of Vegetation Patterns Observed with Multitemporal Remotely Sensed Images

Rapid deforestation i n Mexico, when cozrpled w i t h poor access to current and consistent ecological information across the col lntry underscores the need for a n ecological classification sys t em that can be readily updated and n e w data beconze available. In this s tudy, regiorzal vegetation resources i n Mexico were evaluated us ing remotely sensed information. Mtllt i temporal Global Vegetation l n d e x { G V I) data from Advanced V e r y High Resolution Radiometer images provided ecological infornzation at regional scales b y being interpreted as plzenological patterns ofvegetat ion product iv i ty and seasonality. Principal component analysis o n G V I lnonthly composites identified spatial and temporal vegetation patterns, reducing their variation to f ive phenologically m e a n i n d u l components. S i x t y land-cover and natural vegetation classes zuer: then derived via unsupervised classification f rom the f ive principal components. Addi t ional phenoloipical i~zfontzation (e.g., onset and peak of greenness, periods of growth) was obtained for each class. These data, along wltlz seasonality measures (e.g., szrnzmer us. w in ter peak ofgreenness) were used as criteriafor group ing similar vegetation and land-cover types in to a classification for Mexico