Smigaj Magdalena, Hackney Christopher R, Diem Phan Kieu, Tri Van Pham Dang, Ngoc Nguyen Thi, Bui Duong Du, Darby Stephen E, Leyland Julian
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands; School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
Sci Total Environ. 2023 Feb 20;860:160363. doi: 10.1016/j.scitotenv.2022.160363. Epub 2022 Nov 22.
Mass urbanisation and intensive agricultural development across river deltas have driven ecosystem degradation, impacting deltaic socio-ecological systems and reducing their resilience to climate change. Assessments of the drivers of these changes have so far been focused on human activity on the subaerial delta plains. However, the fragile nature of deltaic ecosystems and the need for biodiversity conservation on a global scale require more accurate quantification of the footprint of anthropogenic activity across delta waterways. To address this need, we investigated the potential of deep learning and high spatiotemporal resolution satellite imagery to identify river vessels, using the Vietnamese Mekong Delta (VMD) as a focus area. We trained the Faster R-CNN Resnet101 model to detect two classes of objects: (i) vessels and (ii) clusters of vessels, and achieved high detection accuracies for both classes (f-score = 0.84-0.85). The model was subsequently applied to available PlanetScope imagery across 2018-2021; the resultant detections were used to generate monthly, seasonal and annual products mapping the riverine activity, termed here the Human Waterway Footprint (HWF), with which we showed how waterborne activity has increased in the VMD (from approx. 1650 active vessels in 2018 to 2070 in 2021 - a 25 % increase). Whilst HWF values correlated well with population density estimates (R = 0.59-0.61, p < 0.001), many riverine activity hotspots were located away from population centres and varied spatially across the investigated period, highlighting that more detailed information is needed to fully evaluate the extent, and type, of human footprint on waterways. High spatiotemporal resolution satellite imagery in combination with deep learning methods offers great promise for such monitoring, which can subsequently enable local and regional assessment of environmental impacts of anthropogenic activities on delta ecosystems around the globe.
河流三角洲地区大规模的城市化和集约化农业发展导致了生态系统退化,影响了三角洲社会生态系统,并降低了它们对气候变化的适应能力。到目前为止,对这些变化驱动因素的评估主要集中在三角洲平原陆地上的人类活动。然而,三角洲生态系统的脆弱性以及全球范围内生物多样性保护的需求,要求更准确地量化人类活动在整个三角洲水道上的足迹。为满足这一需求,我们以越南湄公河三角洲(VMD)为重点区域,研究了深度学习和高时空分辨率卫星图像识别内河船只的潜力。我们训练了Faster R-CNN Resnet101模型来检测两类物体:(i)船只和(ii)船只集群,并在这两类物体上都取得了较高的检测准确率(F值=0.84-0.85)。该模型随后被应用于2018年至2021年期间可用的PlanetScope图像;由此产生的检测结果被用于生成月度、季节和年度产品,绘制内河活动地图,在此称为人类水道足迹(HWF),通过它我们展示了VMD内河活动是如何增加的(从2018年约1650艘活跃船只增加到2021年的2070艘——增长了25%)。虽然HWF值与人口密度估计值相关性良好(R=0.59-0.61,p<0.001),但许多内河活动热点位于远离人口中心的地方,并且在整个调查期间空间分布有所不同,这突出表明需要更详细的信息来全面评估人类在水道上足迹的范围和类型。高时空分辨率卫星图像与深度学习方法相结合,为这种监测提供了巨大的前景,这随后可以对全球范围内人类活动对三角洲生态系统的环境影响进行局部和区域评估。