精确的保护项目在正确的地方, 正确的比例, 尺寸合适, 合适的时机, and making sure they are working—is going to redefine how landscape conservation is approached. Using the latest high-resolution datasets to conduct advanced geospatial analysis allows us to better support conservation and restoration planning and implementation watershed-wide.了解更多
正规外围买球app区，美国.S. Geological Survey (USGS) and University of Vermont Spatial 分析 Lab (UVM SAL) are collaborating, 由切萨皮克湾项目(CBP)资助, to produce 1-meter resolution land cover and land use/land cover datasets for the Chesapeake Bay watershed regional area (206 counties, 超过250,000 km2). 这些数据是基础数据, 权威的, and transformative looks at the landscape and its management throughout the region.
The production of the CBP 1-meter “land cover” data involves the identification and classification of image objects derived from aerial imagery (National Agriculture Imagery Program, 简要), 来自激光雷达的地面高度信息, 以及其他辅助数据. Land cover represents the surface characteristics of the land with classes such as impervious cover, 树的树冠, 草本, 和贫瘠的. 与此形成鲜明对比的是, “land use” represents how humans use and manage the land with classes such as turf grass, 农田, 木材收获. Producing land use from land cover data requires a variety of ancillary datasets combined with spatial rules that leverage the contextual information inherent in the land cover data. The CBP’s land use/land cover (LULC) data are so named because they represent a combination of cover and use classes (e.g., extractive-barren, solar-草本) that are critical for understanding the impact of human activities on the Chesapeake Bay. For example: one land cover class (草本 vegetation) encapsulates both the highest polluting land use (e.g.(如玉米产量)或最低之一(如玉米产量).g.(自然演替). The LULC data contextualize the land cover classes for decision-making, such as informing outcomes in the Chesapeake Bay Watershed Agreement and serving as the basis for developing the next generation of watershed and land change models.
These data are unique in both the spatial and categorical resolution they hold. This project is the largest dataset for open LULC data at a 1-meter resolution, boasting 900 times more detail than the readily available 30-meter resolution National Land Cover Dataset (NLCD). 另外, the CBP 1-meter LULC data has over 50 unique classes, providing more categorical context than the 13-class CBP land cover data or the 17-class NLCD data. This detailed classification scheme is necessary to ensure these data are widely applicable for supporting data-driven decision-making by the Chesapeake Bay Program and other regional stakeholders.
除了LULC和土地覆盖数据, the project team has also collaborated to create Land Use/Land Cover Change and Land Cover Change datasets for the entire Chesapeake Bay watershed region. These data elevate LULC mapping from snapshots in time to the beginning of long-term change monitoring. 根据这些数据, we begin to see where some LULC classes are experiencing fluctuations—gains and losses, 无论是永久的还是暂时的. The land cover change data represents 1-meter pixel-scale differences in the imagery and ancillary data showing shifts from one class to another between time one (2013/14) and time two (2017/18). This spectral analysis identifies shifts that were then used to inform a land use change detection.
These spatial data layers have been summarized in tabular change matrices that are available for download, to help stakeholders understand shifts in land use/land cover between 2013/14-2017/18. The pivot tables show changes to and from each individual class, and are available for both the detailed 54 class scheme and generalized 18 class scheme.
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