流域生态系统对城市化的时空响应
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摘要
以浙江省钱塘江流域为例,在生态系统管理的框架下,把流域看作一个整体的生态系统,将其划分为植被(农地、林地)、土壤、水3个子系统。借助遥感和地理信息系统,在4个时间跨度(1979-1985、1985-1994、1994-2003、2003-2009)以及3个空间尺度(流域、行政区、生态区)上,分析了钱塘江流域城市化的时空格局与过程。通过直接(监测数据分析)和间接(景观生态学)的方法,借助地理权重回归方法,定量分析了各个子系统响应城市化的时空机制。最后,利用突变理论建立了流域生态系统复合指标模型,并借助地理权重回归方法分析了流域生态系统响应城市化的空间机制。主要结果和结论如下：
1979-2009年间,钱塘江流域总人口、非农人口比例、国内生产总值(Gross Domestic Product, GDP)均显著增加,建筑用地扩张剧烈。在这种快速城市化过程中,农地景观格局的变化趋势为：面积和聚集度减少,破碎度、不稳定性、形状不规则度增加。农地景观格局变化与城市化的定量关系存在显著的空间不平稳性。总体来说：(1)在流域尺度上,非农人口比例、GDP、总人口、以及建筑物面积等城市化因子,均可以较好预测农地景观格局的变化；(2)在行政尺度上,城市化强度指数(Urbanization Intensity Index, UII)和GDP可以有效解释农地景观格局的变化。非农人口比例可以有效预测农地景观破碎度和不稳定性的变化(1979-1985)；(3)在生态区尺度上,UⅡ和GDP可以较好的解释农地景观格局变化,此外人口可以较好的预测农地景观格局变化(1985-1994)。
2003-2009年间,在200米及以下的高程带上,各尺度上林地NDVI均显著增加。在5°及以下的坡度带上,行政区林地NDVI显著增加。这些结果可能与林地种植结构改变有关。在600米及以上的高程带上,以及15°以上坡度带上,各尺度上林地NDVI显著下降。这些结果可能与矿山开采以及降水变化有关。
1979-2009年间,钱塘江流域土壤景观变得破碎、不稳定、形状不规则。此外,其连通度、多样性、聚集度也降低。土壤景观变化与城市化的定量关系具有显著的空间不平稳性。总体来说：(1)在流域尺度上,UII、GDP、总人口、以及建筑物面积等城市化因子,均可以有效预测土壤景观格局变化；(2)在行政区尺度上,UⅡ可以有效预测土壤景观格局变化。GDP和人口可以有效预测土壤景观破碎度和不稳定性变化(1985-2003)；(3)在生态区尺度上,UⅡ和GDP可以有效预测土壤景观格局的变化,人口增长会引起土壤破碎度和不稳定性的变化。
1996-2004年间,钱塘江流域的石油、以及磷污染逐渐变为一个全局性的问题,而重金属污染逐渐成为一个局部问题。空间回归结果显示,土地利用变化对重金属的时空动态有显著影响。而石油和氨氮的时空动态主要受人口和经济因素影响。对于溶解氧和总磷的时空变化而言,小流域尺度上的影响因子为人口和经济增长,而土地利用变化是500m缓冲区尺度上的主要影响因子。
2003年,流域生态系统复合指标数值在各尺度上均表现出较大的空间异质性,并与城市化的关系存在显著的空间不平稳性。总体来说：(1)在行政区尺度上,经济发展较快或非农人口比例较高的县市,其生态质量往往较低；(2)在生态区尺度上,经济发展较快或人口密度较高的生态区,其生态质量往往较低。
本研究取得的进展主要有：(1)分析了不同时空尺度上农地景观格局变化、土壤景观格局与城市化的定量关系,证明了这些关系具有空间不平稳性；(2)提出了基于管理的水污染源解析框架,解析了河流水质时空动态变化的影响因子；(3)将分层划分生态系统的方法与突变理论进行结合,提出了流域生态系统复合指标模型。定量分析了不同空间尺度上,流域生态系统对城市化的响应机制。本论文仍然存在一定的局限和不足,在今后的研究中,需要在变量选择、时空尺度选择、回归模型选择、复合指标模型建立、流域生态系统划分、不确定性分析、以及预测模型的建立等方面进行进一步的研究。
Based on the concept of Ecosystem Based Management, the whole watershed was regarded as an integrated ecosystem. The watershed ecosystem was divided into several sub-systems named vegetation (agricultural land and forest), soil and water, with a case of Qiantang River watershed in Zhejiang Province. The spatiotemporal dynamics of urbanization across the Qiantang River watershed were characterized by remote sensing and geographical information system at four temporal intervals (1979-1985,1985-1994,1994-2003,2003-2009) and three spatial scales (watershed, administration, and ecoregion). The dynamics of the four sub-systems were analyzed by direct (monitoring data based) and indirect (landscape ecology based) approaches, and their dynamic response to urbanization were investigated by geographically weighted regression. A composite indicator model was then developed by catastrophe theory, and the spatial response of watershed ecosystem to urbanization was finally analyzed by geographically weighted regression. The major findings and conclusions were summarized as follows:
