Maintaining the health and productivity of rangelands by controlling the livestock stocking rate to remain within carrying capacity is of significance to ensure sustainable management of rangeland ecosystems. But we know little about the safe carrying capacity in particular rangeland landscapes. This has hampered efforts to use rangelands in a risk-averse manner in fluctuating rainfall environments, and especially in arid and semiarid areas. To address this lack of information, we took Kailash Sacred Landscape in China (KSL-China) as our study site and used remote sensing data, meteorological data and statistical data from 2000 to 2015 to analyze rangeland carrying capacity, stocking rate, and major influencing factors. Rangeland carrying capacity presented an increasing trend, while stocking rate was gradually decreasing, resulting in an increase of carrying rate in the study area. The increased carrying capacity was closely related to increased rainfall. Stocking rate declined owing to government regulations, particularly implementation in 2004 of the national policy of Returning Grazing Land to Grassland. There was a sharp reduction of livestock number below 200 000 standard sheep units (SU) after 2005. The decrease of stocking rate had a stronger effect on rangeland carrying rate than did the increase of carrying capacity. Ecosystem restoration programs have provided subsidies to pastoralists to encourage them to reduce livestock numbers. Our findings suggest that a safe rangeland carrying capacity is ca. 170 000 SU in KSL-China. There is a carrying capacity surplus of ca. 50 000 SU for safe animal husbandry development in the study area. More importantly, future climate warming and increases in grazing may jointly play a key role in affecting rangeland carrying capacity. 相似文献
基于马尔科夫随机场(Markov Random Field,MRF)模型下的遥感图像变化检测因固定组合能量函数导致的边缘分割模糊问题,提出了一种改进的变权重MRF遥感图像变化检测方法。该方法首先通过模糊C均值(Fuzzy C-means,FCM)算法对差值图像进行聚类分割,并依此分割结果作为变权重MRF的初始分割条件进行最终的分割;最后对分割结果进行掩膜处理,得到最终的变化检测结果。采用真实遥感影像进行对比实验,结果表明所提方法变化检测精度更高,边缘检测更加平滑,区域一致性更好。 相似文献
Total Cloud Cover (TCC) over China determined from four climate datasets including the International Satellite Cloud Climatology Project (ISCCP), the 40-year Re-Analysis Project of the European Centre for Medium-Range Weather Forecasts (ERA-40), Climate Research Unit Time Series 3.0 (CRU3), and ground station datasets are used to show spatial and temporal variation of TCC and their differences. It is demonstrated that the four datasets show similar spatial pattern and seasonal variation. The maximum value is derived from ISCCP. TCC value in North China derived from ERA-40 is 50% larger than that from the station dataset; however, the value is 50% less than that in South China. The annual TCC of ISCCP, ERA-40, and ground station datasets shows a decreasing trend during 1984-2002; however, an increasing trend is derived from CRU3. The results of this study imply remarkable differences of TCC derived from surface and satellite observations as well as model simulations. The potential effects of these differences on cloud climatology and associated climatic issues should be carefully considered. 相似文献