Classifying the land surface into climate types provides means of diagnosing relations between Earth's physical and biological systems and the climate. Global climate classifications are also used to visualize climate change.Clustering climate datasets provides a natural approach to climate classification, but the rule-based Köppen-Geiger classification (KGC) is the one most widely used. Here, a comprehensive approach to the clusteringbased classification of climates is presented. Local climate is defined as a multivariate time series of mean monthly climatic variables and the authors propose to use dynamic time warping (DTW) as a measure of dissimilarity between local climates. Also discussed are the choice of climatic variables, the importance of their proper normalization, and the advantage of using distance-based clustering algorithms. Using the WorldClim global climate dataset and different combinations of clustering parameters, 32 different clustering-based classifications are calculated. These classifications are compared between themselves and to the KGC using the information-theoretic V measure. It is found that the best classifications are obtained using three climate variables (temperature, precipitation, and temperature range), a data normalization that takes into account the skewed distribution of precipitation values, and the partitioning around medoids clustering algorithm. Two such classifications are compared in detail between each other and to the KGC. About half of the climate types found by clustering can be matched to the familiar KGC classes, but the rest differ in their climatic character and spatial distribution. Finally, it is demonstrated that clustering-based classification results in climate types that are internally more homogeneous and externally more distinct than climate types in the KGC.

Monitoring global land cover changes is important because of concerns about their impact on environment and climate. The release by the European Space Agency (ESA) of a set of worldwide annual land cover maps covering the 1992-2015 period makes possible a quantitative assessment of land change on the global scale. While ESA land cover mapping effort was motivated by the need to better characterize global and regional carbon cycles, the dataset may benefit a broad range of disciplines. To facilitate utilization of ESA maps for broad-scale problems in landscape ecology and environmental studies, we have constructed a GIS-based vector database of mesoscale landscapespatterns of land cover categories in 9km × 9km tracts of land. First, we reprojected ESA maps to the Fuller projection to assure that each landscape in the database has approximately the same size and shape so the patterns of landscapes at different locations can be compared. Second, we calculated landscape attributes including its compositions in 1992 and 2015, magnitude of pattern change, categories transition matrix for detailed characterization of change, fractional abundances of plant functional types (PFTs) in 1992 and 2015, and change trend type -a simple, overall descriptor of the character of landscape change. Combining change trends and change magnitude information we constructed a global, thematic map of land change; this map offers a visualization of what, where, and to what degree has changed between 1992 and 2015. The database is SQL searchable and supports all GIS vector operations. Using change magnitude attribute we calculated that only 22% of total landmass experienced significant landscape change during the 1992-2015 period, but that change zone accounted for 80% of all pixel-based transitions. Dominant land cover transitions were forest → agriculture followed by agriculture → forest. Using PFTs attributes to calculate global aggregation of gross and net changes for major PFTs yielded results in agreement with other recent estimates.

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The United States is increasingly becoming a multi-racial society. To understand multiple consequences of this overall trend to our neighborhoods we need a methodology capable of spatio-temporal analysis of racial diversity at the local level but also across the entire U.S. Furthermore, such methodology should be accessible to stakeholders ranging from analysts to decision makers. In this paper we present a comprehensive framework for visualizing and analyzing diversity data that fulfills such requirements. The first component of our framework is a U.S.-wide, multi-year database of race sub-population grids which is freely available for download. These 30 m resolution grids have being developed using dasymetric modeling and are available for 1990-2000-2010. We summarize numerous advantages of gridded population data over commonly used Census tract-aggregated data. Using these grids frees analysts from constructing their own and allows them to focus on diversity analysis. The second component of our framework is a set of U.S.-wide, multi-year diversity maps at 30 m resolution. A diversity map is our product that classifies the gridded population into 39 communities based on their degrees of diversity, dominant race, and population density. It provides spatial information on diversity in a single, easy-to-understand map that can be utilized by analysts and end users alike. Maps based on subsequent Censuses provide information about spatio-temporal dynamics of diversity. Diversity maps are accessible through the GeoWeb application SocScape (http://sil.uc.edu/webapps/socscape_usa/) for an immediate online exploration. The third component of our framework is a proposal to quantitatively analyze diversity maps using a set of landscape metrics. Because of its form, a grid-based diversity map could be thought of as a diversity “landscape” and analyzed quantitatively using landscape metrics. We give a brief summary of most pertinent metrics and demonstrate how they can be applied to diversity maps.

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Variation in tree stem form depends on species, age, site conditions, etc. Stem taper models that estimate stem diameter at any height and volume should comply with this complexity. In the paper, we propose new methods taking into account both unbiased estimates and stem variability: (i) an expert model based on an artificial neural network (ANN) and (ii) a statistical model built using a regression tree (REG). We used the variable-exponent taper equation (STE) as a reference for these two models. Input data contain information about 2856 trees representing eight dominant forest-forming tree species in Poland (birch, beech, oak, fir, larch, alder, pine, and spruce). The trees were selected across stands varied in terms of age and site conditions. Based on the data, we built ANN and REG models and calculated both stem taper and tree volumes. The results show that ANN is a universal approach that offers the most precise estimation of stem diameter at a particular stem height for different tree species. The results for alder are an exception. In this case, the REG model performs slightly better than ANN. In terms of volume prediction, the ANN model provides the most accurate predictions for coniferous and beech. In general, flexibility and predictive performance of the ANN are better than REG and reference the STE equation.