土壤烧伤严重程度的数字土壤测绘

Stewart G. Wilson, Samuel Prentice
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引用次数: 0

摘要

火灾会改变土壤的水文特性,导致灾难性泥石流和火灾后洪水泛滥的风险增加。因此,美国联邦机构通过直接土壤观测和调整烧毁区域反射率栅格来绘制土壤烧毁严重程度(SBS)图。在内华达山脉烧毁了 154,000 公顷土地的克里克大火中,我们开发了一种独特的数字土壤制图 (DSM) 应用,用于绘制 SBS 图。我们利用 169 个基于地面的 SBS 观测数据,结合土壤形成因素、火灾前燃料状况和火灾对植被影响的栅格代用指标,使用随机森林算法建立了土壤燃烧严重程度的数字土壤测绘模型(DSMSBS),并将 DSMSBS 地图与已建立的 SBS 地图进行了比较。DSMSBS 模型的交叉验证准确率为 48%。既有技术在实地观测和像素之间的一致性为 46%。然而,由于既定技术是人工操作的,因此无法通过交叉验证与 DSMSBS 模型进行比较。我们制作了 SBS 类别不确定性图,该图显示实地观测点周围的预测概率较高,而远离实地观测点的预测概率较低。SBS 预测概率可帮助火后评估团队确定样本的优先次序。我们的报告显示,与现有技术相比,被归类为高和中等 SBS 的面积增加了 107 平方公里。我们的结论是,将基于土壤形成因子的绘图与植被燃烧严重程度绘图相结合,可以改善 SBS 绘图。这表明 SBS 测绘已从验证遥感反射图像转向定量土壤景观模型,该模型结合了火灾和土壤信息,可直接预测 SBS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digital soil mapping of soil burn severity

Fire alters soil hydrologic properties leading to increased risk of catastrophic debris flows and post-fire flooding. As a result, US federal agencies map soil burn severity (SBS) via direct soil observation and adjustment of rasters of burned area reflectance. We developed a unique application of digital soil mapping (DSM) to map SBS in the Creek Fire which burned 154,000 ha in the Sierra Nevada. We utilized 169 ground-based observations of SBS in combination with raster proxies of soil forming factors, pre-fire fuel conditions, and fire effects to vegetation to build a digital soil mapping model of soil burn severity (DSMSBS) using a random forest algorithm and compared the DSMSBS map to the established SBS map. The DSMSBS model had a cross-validation accuracy of 48%. The established technique had 46% agreement between field observations and pixels. However, since the established technique is manual, it could not be compared to the DSMSBS model via cross-validation. We produced SBS class uncertainty maps, which showed high prediction probabilities around field observations, and low probabilities away from field observations. SBS prediction probabilities could aid post-fire assessment teams with sample prioritization. We report 107 km2 more area classified as high and moderate SBS compared to the established technique. We conclude that blending soil forming factors based mapping and vegetation burn severity mapping can improve SBS mapping. This represents a shift in SBS mapping away from validating remotely sensed reflectance imagery and toward a quantitative soil landscape model, which incorporates both fire and soils information to directly predict SBS.

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