Johnston Draw (Idaho) High Resolution Pre-Fire Vegetation Map 2023

The variability of vegetation in rangelands can be over generalized in spatial representation and vegetation types mapped by moderate resolution vegetation maps. A high resolution (&lt;1 meter), site specific, vegetation map may better represent the diversity and spatial complexity of rangelands – a necessity for analyzing pre-fire conditions. We pansharpened two 8-band VNIR Worldview 2 scenes to map pre-fire vegetation in Johnston Draw (1.8 square kilometers) in the Reynolds Creek Experimental Watershed in Southwest Idaho. The two Worldview 2 scenes represent peak greenness (June 14, 2023) and pre-fire (September 23, 2023) conditions with spatial resolutions of 50 centimeters and 42 centimeters, respectively. A prescribed fire burned the area on October 6, 2023. We trained a pixel based random forest classifier to map 10 site specific vegetation classes at a 50-centimeter spatial resolution. We applied a majority filter to remove speckling. Map accuracy was 83.3% when validated using a test set of 54, 30-meter diameter, plots selected to represent the following dominant vegetation types: deciduous/riparian (classes were collapsed into a single class for validation), living juniper, dead juniper, sagebrush, mixed low sage and bunchgrass, bitterbrush, and annual grasses. Barren and water classes were not validated. Training data was developed through a combination of site visit based knowledge and a training set of 30-meter diameter dominant vegetation class plots. The 1.5-billion-dollar cost of fire prevention, suppression, and restoration is stretched thin over the vast area of wildfire occurrence, where site-specific high-resolution vegetation maps are essential to mitigate fire potential and address post fire recovery. In addition to the pre-fire vegetation map a post fire burn product will also be submitted to Ag Data Commons and the related materials will be updated to reflect these complimentary submissions.牧场植被的空间变异性，在空间表征过程及中等分辨率植被图所标注的植被类型中，常存在过度泛化的局限。高分辨率（<1米）的场地专属（site-specific）植被图则能更精准地呈现牧场植被的多样性与空间复杂性——这是火灾前植被状况分析的必要前提。我们对两景8波段可见近红外（VNIR）的WorldView 2影像进行全色锐化处理，以绘制爱达荷州西南部雷诺溪实验流域约翰斯顿河谷（面积1.8平方千米）的火灾前植被分布图。该两景WorldView 2影像分别对应植被冠层青绿峰值期（2023年6月14日）与火灾前（2023年9月23日）的地表状况，空间分辨率依次为50厘米与42厘米。研究区域于2023年10月6日实施了计划火烧。我们以50厘米空间分辨率为基准，训练了基于像素的随机森林分类器，以划分10种场地专属植被类别；并通过多数滤波去除影像斑点噪声。我们选取54个直径30米的样地作为验证集，样地覆盖以下主要植被类型：落叶/河岸植被（验证时将相关类别合并为单一类别）、活杜松、枯杜松、山艾树、低山艾与丛生禾草混交群落、苦皮木以及一年生草本。经验证，该植被图的准确率达83.3%。裸地与水体类别未参与验证。训练数据结合了野外实地考察获取的先验知识，以及覆盖主要植被类别的30米直径样地训练集。野火频发的广袤区域中，火灾预防、扑救与生态修复的150亿美元投入捉襟见肘；场地专属的高分辨率植被图，对缓解火灾隐患、开展灾后恢复工作而言不可或缺。除本次提交的火灾前植被分布图外，灾后火烧产物数据集也将同步上传至农业数据共享平台（Ag Data Commons），相关配套材料也将随之更新以收录该补充提交的数据。