SOIL CARBON STOCK AND PARTICLE SIZE FRACTIONS IN THE CENTRAL AMAZON PREDICTED FROM REMOTELY SENSED RELIEF, MULTISPECTRAL AND RADAR DATA

Soil Carbon Stock and Particle Size Fractions in the Central Amazon Predicted from Remotely Sensed Relief, Multispectral and Radar Data

Soils from the remote areas of the Amazon Rainforest in Brazil are poorly mapped due to the presence of dense forest and lack of access routes.The use of covariates derived from multispectral and radar remote sensors allows mapping large areas and has the potential to improve the accuracy of soil attribute maps.The objectives of this study Supports

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Comparison between low-cost and traditional MEMS accelerometers: a case study from the M7.1 Darfield, New Zealand, aftershock deployment

Recent advances in micro-electro-mechanical systems (MEMS) sensing Supports and distributed computing techniques have enabled the development of low-cost, rapidly deployed dense seismic networks.The Quake-Catcher Network (QCN) uses triaxial MEMS accelerometers installed in homes and businesses to record moderate to large earthquakes.Real-time accel

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