21世纪以来闽三角城市群人类景观开发强度的时空演变——基于能值-GIS方法
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摘要
在快速的城市化进程中,城市群作为城市发展到成熟阶段的高级空间组织形式,其在推进城市群区域社会经济一体化的同时,也在一定程度上加剧了城市群的人地关系矛盾和景观开发强度。为量化城市群的人类景观开发强度,揭示自然环境对人类资源消耗过程的压力响应机制,选取闽三角城市群在2000、2005和2013年的夜间灯光数据(DMSP/OLS)作为刻画人类经济活动范围的基础数据,通过能值分析理论,耦合可更新能源(太阳能、风能、地热能和降雨化学能等)与基于统计数据的不可更新能源进行能值核算,并进一步描述人类景观开发强度(Landscape development intensity,LDI)的时空演变特征,基于人类活动对自然生态系统的干扰程度划分为5级进行空间展示与分析。结果表明:(1)在时间尺度上,闽三角城市群的年可更新能值密度均值在2000、2005和2013年,分别为9.42×10~(16) sej/hm~2、7.25×10~(16) sej/hm~2和7.88×10~(16) sej/hm~2,呈现出先减后增的趋势,在空间尺度上,闽三角城市群可更新资源由环海湾地带向内陆地区呈环形逐层增加,其中在2000年表现尤为明显;(2)在不可更新资源的利用上,闽三角城市群的本地不可更新能源能值投入在2000、2005和2013年,分别为4.64×10~(24) sej、4.63×10~(24) sej和4.66×10~(24) sej,只出现轻微浮动,而外部输入能源的能值投入则在2000、2005和2013年,分别为1.41×10~(24) sej、3.18×10~(24) sej和5.71×10~(24) sej呈现逐年上升趋势,且其中对原煤的需求表现最高,占比高达90%;(3)LDI在空间分布上,自内陆至沿海干扰等级逐渐上升,其中,强度干扰与剧烈干扰主要覆盖厦门市、龙海市、晋江市和石狮市;在时间分布上,呈现出强度干扰和剧烈干扰逐渐增大的趋势。研究有助于政府对不同干扰等级区域的城乡建设与规划管理提供决策依据,为实现城市群可持续发展提供数据参考。
In the rapid urbanization processes, urban agglomerations as a senior spatial organization of urban development at a mature stage. Urban agglomerations promote regional societies and economic integration, but can result in conflicts between the natural environment and human activities, particularly because of landscape development intensity. In order to quantify the collisions and reveal the pressure response mechanisms between the natural environment and human resource utilization, this study first opted the Fujian delta urban agglomeration night light data(DMSP/OLS) from 2000, 2005, and 2013 as the fundamental data for an analysis of the realm of human economic activities. And emergy analysis theory was used to account for renewable emergy(solar, wind, geothermal, rainfall chemical emergy, etc.), and non-renewable emergy which were based on statistics in the same year. Integrate renewable and non-renewable emergy to further describe the human landscape development intensity(LDI) at spatio-temporal scales. Accordance with the otherness of human activities intensity with the natural ecological system, the LDI values were divided into 5 levels. The results showed:(1) Over the time scale, the mean values for renewable emergy density in 2000, 2005, and 2013 were 9.42×10~(16) sej/hm~2, 7.25×10~(16) sej/hm~2 and 7.88×10~(16) sej/hm~2, respectively, represented a decreasing trend before increasing again. On the spatial scale, renewable resources demonstrated a increase from the gulf zone to the inland area of annularity in the Fujian delta urban agglomeration, which was particularly evident in 2000;(2) As for the use of non-renewable resources, the investments of local non-renewable emergy value were 4.64×10~(24) sej, 4.63×10~(24) sej, and 4.66×10~(24) sej, respectively in 2000, 2005, and 2013 which was change lightly,while the inflows from the outside emergy were 1.41×10~(24) sej, 3.18×10~(24) sej and 5.71×10~(24) sej in 2000, 2005 and 2013, showing a growing tendency, among which the demand for raw coal was the highest, accounting for 90%;(3) In the spatial distribution of LDI, from the inland area to the ring bay area, LDI levels increased, and disturbances with severe interference were mainly observed in Xiamen, Longhai, Jinjiang, and Shishi. In terms of the temporal distribution, the scope of strength intensity and severe interference gradually increased. This research findings of different disturbance levels will help the government provide of a decision-making foundation for urban construction and planning. Meanwhile, provide a datum reference for the sustainable development of urban agglomerations.
