High-severity wildfire in arid regions has caused ecological state change, transforming previously forested areas into shrublands. This dramatically alters the microclimatic conditions, which can exceed the climatic tolerance of tree seedlings, rendering the likelihood of returning post-wildlife landscapes to their previous state relatively low. Characterizing microclimatic variability across severely burned landscapes could allow for identifying locations where seedling survival is more likely. We used a combination of small unmanned aircraft system imagery, satellite data and in-situ microclimate data recordings, together with a machine learning approach, to model monthly near-ground minimum, mean and max temperature as well as relative humidity and vapor pressure deficit in a previously forested area, which is now dominated by two different shrub species. Spatially explicit models predicted recorded microclimate well (r = 0.73 to 0.97), and model projections highlight that at any given location in the hottest month, the solar buffering capacity of existing vegetation can alter the maximum temperature by ∼12 °C, increase relative humidity by ∼20% and reduced vapor pressure deficit by 0.3mbar relative to open areas. By harnessing these microclimate refugia, the success rate of reforestation efforts in post-wildfire landscapes could be substantially increased and mitigate seedlings from climate warming at local scales.

干旱地区的高强度野火导致生态状态发生变化，将以前的森林地区变成了灌木丛。这极大地改变了小气候条件，这可能会超过树苗的气候耐受性，从而使野生动物后的景观恢复到以前的状态的可能性相对较低。表征严重烧毁景观中的小气候变化可以确定更有可能幼苗存活的位置。我们结合使用小型无人机系统图像、卫星数据和原位微气候数据记录，以及机器学习方法，对每月近地最低、平均和最高温度以及相对湿度和蒸汽压差进行建模。以前被森林覆盖的地区，现在以两种不同的灌木物种为主。空间显式模型很好地预测了记录的小气候（r  = 0.73 到 0.97)，并且模型预测强调，在最热月份的任何给定位置，现有植被的太阳能缓冲能力可以将最高温度改变约 12 °C，将相对湿度增加约 20% 并降低蒸气压相对于开放区域，赤字减少了 0.3 毫巴。通过利用这些小气候避难所，可以大大提高在野火后景观中重新造林努力的成功率，并在当地范围内缓解气候变暖对幼苗的影响。