Food flow data provide unique insights into the debates surrounding the sustainability of land based production and consumption at multiple scales. Trade flows disguise the spatial correspondence of production and consumption and make their connection to land difficult. Two key components of this spatial disjuncture are land use displacement and economic regional decoupling. By displacing the environmental impact associated with food production from one region to another, environmental trajectories can falsely appear to be sustainable at a particular site or scale. When regional coupling is strong, peripheral areas where land based production occurs are strongly linked and proximate to consumption centers, and the environmental impact of production activities is visible. When food flows occur over longer distances, regional coupling weakens, and environmental impact is frequently overlooked. In this study, we present an analysis of a locally collected food flow dataset containing agricultural and livestock products transported to and from counties in Quintana Roo (QRoo). QRoo is an extensively forested border state in southeast Mexico, which was fully colonized by the state and non-native settlers only in the last century and now is home to some of the major tourist destinations. To approximate land displacement and regional decoupling, we decompose flows to and from QRoo by (1) direction; (2) product types and; (3) scale. Results indicate that QRoo is predominantly a consumer state: incoming flows outnumber outgoing flows by a factor of six, while exports are few, specialized, and with varied geographic reach (Yucatan, south and central Mexico, USA). Imports come predominantly from central Mexico. Local production in QRoo accounts for a small portion of its total consumption. In combining both subsets of agricultural and livestock products, we found that in most years, land consumption requirements were above 100% of the available land not under conservation in QRoo, suggesting unsustainable rates of land consumption in a ´business as usual´ scenario. We found evidence of economic regional decoupling at the state level. Full article

Precipitation and temperature enact variable influences on vegetation, impacting the type and condition of land cover, as well as the assessment of change over broad landscapes. Separating the influence of vegetative variability independent and discrete land cover change remains a major challenge to landscape change assessments. The heterogeneous Lerma-Chapala-Santiago watershed of central Mexico exemplifies both natural and anthropogenic forces enacting variability and change on the landscape. This study employed a time series of Enhanced Vegetation Index (EVI) composites from the Moderate Resolution Imaging Spectoradiometer (MODIS) for 2001–2007 and per-pixel multiple linear regressions in order to model changes in EVI as a function of precipitation, temperature, and elevation. Over the seven-year period, 59.1% of the variability in EVI was explained by variability in the independent variables, with highest model performance among changing and heterogeneous land cover types, while intact forest cover demonstrated the greatest resistance to changes in temperature and precipitation. Model results were compared to an independent change uncertainty assessment, and selected regional samples of change confusion and natural variability give insight to common problems afflicting land change analyses. Full article

A 30-year series of global monthly Normalized Difference Vegetation Index (NDVI) imagery derived from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g archive was analyzed for the presence of trends in changing seasonality. Using the Seasonal Trend Analysis (STA) procedure, over half (56.30%) of land surfaces were found to exhibit significant trends. Almost half (46.10%) of the significant trends belonged to three classes of seasonal trends (or changes). Class 1 consisted of areas that experienced a uniform increase in NDVI throughout the year, and was primarily associated with forested areas, particularly broadleaf forests. Class 2 consisted of areas experiencing an increase in the amplitude of the annual seasonal signal whereby increases in NDVI in the green season were balanced by decreases in the brown season. These areas were found primarily in grassland and shrubland regions. Class 3 was found primarily in the Taiga and Tundra biomes and exhibited increases in the annual summer peak in NDVI. While no single attribution of cause could be determined for each of these classes, it was evident that they are primarily found in natural areas (as opposed to anthropogenic land cover conversions) and that they are consistent with climate-related ameliorations of growing conditions during the study period. Full article