植被覆盖度是单位面积内植被垂直投影面积占总面积的百分比,它是重要的生态学参数,同时也是大豆等农作物的重要农艺参数.理想的覆盖度测量耗时短,工具简单,结果准确,受人为因素影响小.本文以'黔豆3号'、'川豆16'和大黑豆等24个品种为研究对象,以照相法测量值作为参考,分析目估法、实地样线法和照片样线法测定大豆植被覆盖度的精度和适用性.结果表明:目估法测量的植被覆盖度误差最大,平均为7.8％,目估误差随株高的增高呈现出先增加后降低的趋势.实地样线法测量精度较高,误差小于3％(2.8％),但耗时最长,每个1m×1m样方需用时6.2min.照片样线法误差小于3％,几乎不需要消耗野外采样时间,室内每个样方照片处理需耗时约3.0 min.相较于目估法,照片样线法具有更高的测量精度,相较于实地样线法,照片样线法能够节省野外采样时间,提高工作效率.因此推荐照片样线法作为大豆和类似植物覆盖度的测量方法,同时两条样线(样线长度合计为2.8 m)即可满足测量误差小于5％的精度要求.未来应进一步探索照片样线法在其他作物覆盖度测量中的适用性.

Context Fine-scale spatial vegetation patterns are ubiquitous and can have profound impacts on large scale ecological processes including surface runoff, soil erosion, and livestock forage efficiency. However, we have limited knowledge of the fine-scale spatial vegetation patterns in humid grasslands. Objectives The objectives were to characterize the spatial vegetation patterns at centimeter scale in humid grasslands, quantify the vegetation patterns variation under different image pixel sizes and plant covers, and explore the potential ecological implications of the spatial vegetation patterns. Methods Seventy plots with plant covers ranging from 30.8–99.3% were selected from seven humid grasslands in southwest China and their spatial vegetation patterns quantified at image pixel sizes of 0.04, 0.25, 1, and 4 cm. Results With increasing pixel size, plant patch density and total edge density decreased, plant patch size increased, and the plant patch shape became more regular. At a plant cover level below 50%, increasing plant cover will result in increasing patch density and patch size, leading to greater spatial heterogeneity. At plant cover levels above 50%, increasing plant cover will cause the rapid expansion of patch size, along with a lower patch density, forming a more homogeneous landscape dominated by plant patches. The small stems, branches, and leaves of grasses fragmented non-plant patches into smaller patches with increasing plant cover; this fragmentation resembles road-induced landscape fragmentation processes. Conclusions Medium plant cover has the highest heterogeneity of spatial vegetation pattern at the fine scale, which may have significant implications on ecological processes and related management practices.

利用调查问卷和地理信息系统获得了贵州61个县(区)冬季耕地利用情况,分析了冬季耕地利用率、利用方式及其影响因素.结果表明:贵州省冬季耕地利用率为46.4％,主要利用方式是种植油菜和其他蔬菜,主要因为它们的种植能够产生较高的直接经济效益.贵州省冬季耕地利用率与务农劳动力投入成正比,务农劳动力的减少,将会造成耕地粗放经营.利用率与温度和降水量等气候因素无关,但根据不同气候条件下耕地利用方式的不同,研究区可划分为东北部白菜区、南部油菜区、温度较低的西北部土豆区、降水量较大的东南部养鱼区.未来应根据不同地区的经济社会、家庭劳动力结构和气候条件推广相应的冬季耕地利用方式,以提高农民收入,促进冬季耕地资源的合理开发利用.

植被覆盖度是植被垂直投影面积占统计区域面积百分比;理想覆盖度实地测量方法耗时短,工具简单,结果准确,受人为因素影响小.以结缕草(Zoysia japonica)、白三叶(Trifolium repens)和雀稗(Paspalum thunbergii)人工草地为研究对象,以照相法测量值为参考,比较样线法、目估法、样针法、点框架法、网格法1×1、网格法5×5、网格点法1×1和网格点法5×5等8种方法测量精度.结果表明:样线法误差小于5％,耗时短,工具简单.目估法误差大于样线法,适用于植被高度低且覆盖度较低或较高样方.样针法误差大于样线法和目估法,误差受植被高度和茎叶硬度影响,适用于植被高度低且硬度较小样方.点框架法、网格法和网格点法误差均较大,一般大于10％.网格法误差受覆盖度和小网格边长影响,误差峰值出现在覆盖度接近65％的样方,误差随着小网格边长增加而增加.8种方法测量值与照相法测量值均呈显著正相关(P＜0.05),因此对于类似人工草地,这些方法测量值可通过本文提供的拟合公式转化为真实值.随着样方数量增加,8种方法误差均呈现出先降低后稳定趋势.本研究推荐样线法作为类似人工草地覆盖度实地测量方法;未来应根据覆盖度、植被高度和硬度等特征选择适合的测量方法.