A new information fusion method for land-use classification using high resolution satellite imagery

Satellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. In this study, a new postprocessing information fusion algorithm for the extraction and representation of land-use information based on high-resolution satellite imagery is presented. This approach can produce land-use maps with sharp interregional boundaries and homogeneous regions. The proposed approach is conducted in five steps. First, a GIS layer - ATKIS data - was used to generate two coarse homogeneous regions, i.e. urban and rural areas. Second, a thematic (class) map was generated by use of a hybrid spectral classifier combining Gaussian Maximum Likelihood algorithm (GML) and ISODATA classifier. Third, a probabilistic relaxation algorithm was performed on the thematic map, resulting in a smoothed thematic map. Fourth, edge detection and edge thinning techniques were used to generate a contour map with pixel-width interclass boundaries. Fifth, the contour map was superimposed on the thematic map by use of a region-growing algorithm with the contour map and the smoothed thematic map as two constraints. For the operation of the proposed method, a software package is developed using programming language C. This software package comprises the GML algorithm, a probabilistic relaxation algorithm, TBL edge detector, an edge thresholding algorithm, a fast parallel thinning algorithm, and a region-growing information fusion algorithm. The county of Landau of the State Rheinland-Pfalz, Germany was selected as a test site. The high-resolution IRS-1C imagery was used as the principal input data.

Land use policies and practices for reducing vulnerability in rural Tajikistan

Tajikistan, with 93% of its surface area taken up by mountains and 65% of its labor force employed in agriculture, is judged to be highly vulnerable to risks, including climate change risks and food insecurity risks. The article examines a set of land use policies and practices that can be used to mitigate the vulnerability of Tajikistan’s large rural population, primarily by increasing family incomes. Empirical evidence from Tajikistan and other CIS countries suggests that families with more land and higher commercialization earn higher incomes and achieve higher well-being. The recommended policy measures that are likely to increase rural family incomes accordingly advocate expansion of smallholder farms, improvement of livestock productivity, increase of farm commercialization through improvement of farm services, and greater diversification of both income sources and the product mix. The analysis relies for supporting evidence on official statistics and recent farm surveys. Examples from local initiatives promoting sustainable land management practices and demonstrating the implementation of the proposed policy measures are presented.