We analyzed 254 deaths caused by diseases in captive Alpine musk deer (Moschus chrysogaster) from 1998 to 2005 at the Xinglongshan Musk Deer Farm,Gansu.Among the eight categories of diseases,respiratory system diseases had the highest incidence rate of 26.8%, followed by motor system diseases (16.5%), digestive and nutritive diseases (14.6%), unidentified diseases (14.2%),cardiovascular system diseases (13%),urinary system diseases (9.8%),nervous system diseases (3.5%), and reproductive system diseases (1.6%).The percentages of dead males were higher than those of the females for deaths caused by digestive system and nutritive diseases(♂62.2%),cardiovascular system diseases (60.6%), nervous system diseases (66.7%), unidentified diseases (61.1%), and particularly the urinary system diseases (up to 84%), and the male to female ratio of deaths caused by all the diseases was (♂∶♀) 1∶0.76, showing a male-skewed mortality. The mortality of newborn and fawns was relatively high, and the percentage of deaths in one-and two-year-old deer was 51.6% of the total, but those decreased with deer age. The relationship between the economic benefit in the farming of musk deer and the prevention of diseases were discussed. Finally, we offered a strategy to control the incidence of diseases by regarding the musk deer as a solitary species.
为给小麦的长势监测与农艺决策提供科学依据,利用高光谱技术实现了小麦冠层叶绿素含量的估测。通过分析18种高光谱指数对叶绿素的估测能力,筛选出可敏感表征叶绿素含量的指数REP,利用地面光谱数据为样本集,以最小二乘支持向量回归(least squares support vector regression,LS-SVR)算法建立了小麦冠层叶绿素含量反演模型,其校正决定系数C-R2与预测决定系数P-R2分别为0.751与0.722,在各指数中反演精度最高。进一步分析表明,REP对叶绿素含量以及LAI值较高与较低的样本均具备良好的预测能力,可有效避免样本取值范围以及冠层郁闭度等因素对叶绿素含量估测的影响。利用LS-SVR反演模型完成了OMIS影像叶绿素含量的遥感填图,并以地面实测值进行检验,其拟合模型R2与RMSE值分别为0.676与1.715。结果表明,高光谱指数REP所建立的LS-SVR模型实现了叶绿素含量的准确估测,可用于小麦叶绿素含量信息的快速、无损获取。
以高光谱遥感技术实现了小麦叶面积指数(leaf area index,LAI)的反演。对18种高光谱指数进行了比较分析,筛选出了可敏感反映小麦LAI的高光谱指数OSAVI,并以地面光谱数据为样本建立了小麦LAI的反演模型。分析表明,指数OSAVI所建立的反演模型校正集与预测集R2分别达0.823与0.818,在各指数中反演精度最高。利用反演模型逐象元对OMIS影像进行解算,实现小麦LAI的空间量化表达,并将反演结果与地面实测值进行回归拟合,发现两组数据的拟合模型R2达0.756,RMSE为0.500,具有较高的相似度。结果表明:以高光谱指数进行小麦LAI的反演是可行的,且OSAVI为优选指数。
