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作 者:杨晓彤[1,2,3,4] 刘慧平[1,3,4] 高啸峰[1,3,4] 刘湘平[1,3,4] 张洋华
机构地区:[1]北京师范大学遥感科学国家重点实验室,北京100875 [2]国家海洋信息中心,天津300171 [3]环境遥感与数字城市北京市重点实验室,北京100875 [4]北京师范大学地理学与遥感科学学院,北京100875
出 处:《农业工程学报》2016年第13期273-278,共6页Transactions of the Chinese Society of Agricultural Engineering
基 金:国家自然科学基金项目(40671127);国土资源部公益性行业科研专项(201411015-03)
摘 要:无资料地区缺少有关耕地经济价值评价的历史数据,该文以西藏省拉萨市为无资料研究区,选择夜间灯光数据,辅以数字高程模型和道路数据,基于农业区位论和GIS空间分析方法,对2010年拉萨市的耕地经济价值进行分级评价,旨在探讨一种适用于无资料地区某个历史时期的耕地经济价值评价方法,以填补该地区耕地经济价值的数据空白。首先选择城关区为试验区,基于2010年地面真实数据验证了研究方法的可行性(总精度达84%),然后以灯光亮度等级划分拉萨8县(区)的经济差异,进而将方法应用于拉萨全市,得到2010年拉萨市耕地历史经济价值评价结果,评价的总精度达82.6%,Kappa系数0.722,表明该文研究方法具有鲁棒性,可以为西藏无资料地区的耕地历史经济价值评价研究提供参考。Data-lacking regions have little information about the economic value of the cultivated land in the historical years. This research chose Lhasa city in Tibet as an example to discuss a method based on GIS (geographic information system), which was to propose the historical economic value evaluation of cultivated land in data-lacking regions and to fill the blanks of the historical economic value data of the cultivated land. This research took 3 factors based on the agricultural location theory from natural and human aspects, i.e., the slope, the stable light at nighttime, and the distance ffom the cultivated land to the roads at different levels. The data resources of this research were defense meteorological satellite program/operational linescan system (DMSP/OLS), digital elevation model and the road data, all of which were free and easy to acquire. In addition, all of the data were in the year of 2010 in order to ensure the data quality and have the corresponding Google Earth data which were treated as the ground true reference. First of all, we graded each factor into 5 classes and assigned them the score of 1, 3, 5, 7 and 9, which indicated their contributions for improving the economic value for the corresponding cultivated land. Second, we decided the weights for each factor in each grade by employing the method of analytic hierarchy process. Third, the spatial weighted overlay based on GIS was introduced to calculate the final score for each cultivated land which was the reference to determine their historical economic value. The method was firstly testified in the Chengguan district, Lhasa city and then applied to the whole city. We classified the cultivated land in Chengguan district into 3 classes, and the first class cultivated land referred to the farmland which had the highest historical economic value and the third class referred to the lowest one. After that we introduced the stratified random sampling method into this research to perform the accuracy assessment. We selected 100 pieces of
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