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首页> 外文期刊>Earth and Space Science >Quantifying Spatiotemporal Post‐Disturbance Recovery Using Field Inventory, Tree Growth, and Remote Sensing
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Quantifying Spatiotemporal Post‐Disturbance Recovery Using Field Inventory, Tree Growth, and Remote Sensing

机译：使用现场清单，树木生长和遥感对时空后干扰恢复进行量化

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Forest recovery following a disturbance lasts decades to centuries, and the rate depends on pre‐ and post‐disturbance condition and local environmental factors. Existing approaches of field observations, remote sensing, statistical chronosequence, and ecological modeling have one or more drawbacks, including short time frames, generalized details, indirect indicators, hard parameterization, and defective assumptions. Using aboveground live biomass (AGLB) as an example, we developed an approach called “Disturbance and Recovery Assessment across Space and Time (DRAST).” For a specific post‐disturbance year, DRAST utilizes Field Inventory and Analysis data sets and the Forest Vegetation Simulator, as well as pre‐ and post‐disturbance remote sensing to create two rasters: (1) what the AGLB would look like over the disturbed area had the disturbance not occurred and (2) what the AGLB would look like over the disturbed area in the actual presence of the disturbance. These two rasters are compared annually to examine the spatiotemporal recovery pattern. We demonstrated DRAST with the 2013 Rim fire in California, United States, by creating two sets of AGLB for 100 years. Our results showed that (1) the AGLB consumed by Rim fire was 3.52 Tg and (2) 45.9% of the burned area needs 5 years to recover, followed by 6.4% (5–10 years), 6.1% (95 years), 5.9% (10–15 years), 5.4% (15–20 years), 4.8% (20–25 years), and 4.3% (25–30 years). In conclusion, DRAST can provide spatially explicit and highly detailed ecological indicators for decades under the two scenarios of “no disturbance” and “actual disturbance occurrence” for recovery analysis.
机译：扰动后的森林恢复持续数十年至几个世纪，其速度取决于扰动前后的情况以及当地的环境因素。现有的野外观测，遥感，统计时序和生态建模方法具有一个或多个缺点，包括时限短，广义细节，间接指标，硬参数化和错误的假设。以地上的活生物质（AGLB）为例，我们开发了一种名为“跨时空干扰与恢复评估（DRAST）”的方法。在特定的灾后年份，DRAST利用现场清单和分析数据集以及森林植被模拟器以及灾前和灾后遥感来创建两个栅格：（1）AGLB在受干扰情况下的外观区域没有发生干扰;（2）在实际存在干扰的情况下，AGLB在受干扰区域的外观如何。每年比较这两个栅格以检查时空恢复模式。我们通过在美国加利福尼亚州制造2013年的Rim大火来演示DRAST，方法是创建两套AGLB，历时100年。我们的结果表明：（1）轮辋大火消耗的AGLB为3.52 Tg，（2）燃烧面积的45.9％需要<5年才能恢复，其次是6.4％（5-10年），6.1％（> 95年） ），5.9％（10-15岁），5.4％（15-20岁），4.8％（20-25岁）和4.3％（25-30岁）。总之，DRAST可以在“无扰动”和“实际扰动发生”两种情况下提供数十年的空间清晰和高度详细的生态指标，以进行恢复分析。

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