Anchorage, Alaska modified ABoVE: Landsat-derived Annual Dominant Land Cover, 1984-2054

We developed a compound modeling approach that enabled us to refine the available evergreen forest category in the original Arctic Boreal Vulnerability Experiment (ABoVE) dataset (https://daac.ornl.gov/ABOVE/guides/Annual_Landcover_ABoVE.html) to include black and white spruce and hemlock. The data is a geotiff (30 meter resolution) with 17 land cover classes.
The published paper with the methods can be found at: https://doi.org/10.3390/f14081577. This archive includes 1984, 1994, 2004, 2014 and predicted 2024, 2034, 2044, and 2054.
Because medium resolution landcover data that include species detail are lacking, we developed a compound modeling approach that enabled us to refine the available evergreen forest category into highly flammable species and less flammable species. We then expanded our refined landcover at decadal time steps from 1984 to 2014. With the aid of an existing burn model, FlamMap, and simple succession rules, we were able to predict future landcover at decadal steps until 2054. Our resulting land covers provide important information to communities in our study area on current and future wildfire risk and vegetation changes and could be developed in a similar fashion for other areas. These data will then be used to assess wildfire hazards and risk.

本研究开发了一种复合建模方法，对原始北极北方脆弱性实验（Arctic Boreal Vulnerability Experiment, ABoVE）数据集（https://daac.ornl.gov/ABOVE/guides/Annual_Landcover_ABoVE.html）中的常绿森林类别进行细化，新增了黑云杉、白云杉与铁杉树种。该数据为分辨率30米的GeoTIFF格式文件，包含17种土地覆盖类型。
相关方法的正式发表论文可参阅：https://doi.org/10.3390/f14081577。本数据集涵盖1984年、1994年、2004年、2014年的实测数据，以及2024年、2034年、2044年与2054年的预测数据。
鉴于现有中分辨率土地覆盖数据缺乏物种细节信息，本研究再次采用复合建模方法，将原有常绿森林类别划分为高易燃性树种与低易燃性树种两类。随后，我们以10年为时间步长，将细化后的土地覆盖数据从1984年扩展至2014年。借助现有火灾模型FlamMap与简单的植被演替规则，我们得以预测至2054年的每10年步长的土地覆盖变化。本研究生成的土地覆盖数据可为研究区域内的社区提供当前及未来野火风险与植被变化的关键参考，该方法亦可通过类似思路推广至其他区域。本数据集后续将用于野火灾害与风险评估。