Modelling remote barrier detection to achieve free-flowing river targets

Millicent V Parks, C. Garcia de Leaniz, Peter E. Jones, Josh Jones
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Abstract

Fragmentation caused by artificial barriers is one of the main stressors of rivers worldwide. However, many barrier inventories only record large barriers, which underestimates barrier numbers, and hence fragmentation. Corrected barrier numbers can be obtained via river walkovers, but these are costly and time consuming. We assessed the performance of remote sensing as an alternative to river walkovers for barrier discovery by comparing the number and location of barriers detected in the field with those detected using Google Earth imagery. Only 56% of known barriers could be detected remotely, but machine learning models predicted the likelihood of remote detection with 62-65% accuracy. Barriers located downstream were twice as likely to be detected remotely than those in the headwaters, the probability of detection diminishing by 3-4% for every decrease in Strahler stream order and for every 10km increase in distance from the river mouth. Barriers located in forested reaches were 35% less likely to be detected than those in open reaches. Observer skills also affected the ability to locate barriers remotely and detection rate varied by 11% between experienced and less experienced observers, suggesting that training might improve barrier detection. Our findings have implications for estimates of river fragmentation because they show that the most under-represented structures in barrier inventories, i.e. small barriers located in forested headwaters, are unlikely to be detected remotely. Although remote sensing cannot fully replace ‘boots on the ground’ field surveys for filling barrier data gaps, it can reduce the field work necessary to improve barrier inventories and help inform optimal strategies for barrier removal under data-poor scenarios.
建立远程障碍物探测模型,实现河流自由流动目标
人工障碍物造成的支离破碎是全球河流面临的主要压力之一。然而,许多障碍物清单只记录大型障碍物,这就低估了障碍物的数量,从而也低估了破碎化程度。通过河流漫步可以获得经过校正的障碍物数量,但这样做既费钱又费时。我们通过比较实地发现的障碍物数量和位置与使用谷歌地球图像发现的障碍物数量和位置,评估了遥感技术作为河流漫步发现障碍物的替代方法的性能。只有 56% 的已知障碍物能被遥感检测到,但机器学习模型预测遥感检测可能性的准确率为 62-65%。位于下游的障碍物被远程检测到的几率是上游障碍物的两倍,Strahler溪流顺序每降低一个等级,距离河口每增加10公里,检测到的几率就会降低3-4%。位于森林河段的障碍物被探测到的概率比位于开阔河段的障碍物低 35%。观察者的技能也会影响远程定位障碍物的能力,经验丰富的观察者和经验不足的观察者的检测率相差 11%,这表明培训可以提高障碍物的检测率。我们的研究结果对河流破碎化的估算有影响,因为这些结果表明,在障碍物清单中代表性最弱的结构,即位于森林上游的小型障碍物,不太可能被遥感探测到。虽然遥感技术不能完全取代 "实地 "实地调查来填补障碍物数据缺口,但它可以减少改进障碍物清单所需的实地工作,并有助于为数据匮乏情况下的最佳障碍物清除策略提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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