using an empirical greenness-based approach in combination with data of the Airborne Prism Experiment (APEX) imaging spectrometer. Last, we investigate the relationship between TD and FP and evaluate the impact of topography (i.e., surface exposition and elevation) on these two important forest variables.