南京市植被覆盖管理措施因子的时空格局动态变化
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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~(137)Cs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 ~(137)Cs 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 firstly decreased from 15.66%(1988)to 9.43%(before 2006)and then gradually increased to 12.07%. For the region where C values were greater than 0.3,the area with weaker resistance to soil erosion firstly slowly increased from 7.29% to 9.22%(2002),then it quickly increased to 12.31%(2002-2006)and then slowly decreased to 11.77%. Therefore,it is feasible to retrieve the long-term sequence C factor based on BP neural network and LAI proposed in this paper,which provides a new way for quantitative remote sensing monitoring of soil erosion on regional scale.
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 ~(137)Cs 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 firstly decreased from 15.66%(1988)to 9.43%(before 2006)and then gradually increased to 12.07%. For the region where C values were greater than 0.3,the area with weaker resistance to soil erosion firstly slowly increased from 7.29% to 9.22%(2002),then it quickly increased to 12.31%(2002-2006)and then slowly decreased to 11.77%. Therefore,it is feasible to retrieve the long-term sequence C factor based on BP neural network and LAI proposed in this paper,which provides a new way for quantitative remote sensing monitoring of soil erosion on regional scale.
引文
[1]VATANDA?LAR C,YAVUZ M.Modeling Cover Management Factor of RUSLE Using Very High?Resolution Satellite Imagery in a Semiarid Watershed[J].Environmental Earth Sciences,2017,76(2):65.DOI:10.1007/s12665-017-6388-0.
[2]WIJESUNDARA N C,ABEYSINGHA N S,DISSANAYAKE D MS L B.GIS?Based Soil Loss Estimation Using RUSLE Model:ACase of Kirindi Oya River Basin,Sri Lanka[J].Modeling Earth Systems and Environment,2018,4(1):251-262.
[3]LU D,LI G,VALLADARES G S,et al.Mapping Soil Erosion Risk in Rond?nia,Brazilian Amazonia:Using RUSLE,Remote Sensing and GIS[J].Land Degradation and Development,2004,15(5):499-512.
[4]RAWAT K S,SINGH S K.Appraisal of Soil Conservation Capaci?ty Using NDVI Model?Based C Factor of RUSLE Model for a Semi Arid Ungauged Watershed:A Case Study[J].Water Conser?vation Science and Engineering,2018,3(1):47-58.
[5]林杰,张金池,顾哲衍,等.基于叶面积指数的植被覆盖管理措施因子C的遥感定量估算[J].林业科学,2013,49(2):86-92.[LIN Jie,ZHANG Jin?chi,GU Zhe?yan,et al.Quantitative As?sessment of Vegetation Cover and Management Factor Based on Leaf Area Index and Remote Sensing[J].Scientia Silvae Sinicae,2013,49(2):86-92.]
[6]杨勤科,罗万勤,马宏斌,等.区域水土流失植被因子的遥感提取[J].水土保持研究,2006,13(5):267-268,271.[YANGQin?ke,LUO Wan?qin,MA Hong?bin,et al.NDVI Extraction of Regional Soil Erosion Based on Remote Sensing[J].Research of Soil and Water Conservation,2006,13(5):267-268,271.]
[7]LIN J,ZHANG J C,GU Z Y,et al.A New Approach of Assessing Soil Erosion Using the Remotely Sensed Leaf Area Index and Its Application in the Hilly Area[J].Vegetos,2014,27(2):1-12.DOI:10.5958/2229-4473.2014.00008.1.
[8]冯强,赵文武.USLE/RUSLE中植被覆盖与管理因子研究进展[J].生态学报,2014,34(16):4461-4472.[FENG Qiang,ZHAO Wen?wu.The Study on Cover?Management Factor in USLE and RUSLE:A Review[J].Acta Ecologica Sinica,2014,34(16):4461-4472.]