Between1979and2009, the total population, non-agriculture population (%), and Gross Domestic Product (GDP) all increased significantly. Built-up land expanded vigorously. Under such rapid urbanization, agricultural landscapes became lost, fragmented, irregular, instable and less aggregated. Relationships between agricultural landscape patterns and urbanization presented significant spatial non-stationarity. Generally,(1) at watershed scale, urbanization indicators like total population, non-agriculture population (%), GDP, and area of built-ups could all effectively predict agricultural landscape pattern changes;(2) at administrative scale, urbanization intensity index (UII) and GDP could effectively predict agricultural landscape pattern changes. Changes of agricultural landscape fragmentation and instability could be explained by non-agriculture population (%);(3) at ecoregional scale, UII and GDP could effectively predict agricultural landscape pattern changes. Between1985and1994, total population was a good indicator for agricultural landscape pattern changes.
Between2003and2009, the forest NDVI at all scales increased significantly across the zones with elevation less than200m or zones with slope below5°. Changes of plantation structure may account for such results. For zones with elevation greater than600m or zones with slope greater than5°, the forest NDVI at all scales decreased significantly. These results may relate to mining activities and rainfall.
Between1979and2009, soil landscape patterns became fragmented, irregular, instable, and became less aggregated, connected and diverse. Relationships between soil landscape patterns and urbanization presented significant spatial non-stationarity. Generally,(1) at watershed scale, urbanization indicators like total population, non-agriculture population (%), GDP, and area of built-ups could all effectively predict soil landscape pattern changes;(2) at administrative scale, UII could effectively predict soil landscape pattern changes. Changes of soil landscape fragmentation and instability could be explained by GDP and total population;(3) at ecoregional scale, UII and GDP could effectively predict soil landscape pattern changes. Total population was a good indicator for soil landscape fragmentation and instability.
Between1996and2004, the river water pollution of petroleum, and phosphorus gradually became a regional problem, while heavy metal pollution became a local problem. Spatial regression showed that LULC changes were good predictors for changes of heavy metals. However, GDP and population density contributed to petroleum and ammonia dynamics. For dynamics of dissolved oxygen and total phosphorus, population and GDP were major determinants at sub-basin scale, while LULC changes were determinants at500m buffer scale.
Watershed ecosystem composite indicator presented obvious spatial variations in2003. Relationship between watershed ecosystem composite indicator and urbanization exhibited significant spatial non-stationarity. Generally,(1) at administrative scale, cities or counties, with faster economic development and higher non-agriculture population (%), had lower ecological quality;(2) at ecoregional scale, regions, with faster economic development and higher population, had lower ecological quality.
