蒙古国草地系统的健康状况关系着其畜牧业效益和国内外生态安全。衡量牲畜放牧密度并合理控制放牧密度对于维护蒙古国草地生态系统健康以及实现畜牧业的可持续发展具有重要意义。空间放牧密度梯度信息的缺失阻碍了对草地承载力相关研究...蒙古国草地系统的健康状况关系着其畜牧业效益和国内外生态安全。衡量牲畜放牧密度并合理控制放牧密度对于维护蒙古国草地生态系统健康以及实现畜牧业的可持续发展具有重要意义。空间放牧密度梯度信息的缺失阻碍了对草地承载力相关研究的推进。本研究基于2015年世界网格化牲畜数据集(gridded livestock of the world,GLW)、牧区人口密度、土壤水分、年降水、地表温度和净初级生产力(net primary productivity,NPP)等空间数据,利用谷歌地球引擎(Google Earth Engine,GEE)云平台运行随机森林回归算法,建立了蒙古国放牧密度估算模型;基于省域牲畜存栏量统计数据检验了模型的准确性,并结合不同年份预测因子数据,模拟了蒙古国2006—2020年放牧密度空间分布。为确保数据集的准确性,采用判定系数(R2)、平均绝对误差(MAE)和均方根误差(RMSE)三个误差测量指标对数据集进行校验。模拟结果显示,2006—2020年蒙古国放牧密度在空间上整体呈现北高南低的特点;2006—2010年蒙古国放牧密度扩张明显,放牧密度高于5 TLU/km^(2)区域面积占比由0.223%增加到51.390%;2010—2020年,蒙古国大部分地区放牧密度无显著变化。检验结果表明,该数据集较好地实现了蒙古国放牧密度空间化的模拟,2006、2010、2015和2020年模拟数据与蒙古国省域牲畜存栏量拟合R2分别为0.844、0.734、0.914、和0.926,均通过显著性检验,MAE分别为5.195、3.513、2.336、3.461,RMSE分别为8.135、5.257、4.200、5.909。本研究提供的蒙古国放牧密度数据集对该地区草地生态系统的可持续发展以及牧民的生计安全提供了重要信息支撑。展开更多
Large amounts of data at various temporal and spatial scales require terabyte(TB) level storage and computation, both of which are not easy for researchers to access. Cloud data and computing services provide another ...Large amounts of data at various temporal and spatial scales require terabyte(TB) level storage and computation, both of which are not easy for researchers to access. Cloud data and computing services provide another solution to store, process, share and explore environmental data with low costs, stronger computation capacity and easy access. The purpose of this paper is to examine the benefits and challenges of using freely available satellite data products from Australian Geoscience DataCube and Google Earth Engine(GEE) online data with time series for integrative environmental analysis of the Macquarie-Castlereagh Basin in the last 15 years as a case study. Results revealed that the cloud platform simplifies the procedure of traditional catalog data processing and analysis. The integrated analysis based on the cloud computing and traditional methods represents a great potential as a low-cost, efficient and user-friendly method for global and regional environmental study. The user can save considerable time and cost on data integration. The research shows that there is an excellent promise in performing regional environmental analysis by using a cloud platform. The incoming challenge of the cloud platform is that not all kinds of data are available on the cloud platform. How data are integrated into a single platform while protecting or recognizing the data property, or how one portal can be used to explore data archived on different platforms represent considerable challenges.展开更多
文摘蒙古国草地系统的健康状况关系着其畜牧业效益和国内外生态安全。衡量牲畜放牧密度并合理控制放牧密度对于维护蒙古国草地生态系统健康以及实现畜牧业的可持续发展具有重要意义。空间放牧密度梯度信息的缺失阻碍了对草地承载力相关研究的推进。本研究基于2015年世界网格化牲畜数据集(gridded livestock of the world,GLW)、牧区人口密度、土壤水分、年降水、地表温度和净初级生产力(net primary productivity,NPP)等空间数据,利用谷歌地球引擎(Google Earth Engine,GEE)云平台运行随机森林回归算法,建立了蒙古国放牧密度估算模型;基于省域牲畜存栏量统计数据检验了模型的准确性,并结合不同年份预测因子数据,模拟了蒙古国2006—2020年放牧密度空间分布。为确保数据集的准确性,采用判定系数(R2)、平均绝对误差(MAE)和均方根误差(RMSE)三个误差测量指标对数据集进行校验。模拟结果显示,2006—2020年蒙古国放牧密度在空间上整体呈现北高南低的特点;2006—2010年蒙古国放牧密度扩张明显,放牧密度高于5 TLU/km^(2)区域面积占比由0.223%增加到51.390%;2010—2020年,蒙古国大部分地区放牧密度无显著变化。检验结果表明,该数据集较好地实现了蒙古国放牧密度空间化的模拟,2006、2010、2015和2020年模拟数据与蒙古国省域牲畜存栏量拟合R2分别为0.844、0.734、0.914、和0.926,均通过显著性检验,MAE分别为5.195、3.513、2.336、3.461,RMSE分别为8.135、5.257、4.200、5.909。本研究提供的蒙古国放牧密度数据集对该地区草地生态系统的可持续发展以及牧民的生计安全提供了重要信息支撑。
基金Under the auspices of National Key Research and Development Program of China(No.2016YFA0600304)
文摘Large amounts of data at various temporal and spatial scales require terabyte(TB) level storage and computation, both of which are not easy for researchers to access. Cloud data and computing services provide another solution to store, process, share and explore environmental data with low costs, stronger computation capacity and easy access. The purpose of this paper is to examine the benefits and challenges of using freely available satellite data products from Australian Geoscience DataCube and Google Earth Engine(GEE) online data with time series for integrative environmental analysis of the Macquarie-Castlereagh Basin in the last 15 years as a case study. Results revealed that the cloud platform simplifies the procedure of traditional catalog data processing and analysis. The integrated analysis based on the cloud computing and traditional methods represents a great potential as a low-cost, efficient and user-friendly method for global and regional environmental study. The user can save considerable time and cost on data integration. The research shows that there is an excellent promise in performing regional environmental analysis by using a cloud platform. The incoming challenge of the cloud platform is that not all kinds of data are available on the cloud platform. How data are integrated into a single platform while protecting or recognizing the data property, or how one portal can be used to explore data archived on different platforms represent considerable challenges.
