Kleindl William J, Powell Scott L, Hauer F Richard
Flathead Lake Biological Station and Montana Institute on Ecosystems, University of Montana, Missoula, MT, 59812, USA,
Environ Monit Assess. 2015 Jun;187(6):321. doi: 10.1007/s10661-015-4546-y. Epub 2015 May 5.
Advancements in remote sensing and computational tools have increased our awareness of large-scale environmental problems, thereby creating a need for monitoring, assessment, and management at these scales. Over the last decade, several watershed and regional multi-metric indices have been developed to assist decision-makers with planning actions of these scales. However, these tools use remote-sensing products that are subject to land-cover misclassification, and these errors are rarely incorporated in the assessment results. Here, we examined the sensitivity of a landscape-scale multi-metric index (MMI) to error from thematic land-cover misclassification and the implications of this uncertainty for resource management decisions. Through a case study, we used a simplified floodplain MMI assessment tool, whose metrics were derived from Landsat thematic maps, to initially provide results that were naive to thematic misclassification error. Using a Monte Carlo simulation model, we then incorporated map misclassification error into our MMI, resulting in four important conclusions: (1) each metric had a different sensitivity to error; (2) within each metric, the bias between the error-naive metric scores and simulated scores that incorporate potential error varied in magnitude and direction depending on the underlying land cover at each assessment site; (3) collectively, when the metrics were combined into a multi-metric index, the effects were attenuated; and (4) the index bias indicated that our naive assessment model may overestimate floodplain condition of sites with limited human impacts and, to a lesser extent, either over- or underestimated floodplain condition of sites with mixed land use.
遥感技术和计算工具的进步提高了我们对大规模环境问题的认识，从而产生了对这些尺度上的监测、评估和管理的需求。在过去十年中，已经开发了几种流域和区域多指标指数，以协助决策者规划这些尺度上的行动。然而，这些工具使用的遥感产品存在土地覆盖误分类问题，而这些误差很少纳入评估结果中。在这里，我们研究了景观尺度多指标指数（MMI）对专题土地覆盖误分类误差的敏感性，以及这种不确定性对资源管理决策的影响。通过一个案例研究，我们使用了一个简化的洪泛平原MMI评估工具，其指标来自陆地卫星专题地图，最初提供了对专题误分类误差不敏感的结果。然后，我们使用蒙特卡罗模拟模型，将地图误分类误差纳入我们的MMI，得出了四个重要结论：（1）每个指标对误差的敏感性不同；（2）在每个指标内，不考虑误差的指标得分与纳入潜在误差的模拟得分之间的偏差，其大小和方向因每个评估地点的基础土地覆盖而异；（3）总体而言，当将这些指标组合成一个多指标指数时，影响会减弱；（4）指数偏差表明，我们的简单评估模型可能高估了人类影响有限的地点的洪泛平原状况，在较小程度上，高估或低估了土地利用混合的地点的洪泛平原状况。
