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首页> 外文学位 >Using kriging to predict distribution of arid vegetation, with discussion of co-kriging field data and satellite imagery.
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Using kriging to predict distribution of arid vegetation, with discussion of co-kriging field data and satellite imagery.

机译：利用克里金法预测干旱植被的分布，并讨论了克里金法的野外数据和卫星图像的共同讨论。

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The regionalized variable, vegetation canopy class, was kriged to predict distribution of selected plant species. A 5000 ha site representative of the northern Chihuahuan Desert was selected to test prediction of spatial distribution of vegetation. This method predicts based on the variation over increasing linear distances separating pairs of observations of a variable. Estimates of canopy class of a plant species at unsampled sites were weighted by the spatial variation of canopy class of sampled sites. Canopy class was measured with a modified Daubenmire (1970) method with randomly located transects 53 and 47, respectively, in 2 pastures of the USDA-ARS Jornada Experimental Range, Las Cruces, New Mexico. For all transects, 100 Daubenmire plots, 50 x 50 cm, were placed and canopy for all live species occurring in the quadrats was classified. Classes were 0, 1-5, 6-25, 26-50, 51-75, 76-95, and 96-100%. Of the 56 species identified, 18 species were selected for analysis, based on their presence in 30 or more plots. Semi-variograms were calculated for these 18 species per pasture. Out of a potential of 36 models, 19 were identified. Some plant species, such as black grama (Bouteloua eriopoda), snakeweed (Gutierrezia sarothrae), and honey mesquite (Prosopis glandulosa) occurred in both pastures but exhibited different parameter estimates for each pasture. These differences may be driven by plant growth form, seedling establishment requirements, historic utilization patterns by herbivores, climate patterns and (or) edaphic characteristics. Results show that kriging can predict vegetation distribution. Co-kriging LandSat Thematic Mapper image and vegetation data was confounded by scale and year differences in data sets. Ordinary kriging successfully predicted canopy classes of dominant species. Ordinary kriging should be applicable for predicting vegetation distribution in similar landscapes, when applying the following guidelines: a preliminary nested survey to reveal general spatial variation needed for designing representative sampling methods, estimation of cover class for only 4-6 dominant species, estimation of cover class for bare ground, match sampling scale with that of remotely-sensed data (for co-kriging analysis).
机译：克里格地区化变量，植被冠层类别，以预测所选植物物种的分布。选择了一个代表北部奇瓦瓦沙漠的5000公顷土地来测试植被空间分布的预测。该方法基于线性距离的变化预测，该线性距离将变量的观测对分开。通过采样地点的冠层类别的空间变化对未采样地点的植物物种的冠层类别的估计进行加权。在新墨西哥州拉斯克鲁塞斯的USDA-ARS Jornada实验范围的2个牧场中，分别使用随机定位的53和47样线，采用改良的Daubenmire（1970）方法测量了冠层类别。对于所有样带，放置100个50 x 50厘米的道本奈尔样地，并对发生在该样方中的所有活物种的冠层进行分类。等级为0、1-5、6-25、26-50、51-75、76-95和96-100％。在确定的56个物种中，根据其在30个或更多样地中的存在，选择了18个物种进行分析。每个牧场计算了这18种物种的半变异函数。在潜在的36种模型中，有19种被确定。两种草场都出现了一些植物物种，例如黑格拉玛（Bouteloua eriopoda），蛇毒草（Gutierrezia sarothrae）和蜂蜜豆科灌木（Prosopis glandulosa），但每种草场的参数估算值不同。这些差异可能是由植物的生长形式，幼苗的建立要求，草食动物的历史利用模式，气候模式和（或）食性特征所驱动。结果表明，克里金法可以预测植被分布。由于数据集的规模和年份差异，共同绘制LandSat Thematic Mapper图像和植被数据是令人困惑的。普通克里金法成功地预测了优势物种的冠层类别。当应用以下准则时，普通克里金法应适用于预测相似景观中的植被分布：初步的嵌套调查以揭示设计代表性采样方法所需的一般空间变化，仅对4-6个优势种的覆盖度类别的估计，覆盖度的估计裸地分类，使采样比例与遥感数据的比例相匹配（用于共同克里金分析）。

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