This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities’ increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics.

Sudden short-term severe droughts have major impacts on ecosystem balance. Synoptic and replicable measurements from remotely sensed data are essential for calculating changes to land use/cover caused by severe drought conditions. In the US, Texas experienced a particularly severe drought in 2011, which adversely affected forest and grassland ecosystems in addition to $7.62 billion of agricultural loss. To assess the extent and severity of the drought we use satellite sensor data and image processing techniques to measure changes in land use/cover. Our methodology uses change vector analysis (CVA), the normalized difference vegetation index, the normalized difference moisture index, and three variables-brightness, greenness, and wetness-extracted from tasseled cap transforms (TCT). All are established techniques in remote sensing but have as yet been applied in combination to measure land use/cover changes affected by intense short-term drought conditions. Our objective is to calculate not only vegetation and bare soil indices, but also the intensity of change (magnitude) and the type of change (direction). For CVA direction, we include an improved methodology using the arctangent function based on two arguments, ATAN2 which produces results in all four possible quadrants, and complete characterization of all possible change directions. The three variables of TCT are applied to CVA magnitude and direction using vectors in three dimensions, resulting in eight change categories. Our results are based on Landsat TM sensor data for the years 2009, 2010 and 2011, which represent a short period of severe drought, above average precipitation, and severe drought respectively, for two study sites in Texas. Results indicate that land use/cover changes were affected by both an increase in precipitation in 2010 as well as a considerable decrease of precipitation in 2011 resulting in the devastating sudden drought.

Trumbo’s (1981) ideas on bivariate choropleth design have been underexplored and underutilized. He noted that effective map design (including color selection) is directly informed by the intended goal or use of the map (i.e., what questions might the map answer), and he identified three common spatial relationships that can be displayed by a bivariate choropleth: inverse relationships, a range of one variable within another, and direct relationships. Each is best suited to answering different map readers’ questions. Trumbo also suggested sample color palettes to focus the map reader’s attention on pertinent data. In consultation with Trumbo, we extended his ideas, first by creating focal models that illustrate his three spatial relationships. We then constructed sample maps to examine each of the focal models, and finally compared each model by mapping the same two data sets (of obesity and inactivity). We investigated the visual differences in each of the resulting maps, and asked spatial questions regarding the relationships between obesity and inactivity. Our work validates Trumbo’s ideas on bivariate choropleth map design, and we hope our focal models guide cartographers towards making color choices by linking their map purpose to the appropriate focal model.

Spatial indices measure the geometric arrangement of land use and land cover classes at various scales and are computationally adaptive with wavelet transform coefficients. Decision rules built on permutations of three spatial indices – energy, log energy and Shannon's diversity – are used to improve the accuracy of multi-resolution hierarchical wavelet-based classifications. Comparisons are made with classification results derived from other texture measures, as well as with classification results calculated from more conventional per-pixel techniques. The first decision rule that computes one distance for each class across all bands to select the shortest distance was the most effective decision rule, resulting in the highest overall classification accuracy using the energy spatial index.

In the United States, the Centers for Disease Control and Prevention (CDC) is the national agency that conducts and supports public health research and practice. Among the CDC’s many achievements is the development of a social vulnerability index (SVI) to aid planners and emergency responders when identifying vulnerable segments of the population, especially during natural hazard events. The index includes an overall social vulnerability ranking as well as four individual themes: socioeconomic, household composition & disability, ethnicity & language, and housing & transportation. This makes the SVI dataset multivariate, but it is typically viewed via maps that show one theme at a time. This paper explores a suite of cartographic techniques that can represent the SVI beyond the univariate view. Specifically, we recommend three techniques: (1) bivariate mapping to illustrate overall vulnerability and population density, (2) multivariate mapping using cartographic glyphs to disaggregate levels of the four vulnerability themes, and (3) visual analytics using Euler diagrams to depict overlap between the vulnerability themes. The CDC’s SVI, and by extension, vulnerability indices in other countries, can be viewed in a variety of cartographic forms that illustrate the location of vulnerable groups of society. Viewing data from various perspectives can facilitate the understanding and analysis of the growing amount and complexity of data.