five

Columbia River Estuary Ecosystem Classification Geomorphic Catena|生态系统分类数据集|地貌分类数据集

收藏
DataONE2016-10-29 更新2024-06-26 收录
生态系统分类
地貌分类
下载链接:
https://search.dataone.org/view/1e4c5828-70ec-413f-8c58-7462676f021e
下载链接
链接失效反馈
资源简介:
Estuarine ecosystems are controlled by a variety of processes that operate at multiple spatial and temporal scales. Understanding the hierarchical nature of these processes will aid in prioritization of restoration efforts. This hierarchical Columbia River Estuary Ecosystem Classification (henceforth "Classification") of the Columbia River estuary is a spatial database of the tidally-influenced reaches of the lower Columbia River, the tidally affected parts of its tributaries, and the landforms that make up their floodplains for the 230 kilometers between the Pacific Ocean and Bonneville Dam. This work is a collaborative effort between University of Washington School of Aquatic and Fishery Sciences (henceforth "UW"), U.S. Geological Survey (henceforth "USGS"), and the Lower Columbia Estuary Partnership (henceforth "EP"). Consideration of geomorphologic processes will improve the understanding of controlling physical factors that drive ecosystem evolution along the tidal Columbia River. The Classification is organized around six hierarchical levels, progressing from the coarsest, regional scale to the finest, localized scale: (1) Ecosystem Province; (2) Ecoregion; (3) Hydrogeomorphic Reach; (4) Ecosystem Complex; (5) Geomorphic Catena; and (6) Primary Cover Class. For Levels 4 and 5, we mapped landforms within the Holocene floodplain primarily by visual interpretation of Light Detection and Ranging (LiDAR) topography supplemented with aerial photographs, Natural Resources Conservation Service (NRCS) soils data, and historical maps. Mapped landforms are classified as to their current geomorphic function, the inferred process regime that formed them, and anthropogenic modification. Channels were classified primarily by a set of depth-based rules and geometric relationships. Classification Level 5 floodplain landforms ("geomorphic catenae") were further classified based on multivariate analysis of land-cover within the mapped landform area and attributed as "sub-catena". The extent of detailed mapping is the interpreted Holocene geologic floodplain of the tidal Columbia River and its tributaries to the estimated head of tide. The extent of this dataset also includes tributary valleys that are not mapped in detail. The upstream extents of tributary valleys are an estimation of the limit of Columbia River influence and are for use as containers in future analyses. The geologic floodplain is the geomorphic surface that is actively accumulating sediment through occasional overbank deposition. Most features within the geologic floodplain are considered to be formed during the recent (Holocene-epoch) climatic regime. There are bedrock and pre-Holocene sedimentary deposits included where they are surrounded by Holocene sediment accumulations or have been shaped by Holocene floods. In some places, Holocene landforms such as landslides, tributary fans, and coastal dunes are mapped that extend outside of the modern floodplain. This map is not a floodplain hazard map or delineation of actual flood boundaries. Although wetlands are included in the Classification, they are based on different criteria than jurisdictional wetlands. The extent of mapping may differ from the actual limit of tidal influence.
创建时间:
2016-10-29
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4098个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

DermNet

DermNet是一个包含皮肤病图像的数据集,涵盖了多种皮肤病类型,如痤疮、湿疹、牛皮癣等。该数据集主要用于皮肤病诊断和研究。

www.dermnetnz.org 收录

38-Cloud

该数据集包含38幅Landsat 8场景图像及其手动提取的像素级云检测地面实况。数据集被分割成多个384*384的补丁,适合深度学习语义分割算法。训练集有8400个补丁,测试集有9201个补丁。每个补丁包含4个对应的谱通道:红色、绿色、蓝色和近红外。

github 收录

基于站点观测的中国1km土壤湿度日尺度数据集(2000-2022)

本研究提供了中国范围1km高质量的土壤湿度数据集-SMCI1.0(Soil Moisture of China by in situ data, version 1.0),SMCI1.0是包含2000-2022年、日尺度、以10厘米为间隔10层深度(10-100cm)的高时空分辨率土壤湿度,数据单位为0.001m³/m³,缺失值为-999,投影为WGS1984。该数据集是以中国气象局提供的1,648个站点观测10层土壤湿度作为基准,使用ERA5_Land气象强迫数据、叶面积指数(LAI)、土地覆盖类型(Landtypes)、地形(DEM)和土壤特性(Soil properties)作为协变量,通过机器学习方式获得。本研究进行了两组实验以验证SMCI1.0的精度,时间尺度上:ubRMSE为0.041-0.052,R为0.883-0.919;空间尺度上:ubRMSE为0.045-0.051,R为0.866-0.893。 由于SMCI1.0是基于实地观测的土壤湿度,它可以作为现有基于模型和卫星数据集的有效补充。该数据产品可用于各种水文、气象、生态分析和建模,尤其在需要高质量、高分辨率土壤湿度的应用上至关重要。有关数据集的引用及详细描述,请阅读说明文档。为便于使用,本研究提供了两种不同分辨率的版本:30 秒(~1km)和0.1度(~9km)。

国家青藏高原科学数据中心 收录

MOOCs Dataset

该数据集包含了大规模开放在线课程(MOOCs)的相关数据,包括课程信息、用户行为、学习进度等。数据主要用于研究在线教育的行为模式和学习效果。

www.kaggle.com 收录

中国区域1km分辨率逐月平均风速数据集(2000-2020年)

中国区域1km分辨率逐月平均风速数据集(2001-2020年),是基于再分析气候数据经过空间降尺度得到,包括中国陆地范围,空间分辨率1km,时间分辨率为逐月。可以为气候变化、生态学、农学等研究提供逐月平均风速数据。

国家地球系统科学数据中心 收录