Some context: I'm working with deforestation areas. An research institute provides time series data of deforested areas (in km²) in two ways: monthly and annual. Each one of them is obtained by a different methodology (like different satellites, image resolutions, etc). So, in my study, I selected the years from 2009-2016, obtaining 8 observations (one for year 2009, one for 2010, ...) in the annual series and 96 in the monthly series (jan-2009, feb-2009, ..., dec-2016). Since they're different methodologies, the monthly one doesn't provide the exact number of the annual series if we add up them.
My question is: Are there any ways/tests to show that the annual time series can share the same information (like periodicity/seasonality) as the monthly one?
What I have found so far:
a. I started studying time series recently using the R software and found the DTW (Dynamic Time Warping) package. Is searching for similarities using the short series as query and the other as a reference a good idea in this case?
b. I also found some dimensionality reductions like Piecewise regression and Piecewise Aggregate Approximation so I can reduce the monthly time series. Can this help with the comparison?
c. ARMA models were discussed here, but doing that was not indicated.
Thanks in advance for any help provided.