The original contributions of this study were summarized as follows:(1) Spatially varying relationships between agricultural landscape patterns, soil landscape patterns and urbanization at different scales were quantitatively analyzed.(2) This study developed an innovative management oriented source apportionment framework; and analyzed the spatial determinants of river water quality dynamics.(3) This study proposed a new composite indicator watershed ecosystem model based on catastrophe theory; and quantitatively analyzed the dynamic response of watershed ecosystem to urbanization at different scales. This study incorporated several limitations and further study should focus on the selection of scales, regression models, composite indicator models, division of watershed ecosystem, uncertainty analysis and development of predicting models.
1979-2009年间,钱塘江流域总人口、非农人口比例、国内生产总值(Gross Domestic Product, GDP)均显著增加,建筑用地扩张剧烈。在这种快速城市化过程中,农地景观格局的变化趋势为：面积和聚集度减少,破碎度、不稳定性、形状不规则度增加。农地景观格局变化与城市化的定量关系存在显著的空间不平稳性。总体来说：(1)在流域尺度上,非农人口比例、GDP、总人口、以及建筑物面积等城市化因子,均可以较好预测农地景观格局的变化；(2)在行政尺度上,城市化强度指数(Urbanization Intensity Index, UII)和GDP可以有效解释农地景观格局的变化。非农人口比例可以有效预测农地景观破碎度和不稳定性的变化(1979-1985)；(3)在生态区尺度上,UⅡ和GDP可以较好的解释农地景观格局变化,此外人口可以较好的预测农地景观格局变化(1985-1994)。
2003-2009年间,在200米及以下的高程带上,各尺度上林地NDVI均显著增加。在5°及以下的坡度带上,行政区林地NDVI显著增加。这些结果可能与林地种植结构改变有关。在600米及以上的高程带上,以及15°以上坡度带上,各尺度上林地NDVI显著下降。这些结果可能与矿山开采以及降水变化有关。
1979-2009年间,钱塘江流域土壤景观变得破碎、不稳定、形状不规则。此外,其连通度、多样性、聚集度也降低。土壤景观变化与城市化的定量关系具有显著的空间不平稳性。总体来说：(1)在流域尺度上,UII、GDP、总人口、以及建筑物面积等城市化因子,均可以有效预测土壤景观格局变化；(2)在行政区尺度上,UⅡ可以有效预测土壤景观格局变化。GDP和人口可以有效预测土壤景观破碎度和不稳定性变化(1985-2003)；(3)在生态区尺度上,UⅡ和GDP可以有效预测土壤景观格局的变化,人口增长会引起土壤破碎度和不稳定性的变化。
1996-2004年间,钱塘江流域的石油、以及磷污染逐渐变为一个全局性的问题,而重金属污染逐渐成为一个局部问题。空间回归结果显示,土地利用变化对重金属的时空动态有显著影响。而石油和氨氮的时空动态主要受人口和经济因素影响。对于溶解氧和总磷的时空变化而言,小流域尺度上的影响因子为人口和经济增长,而土地利用变化是500m缓冲区尺度上的主要影响因子。
2003年,流域生态系统复合指标数值在各尺度上均表现出较大的空间异质性,并与城市化的关系存在显著的空间不平稳性。总体来说：(1)在行政区尺度上,经济发展较快或非农人口比例较高的县市,其生态质量往往较低；(2)在生态区尺度上,经济发展较快或人口密度较高的生态区,其生态质量往往较低。
本研究取得的进展主要有：(1)分析了不同时空尺度上农地景观格局变化、土壤景观格局与城市化的定量关系,证明了这些关系具有空间不平稳性；(2)提出了基于管理的水污染源解析框架,解析了河流水质时空动态变化的影响因子；(3)将分层划分生态系统的方法与突变理论进行结合,提出了流域生态系统复合指标模型。定量分析了不同空间尺度上,流域生态系统对城市化的响应机制。本论文仍然存在一定的局限和不足,在今后的研究中,需要在变量选择、时空尺度选择、回归模型选择、复合指标模型建立、流域生态系统划分、不确定性分析、以及预测模型的建立等方面进行进一步的研究。
Based on the concept of Ecosystem Based Management, the whole watershed was regarded as an integrated ecosystem. The watershed ecosystem was divided into several sub-systems named vegetation (agricultural land and forest), soil and water, with a case of Qiantang River watershed in Zhejiang Province. The spatiotemporal dynamics of urbanization across the Qiantang River watershed were characterized by remote sensing and geographical information system at four temporal intervals (1979-1985,1985-1994,1994-2003,2003-2009) and three spatial scales (watershed, administration, and ecoregion). The dynamics of the four sub-systems were analyzed by direct (monitoring data based) and indirect (landscape ecology based) approaches, and their dynamic response to urbanization were investigated by geographically weighted regression. A composite indicator model was then developed by catastrophe theory, and the spatial response of watershed ecosystem to urbanization was finally analyzed by geographically weighted regression. The major findings and conclusions were summarized as follows:
