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首页> 外文期刊>The Science of the Total Environment >Disturbance automated reference toolset (DART): Assessing patterns in ecological recovery from energy development on the Colorado Plateau
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Disturbance automated reference toolset (DART): Assessing patterns in ecological recovery from energy development on the Colorado Plateau

机译:干扰自动参考工具集(DART):评估科罗拉多高原能源开发中的生态恢复模式

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A new disturbance automated reference toolset (DART) was developed to monitor human land surface impacts using soil-type and ecological context. DART identifies reference areas with similar soils, topography, and geology; and compares the disturbance condition to the reference area condition using a quantile-based approach based on a satellite vegetation index. DART was able to represent 26-55% of variation of relative differences in bare ground and 26-41% of variation in total foliar cover when comparing sites with nearby ecological reference areas using the Soil Adjusted Total Vegetation Index (SATVI). Assessment of ecological recovery at oil and gas pads on the Colorado Plateau with DART revealed that more than half of well-pads were below the 25th percentile of reference areas. Machine learning trend analysis of poorly recovering well-pads (quantile < 0.23) had out-of-bag error rates between 37 and 40% indicating moderate association with environmental and management variables hypothesized to influence recovery. Well-pads in grasslands (median quantile [MQ] = 13%), blackbrush (Coleogyne ramosissima) shrublands (MQ = 18%), arid canyon complexes (MQ = 18%), warmer areas with more summer-dominated precipitation, and state administered areas (MQ = 12%) had low recovery rates. Results showcase the usefulness of DART for assessing discrete surface land disturbances, and highlight the need for more targeted rehabilitation efforts at oil and gas well-pads in the arid southwest US.
机译:开发了一种新的干扰自动参考工具集(DART),以使用土壤类型和生态环境来监视人类土地表面的影响。 DART会确定具有相似土壤,地形和地质特征的参考区域;并使用基于卫星植被指数的基于分位数的方法将干扰条件与参考区域条件进行比较。当使用土壤调整的总植被指数(SATVI)将站点与附近的生态参考区域进行比较时,DART能够代表裸地相对差异的26-55%的变化和总叶面覆盖率的26-41%的变化。使用DART对科罗拉多高原油气垫的生态恢复进行的评估显示,超过一半的井垫低于参考地区的25%。回收不良的垫板(分位数<0.23)的机器学习趋势分析的袋外错误率在37%和40%之间,表明与假设的环境和管理变量有中等关联,这些变量被认为会影响回收率。草原的垫层(中位数[MQ] = 13%),灌木丛(Coleogyne ramosissima)灌木丛(MQ = 18%),干旱的峡谷群(MQ = 18%),较温暖的地区,夏季降水更多,国家管理区域(MQ = 12%)的恢复率较低。结果证明了DART在评估离散的地面干扰方面的有用性,并强调了在美国西南干旱地区的油气井垫上进行更有针对性的修复工作的必要性。

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