In the rapid urbanization processes, urban agglomerations as a senior spatial organization of urban development at a mature stage. Urban agglomerations promote regional societies and economic integration, but can result in conflicts between the natural environment and human activities, particularly because of landscape development intensity. In order to quantify the collisions and reveal the pressure response mechanisms between the natural environment and human resource utilization, this study first opted the Fujian delta urban agglomeration night light data(DMSP/OLS) from 2000, 2005, and 2013 as the fundamental data for an analysis of the realm of human economic activities. And emergy analysis theory was used to account for renewable emergy(solar, wind, geothermal, rainfall chemical emergy, etc.), and non-renewable emergy which were based on statistics in the same year. Integrate renewable and non-renewable emergy to further describe the human landscape development intensity(LDI) at spatio-temporal scales. Accordance with the otherness of human activities intensity with the natural ecological system, the LDI values were divided into 5 levels. The results showed:(1) Over the time scale, the mean values for renewable emergy density in 2000, 2005, and 2013 were 9.42×10~(16) sej/hm~2, 7.25×10~(16) sej/hm~2 and 7.88×10~(16) sej/hm~2, respectively, represented a decreasing trend before increasing again. On the spatial scale, renewable resources demonstrated a increase from the gulf zone to the inland area of annularity in the Fujian delta urban agglomeration, which was particularly evident in 2000;(2) As for the use of non-renewable resources, the investments of local non-renewable emergy value were 4.64×10~(24) sej, 4.63×10~(24) sej, and 4.66×10~(24) sej, respectively in 2000, 2005, and 2013 which was change lightly,while the inflows from the outside emergy were 1.41×10~(24) sej, 3.18×10~(24) sej and 5.71×10~(24) sej in 2000, 2005 and 2013, showing a growing tendency, among which the demand for raw coal was the highest, accounting for 90%;(3) In the spatial distribution of LDI, from the inland area to the ring bay area, LDI levels increased, and disturbances with severe interference were mainly observed in Xiamen, Longhai, Jinjiang, and Shishi. In terms of the temporal distribution, the scope of strength intensity and severe interference gradually increased. This research findings of different disturbance levels will help the government provide of a decision-making foundation for urban construction and planning. Meanwhile, provide a datum reference for the sustainable development of urban agglomerations.
引文
[1] 方创琳. 中国城市群形成发育的新格局及新趋向. 地理科学, 2011, 31(9): 1025- 1034.
[2] 王静, 周伟奇, 许开鹏, 颜景理. 京津冀地区城市化对植被覆盖度及景观格局的影响. 生态学报, 2017, 37(21): 7019- 7029.
[3] Liao F J, Wang X M. Urban vegetation landscape fragmentation and the change of ecological values in Shenzhen. Journal of Landscape Research, 2017, 9(5): 67- 70, 73- 73.
[4] 尹海伟, 孔繁花, 祈毅, 王红扬, 周艳妮, 秦正茂. 湖南省城市群生态网络构建与优化. 生态学报, 2011, 31(10): 2863- 2874.
[5] Buyantuyev A, Wu J G. Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landscape Ecology, 2010, 25(1): 17- 33.
[6] 李志刚, 曾焕忱, 叶静文, 李军, 韩诗畴. 珠三角重要生态区域蝶类多样性及其对区域环境的指示. 生态科学, 2015, 34(5): 167- 171.
[7] Pauw A, Louw K. Urbanization drives a reduction in functional diversity in a guild of nectar-feeding birds. Ecology and Society, 2012, 17(2): 27.
[8] 叶鑫, 邹长新, 刘国华, 林乃峰, 徐梦佳. 生态安全格局研究的主要内容与进展. 生态学报, 2018, 38(10): 3382- 3392.
[9] 黄国和, 安春江, 范玉瑞, 徐琳瑜, 李永平, 蔡宴朋, 李延峰, 李锋. 珠江三角洲城市群生态安全保障技术研究. 生态学报, 2016, 36(22): 7119- 7124.
[10] 曹祺文, 张曦文, 马洪坤, 吴健生. 景观生态风险研究进展及基于生态系统服务的评价框架: ESRISK. 地理学报, 2018, 73(5): 843- 855.
