We evaluated the fire severity and recovery process of the Latroon dry forest in Jordan following the 2003 fire. A series of multi-temporal Landsat-ETM + data and the delta normalized burn ratio (dNBR) were used to map the fire severity immediately following the fire and 1,5,9,13 and 17 years after. In addition, combined field morpho-physiological measurements, unmanned aerial vehicle (UAV) were also used in 2020 to assess the forest recovery. Landsat-dNBR images revealed that about 65% of the forest was burned in 2003. In 2020, about 90% of the burned area recovered to condition before fire. UAV means were similar to ground measurement data across the severity classes and over the tested species. Landsat-dNBR images showed that most moderate and highly severe burned area in 2003 had recovered in 2020 but ground measurements showed that the severely burned area trees were significantly shorter (p < 0.001) than those from the moderate severity across the studied species. Therefore, Landsat-dNBR did not detect tree height changes. While UAV can potentially estimate the tree height, Landsat-ETM+ (near-infrared, chlorophyll; shortwave-infrared, water status) hold promise for estimating the physiology of the canopy. Overall, different remote sensing levels are required to track different kinds of changes in the recovered forests.

我们评估了 2003 年火灾后约旦 Latroon 干旱森林的火灾严重程度和恢复过程。一系列多时相 Landsat-ETM + 数据和 delta 归一化燃烧比 (dNBR) 用于绘制火灾后立即以及 1、5、9、13 和 17 年后的火灾严重程度。此外，2020 年还使用联合野外形态生理测量、无人机 (UAV) 来评估森林恢复情况。Landsat-dNBR 图像显示，2003 年约有 65% 的森林被烧毁。2020 年，约 90% 的烧毁面积恢复到火灾前的状态。无人机平均值与严重等级和测试物种的地面测量数据相似。Landsat-dNBR 图像显示，2003 年的大多数中度和高度严重烧毁区域已在 2020 年恢复，但地面测量显示，在所研究物种中，严重烧毁区域的树木明显短于中等严重程度的树木（p < 0.001）。因此，Landsat-dNBR 没有检测到树高变化。虽然无人机可以潜在地估计树高，但 Landsat-ETM+（近红外，叶绿素；短波红外，水状态）有望用于估计树冠的生理机能。总体而言，需要不同的遥感水平来跟踪恢复森林的不同类型的变化。虽然无人机可以潜在地估计树高，但 Landsat-ETM+（近红外，叶绿素；短波红外，水状态）有望用于估计树冠的生理机能。总体而言，需要不同的遥感水平来跟踪恢复森林的不同类型的变化。虽然 UAV 可以潜在地估计树高，但 Landsat-ETM+（近红外，叶绿素；短波红外，水状态）有望用于估计树冠的生理机能。总体而言，需要不同的遥感水平来跟踪恢复森林的不同类型的变化。