Free spherical image data for one full year at Harvard Forest!
A Raspberry pi based multi-spectral camera. A cost effective solution for vegetation monitoring.
We used a combination of historical (1958) aerial photography and contemporary remote sensing data to map long-term changes in the extent and structure of the tropical forest surrounding Yangambi (DR Congo) in the central Congo Basin.
Smartphone repeat imagery quantifies important phenological stages of winter wheat. Small scale phenology or disturbances are not captured by satellite remote sensing. Using smartphone imagery can improve crop modeling and insurance for small farmers.
We detail the procedure for the implementation of cameras on Integrated Carbon Observation System flux towers and how these images will help us understand the impact of leaf phenology and ecosystem function, distinguish changes in canopy structure from leaf physiology and at larger scales will assist in the validation of (future) remote sensing products.
We present the phenor r package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network, the Pan European Phenology Project, MODIS phenology combined with (global) retrospective and projected climate data.
We provide remote sensing validation expertise in support of product development.
We used a combination of historical (1958) aerial photography and contemporary remote sensing data to map long-term changes in the extent and structure of the tropical forest surrounding Yangambi (DR Congo) in the central Congo Basin.
We detail the protocol for the implementation of cameras on Integrated Carbon Observation System flux towers.
We provide protocol development and data processing expertise in support of this IFPRI led project to mitigate basic risk in smallholder farmer insurance.