Del Giudice Liliana, Arca Bachisio, Scarpa Carla, Pellizzaro Grazia, Duce Pierpaolo, Salis Michele
National Research Council of Italy, Institute of BioEconomy (CNR IBE), Sassari, Italy.
Data Brief. 2021 Sep 12;38:107355. doi: 10.1016/j.dib.2021.107355. eCollection 2021 Oct.
We applied a geographical information system analysis to reclassify and characterize anthropic buildings based on structure density and area covered, land type, and proximity to wildlands able to originate intense wildfires and spot fires. The methodology was carried out in the 93,000 km Italy-France Maritime cooperation area (which includes the Regions of Sardinia, Tuscany, and Liguria, in Italy, and Corsica, and Provence-Alpes-Côte d'Azur, in France). We produced a 100-m raster dataset that characterizes and maps medium-high anthropic presence, wildland-anthropic areas, dispersed anthropic areas, and non-anthropic zones, in the whole study area. The study allowed to highlight variations in wildland anthropic interfaces among and within Regions as a function of anthropic presence and types and the surrounding wildlands. The spatial dataset provided with this work represents a valuable contribution to support landscape and urban planning and inform strategies to limit wildfire impacts nearby anthropic areas.
我们应用地理信息系统分析，根据建筑结构密度、占地面积、土地类型以及与可能引发强烈野火和飞火的荒地的距离，对人工建筑进行重新分类和特征描述。该方法在意大利 - 法国93,000平方公里的海洋合作区域（包括意大利的撒丁岛、托斯卡纳和利古里亚大区，以及法国的科西嘉岛和普罗旺斯 - 阿尔卑斯 - 蓝色海岸大区）实施。我们生成了一个100米分辨率的栅格数据集，该数据集对整个研究区域内的中高人口密集区、荒地 - 人工区域、分散人工区域和非人工区域进行了特征描述和制图。该研究突出了不同大区之间以及大区内部荒地与人工区域界面的差异，这些差异是人口密集程度、类型以及周边荒地的函数。这项工作所提供的空间数据集对于支持景观和城市规划以及为限制人工区域附近野火影响的策略提供信息具有重要价值。
