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Heat requirements for grapevine varieties have been widely used to characterize potential growing regions for viticulture. One of the most common landscape classifications is based on the Winkler Index (WI) values, defined as the total summation of daily average air temperature above 10 °C from April 1 to October 31 in the Northern Hemisphere. Mapping WI is commonly performed by spatial interpolation of temperature data collected with weather station (WS) networks. However, in complex terrain such as the European Alps, these are usually irregularly and sparsely distributed or unavailable. This renders traditional geospatial interpolation approaches unreliable. As an alternative, thermal remote sensing data, which are intrinsically spatialized, can be used. The aims of this paper are the following: (1) to provide time series of WI-value maps from 2003 to 2010, by means of the Moderate-resolution Imaging Spectroradiometer (MODIS) land surface temperature (MOD11A1 and MYD11A1) daily data and (2) to calibrate them using ground truth data, collected by two WS networks, (3) leading to a classification of the study area into WI regions. WI values from WS data and from MODIS data exhibit a strong linear regression, close to the 1 : 1 prediction with an offset of 42.02 and a slope of 0.90. These coefficients have been used to calibrate the original map, before the following classification. The study area was found to have growing degree days that correspond to four Winkler Regions. The use of MODIS data represents a robust and straightforward method for selectively detecting areas suitable for grapevine varieties, particularly in regions with sparsely distributed WSs.

Aerial photographs provide the basis for developing indices of landscape composition and structure as sensitive measures of large-scale environmental change over relatively long periods of time. In view of this, proper image rectification is needed to enable geometrically unbiased application of landscape metrics in order to obtain meaningful results. It is also particularly important to provide researchers with image rectification tools within an open source environment, in order to: (i) guarantee free and robust tools for processing remote sensing data, (ii) facilitate customization, and (iii) provide useful support via forums and email lists. In this paper we provide a complete description of a robust and freely licensed toolchain for orthorectifying images, which is available in the open source software GRASS GIS. We will first sketch the theoretical background behind rectification and then illustrate the workflow of the orthorectification procedure in GRASS GIS.