<正>对盒维数法、双表面积法及贝氏法等3种树冠分维数估算方法在杨树无性系上的应用结果进行了比较。结果表明：双表面积法操作复杂且工作量大；贝氏法虽然操作简单但是精度太低；盒维数法具有操作简便、快速、重演性高、能充分反映树冠内叶面积的空间分布模式等优点，但目前仅能估算2维平面的分维数。相对而言，盒维数法应该是求算树冠分维数最理想的方法。树冠的分维数与树冠形状、树冠大小相关性不大，而与树冠内叶面积的多少密切相关，一般地，树冠内叶面积越多，树冠的分维数越大。

Geospatial distribution of natural vegetation is among some of very important environmental parameters required for applications ranging from global climate change to monitoring of natural hazards, monitoring of ecosystem vitality, and fire management practices. Increasingly sophisticated applications require vegetation datasets to cover large areas at a suitable scale and provide sufficiently detailed information. In this paper, we describe a research effort to develop a remote sensing methodology capable of producing 30 m resolution, wallto-wall coverage of natural vegetation types and structure variables in support of a multi-agency fire fuels and fire risks assessment project. Success of this remote sensing research effort is dependent on improved sensor and data qualities, a thorough understanding of regional and local vegetation ecology, successful integration of remote sensing with large amount of field plot data, and flexible mapping algorithms. Preliminary results produced in Wasatch Range and Uinta Mountains of central Utah include 28 vegetation types with an overall accuraccy of 60% (average by life forms), percent canopy density (sub-pixel density) of forest, sbrub, and herbaceous cover, and average top canopy height of forest, shrub, and herbaceous cover. Techniques to improve the first-round results are discussed, including refinements of mapping models and use of relevant environmental gradients and potential vegetation classification associated with actual vegetation types.