Middle Rio Grande Multitemporal Land Cover Classifications - 1935, 1962, 1987, 1999, and 2014

Riparian ecosystems are valuable to the ecological and human communities which depend on them. Over the past century, they have been subject to shifting management practices to maximize human use and ecosystem services, creating a complex relationship between water policy, management, and the natural ecosystem. This has necessitated research on the spatial and temporal dynamics of riparian vegetation change. The San Acacia Reach of the Middle Rio Grande has experienced multiple management and river flow fluctuations, resulting in threats to its riparian and aquatic ecosystems. This research uses remote sensing data, GIS, a review of management decisions, and an assessment of climate to quantify how riparian vegetation has been altered over time. This research focused on four management phases from 1935-2014, each highlighting different management practices and climate-driven river patterns, providing unique opportunities to observe a direct relationship between river management, climate, and riparian response. A land cover classification was created for each date constraining the management phases, 1935, 1962, 1987 (twice), 1999, and 2014. The 1935, 1962, and first 1987 classifications were produced by digitizing aerial imagery. The second 1987 classification, 1999, and 2014 classifications were created using a Classification and Regression Tree (CART) model. This metadata file pertains to the overall data release.

河岸生态系统（Riparian ecosystems）对依赖其存续的生态系统与人类社群均具有重要价值。过去一个世纪以来，为最大化人类利用效益与生态系统服务功能，其管理模式持续变迁，由此在水资源政策、河道管理与自然生态系统之间形成了复杂的关联。这一现状使得针对河岸植被变化的时空动态开展研究成为必要。里奥格兰德河中游的圣阿卡西亚河段（San Acacia Reach of the Middle Rio Grande）曾经历多轮管理调整与河流流量波动，对其河岸与水生生态系统构成威胁。本研究借助遥感数据、地理信息系统（GIS）、管理决策复盘与气候评估手段，量化分析了河岸植被随时间的变化情况。研究聚焦1935年至2014年的四个管理阶段，各阶段均对应独特的管理实践与气候驱动的河流水文模式，为观测河流管理、气候变化与河岸生态响应之间的直接关联提供了绝佳契机。针对各对应管理阶段的时间节点——1935年、1962年、1987年（两次）、1999年与2014年——均生成了土地覆盖分类结果。其中，1935年、1962年与首次1987年的分类结果通过数字化航空影像生成；第二次1987年、1999年与2014年的分类结果则借助分类与回归树（Classification and Regression Tree, CART）模型完成构建。本元数据文件对应本次整体数据集的发布。