Bed level data of Advancing Resilience Measures for Vegetated Coastlines (ARM4VEG), Guyana

This collection contains data collected during mangrove-mudflat campaign in Guyana, during the PhD Thesis project of Uwe Best. It is being made public both to act as supplementary data for publications and the final PhD thesis document, as well as to allow for open usage by other researchers in their own work.<br>This dataset covers the bathymetric Survey of the Intertidal Area, Chateau Margot Coastline, Guyana during Dec 2019 - Jan 2020. It also captures the development of the bed over a mudbank cycle (~ 20 - 30 years). <br>Within the mangroves, a combination of traditional surveying techniques were used with the automatic level and theodolite. Within the mangroves, limited satellite coverage prevented the use of the DGPS. A permanent bench mark (PBM) was established and used to setup the eleven transects along the field area (1000 m x 3500 m). Along the mudflat, the depths were attained using an echo sounder. boat itself. These measurements were taken over four days to complete ten (10) transects. <br>In November/December 2003 lines were surveyed with a spacing of 2 km (in offshore blocks). In May/June 2004 the intermediate lines were surveyed. The measured depths were corrected using measured water levels at Georgetown. The bed level measurements were supplemented by the global bathymetric charts for the years 2004, 2008 and 2019.<br>

本数据集收录了乌韦·贝斯特（Uwe Best）博士论文项目期间，在圭亚那开展的红树林-潮滩野外科考活动所获取的数据。本数据集公开的目的，一是为相关学术论文及最终博士学位论文提供补充数据，二是供其他研究人员在其研究工作中开放使用。
本数据集涵盖2019年12月至2020年1月期间，针对圭亚那玛戈堡（Chateau Margot）海岸线潮间带开展的水深勘测（bathymetric survey）数据，同时记录了潮滩床面在约20至30年的潮滩周期内的演变过程。
在红树林区域内，本次调查采用自动水准仪（automatic level）与经纬仪（theodolite）相结合的传统测量技术。由于该区域卫星信号覆盖有限，无法使用差分全球定位系统（DGPS）。本次调查设置了永久基准点（permanent bench mark, PBM），并以此为基准在1000米×3500米的野外作业区内布设了11条测线（transect）。在潮滩区域，调查人员借助搭载于作业船只的回声测深仪（echo sounder）获取水深数据。本次水深测量耗时四天，共完成10条测线的数据采集工作。
2003年11月至12月，调查人员在近海区块内对间距为2千米的测线开展了勘测工作；2004年5月至6月，完成了中间加密测线的勘测。所有实测水深均以乔治敦（Georgetown）实测水位数据进行了校正。床面高程测量数据还补充采用了2004年、2008年及2019年的全球水深图数据。