[11] 康鹏, 陈卫平, 王美娥. 基于生态系统服务的生态风险评价研究进展. 生态学报, 2016, 36(5): 1192- 1203.
[12] 严珅, 孙然好. 京津冀县域城镇化与景观格局变化的协调性研究. 生态环境学报, 2018, 27(1): 62- 70.
[13] 刘春玲, 王永, 姚翔龙, 童立强, 祁生文, 贺鹏. 天津滨海新区重点海岸带土地生态景观综合分析与评价. 第四纪研究, 2018, 38(2): 505- 511.
[14] 胡志仁, 龚建周, 李天翔, 孙家仁. 珠江三角洲城市群生态安全评价及态势分析. 生态环境学报, 2018, 27(2): 304- 312.
[15] 朱卫红, 苗承玉, 郑小军, 曹光兰, 王凡凡. 基于3S技术的图们江流域湿地生态安全评价与预警研究. 生态学报, 2014, 34(06): 1379- 1390.
[16] 熊鹰, 陈昊林. 基于属性理论的长株潭城市群生态系统健康评价. 生态环境学报, 2010, 19(6): 1422- 1427.
[17] Wang Z Y, Tang L N, Qiu Q Y, Chen H X, Wu T, Shao G F. Assessment of regional ecosystem health—a case study of the golden triangle of southern Fujian Province, China. International Journal of Environmental Research and Public Health, 2018, 15(4): 802.
[18] Wu L Y, You W B, Ji Z R, Xiao S H, He D J. Ecosystem health assessment of Dongshan Island based on its ability to provide ecological services that regulate heavy rainfall. Ecological Indicators, 2018, 84: 393- 403.
[19] Brown M T, Vivas M B. Landscape development intensity index. Environmental Monitoring and Assessment, 2005, 101(1/3): 289- 309.
[20] Chen T S, Lin H J. Application of a landscape development intensity index for assessing wetlands in Taiwan. Wetlands, 2011, 31(4): 745- 756.
[21] Bonzongo J C J, Donkor A K, Attibayeba A, Gao J. Linking landscape development intensity within watersheds to methyl-mercury accumulation in river sediments. Ambio, 2016, 45(2): 196- 204.
[22] Odum H T. Environmental Accounting: Emergy and Environmental Decision Making. New York: John Wiley & Sons, 1996.
[23] 林金煌, 陈文惠, 祁新华, 程瑞彤, 陈增文. 闽三角城市群生态系统格局演变及其驱动机制. 生态学杂志, 2018, 37(1): 203- 210.
[24] Su Y X, Chen X Z, Wang C Y, Zhang H G, Liao J S, Ye Y Y, Wang C J. A new method for extracting built-up urban areas using DMSP-OLS nighttime stable lights: a case study in the Pearl River Delta, southern China. GIScience & Remote Sensing, 2015, 52(2): 218- 238.
[25] 吴小语, 张鹏林. 融合DMSP-OLS和Landsat影像的城区边界提取. 应用科学学报, 2016, 34(1): 67- 74.
[26] Ma T, Zhou C H, Pei T, Haynie S, Fan J F. Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: a comparative case study from China′s cities. Remote Sensing of Environment, 2012, 124: 99- 107.
[27] Pandey B, Joshi P K, Seto K C. Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data. International Journal of Applied Earth Observation and Geoinformation, 2013, 23: 49- 61.
[28] Tripathy B R, Tiwari V, Pandey V, Elvidge C D, Rawat J S, Sharma M P, Prawasi R, Kumar P. Estimation of urban population dynamics using DMSP-OLS night-time lights time series sensors data. IEEE Sensors Journal, 2017, 17(4): 1013- 1020.
[29] 周玉科, 高锡章, 倪希亮. 利用夜间灯光数据分析我国社会经济发展的区域不均衡特征. 遥感技术与应用, 2017, 32(6): 1107- 1113.
[30] Li X K, Zhang C R, Li W D, Liu K. Potential application of DMSP/OLS nighttime light data for estimating ground-level PM2.5 concentrations//Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China: IEEE, 2016: 5749- 5752.
[31] 杨雪, 张文忠. 基于栅格的区域人居自然和人文环境质量综合评价——以京津冀地区为例. 地理学报, 2016, 71(12): 2141- 2154.