[9]夏天,吴文斌,周清波,等.冬小麦叶面积指数高光谱遥感反演方法对比[J].农业工程学报,2013,29(3):139-147.[XIATian,WU Wen?bin,ZHOU Qing?bo,et al.Comparison of Two In?version Methods for Winter Wheat Leaf Area Index Based on Hy?perspectral Remote Sensing[J].Transactions of the Chinese Soci?ety of Agricultural Engineering(Transactions of the CSAE),2013,29(3):139-147.]
[10]陈健,倪绍祥,李云梅.基于神经网络方法的芦苇叶面积指数遥感反演[J].国土资源遥感,2008(2):62-67.[CHEN Jian,NI Shao?xiang,LI Yun?mei.LAI Retrieval of Reed Canopy Using the Neural Network Method[J].Remote Sensing for Land&Re?sources,2008(2):62-67.]
[11]NOBLE P A,TRIBOU E H.Neuroet:An Easy?to?Use Artificial Neural Network for Ecological and Biological Modeling[J].Eco?logical Modelling,2007,203(1):87-98.
[12]杨敏,林杰,顾哲衍,等.基于Landsat 8 OLI多光谱影像数据和BP神经网络的叶面积指数反演[J].中国水土保持科学,2015,13(4):86-93.[YANG Min,LIN Jie,GU Zhe?yan,et al.Leaf Area Index Retrieval Based on Landsat 8 OLI Multi?Spectral Image Data and BP Neural Network[J].Science of Soil and Wa?ter Conservation,2015,13(4):86-93.]
[13]黄荣珍,张金池,李凤,等.GIS在南京市土壤侵蚀监测中的应用[J].南昌工程学院学报,2007,26(4):49-53.[HUANGRong?zhen,ZHANG Jin?chi,LI Feng,et al.Application of GIS in Soil Erosion Monitoring in Nanjing[J].Journal of Nanchang Insti?tute of Technology,2007,26(4):49-53.]
[14]马力,卜兆宏,彭桂兰,等.南京市2001-2008年水土流失的时空变化与治理成效研究[J].土壤学报,2011,48(4):683-692.[MA Li,BU Zhao?hong,PENG Gui?lan,et al.Temporal and Spatial Variation of Soil and Water Loss and Its Control in Nan?jing Area From 2001 to 2008[J].Acta Pedologica Sinica,2011,48(4):683-692.]
[15]邵方泽,张慧,缪旭波.基于RUSLE模型的南京市2006-2014年水土侵蚀时空分布特征[J].江苏农业科学,2017,45(17):264-269.
[16]吴雨伦,胡海波.南京市生态清洁小流域建设现状与措施体系[J].中国水土保持,2015(8):18-20.
[17]薛雪,杨静,郑云峰,等.南京城市杂交马褂木林小气候特征研究[J].水土保持研究,2016,23(4):226-232.[XUE Xue,YANG Jing,ZHENG Yun?feng,et al.Microclimate Characteris?tics of Liriodendron chinense×tulipifera Forest in Nanjing[J].Re?search of Soil and Water Conservation,2016,23(4):226-232.]
[18]岳健敏,张金池,庄家尧,等.南京毛竹林小流域SCS-CN方法初损率λ取值研究[J].中国水土保持科学,2015,13(5):9-15.[YUE Jian?min,ZHANG Jin?chi,ZHUANG Jia?yao,et al.Calibration of SCS-CN Initial Abstraction Ration of a Small Wa?tershed in Nanjing Bamboo Forest[J].Science of Soil and Water Conservation,2015,13(5):9-15.]
[19]李苗苗,吴炳方,颜长珍,等.密云水库上游植被覆盖度的遥感估算[J].资源科学,2004,26(4):153-159.[LI Miao?miao,WU Bing?fang,YAN Chang?zhen,et al.Estimation of Vegetation Fraction in the Upper Basin of Miyun Reservoir by Remote Sens?ing[J].Resource Science,2004,26(4):153-159.]
