Title :
Fraction images derived from EO-1 Hyperion multitemporal data for dry season green up analysis in Tapajós National Forest, Brazilian Amazonia
Author :
Freitas, Ramon M. ; Shimabukuro, Yosio E. ; Rosa, Reinaldo Roberto ; Huete, Alfredo
Author_Institution :
Inst. Nac. de Pesquisas Espaciais (INPE), Sao Jose dos Campos, Brazil
Abstract :
In this study, we present an approach for phenology analysis of Amazon green-up using Linear Spectral Mixing Model applied to Hyperion multitemporal data. The study area was selected in the Tapajo¿s National Forest located in Para¿ State, Brazilian Amazonia. The region has well-defined dry and wet seasons with yearly rain about 2, 100 mm a dry season occurring from June to October. The study area is primarily covered by dense tropical rain forest (¿Floresta Ombro¿fila Densa¿) with a high number of emergent tree species. The EO-1 Hyperion data were acquired in July, August and September 2001, corresponding to the dry season in this region. The Linear Spectral Mixing Model was applied on each calibrated surface reflectance data, generating vegetation, soil, and shade fraction images. Then fundamental statistical analyses were carried out to evaluate the differences within the vegetation and shade fraction images derived from medium spatial resolution Hyperion images for rainforest phenology analysis.
Keywords :
forestry; geophysical image processing; phenology; vegetation mapping; AD 2001 07 to 09; Brazilian Amazonia; EO-1 hyperion multitemporal data; Hyperion multitemporal data; Linear Spectral Mixing Model; Para State; Tapajos National Forest; dry season green up analysis; forestry; fraction images; rainforest phenology analysis; soil images; spatial resolution Hyperion images; statistical analyses; surface reflectance data; tropical rain forest; vegetation; Atmospheric measurements; Electronic mail; Image analysis; MODIS; Rain; Reflectivity; Remote sensing; Soil; Spatial resolution; Vegetation mapping; Amazonia; Forestry; Green-Up; Hyperion; Linear Spectral Mixing Model;
Conference_Titel :
Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009
Conference_Location :
Cape Town
Print_ISBN :
978-1-4244-3394-0
Electronic_ISBN :
978-1-4244-3395-7
DOI :
10.1109/IGARSS.2009.5417552