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南京市植被覆盖管理措施因子的时空格局动态变化 预览

Spatial and Temporal Dynamic Changes of Vegetation Cover and Management Factor in Nanjing City
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摘要 修正的通用土壤侵蚀方程(revised universal soil loss equation,RUSLE)模型中的植被覆盖管理措施因子(C)可用来表示植被覆盖对土壤的防蚀作用,是评价土壤侵蚀的关键参数之一,而区域尺度高质量时间序列C因子的合理估算及空间分布特征是预测区域土壤侵蚀动态变化的重要环节。因此,及时准确地掌握区域尺度长时间序列的C因子,对研究土壤侵蚀动态变化与植被的关系至关重要。选择南京市1988-2013年10期遥感影像,基于后向反射(back propagation,BP)神经网络和遥感数据反演植被结构因子叶面积指数(leaf area index,LAI),构建LAI与C因子的量化耦合模型,通过137Cs同位素示踪技术获取C因子的实测值,验证并探讨反演模型的精度。结果表明:(1)基于归一化植被指数(normalized difference vegetation index,NDVI)计算的传统植被覆盖度在整体上比基于LAI计算的植被方向性覆盖度偏大,得到的C值与实测值相比整体上偏小,均方根误差(root mean square error,RMSE)为87. 829%;而基于LAI计算的C值与实测值接近,RMSE为30. 017%,能更好地反映实际的植被结构信息;(2)C值大于0. 3的区域主要分布于建筑物较为密集、植被稀疏且植被结构简单甚至无植被覆盖的市区;C值小于0. 05的区域主要分布在植被密集且植被结构复杂的丘陵山区,南京市C值的分布与植被覆盖和土地利用类型关系密切。(3)从全市整体来看,1988-2013年C值小于0. 05的抵抗土壤侵蚀能力较强的区域面积先由南京市总面积的15. 66%(1988年)减小到9. 43%(2006年以前),后逐渐增大到12. 07%;C值大于0. 3的抵抗土壤侵蚀能力较弱的区域面积先由南京市总面积的7. 29%缓慢增大到9. 22%(2002年),后迅速增大到12. 31%(2002-2006年),然后缓慢减小至11. 77%。所提出的基于BP神经网络和LAI反演的长时间序列C因子估算方法是可靠的,可为区域尺度土壤侵蚀定量遥感监测提供新途径。 The vegetation cover and management factor(C)in the revised universal soil loss equation(RUSLE) model is used to indicate the effect of vegetation cover on soil erosion,and is one of the key parameters for evaluating soil erosion.The precise estimation and the spatial distribution characteristics of the long-term sequence of C factor at the regional scale are important for the prediction of dynamics of regional soil erosion. Therefore,timely and accurate grasping of the long-term sequence of C factor at the regional scale is very important for studying the dynamic relationship between soil erosion and vegetation. The remote sensing image in Nanjing City from 1988 to 2013 was selected and used. The quantification coupling model between leaf area index(LAI) and C factor was constructed based on BP neural network and the vegetation structure factor LAI inversed by remote sensing. The field measurement of C factor was obtained by 137Cs isotope tracer technique,and the accuracies of the inversion models were verified. The results showed that:(1)The traditional vegetation coverage based on normalized difference vegetation index(NDVI)was generally larger than that based on LAI. The C value was generally smaller than the measured value,and the RMSE was 87.829%. While the C value calculated by LAI was close to the measured value,and the RMSE was 30.017%. Therefore,the C value calculated by LAI can reflect the actual vegetation structure information better than by NDVI.(2)The area with C value greater than0.3 was mainly distributed in the urban areas with dense buildings,sparse vegetation and simple vegetation structure or even no vegetation cover. The area with C value less than 0.05 was mainly distributed in the hills and mountainous regions with dense vegetation structure. The distribution of C value in Nanjing City was closely related to vegetation cover and land use types.(3)Over the whole city where the C values were less than 0.05,during the period from 1988 to 2013,the area with strong resistance to soil erosion fi
作者 林杰 董波 潘颖 杨敏 朱茜 LIN Jie;DONG Bo;PAN Ying;YANG Min;ZHU Xi(Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province/Key Laboratory of Soil and Water Conservation and Ecological Restoration of Jiangsu Province,Nanjing Forestry University,Nanjing 210037,China)
出处 《生态与农村环境学报》 CAS CSCD 北大核心 2019年第5期617-626,共10页 Journal of Ecology and Rural Environment
基金 国家重点研发项目(2017YFC0505505) 国家自然科学基金面上项目(31870600) 江苏高校优势学科建设工程资助项目.
关键词 BP神经网络 长时间序列 叶面积指数(LAI) 土壤侵蚀 BP neural network long-term sequence leaf area index(LAI) soil erosion
作者简介 通讯作者:林杰(1976-),女,辽宁丹东人,副教授,博士,研究方向为土壤侵蚀遥感监测。E-mail:jielin@njfu.edu.cn.
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