Guangxi Key Laboratory of Forest Ecology and Conservation, College of Forestry, Guangxi University, Nanning, 530004, China.
Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, New Brunswick, E3B 5A3, Canada.
Sci Rep. 2017 Sep 8;7(1):10998. doi: 10.1038/s41598-017-11381-z.
Forest ecosite reflects the local site conditions that are meaningful to forest productivity as well as basic ecological functions. Field assessments of vegetation and soil types are often used to identify forest ecosites. However, the production of high-resolution ecosite maps for large areas from interpolating field data is difficult because of high spatial variation and associated costs and time requirements. Indices of soil moisture and nutrient regimes (i.e., SMR and SNR) introduced in this study reflect the combined effects of biogeochemical and topographic factors on forest growth. The objective of this research is to present a method for creating high-resolution forest ecosite maps based on computer-generated predictions of SMR and SNR for an area in Atlantic Canada covering about 4.3 × 10 hectares (ha) of forestland. Field data from 1,507 forest ecosystem classification plots were used to assess the accuracy of the ecosite maps produced. Using model predictions of SMR and SNR alone, ecosite maps were 61 and 59% correct in identifying 10 Acadian- and Maritime-Boreal-region ecosite types, respectively. This method provides an operational framework for the production of high-resolution maps of forest ecosites over large areas without the need for data from expensive, supplementary field surveys.
森林生态位反映了对森林生产力以及基本生态功能有意义的局部地点条件。通常采用植被和土壤类型的实地评估来识别森林生态位。然而，由于空间变化较大，以及相关的成本和时间要求，从实地数据插值生成大面积高分辨率生态位地图是困难的。本研究中引入的土壤湿度和养分状况指数（即 SMR 和 SNR）反映了生物地球化学和地形因素对森林生长的综合影响。本研究的目的是提出一种基于计算机生成的 SMR 和 SNR 预测值为加拿大大西洋地区约 4.3×10 公顷（ha）林地创建高分辨率森林生态位地图的方法。使用 1507 个森林生态系统分类图的实地数据来评估所生成的生态位地图的准确性。仅使用 SMR 和 SNR 的模型预测，生态位地图在识别 10 个阿卡迪亚和海洋北方地区生态位类型方面的正确率分别为 61%和 59%。该方法为在不依赖昂贵的补充实地调查数据的情况下，在大面积地区生成高分辨率森林生态位地图提供了一个操作框架。