[20]李登科,范建忠,王娟.陕西省植被覆盖度变化特征及其成因[J].应用生态学报,2010,21(11):2896-2903.[LI Deng?ke,FAN Jian?zhong,WANG Juan.Change Characteristics and Their Causes of Fractional Vegetation Coverage(FVC)in Shaanxi Prov?ince[J].Chinese Journal of Applied Ecology,2010,21(11):2896-2903.]
[21]CHEN J M,RICH P M,GOWER S T,et al.Leaf Area Index of Bo?real Forests:Theory,Techniques,and Measurements[J].Journal of Geophysical Research,1997,102(D24):29429-29443.
[22]KUUSK A,LANG M,NILSON T.Simulation of the Reflectance of Ground Vegetation in Sub?Boreal Forests[J].Agricultural and Forest Meteorology,2004,126(1):33-46.
[23]蔡崇法,丁树文,史志华,等.应用USLE模型与地理信息系统IDRISI预测小流域土壤侵蚀量的研究[J].水土保持学报,2000,14(2):19-24.[CAI Chong?fa,DING Shu?wen,SHIZhi?hua,et al.Study of Applying USLE and Geographical Infor?mation System IDRISI to Predict Soil Erosion in Small Watershed[J].Journal of Soil and Water Conservation,2000,14(2):19-24.]
[24]王琳贤.镇江地区土壤侵蚀的137Cs和210Pbex双同位素法示踪研究[D].南京:南京师范大学,2011.
[25]张信宝.137Cs法测算黄土高原土壤侵蚀速率的初步研究[J].地球化学,1991,20(3):212-218.[ZHANG Xin?bao.A Prelimi?nary Assessment of the Potential to Use137Cs to Estimate the Rates of Soil Erosion in the Loss Plateau of China[J].Geochimi?ca,1991,20(3):212-218.]
[26]杨浩,杜明远,赵其国,等.利用137Cs示踪农业耕作土壤侵蚀速率的定量模型[J].土壤学报,2000,37(3):296-305.[YANG Hao,DU Ming?yuan,ZHAO Qi?guo,et al.Quantitative Model to Estimate Soil Erosion Rates Using Caesium?137 for Cul?tivated Soil[J].Acta Pedologica Sinica,2000,37(3):296-305.]
[27]唐克丽.中国水土保持[M].北京:科学出版社,2004:321.
[28]赵其国.我国南方当前水土流失与生态安全中值得重视的问题[J].水土保持通报,2006,26(2):1-8.[ZHAO Qi?guo.Some Considerations for Present Soil and Water Conservation and Ecol?ogy Security of South China[J].Bulletin of Soil and Water Con?servation,2006,26(2):1-8.]
[2]WIJESUNDARA N C,ABEYSINGHA N S,DISSANAYAKE D MS L B.GIS?Based Soil Loss Estimation Using RUSLE Model:ACase of Kirindi Oya River Basin,Sri Lanka[J].Modeling Earth Systems and Environment,2018,4(1):251-262.
[3]LU D,LI G,VALLADARES G S,et al.Mapping Soil Erosion Risk in Rond?nia,Brazilian Amazonia:Using RUSLE,Remote Sensing and GIS[J].Land Degradation and Development,2004,15(5):499-512.
[4]RAWAT K S,SINGH S K.Appraisal of Soil Conservation Capaci?ty Using NDVI Model?Based C Factor of RUSLE Model for a Semi Arid Ungauged Watershed:A Case Study[J].Water Conser?vation Science and Engineering,2018,3(1):47-58.
[5]林杰,张金池,顾哲衍,等.基于叶面积指数的植被覆盖管理措施因子C的遥感定量估算[J].林业科学,2013,49(2):86-92.[LIN Jie,ZHANG Jin?chi,GU Zhe?yan,et al.Quantitative As?sessment of Vegetation Cover and Management Factor Based on Leaf Area Index and Remote Sensing[J].Scientia Silvae Sinicae,2013,49(2):86-92.]
