NASA CMS: Tree Canopy Cover and Canopy Height at 1-Meter Resolution in Maine, USA – Part 1
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This dataset provides 1-meter resolution tree canopy cover and canopy height data for the state of Maine. The data were derived using a rules-based expert system that integrated leaf-on LiDAR and imagery data into a single classification workflow, utilizing spectral, height, and spatial information from the datasets. The canopy cover and canopy height data are organized by county. In the canopy cover file, a grid cell value of 1 indicates the presence of canopy cover. In the canopy height files, the values represent canopy height in meters, with a scaling factor of 100 applied to reduce file size. This dataset has been used as input for a novel forest carbon monitoring and modeling system, which integrates a mechanistic model (i.e., Ecosystem Demography EDv3), multi-source remote sensing data, meteorological reanalysis, and soil properties. It enables the estimation of carbon dynamics from the past to the present and projects future carbon sequestration (Hurtt et al.,2019 and 2024; Huang et al.,2019; Ma et al 2021; Tang et al., 2021). This dataset is part 1 of 3 of the full dataset. See also: part 1 (DOI: 10.5281/zenodo.17049013), part 2 (DOI: 10.5281/zenodo.19009369), part 3 (DOI:10.5281/zenodo.19009469) Similar data for other states in the northeastern U.S. are archived in other repositories. Reference 1. Hurtt, G., Zhao, M., Sahajpal, R., Armstrong, A., Birdsey, R., Campbell, E., ... & Tang, H. (2019). Beyond MRV: high-resolution forest carbon modeling for climate mitigation planning over Maryland, USA. Environmental Research Letters, 14(4), 045013. 2. Huang, W., Dolan, K., Swatantran, A., Johnson, K., Tang, H., O’Neil-Dunne, J., ... & Hurtt, G. (2019). High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA. Environmental Research Letters, 14(9), 095002. 3. Ma, L., Hurtt, G., Tang, H., Lamb, R., Campbell, E., Dubayah, R., Guy, M., Huang, W., Lister, A., Lu, J., Dunne, J. O., Rudee, A., Shen, Q. & Silva, C. High-resolution forest carbon modelling for climate mitigation planning over the RGGI region, USA. Environmental Research Letters (2021). doi:https://doi.org/10.1088/1748-9326/abe4f4 4. Tang, H., Ma, L., Lister, A., O'Neil-Dunne, J., Lu, J., Lamb, R. L., Dubayah, R. & Hurtt, G. High-resolution forest carbon mapping for climate mitigation baselines over the RGGI region, USA. Environ Res Lett 16, 035011 (2021). 5. Hurtt, G. C., Ma, L., Lamb, R., Campbell, E., Dubayah, R. O., Hansen, M., ... & Tang, H. (2024). Beyond MRV: combining remote sensing and ecosystem modeling for geospatial monitoring and attribution of forest carbon fluxes over Maryland, USA. Environmental Research Letters, 19(12), 124058.
本数据集提供美国缅因州1米分辨率的树冠覆盖度与树冠高度数据。该数据通过基于规则的专家系统生成，将有叶期激光雷达（LiDAR）与影像数据整合至统一分类流程中，利用数据集的光谱、高度与空间信息完成处理。树冠覆盖度与树冠高度数据按县进行组织。在树冠覆盖度文件中，网格单元值为1代表存在树冠覆盖；在树冠高度文件中，数值以米为单位表示树冠高度，为压缩文件体积，已应用100倍的缩放因子。 本数据集已作为新型森林碳监测与建模系统的输入数据，该系统整合了机理模型（生态系统人口模型，Ecosystem Demography EDv3）、多源遥感数据、气象再分析数据与土壤属性数据，可实现从过去到当前的碳动态估算，并对未来碳固存情况进行预测（Hurtt等，2019年与2024年；Huang等，2019年；Ma等，2021年；Tang等，2021年）。 本数据集为完整数据集的3个组成部分中的第1部分。相关数据集参见：第1部分（DOI：10.5281/zenodo.17049013）、第2部分（DOI：10.5281/zenodo.19009369）、第3部分（DOI：10.5281/zenodo.19009469）。 美国东北部其他州的同类数据已归档至其他数据仓库中。 参考文献 1. Hurtt, G., Zhao, M., Sahajpal, R., Armstrong, A., Birdsey, R., Campbell, E., 等 & Tang, H. (2019). 超越监测、报告与核查（MRV）：美国马里兰州气候减缓规划用高分辨率森林碳建模. 《环境研究快报》, 14(4), 045013. 2. Huang, W., Dolan, K., Swatantran, A., Johnson, K., Tang, H., O’Neil-Dunne, J., 等 & Hurtt, G. (2019). 美国马里兰州、宾夕法尼亚州与特拉华州三州区域森林碳监测系统地上生物量高分辨率制图. 《环境研究快报》, 14(9), 095002. 3. Ma, L., Hurtt, G., Tang, H., Lamb, R., Campbell, E., Dubayah, R., Guy, M., Huang, W., Lister, A., Lu, J., Dunne, J. O., Rudee, A., Shen, Q. & Silva, C. 美国区域温室气体倡议（Regional Greenhouse Gas Initiative，RGGI）区域气候减缓规划用高分辨率森林碳建模. 《环境研究快报》(2021). doi:https://doi.org/10.1088/1748-9326/abe4f4 4. Tang, H., Ma, L., Lister, A., O'Neil-Dunne, J., Lu, J., Lamb, R. L., Dubayah, R. & Hurtt, G. 美国RGGI区域气候减缓基线高分辨率森林碳制图. 《环境研究快报》16, 035011 (2021). 5. Hurtt, G. C., Ma, L., Lamb, R., Campbell, E., Dubayah, R. O., Hansen, M., 等 & Tang, H. (2024). 超越MRV：结合遥感与生态系统模型对美国马里兰州森林碳通量进行地理空间监测与归因. 《环境研究快报》, 19(12), 124058.
