High Resolution Climate Spatial Analysis of European Winegrowing Regions

IXe International Terroirs Congress 2012

Benjamin BOIS, Aurélie BLAIS, Marco MORIONDO, and Gregory V. JONES

“Climate strongly affects the geographical distribution of grape varieties, grapevine cultivation techniques and wine organoleptic properties. The current study aims at comparing the climatic features of European winegrowing regions. A geodatabase of 260 wine producing areas within 18 countries of the European Community was first established by means of maps collected from various sources (e.g. atlases and national wine and vine services). Within the 247 of the 260 initially delimited regions, areas actually planted with vine were identified by means of the Corine Land Cover database, for a total of 6 million of hectares. Each of the 1 km resolution pixels of the WorldClim 1950-2000 monthly climatic database located within these planted areas were used to calculate agroclimatic indices. The Huglin index, the Cool night index and the Dryness index, as described by the Multicriteria Climatic Classification system, as well as a winter freeze risk index, a spring frost risk index and a heat stress index were calculated.

CLARA cluster group representations of the WorldCLim pixels located within winegrowing regions

CLARA cluster group representations of the WorldCLim pixels located within winegrowing regions (Axes correspond to the two first principal components of a PCA performed on the agroclimatic indices).

“The use of a clustering algorithm (CLARA) with each of these 1 km resolution gridded indices resulted in the identification of six climate types: (1) sub-humid temperate, (2) sub-humid cool with very cool nights and high spring frost risk, (3) moderately dry and temperate with cool nights, (4) dry and temperate warm with temperate nights, (5) sub-humid temperate with strong frost risks, and (6) very dry and hot, with cool nights climates. Each of the 247 winegrowing regions was classified according to the type of climate that covers the largest part of its territory. Despite the clustering, the type 4 climate still exhibits a large diversity of climatic characteristics. It is located mainly within winegrowing regions located close to the Mediterranean Sea. To our knowledge the current work is the largest spatial climate analysis of winegrowing regions that have been performed so far.”

Using MODIS-NDVI for the Modeling of Post-Wildfire Vegetation Response as a Function of Environmental Conditions and Pre-Fire Restoration Treatments

Remote Sensing, 2012, 4(3), 598-621

Jose Raul Romo Leon, Willem J.D. van Leeuwen, and Grant M. Casady

“Post-fire vegetation response is influenced by the interaction of natural and anthropogenic factors such as topography, climate, vegetation type and restoration practices. Previous research has analyzed the relationship of some of these factors to vegetation response, but few have taken into account the effects of pre-fire restoration practices. We selected three wildfires that occurred in Bandelier National Monument (New Mexico, USA) between 1999 and 2007 and three adjacent unburned control areas. We used interannual trends in the Normalized Difference Vegetation Index (NDVI) time series data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess vegetation response, which we define as the average potential photosynthetic activity through the summer monsoon. Topography, fire severity and restoration treatment were obtained and used to explain post-fire vegetation response.

Monsoon trend for each pixel within the fire and the reference areas were computed.

Monsoon trend for each pixel within the fire and the reference areas were computed. For Frijoles Canyon (A) the trend is calculated from 2008 to 2010, for Mid Elevation Mesas (B) from 2000 to 2010, and for Capulin (C) from 2006 to 2010.

“We applied parametric (Multiple Linear Regressions-MLR) and non-parametric tests (Classification and Regression Trees-CART) to analyze effects of fire severity, terrain and pre-fire restoration treatments (variable used in CART) on post-fire vegetation response. MLR results showed strong relationships between vegetation response and environmental factors (p < 0.1), however the explanatory factors changed among treatments. CART results showed that beside fire severity and topography, pre-fire treatments strongly impact post-fire vegetation response. Results for these three fires show that pre-fire restoration conditions along with local environmental factors constitute key processes that modify post-fire vegetation response.”