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Combining LPJ-GUESS and HASM to simulate the spatial distribution of forest vegetation carbon stock in China简 总被引:5,自引:2,他引:3
It is very important in accurately estimating the forests' carbon stock and spatial distribution in the regional scale because they possess a great rate in the carbon stock of the terrestrial ecosystem. Yet the current estimation of forest carbon stock in the regional scale mainly depends on the forest inventory data, and the whole process consumes too much labor, money and time. And meanwhile it has many negative influences on the forest carbon storage updating. In order to figure out these problems, this paper, based on High Accuracy Surface Modeling (HASM), proposes a forest vegetation carbon storage simulation method. This new method employs the output of LPJ-GUESS model as initial values of HASM and uses the inventory data as sample points of HASM to simulate the distribution of forest carbon storage in China. This study also adopts the seventh forest resources statistics of China as the data source to generate sample points, and it also works as the simulation accuracy test. The HASM simulation shows that the total forest carbon storage of China is 9.2405 Pg, while the calculated value based on forest resources statistics are 7.8115 Pg. The forest resources statistics is taken based on a forest canopy closure, and the result of HASM is much more suitable to the real forest carbon storage. The simulation result also indicates that the southwestern mountain region and the northeastern forests are the important forest carbon reservoirs in China, and they account for 39.82% and 20.46% of the country's total forest vegetation carbon stock respectively. Compared with the former value (1975-1995), it mani- fests that the carbon storage of the two regions do increase clearly. The results of this re- search show that the large-scale reforestation in the last decades in China attains a signifi- cant carbon sink. 相似文献
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基于阈值分割的黑龙江省森林类型遥感识别 总被引:1,自引:0,他引:1
全球变化背景下,准确获取森林覆盖是监测森林资源动态、实现林业可持续发展的重要基础。为将省级尺度森林资源清查面积资料空间化,以黑龙江省为例,利用1999-2003年该省森林资源清查面积数据,结合2000年500 m分辨率的MODIS数据,构建了基于阈值分割的森林类型遥感识别方法。该方法利用不同地表覆被类型归一化植被指数时间序列的季节分异特征,以森林资源清查面积为标准,设定森林类型的划分阈值,识别了黑龙江省森林类型的空间分布。最后,基于分层随机抽样和精度评价方法,表明森林类型识别结果与地面参考数据具有较高的一致性,总体分类精度为78.1%;特别是季节特征明显的落叶林,精度可达80%以上。本文所构建的方法可将森林清查统计数据进行准确的空间定位,同时结合多期森林资源连续清查资料和遥感信息,可为识别并量化区域生态系统生物量和碳库变化等提供科技支撑。 相似文献
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结合黑龙江省自然资源清查整治工作的实际需求,基于测绘地理信息数据、技术和信息系统的优势,设计了测绘地理信息服务自然资源清查整治工作的总体技术路线,给出了实现方法,并介绍了黑龙江省的具体应用和实践情况.研究成果可为自然资源清查整治相关工作提供参考. 相似文献
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