[6]杨勤科,罗万勤,马宏斌,等.区域水土流失植被因子的遥感提取[J].水土保持研究,2006,13(5):267-268,271.[YANGQin?ke,LUO Wan?qin,MA Hong?bin,et al.NDVI Extraction of Regional Soil Erosion Based on Remote Sensing[J].Research of Soil and Water Conservation,2006,13(5):267-268,271.]
[7]LIN J,ZHANG J C,GU Z Y,et al.A New Approach of Assessing Soil Erosion Using the Remotely Sensed Leaf Area Index and Its Application in the Hilly Area[J].Vegetos,2014,27(2):1-12.DOI:10.5958/2229-4473.2014.00008.1.
[8]冯强,赵文武.USLE/RUSLE中植被覆盖与管理因子研究进展[J].生态学报,2014,34(16):4461-4472.[FENG Qiang,ZHAO Wen?wu.The Study on Cover?Management Factor in USLE and RUSLE:A Review[J].Acta Ecologica Sinica,2014,34(16):4461-4472.]
[9]夏天,吴文斌,周清波,等.冬小麦叶面积指数高光谱遥感反演方法对比[J].农业工程学报,2013,29(3):139-147.[XIATian,WU Wen?bin,ZHOU Qing?bo,et al.Comparison of Two In?version Methods for Winter Wheat Leaf Area Index Based on Hy?perspectral Remote Sensing[J].Transactions of the Chinese Soci?ety of Agricultural Engineering(Transactions of the CSAE),2013,29(3):139-147.]
[10]陈健,倪绍祥,李云梅.基于神经网络方法的芦苇叶面积指数遥感反演[J].国土资源遥感,2008(2):62-67.[CHEN Jian,NI Shao?xiang,LI Yun?mei.LAI Retrieval of Reed Canopy Using the Neural Network Method[J].Remote Sensing for Land&Re?sources,2008(2):62-67.]
[11]NOBLE P A,TRIBOU E H.Neuroet:An Easy?to?Use Artificial Neural Network for Ecological and Biological Modeling[J].Eco?logical Modelling,2007,203(1):87-98.
[12]杨敏,林杰,顾哲衍,等.基于Landsat 8 OLI多光谱影像数据和BP神经网络的叶面积指数反演[J].中国水土保持科学,2015,13(4):86-93.[YANG Min,LIN Jie,GU Zhe?yan,et al.Leaf Area Index Retrieval Based on Landsat 8 OLI Multi?Spectral Image Data and BP Neural Network[J].Science of Soil and Wa?ter Conservation,2015,13(4):86-93.]
[13]黄荣珍,张金池,李凤,等.GIS在南京市土壤侵蚀监测中的应用[J].南昌工程学院学报,2007,26(4):49-53.[HUANGRong?zhen,ZHANG Jin?chi,LI Feng,et al.Application of GIS in Soil Erosion Monitoring in Nanjing[J].Journal of Nanchang Insti?tute of Technology,2007,26(4):49-53.]
[14]马力,卜兆宏,彭桂兰,等.南京市2001-2008年水土流失的时空变化与治理成效研究[J].土壤学报,2011,48(4):683-692.[MA Li,BU Zhao?hong,PENG Gui?lan,et al.Temporal and Spatial Variation of Soil and Water Loss and Its Control in Nan?jing Area From 2001 to 2008[J].Acta Pedologica Sinica,2011,48(4):683-692.]
[15]邵方泽,张慧,缪旭波.基于RUSLE模型的南京市2006-2014年水土侵蚀时空分布特征[J].江苏农业科学,2017,45(17):264-269.
[16]吴雨伦,胡海波.南京市生态清洁小流域建设现状与措施体系[J].中国水土保持,2015(8):18-20.