[32] Wang C D, Zhang S Y, Yan W L, Wang R Q, Liu J, Wang Y T. Evaluating renewable natural resources flow and net primary productivity with a GIS-Emergy approach: a case study of Hokkaido, Japan. Scientific Reports, 2016, 6: 37552.
[33] Brown M T, Ulgiati S. Updated evaluation of exergy and emergy driving the geobiosphere: a review and refinement of the emergy baseline. Ecological Modelling, 2010, 221(20): 2501- 2508.
[34] Mellino S, Ripa M, Ulgiati S. Spatial accounting of environmental pressure and resource consumption using night-light satellite imagery. Journal of Environmental Accounting and Management, 2013, 1(4): 361- 379.
[35] 张淼, 刘俊国, 赵旭, 陈忞忞. 基于景观开发强度法的湿地健康变化研究. 水土保持研究, 2014, 21(3): 157- 162.
[36] 楚芳芳, 蒋涤非. 基于能值分析的长株潭城市群生态经济系统演变态势分析. 经济地理, 2012, 32(2): 143- 148.
[37] Tripathy B R, Sajjad H, Elvidge C D, Ting Y, Pandey P C, Rani M, Kumar P. Modeling of electric demand for sustainable energy and management in India using spatio-temporal DMSP-OLS night-time data. Environmental Management, 2018, 61(4): 615- 623. 附录
[2] 王静, 周伟奇, 许开鹏, 颜景理. 京津冀地区城市化对植被覆盖度及景观格局的影响. 生态学报, 2017, 37(21): 7019- 7029.
[3] Liao F J, Wang X M. Urban vegetation landscape fragmentation and the change of ecological values in Shenzhen. Journal of Landscape Research, 2017, 9(5): 67- 70, 73- 73.
[4] 尹海伟, 孔繁花, 祈毅, 王红扬, 周艳妮, 秦正茂. 湖南省城市群生态网络构建与优化. 生态学报, 2011, 31(10): 2863- 2874.
[5] Buyantuyev A, Wu J G. Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landscape Ecology, 2010, 25(1): 17- 33.
[6] 李志刚, 曾焕忱, 叶静文, 李军, 韩诗畴. 珠三角重要生态区域蝶类多样性及其对区域环境的指示. 生态科学, 2015, 34(5): 167- 171.
[7] Pauw A, Louw K. Urbanization drives a reduction in functional diversity in a guild of nectar-feeding birds. Ecology and Society, 2012, 17(2): 27.
[8] 叶鑫, 邹长新, 刘国华, 林乃峰, 徐梦佳. 生态安全格局研究的主要内容与进展. 生态学报, 2018, 38(10): 3382- 3392.
[9] 黄国和, 安春江, 范玉瑞, 徐琳瑜, 李永平, 蔡宴朋, 李延峰, 李锋. 珠江三角洲城市群生态安全保障技术研究. 生态学报, 2016, 36(22): 7119- 7124.
[10] 曹祺文, 张曦文, 马洪坤, 吴健生. 景观生态风险研究进展及基于生态系统服务的评价框架: ESRISK. 地理学报, 2018, 73(5): 843- 855.
[11] 康鹏, 陈卫平, 王美娥. 基于生态系统服务的生态风险评价研究进展. 生态学报, 2016, 36(5): 1192- 1203.
[12] 严珅, 孙然好. 京津冀县域城镇化与景观格局变化的协调性研究. 生态环境学报, 2018, 27(1): 62- 70.
[13] 刘春玲, 王永, 姚翔龙, 童立强, 祁生文, 贺鹏. 天津滨海新区重点海岸带土地生态景观综合分析与评价. 第四纪研究, 2018, 38(2): 505- 511.
[14] 胡志仁, 龚建周, 李天翔, 孙家仁. 珠江三角洲城市群生态安全评价及态势分析. 生态环境学报, 2018, 27(2): 304- 312.
[15] 朱卫红, 苗承玉, 郑小军, 曹光兰, 王凡凡. 基于3S技术的图们江流域湿地生态安全评价与预警研究. 生态学报, 2014, 34(06): 1379- 1390.
[16] 熊鹰, 陈昊林. 基于属性理论的长株潭城市群生态系统健康评价. 生态环境学报, 2010, 19(6): 1422- 1427.
[17] Wang Z Y, Tang L N, Qiu Q Y, Chen H X, Wu T, Shao G F. Assessment of regional ecosystem health—a case study of the golden triangle of southern Fujian Province, China. International Journal of Environmental Research and Public Health, 2018, 15(4): 802.
