Kammerer Melanie, Iverson Aaron L, Li Kevin, Goslee Sarah C
USDA-ARS Pasture Systems and Watershed Management Research Unit, University Park, PA, 16802, USA.
Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37830, USA.
Sci Data. 2024 Jan 26;11(1):137. doi: 10.1038/s41597-024-02979-w.
Due to the key role surrounding landscape plays in ecological processes, a detailed characterization of land cover is critical for researchers and conservation practitioners. Unfortunately, in the United States, land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this gap, we merged two datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated 'Spatial Products for Agriculture and Nature' (SPAN). Our workflow leveraged strengths of the NVC and the CDL to create detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN annually from 2012-2021 for the conterminous United States, quantified agreement and accuracy of SPAN, and published the complete computational workflow. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved most conflicts, leaving only 0.6% of agricultural pixels unresolved in SPAN. These ready-to-use rasters characterizing both agricultural and natural land cover will be widely useful in environmental research and management.
由于周边景观在生态过程中发挥着关键作用,土地覆盖的详细特征描述对于研究人员和保护从业者至关重要。不幸的是,在美国,土地覆盖数据分散在强调农业或自然植被的专题数据集中,而非两者兼顾。为填补这一空白,我们合并了两个数据集,即LANDFIRE国家植被分类(NVC)和美国农业部国家农业统计局农田数据层(CDL),以生成综合的“农业与自然空间产品”(SPAN)。我们的工作流程利用了NVC和CDL的优势,创建了包含农业和自然土地覆盖类别的详细栅格数据。我们在2012年至2021年期间每年为美国本土生成SPAN,量化了SPAN的一致性和准确性,并公布了完整的计算工作流程。在我们的验证分析中,我们发现约5.5%的NVC农业像素与CDL存在冲突,但我们解决了大多数冲突,在SPAN中仅留下0.6%的农业像素未解决。这些表征农业和自然土地覆盖的即用型栅格数据将在环境研究和管理中广泛有用。