中文摘要：

Qinghai 西藏高原是最高的世界和最大的高原。由于为环境探索和旅游增加要求，一个大过渡区域为高度改编被要求。Hehuang 山谷，在在黄土高原和 Qinghai 西藏高原之间的转变地区定位，有方便交通和相对低的举起。我们的问题是这里的地理条件是否为在走进 Qinghai 西藏高原前的改编停留是适当的。在这研究，因此，我们为印射人的解决的当前、潜在的分发模式检验了生态的壁龛建模的潜在的使用(ENM ) 。我们选择了最大的熵方法(Maxent ) ，集成气候的 ENM，遥感和地理数据，为分布建模并且为转变区域估计陆地适用性。在预处理和选择以后，在变量和空间自相关输入数据之间的关联被移开， 106 个出现点和 9 环境层作为模型输入坚定。阀值无关的模型表演根据模型跑，与在曲线(AUC ) 下面的区域珍视的 10 次是合理的是 0.917 Gongga 在过去的二十年的山作为的显示出类似的趋势北半球、全球。在过去的二十年，温度增加了 0.35ceabl 吗？？

英文摘要：

The Qinghai-Tibet Plateau is the word＇s highest and largest plateau. Due to increasing demands for environment exploration and tourism, a large transitional area is required for altitude adaptation. Hehuang valley, which locates in the transition zone between the Loess Plateau and the Qinghai-Tibet Plateau, has convenient transportation and relatively low elevation. Our question is whether the geographic conditions here are appropriate for adapted stay before going into the Qinghai-Tibet Plateau. Therefore, in this study, we examined the potential use of ecological niche modeling （ENM） for mapping current and potential distribution patterns of human settlements. We chose the Maximum Entropy Method （Maxent）, an ENM which integrates climate, remote sensing and geographical data, to model distributions and assess land suitability for transition areas. After preprocessing and selection, the correlation between variables and spatial auto- correlation input data were removed and 106 occurrence points and 9 environmental layers were determined as the model inputs. The threshold- independent model performance was reasonable according to lO times model running, with the area under the curve （AUC） values being 0.917± 0.01, and 0.923±0.002 for test data. Cohen＇s kappa coefficient of model performance was 0.848. Results showed that 82.22% of the study extent was not suitable for human settlement. Of the remaining areas, highly suitable areas aceounted for 1.19%, moderately for 5.3% and marginally for 11.28%. These suitable areas totaled 418.79 km2, and 86.25% of the sample data was identified in the different gradient of suitable area.The decisive environmental factors were slope and two climate variables： mean diurnal temperature range and temperature seasonality. Our model showed a good performance in mapping and assessing human settlements. This study provides the first predicted potential habitat distribution map for human settlement in Ledu County, which could also help in land use management.