5 th International Conference on Cartography and GIS June 15-20, 2014, Riviera, Bulgaria 756 Estimation and mapping of forest fire danger by action of the focused sunlight in geographical information system Dr. Nikolay V. Baranovskiy, Elena P. Yankovich National Research Tomsk Polytechnic University 634050, Russian Federation, Tomsk, Lenin av., 30 Ph.: +7-913-874-22-37, e-mail: firedanger@yandex.ru, e-mail: yankovich@tpu.ru This study presents development of algorithms and the corresponding software for estimating and mapping forest fire danger caused by focused sunlight. The development includes an extension of existing ArcGIS system with several valuable options for spatial modeling. Our extended version of ArcGIS system involves additional options for representing both numerically and visually a potential impact of focused sunlight on the forest fire danger. Performance of the developed algorithms is illustrated for a representative case (~10x10km 2 , Siberian region) using datasets obtained from conventional inventory reports. INTRODUCTION Modern information technologies offer valuable capabilities for estimating and monitoring of forest fire danger (FFD) [1] for different model setups [2] and environmental conditions [3]. Therefore, these technologies are highly relevant for analyzing large datasets collected with support from both private and federal forestry agencies for representing diverse species, regions and sites. The Russian Federal Forestry Agency is one of these agencies. The collected datasets with several levels of complexity are based on primary inventory unit (PIU) information [4]. Such information is important for characterizing local conditions and, thus for planning forest management activities and applications [5], including estimation of FFD associated with focused sunlight [6]. The main objective of this study is development of algorithms and the corresponding software for estimating and mapping FFD caused by focused sunlight using datasets from available inventory reports. FOREST FIRE DANGER AND ITS ESTIMATION Natural [7,8] and anthropogenic [9] factors are responsible for forest fires, and a large effort has been exerted over the past several decades for understanding these factors and for preventing and predicting forest fires. In particular, several forecasting schemes have been suggested for estimating probability of FFD [10-12]. However, it is still unclear how to assess accurately this probability at different temporal and spatial scales [1]. The need in such assessment has motivated development of advanced models [13,14], and one of these models estimates probability of FFD associated with sunlight focused by large drops of resin of coniferous trees, glass containers and their splinters [15]. For a given area, forest fuel layer flammability (relative to the focused sunlight) depends largely on fuel type and moisture content. For example, forest fires do not coincide with “unfavorable” conditions when fuel moisture content is high due to the vicinity of a given area to substantial water supply (e.g., wet weather conditions, near-water location). Fires also do not occur largely in valley bottoms and coniferous forests. In contrast, areas with conifer forests exhibit the highest probabilities of wildfire. Therefore, regions, which can be described as “Floodland/Wetland/Water Land Cover”, “Valley Bottom”, or “Coniferous Forest”, are less fire-prone than those identified as “Conifer Forests”. The likelihood of forest fires tends to increase with the forest growing (old-growth conifer forest versus young-growth conifer forest). To illustrate selection of the most fire-prone site within a study area, let us consider a schematic diagram (Fig. 1) with identified eight types of forest- and land-covers: (1) forest clear-cuts near road (label “R”), (2) water land cover (label “W”), (3) wetland (label “B”), (4) valley bottom (label “L”), (5) coniferous forest (label “D”), (6) mixed forest (label “M”), (7) young-growth conifer forest (label “CY”) and (8) old-growth conifer forest (label “CO”). The selection includes a number of successive screenings of sites with small and moderate probabilities of forest fire (Fig. 1).