Locally adaptive, spatially explicit projection of US population for 2030 and 2050.

Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Building on the spatial interpolation technique previously developed for high-resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically informed spatial distribution of projected population of the contiguous United States for 2030 and 2050, depicting one of many possible population futures. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection model departs from these by accounting for multiple components that affect population distribution. Modeled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the US Census's projection methodology, with the US Census's official projection as the benchmark. Applications of our model include incorporating multiple various scenario-driven events to produce a range of spatially explicit population futures for suitability modeling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.

Hyperspectral agricultural mapping using support vector machine-based endmember extraction (SVM-BEE).

Extracting endmembers from remotely-sensed images of vegetated areas can present difficulties. In this research, we applied a recently-developed endmember-extraction algorithm based on Support Vector Machines to the problem of semi-autonomous estimation of vegetation endmembers from a hyperspectral image. This algorithm, referred to as Support Vector Machine-Based Endmember Extraction (SVM-BEE), accurately and rapidly yields a computed representation of hyperspectral data that can accommodate multiple distributions. The number of distributions is identified without prior knowledge, based upon this representation. Prior work established that SVM-BEE is robustly noise-tolerant and can semi-automatically estimate endmembers; synthetic data and a geologic scene were previously analyzed. Here we compared the efficacies of SVM-BEE, N-FINDR, and SMACC algorithms in extracting endmembers from a real, predominantly-agricultural scene. SVM-BEE estimated vegetation and other endmembers for all classes in the image, which N-FINDR and SMACC failed to do. SVM-BEE was consistent in the endmembers that it estimated across replicate trials. Spectral angle mapper (SAM) classifications based on SVM-BEE-estimated endmembers were significantly more accurate compared with those based on N-FINDR- and (in general) SMACC-endmembers. Linear spectral unmixing accrued overall accuracies similar to those of SAM.