I’m using ArcMap 10.3. I’m a student working on a research problem where I’m taking Landsat imagery (at 30-meters resolution) and regressing it against other datasets captured at different scales (up to about 1 km^2). I am then resampling everything down to 30 square meters prior to running the regression.
My ultimate goal is to understand vegetation dynamics, which is why I wanted to bring the whole analysis down to Landsat’s 30 square meters. Or is this thinking incorrect, ie, should I run the analysis at the scale of the coarsest dataset, being 1 km^2?
From what I’ve read- in particular, Hengl 2006 “Finding the right pixel size”, there is no “ideal” grid resolution and it is dependent on the question. Another paper I read, Tarnavsky 2007 “Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products” recommended choosing a pixel size to minimize the spatial variability between sensors. Yet other papers suggest statistical corrections in order to use multiscale data.
I can do the regression. My question is whether there are any standards for how to approach an analysis where all the input data is at a different scale, in my case, ranging from 30 square meters up to 1 km^2.