Improved forest classification in the northern Lake States using multi-temporal Landsat imagery
Improved forest classification in the northern Lake States using multi-temporal Landsat imagery
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DOI：
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发表时间：
1995
影响因子：
1.3
通讯作者：
P. Wolter;D. Mladenoff;G. Host;T. Crow
中科院分区：
文献类型：
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作者：
P. Wolter;D. Mladenoff;G. Host;T. Crow
利用单一日期Landsat TM数据进行森林分类，在分离湖州北部地区的森林覆盖类型方面只取得了中等程度的成功。很少有区域森林分类达到属或种级别的精度。我们利用初夏的TM数据和4个MSS数据建立了更具体的森林覆盖分类，以捕捉不同树种的物候变化。在分类的22种森林类型中，多时相图像分析有助于分离出13种类型。最重要的是，颤栗杨、糖枫、北方红橡树、北方针橡树、黑灰和柽柳被成功分类。总体分类精度为83.2%，森林分类精度为80.1%。这种方法可能对其他地区的大规模森林覆盖监测有用，特别是在没有辅助数据层的地方。
Forest classifications using single date Landsat TM data have been only moderately successful in separating forest cover types in the northern Lake States region. Few regional forest classifications have been presented that achieve genus or species level accuracy. We developed a more specific forest cover classification using TM data from early summer in conjunction with four MSS dates to capture phenological changes of different tree species. Among the 22 forest types classified, multi-temporal image analysis aided in separating 13 types. Of greatest significance, trembling aspen, sugar maple, northern red oak, northern pin oak, black ash, and tamarack were successfully classified. The overall classification accuracy was 83.2 percent and the forest classification accuracy was 80.1 percent. This approach may be useful for broad-scale forest cover monitoring in other areas, particularly where ancillary data layers are not available.