朱鹮的潜在繁殖地

利用ErdasImagine8.4和ArcViewGIS对陕西省部分地区的卫片进行解译,分析其中的栖息地组成。结果表明,现有野生朱种群的最大繁殖地—花园繁殖区的栖息地组成为:马尾松林占73.1%,次生疏林占16.9%,农田占4.8%,稀疏灌木林占4.2%,油松栎类混交林占1.0%。使用彩色数字环标记重见法研究了朱繁殖期活动区的栖息地组成。根据花园繁殖地和朱鹮活动区内的栖息地组成,给各种栖息地赋予相应的适宜度值,以此作为朱鹮潜在繁殖地的评估标准。对卫片区域的评估结果表明,研究区内潜在的很适宜的繁殖地主要分布于洋县华阳镇周边、洋县花园乡周围地区、酉水河两岸山区、南郑县东南部和汉中西北部等地区,占总面积的5.2%;适宜的繁殖地占19.4%,不适宜繁殖的区域占75.4%。根据这个评估结果,野生朱鹮1995-2004年的97个巢中(花园繁殖地以外),87个(占89.7%)落入适宜的繁殖地中;野外核查的结果也表明,该评估方法的准确度达到88.4%。评估的误差主要源于遥感很难准确判别林木的生长状况(树高、胸径、密度等)和混交林中某些适于营巢的树种的存在,并忽略了小面积地块。因此,适当的野外核查可以提高该评估方法的准确度

Potential breeding sites of crested ibis Nipponia nippon

Crested ibis Nipponia nippon is one of the most endangered species in the world. There is only one wild population of about 450 individuals distributed in Yang County of Shaanxi Province, China. With the population size increasing in recent years, the breeding sites of the Crested Ibis in Yangxian are becoming saturated, and the ibises have expended their breeding range. We evaluated the potential breeding sites of crested ibis adjacent to Yangxian County using Remote Sensing (RS) and Geographic Information System (GIS). The available habitat at the main breeding site of Huayuan was composed of Chinese red pine 73.1%, secondary sparse forest 16.9%, paddy field 4.8%, sparse shrubbery 4.2%, and oil pine-robur mixed forest 1.0%. We qualified ibis habitat utilization during breeding season using color band marking. Based on habitat avaliability and ibis habitat utilization, we used a combined suitability index based on the summation of six habitat suitability indices to evaluate potential breeding sites. The evaluation indicated that the most suitable, suitable, and not suitable breeding sites accounted for 5.2%, 19.4% and 75.4% of the study area, respectively. The most suitable areas were located at Huayang and Huayuan of Yang County, the area along Youshui River, southeastern Nanzheng County, and Northwestern Hanzhong city. Eighty seven (89.7%) of 97 nests (excluding Huayuan breeding area) of crested ibis from 1995 to 2004 were in the suitable sites. Field checking showed that the accuracy of the evaluation was 88.4%. Most errors were resulted from that Remote Sensing could not detect accurately the growth condition of the forest (Height, diameter at breast height, and density) and small patches. Field checking was useful to improve the accuracy of habitat evaluation