Quality assessment of a landslide inventory map and its application to land‐use planning. A case study in the Northern Apennines (Emilia‐Romagna region, Italy)

Landslide hazard and risk are growing as a consequence of climate change and demographic pressure. Land‐use planning represents a powerful tool to manage this socio‐economic problem and build sustainable and landslide resilient communities. Landslide inventory maps are a cornerstone of land‐use planning and, consequently, their quality assessment represents a burning issue. This work aimed to define the quality parameters of a landslide inventory and assess its spatial and temporal accuracy with regard to its possible applications to land‐use planning. In this sense, I proceeded according to a two‐steps approach. An overall assessment of the accuracy of data geographic positioning was performed on four case study sites located in the Italian Northern Apennines. The quantification of the overall spatial and temporal accuracy, instead, focused on the Dorgola Valley (Province of Reggio Emilia). The assessment of spatial accuracy involved a comparison between remotely sensed and field survey data, as well as an innovative fuzzylike analysis of a multi‐temporal landslide inventory map. Conversely, long‐ and short‐term landslide temporal persistence was appraised over a period of 60 years with the aid of 18 remotely sensed image sets. These results were eventually compared with the current Territorial Plan for Provincial Coordination (PTCP) of the Province of Reggio Emilia. The outcome of this work suggested that geomorphologically detected and mapped landslides are a significant approximation of a more complex reality. In order to convey to the end‐users this intrinsic uncertainty, a new form of cartographic representation is needed. In this sense, a fuzzy raster landslide map may be an option. With regard to land‐use planning, landslide inventory maps, if appropriately updated, confirmed to be essential decision‐support tools. This research, however, proved that their spatial and temporal uncertainty discourages any direct use as zoning maps, especially when zoning itself is associated to statutory or advisory regulations.

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.