Ecological Communities Classification (2011)

This hosted feature layer has been published in RI State Plane Feet NAD 83. This layer should be used in place of the Ecological Communities Classification (2011) published in 2018. This is a statewide, seamless digital dataset of the ecological communities for the State of Rhode Island, which was derived using automated and semi-automated methods and based on imagery captured in 2011. The project area encompasses the State of Rhode Island and also extends 1/2 mile into the neighboring states of Connecticut and Massachusetts or to the limits of source orthophotography. Geographic feature accuracy meets the National Mapping Standards for 1:5000 scale mapping with respect to base level data (roads, hydrography, and orthos). The minimum mapping unit for this dataset is .5 acre. The ecological communities classification scheme used for these data was based on the Rhode Island Ecological Communities Classification document created by Richard W. Enser on October 4, 2011. Geography for the dataset was based on ground conditions of 2011 four-band orthophotography with a spatial resolution of 0.5 ft and 2011 LiDAR data and data derivatives with a nominal post spacing of 1m. Additional ancillary data used in the production of this dataset were provided by the State of Rhode Island and included 2011 land cover/land use, 2011 impervious, road centerline, hydrography, railroads, state boundary, municipal boundary, coastline, location of schools, hospitals, governmental facilities, waste disposal sites, etc.本托管要素图层采用罗德岛州平面英尺坐标系（NAD 83）发布。本图层应替代2018年发布的《2011年生态群落分类》成果。本数据集为罗德岛州全域无缝生态群落数字数据集，采用自动化与半自动化方法生成，数据基础为2011年采集的遥感影像。项目覆盖范围包含罗德岛州全域，且向邻近的康涅狄格州、马萨诸塞州延伸0.5英里，或延伸至原始正射影像的覆盖边界。针对基础层数据（道路、水文要素与正射影像），本数据集的地理要素精度符合1:5000比例尺制图的国家测绘标准。本数据集的最小制图单元为0.5英亩。本数据集所采用的生态群落分类体系，基于理查德·W·恩瑟（Richard W. Enser）于2011年10月4日编制的《罗德岛州生态群落分类》文档。数据集的地理空间信息基于2011年采集的空间分辨率为0.5英尺的四波段正射影像，以及2011年激光雷达（LiDAR）数据及其标称点间距为1米的数据衍生产品。本数据集生产过程中使用的其他辅助数据由罗德岛州提供，涵盖2011年土地覆盖/土地利用数据、2011年不透水面数据、道路中心线数据、水文要素数据、铁路数据、州界数据、市政边界数据、海岸线数据以及学校、医院、政府设施、垃圾处理场等场所的位置数据等。