Facultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Río Papaloapan y Blvd, Durango S/N Col. Valle del Sur, 34120 Durango, Mexico.
Facultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Río Papaloapan y Blvd, Durango S/N Col. Valle del Sur, 34120 Durango, Mexico.
Sci Total Environ. 2020 May 20;718:137313. doi: 10.1016/j.scitotenv.2020.137313. Epub 2020 Feb 15.
Identifying the relative importance of human and environmental drivers on fire occurrence in different regions and scales is critical for a sound fire management. Nevertheless, studies analyzing fire occurrence spatial patterns at multiple scales, covering the regional to national levels at multiple spatial resolutions, both in the fire occurrence drivers and in fire density, are very scarce. Furthermore, there is a scarcity of studies that analyze the spatial stationarity in the relationships of fire occurrence and its drivers at multiple scales. The current study aimed at predicting the spatial patterns of fire occurrence at regional and national levels in Mexico, utilizing geographically weighted regression (GWR) to predict fire density, calculated with two different approaches -regular grid density and kernel density - at spatial resolutions from 5 to 50 km, both in the dependent and in the independent human and environmental candidate variables. A better performance of GWR, both in goodness of fit and residual correlation reduction, was observed for prediction of kernel density as opposed to regular grid density. Our study is, to our best knowledge, the first study utilizing GWR to predict fire kernel density, and the first study to utilize GWR considering multiple scales, both in the dependent and independent variables. GWR models goodness of fit increased with fire kernel density search radius (bandwidths), but saturation in predictive capacity was apparent at 15-20 km for most regions. This suggests that this scale has a good potential for operational use in fire prevention and suppression decision-making as a compromise between predictive capability and spatial detail in fire occurrence predictions. This result might be a consequence of the specific spatial patterns of fire occurrence in Mexico and should be analyzed in future studies replicating this methodology elsewhere.
确定人类和环境驱动因素对不同地区和尺度火灾发生的相对重要性对于健全的火灾管理至关重要。然而，分析多个尺度火灾发生空间格局的研究，包括在火灾发生驱动因素和火灾密度方面，涵盖区域到国家各级和多个空间分辨率的研究非常稀缺。此外，分析火灾发生及其驱动因素在多个尺度上的空间稳定性的研究也很少。本研究旨在利用地理加权回归（GWR）预测墨西哥区域和国家各级的火灾发生空间格局，以预测两种不同方法（规则网格密度和核密度）计算的火灾密度，空间分辨率从 5 到 50 公里，在依赖和独立的人类和环境候选变量中。与规则网格密度相比，GWR 对核密度的预测表现出更好的拟合优度和残差相关性降低。据我们所知，我们的研究是首次利用 GWR 预测火灾核密度的研究，也是首次利用 GWR 考虑多个尺度的研究，在依赖和独立变量中均如此。GWR 模型的拟合优度随着火灾核密度搜索半径（带宽）的增加而增加，但在大多数地区，在 15-20 公里处预测能力达到饱和。这表明，该尺度在火灾预防和抑制决策中具有良好的操作潜力，是在火灾发生预测的预测能力和空间细节之间的折衷。这一结果可能是墨西哥火灾发生特定空间格局的结果，应在其他地方复制该方法的未来研究中进行分析。