[18] Wu L Y, You W B, Ji Z R, Xiao S H, He D J. Ecosystem health assessment of Dongshan Island based on its ability to provide ecological services that regulate heavy rainfall. Ecological Indicators, 2018, 84: 393- 403.
[19] Brown M T, Vivas M B. Landscape development intensity index. Environmental Monitoring and Assessment, 2005, 101(1/3): 289- 309.
[20] Chen T S, Lin H J. Application of a landscape development intensity index for assessing wetlands in Taiwan. Wetlands, 2011, 31(4): 745- 756.
[21] Bonzongo J C J, Donkor A K, Attibayeba A, Gao J. Linking landscape development intensity within watersheds to methyl-mercury accumulation in river sediments. Ambio, 2016, 45(2): 196- 204.
[22] Odum H T. Environmental Accounting: Emergy and Environmental Decision Making. New York: John Wiley & Sons, 1996.
[23] 林金煌, 陈文惠, 祁新华, 程瑞彤, 陈增文. 闽三角城市群生态系统格局演变及其驱动机制. 生态学杂志, 2018, 37(1): 203- 210.
[24] Su Y X, Chen X Z, Wang C Y, Zhang H G, Liao J S, Ye Y Y, Wang C J. A new method for extracting built-up urban areas using DMSP-OLS nighttime stable lights: a case study in the Pearl River Delta, southern China. GIScience & Remote Sensing, 2015, 52(2): 218- 238.
[25] 吴小语, 张鹏林. 融合DMSP-OLS和Landsat影像的城区边界提取. 应用科学学报, 2016, 34(1): 67- 74.
[26] Ma T, Zhou C H, Pei T, Haynie S, Fan J F. Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: a comparative case study from China′s cities. Remote Sensing of Environment, 2012, 124: 99- 107.
[27] Pandey B, Joshi P K, Seto K C. Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data. International Journal of Applied Earth Observation and Geoinformation, 2013, 23: 49- 61.
[28] Tripathy B R, Tiwari V, Pandey V, Elvidge C D, Rawat J S, Sharma M P, Prawasi R, Kumar P. Estimation of urban population dynamics using DMSP-OLS night-time lights time series sensors data. IEEE Sensors Journal, 2017, 17(4): 1013- 1020.
[29] 周玉科, 高锡章, 倪希亮. 利用夜间灯光数据分析我国社会经济发展的区域不均衡特征. 遥感技术与应用, 2017, 32(6): 1107- 1113.
[30] Li X K, Zhang C R, Li W D, Liu K. Potential application of DMSP/OLS nighttime light data for estimating ground-level PM2.5 concentrations//Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China: IEEE, 2016: 5749- 5752.
[31] 杨雪, 张文忠. 基于栅格的区域人居自然和人文环境质量综合评价——以京津冀地区为例. 地理学报, 2016, 71(12): 2141- 2154.
[32] Wang C D, Zhang S Y, Yan W L, Wang R Q, Liu J, Wang Y T. Evaluating renewable natural resources flow and net primary productivity with a GIS-Emergy approach: a case study of Hokkaido, Japan. Scientific Reports, 2016, 6: 37552.
[33] Brown M T, Ulgiati S. Updated evaluation of exergy and emergy driving the geobiosphere: a review and refinement of the emergy baseline. Ecological Modelling, 2010, 221(20): 2501- 2508.
[34] Mellino S, Ripa M, Ulgiati S. Spatial accounting of environmental pressure and resource consumption using night-light satellite imagery. Journal of Environmental Accounting and Management, 2013, 1(4): 361- 379.
[35] 张淼, 刘俊国, 赵旭, 陈忞忞. 基于景观开发强度法的湿地健康变化研究. 水土保持研究, 2014, 21(3): 157- 162.
[36] 楚芳芳, 蒋涤非. 基于能值分析的长株潭城市群生态经济系统演变态势分析. 经济地理, 2012, 32(2): 143- 148.
[37] Tripathy B R, Sajjad H, Elvidge C D, Ting Y, Pandey P C, Rani M, Kumar P. Modeling of electric demand for sustainable energy and management in India using spatio-temporal DMSP-OLS night-time data. Environmental Management, 2018, 61(4): 615- 623. 附录