"Researching, modelling and mapping forest productivity in relation to topographically influenced soil properties, including soil moisture"

这项建议涉及了解森林生产力如何根据地形和基质影响的水分和养分状况从山脊到洼地变化。到目前为止，这一连续体一直以专题土壤和森林测绘方法表现出来。然而，高分辨率数字高程模型(DEM)现在实现了一种功能方法来以一种综合的方式建模和绘制这种变化，例如，1m分辨率。这项建议涉及的个别研究问题如下：(1)激光雷达测绘的指标，如制图水深指数(DTW)，在重现基于小区的变化方面的表现如何，这些变化涉及(I)森林生产力，(Ii)土壤、排水和植被类型，(Iii)影响土壤质地、容重(或孔隙空间)、有机质含量和根系深度等变量的生产力，以及(Iv)与深度和土壤水分有关的夏末水平。(2)所有这些指标与DEM衍生的DTW指数和其他DEM衍生的指数，如坡度、曲率和其他广泛使用的地形湿度指数的关系如何？(3)DEM生成的土壤和场地变量(包括DTW)的组合在模拟和绘制整个景观中的树木生长时效果最好？这项研究将在工业促进下在新不伦瑞克进行，特点是自然森林条件下穿插着森林种植园(杰克松、黑云杉、白云杉)。处理最新森林清单以及高分辨率地面图像和地形(激光雷达、点云数据)的数据层已经可用。这项提议支持一个由研究生、暑期助理和技术人员组成的小组开展实地工作(基于地块的土壤和植被调查和采样)、实验室分析、地理信息系统编程和森林生产力建模。这项研究将生成基于地理信息系统的模型，旨在按直接利益的森林管理中的优势树种类型绘制森林生产力图，还将处理与其他地方的模型推广和绘图应用有关的事项。

This proposal deals with learning how forest productivity varies from ridge tops to depressions, according to topography- and substrate-influenced moisture and nutrient regimes. Until now, this continuum has been represented by thematic soil and forest surveying and mapping methods. High-resolution digital elevation models (DEM), however, are now enabling a functional approach to model and map this change in a comprehensive manner at, e.g., 1m resolution. The individual research questions addressed with this proposal are as follows: (1) How well do LiDAR-mapped metrics such as the cartographic depth-to-water index (DTW) perform in reproducing plot-based determinations of ridge-to-depression variations regarding (i) forest productivity, (ii) soil, drainage and vegetation type, (iii) productivity affecting variables such as soil texture, bulk density (or pore space), organic matter content and rooting depth, and (iv) end-of summer levels pertaining to depth-to-water and soil moisture content. (2) How well are all these measures related to the DEM-derived DTW index and other DEM-derived indices such as slope, curvature and other widely used terrain wetness indices? (3)What combination of DEM-generated soil and site variables including DTW work best in modelling and mapping tree growth across the landscape? This research, to be done in New Brunswick with industrial facilitation, features natural forest conditions interspersed by forest plantations (jack pine, black spruce, white spruce). Data layers dealing with up-to-date forest inventories, and high-resolution surface images and topography (LiDAR, point cloud data) are already available. This proposal supports a team involving graduate students, summer assistants, and technical staff to conduct the field work (plot-based soil and vegetation surveying and sampling), laboratory analyses, GIS programming, and forest productivity modelling. This research will generate GIS-based models designed to map forest productivity by dominant species type across the forest management of immediate interest, and will also deal with matters pertaining to model generalization and mapping applications elsewhere.