Which method is most often used to assess forest biomass through remote sensing?

The method most often used to assess forest biomass through remote sensing involves calculating spectral reflectance data. This technique utilizes the unique signatures of different vegetation types as measured in various wavelengths, particularly in the visible and infrared ranges of the electromagnetic spectrum.

Spectral reflectance data provides information about the canopy characteristics of forests, such as leaf area index and chlorophyll content, which are directly related to biomass estimation. By analyzing the reflected light from the forest, researchers can derive quantitative measures of biomass across large areas without the need for extensive ground surveys. This efficiency is particularly beneficial in remote or inaccessible regions.

Other methods mentioned, such as analyzing tree height or monitoring tree species diversity, may contribute to biomass assessments but do not provide the comprehensive and direct measures that spectral analysis does. GPS tracking of tree growth is more focused on individual trees rather than providing an overall assessment of forest biomass across a larger landscape. Therefore, using spectral reflectance data remains the preferred method in remote sensing applications for accurately estimating forest biomass.