基于TM与ASAR遥感数据的扎龙丹顶鹤繁殖栖息地多尺度特征

选取扎龙保护区Landsat TM及Envisat ASAR双极化（HH/HV）两种遥感数据源，利用丹顶鹤巢址GPS位点数据（n=28），在8个尺度（30～240 m）下，采用二元Logistic回归建立丹顶鹤繁殖栖息地选择模型，从而确定不同尺度下栖息地选择关键影响因子；并通过对比两种遥感影像数据模型，探讨了雷达影像在不同尺度下对模型预测能力的影响.结果表明： 两种数据集的回归模型总体精度均较理想（≥69.0%）.在30 m尺度下，TM数据集模型中仅穗帽变换绿度分量(TCA_2)以系数为负值进入模型，表明该尺度下丹顶鹤避免选择植被密度较高的芦苇沼泽；60～120 m尺度下，两种数据集模型的主成分变换中第二主成分(PCA_2)系数均为正值，表明该尺度下丹顶鹤对植被覆盖度和长势要求较高，有利于增强其隐蔽性；90 m尺度交叉极化（HV）的后向散射系数信息进入混合模型，表明该尺度下水分条件是丹顶鹤栖息地选择的重要因子；>120 m尺度下，两种数据源的纹理信息开始进入模型，说明较大尺度下丹顶鹤栖息地选择对植被特性的要求降低，而对人为干扰因子的敏感度增强.在不同尺度的混合模型中，ASAR变量的纳入均显著提高了模型的预测精度.

Breeding habitat characteristics of red-crowned crane at Zhalong of Northeast China: a multi-scale approach based on TM and ASAR image data

Based on the Landsat TM and Envisat ASAR HH/HV imagery data and by using the GPS data of red-crowned crane nesting sites (n = 28) at Zhalong National Nature Reserve of Northeast China, the models of the breeding habitat selection of red-crowned crane at the Reserve were established by binary Logistic regression to identify the key variables for the habitat selection at eight spatial scales (30-240 m). The relative performance of the two models based on the Landsat TM and Envisat ASAR HH/HV databases was compared, and the prediction capacity of the models across the eight scales was approached. The overall precisions of the two models were satisfactory (> or = 69.0%). At scale 30 m, only variable TCA_2 entered with negative value into the model based on Landsat TM database, which indicated that the crane at this scale avoided selecting higher density reed marshes. At scales 60-120 m, the variable PCA_2 entered with positive value into the two models, indicating that the crane at these scales had higher demand of high density reed marshes to improve its concealment. At scale 90 m, the variable HV backward scatting coefficient also entered into the combined model, which indicated that water condition was the important factor for the habitat selection of the crane at this scale. At scales > 120 m, the texture information of the two satellite sensors started to be involved into the two models, indicating that at larger scales, the crane had decreasing demand on the vegetation features for its breeding habitat selection but increasing sensitivity to the anthropogenic disturbance factors. The introduction of ASAR variables into the models increased the prediction accuracy of the models markedly at all scales.