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机构地区:[1]中国科学院地理科学与资源研究所,北京100101 [2]中国科学院大学,北京100049
出 处:《地理学报》2014年第3期312-322,共11页Acta Geographica Sinica
基 金:全球变化研究国家重大科学研究计划(2010CB950901);国家自然科学基金(41271227)~~
摘 要:具有空间信息的历史土地覆被数据集是研究土地覆被变化气候与生态效应的基础数据。本文在潜在植被数据和现代遥感土地利用数据的基础上,确定了土地垦殖前森林植被可能的分布范围;遴选并量化了影响我国土地宜垦性的主导因子,评估了省域尺度上土地的宜垦性;构建了以土地宜垦性为权重的历史森林覆被网格化重建模型(网格大小为10km×10km);重建了我国东北三省1780年和1940年网格化的森林覆被。结果表明:基于省域森林面积数据,该方法较好地再现了历史时期森林空间格局;与2000年遥感有林地空间数据对比分析显示,模型重建结果与遥感数据绝对误差的单样本t检验相伴概率(双尾)为0.12,大于显著性水平0.05,表明模型重建结果与遥感数据不存在显著性差异,网格化重建方法具有较好的可行性;同时,重建结果准确性评估也表明:1780年重建结果在县域尺度上的相对误差在.30%~30%之间的县级政区有99个,占县级政区总数(不含无值区)的74.44%,总体误差相对较小。To research the climatic and ecological effects of historical land cover change, it is necessary to create historical land cover datasets with explicit spatial information. Using potential vegetation and satellite-based land use data, we determined the possible distribution extent of forest cover before reclamation. Then, some factors affecting land reclamation were selected to evaluate land suitability value of China for cultivation and then they were integrated into a model. Furthermore, historical forest gridding reconstruction model (the size of grid cell is 10 km by 10 km) was developed according to the land suitability value. As a case study, we reconstructed spatially explicit forest cover of 1780 and 1940 in Northeast China using this approach. The results demonstrated that the forest gridding reconstruction model we created can transform provincial forest area into spatially explicit data well. To test the model's rationality, we compared satellite-based forest cover and reconstructed forest cover of 2000. And the one sample t-test of the absolute error of them showed that the two-tailed significance was 0.12, larger than the significant level 0.05, suggesting that there was no significant difference between them and the gridding reconstruction method we designed was rational. The relative errors at county scale of forest cover reconstruction in 1780 of Northeast China were calculated. And the number of counties, whose relative error ranged from -30% - 30%, is 99, accounting for 74.44% of the totals (data missing counties are discharged).
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