In the middle of March just before leaving for UC Berkeley I attended a course on using remote sensing data to detect environmental change at the Department of Geography and Geology.The course offered a good chance to get more familiar with the increasing amount of satellite based products that gives us the birds eye view of our planets environmental state. To me, remote sensing is of special relevance when trying to explain some of the large-scale changes in bird population abundance. In my current work on European wide population dynamics of European songbirds a particular challenge is to find environmental variables that affect long-distance migrants. The remote sensing course provided a good opportunity to look more into the interrelation of some of the basic remotely sensed environmental variables.
European long-distance migrants mainly winter in various parts of Africa. However, we often have very little knowledge on when they go where. Therefore it is also hard to know what environmental conditions might matter for them, and the relative roles of conditions in Europe and Africa.
NDVI, satellite based measure of vegetation greenness has been widely used as an overall indicator of general environmental conditions, and I have been using it in my work on population dynamics. I was interested though, to see how precipitation might affect long-distance migrants. However, this is where it gets complicated. Because of the shift in the period of the rainy season within Africa, the crucial period of precipitation that determines winter conditions probably also varies within Africa.
To investigate this I did a lagged correlation between NDVI during winter and precipitation during different months of the year. This should give me a hint of what periods of the year I should focus on in different wintering areas of Africa. The results show marked differences between areas like Western and Southern Africa (see the figure).
I think this analysis will be a good basis to start relating population dynamics of long-distance migrants to variation in precipitation in Africa.