Between1979and2009, the total population, non-agriculture population (%), and Gross Domestic Product (GDP) all increased significantly. Built-up land expanded vigorously. Under such rapid urbanization, agricultural landscapes became lost, fragmented, irregular, instable and less aggregated. Relationships between agricultural landscape patterns and urbanization presented significant spatial non-stationarity. Generally,(1) at watershed scale, urbanization indicators like total population, non-agriculture population (%), GDP, and area of built-ups could all effectively predict agricultural landscape pattern changes;(2) at administrative scale, urbanization intensity index (UII) and GDP could effectively predict agricultural landscape pattern changes. Changes of agricultural landscape fragmentation and instability could be explained by non-agriculture population (%);(3) at ecoregional scale, UII and GDP could effectively predict agricultural landscape pattern changes. Between1985and1994, total population was a good indicator for agricultural landscape pattern changes.
Between2003and2009, the forest NDVI at all scales increased significantly across the zones with elevation less than200m or zones with slope below5°. Changes of plantation structure may account for such results. For zones with elevation greater than600m or zones with slope greater than5°, the forest NDVI at all scales decreased significantly. These results may relate to mining activities and rainfall.
Between1979and2009, soil landscape patterns became fragmented, irregular, instable, and became less aggregated, connected and diverse. Relationships between soil landscape patterns and urbanization presented significant spatial non-stationarity. Generally,(1) at watershed scale, urbanization indicators like total population, non-agriculture population (%), GDP, and area of built-ups could all effectively predict soil landscape pattern changes;(2) at administrative scale, UII could effectively predict soil landscape pattern changes. Changes of soil landscape fragmentation and instability could be explained by GDP and total population;(3) at ecoregional scale, UII and GDP could effectively predict soil landscape pattern changes. Total population was a good indicator for soil landscape fragmentation and instability.
Between1996and2004, the river water pollution of petroleum, and phosphorus gradually became a regional problem, while heavy metal pollution became a local problem. Spatial regression showed that LULC changes were good predictors for changes of heavy metals. However, GDP and population density contributed to petroleum and ammonia dynamics. For dynamics of dissolved oxygen and total phosphorus, population and GDP were major determinants at sub-basin scale, while LULC changes were determinants at500m buffer scale.
Watershed ecosystem composite indicator presented obvious spatial variations in2003. Relationship between watershed ecosystem composite indicator and urbanization exhibited significant spatial non-stationarity. Generally,(1) at administrative scale, cities or counties, with faster economic development and higher non-agriculture population (%), had lower ecological quality;(2) at ecoregional scale, regions, with faster economic development and higher population, had lower ecological quality.
The original contributions of this study were summarized as follows:(1) Spatially varying relationships between agricultural landscape patterns, soil landscape patterns and urbanization at different scales were quantitatively analyzed.(2) This study developed an innovative management oriented source apportionment framework; and analyzed the spatial determinants of river water quality dynamics.(3) This study proposed a new composite indicator watershed ecosystem model based on catastrophe theory; and quantitatively analyzed the dynamic response of watershed ecosystem to urbanization at different scales. This study incorporated several limitations and further study should focus on the selection of scales, regression models, composite indicator models, division of watershed ecosystem, uncertainty analysis and development of predicting models.
引文
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