[17]薛雪,杨静,郑云峰,等.南京城市杂交马褂木林小气候特征研究[J].水土保持研究,2016,23(4):226-232.[XUE Xue,YANG Jing,ZHENG Yun?feng,et al.Microclimate Characteris?tics of Liriodendron chinense×tulipifera Forest in Nanjing[J].Re?search of Soil and Water Conservation,2016,23(4):226-232.]
[18]岳健敏,张金池,庄家尧,等.南京毛竹林小流域SCS-CN方法初损率λ取值研究[J].中国水土保持科学,2015,13(5):9-15.[YUE Jian?min,ZHANG Jin?chi,ZHUANG Jia?yao,et al.Calibration of SCS-CN Initial Abstraction Ration of a Small Wa?tershed in Nanjing Bamboo Forest[J].Science of Soil and Water Conservation,2015,13(5):9-15.]
[19]李苗苗,吴炳方,颜长珍,等.密云水库上游植被覆盖度的遥感估算[J].资源科学,2004,26(4):153-159.[LI Miao?miao,WU Bing?fang,YAN Chang?zhen,et al.Estimation of Vegetation Fraction in the Upper Basin of Miyun Reservoir by Remote Sens?ing[J].Resource Science,2004,26(4):153-159.]
[20]李登科,范建忠,王娟.陕西省植被覆盖度变化特征及其成因[J].应用生态学报,2010,21(11):2896-2903.[LI Deng?ke,FAN Jian?zhong,WANG Juan.Change Characteristics and Their Causes of Fractional Vegetation Coverage(FVC)in Shaanxi Prov?ince[J].Chinese Journal of Applied Ecology,2010,21(11):2896-2903.]
[21]CHEN J M,RICH P M,GOWER S T,et al.Leaf Area Index of Bo?real Forests:Theory,Techniques,and Measurements[J].Journal of Geophysical Research,1997,102(D24):29429-29443.
[22]KUUSK A,LANG M,NILSON T.Simulation of the Reflectance of Ground Vegetation in Sub?Boreal Forests[J].Agricultural and Forest Meteorology,2004,126(1):33-46.
[23]蔡崇法,丁树文,史志华,等.应用USLE模型与地理信息系统IDRISI预测小流域土壤侵蚀量的研究[J].水土保持学报,2000,14(2):19-24.[CAI Chong?fa,DING Shu?wen,SHIZhi?hua,et al.Study of Applying USLE and Geographical Infor?mation System IDRISI to Predict Soil Erosion in Small Watershed[J].Journal of Soil and Water Conservation,2000,14(2):19-24.]
[24]王琳贤.镇江地区土壤侵蚀的137Cs和210Pbex双同位素法示踪研究[D].南京:南京师范大学,2011.
[25]张信宝.137Cs法测算黄土高原土壤侵蚀速率的初步研究[J].地球化学,1991,20(3):212-218.[ZHANG Xin?bao.A Prelimi?nary Assessment of the Potential to Use137Cs to Estimate the Rates of Soil Erosion in the Loss Plateau of China[J].Geochimi?ca,1991,20(3):212-218.]
[26]杨浩,杜明远,赵其国,等.利用137Cs示踪农业耕作土壤侵蚀速率的定量模型[J].土壤学报,2000,37(3):296-305.[YANG Hao,DU Ming?yuan,ZHAO Qi?guo,et al.Quantitative Model to Estimate Soil Erosion Rates Using Caesium?137 for Cul?tivated Soil[J].Acta Pedologica Sinica,2000,37(3):296-305.]
[27]唐克丽.中国水土保持[M].北京:科学出版社,2004:321.
[28]赵其国.我国南方当前水土流失与生态安全中值得重视的问题[J].水土保持通报,2006,26(2):1-8.[ZHAO Qi?guo.Some Considerations for Present Soil and Water Conservation and Ecol?ogy Security of South China[J].Bulletin of Soil and Water Con?servation,2006,26(2):1-8.]