Space observation is acknowledged as quintessential for providing reliable baseline assessment and monitoring strategies for vegetation at multiple scales over extensive territories with a low population and limited accessibility. Optical satellite imagery represents the major source of data and covers an ample continuum of image resolution and swath. Yet vegetation monitoring in both the dry and wet tropics has long been hampered by insufficient pixel resolution that renders the well-mastered, pixel-wise classification techniques inefficient. The increasing availability of images with high spatial resolution (HSR, pixels of 10 m or less) to very high spatial resolution (VHSR, pixels of less than 1 m) has opened up new prospects by allowing the inference of vegetation properties from image texture features (i.e., local inter-pixel variability). In the present paper, we aim to illustrate this potential through recently published case studies dealing with semi-arid vegetation monitoring and baseline above ground biomass assessment in moist tropical forests. In both cases, we applied variants of the FOTO method (Fourier-based textural ordination) to quantify textural features in the images and relate them to meaningful vegetation properties, such as patterns of vegetation vs.bare ground in drylands, or crown and gap size distribution in forest canopy images. Textural ordination based on Fourier spectra provides a powerful and consistent framework for identifying prominent scales of landscape patterns and comparing scaling properties across landscapes. In the case of forest landscapes, texture features relate to crown size distribution and sometimes to inter-crown gaps and therefore are often good predictors of stand structure and biomass.