Superpixel based sea ice segmentation with high-resolution optical images: analysis and evaluation.

By grouping pixels with visual coherence, superpixel algorithms provide an alternative representation of regular pixel grid for precise and efficient image segmentation. In this paper, a multi-stage model is used for sea ice segmentation from the high-resolution optical imagery, including the pre-processing to enhance the image contrast and suppress the noise, superpixel generation and classification, and post-processing to refine the segmented results. Four superpixel algorithms are evaluated within the framework, where the high-resolution imagery of the Chukchi sea is used for validation. Quantitative evaluation in terms of the segmentation quality and floe size distribution, and visual comparison for several selected regions of interest are presented. Overall, the model with TS-SLIC yields the best results, with a segmentation accuracy of 98.19% on average and adhering to the ice edges well

GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing

Volunteer cotton plants germinate and grow at unwanted locations like transport routes and can serve as hosts for a harmful cotton pests called cotton boll weevils. The main objective of this study was to develop a geographic information system (GIS) framework to efficiently locate volunteer cotton plants in the cotton production regions in southern Texas, thus reducing time and economic cost for their removal. A GIS network analysis tool was applied to estimate the most likely routes for cotton transportation, and a GIS model was created to identify and visualize potential areas of volunteer cotton growth. The GIS model indicated that, of the 31 counties in southern Texas that may have habitat for volunteer cotton, Hidalgo, Cameron, Nueces, and San Patricio are the counties at the greatest risk. Moreover, a method based on unmanned aerial vehicle (UAV) remote sensing was proposed to detect the precise locations of volunteer cotton plants in potential areas for their subsequent removal. In this study, a UAV was used to scan limited samples of potential volunteer cotton growth areas identified with the GIS model. The results indicated that UAV remote sensing coupled with the proposed image analysis methods could accurately identify the precise locations of volunteer cotton and could potentially assist in the elimination of volunteer cotton along transport routes

Analyzing the Population Density Pattern in China with a GIS-Automated Regionalization Method: Hu Line Revisited

The famous “Hu Line”, proposed by Hu Huanyong in 1935, divided China into two regions of comparable area sizes that drastically differ in population: about 4% in the northwest part and 96% in the southeast. However, the Hu Line was proposed largely by visual examination of hand-made maps and arduous experiments of numerous configurations, and has been subject to criticism of lack of scientific rigor and accuracy. Furthermore, it has been over eight decades since the Hu Line was proposed. During the time, China sustained several major man-made and natural disasters (e.g., the World War II, the subsequent Civil War and the 1958-62 Great Famine), and also experienced some major government-sponsored migrations, economic growth and unprecedented urbanization. It is necessary to revisit the (in) stability of Hu Line. By using a GIS-automated regionalization method, termed REDCAP (Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning), this study re-visits the Hu Line in three aspects. First, by reconstructing the demarcation line based on the latest census of 2010 county-level population by REDCAP, this study largely validates and refines the classic Hu Line. Secondly, this research also seeks to uncover the underlying physical environment factors that shape such a contrast by proposing a habitation environment suitability index (HESI) model. In the third part, this study examines the population density change and disparity change over time by using all the six censuses (1953, 1964, 1982, 1990, 2000, and 2010) since the founding of the People’s Republic of China. This study advances the methodological rigor in defining the Hu Line, solidifies the inherent connection between physical environment and population settlement, and strengthens the findings by extending the analysis across time epochs