Construction Of Landscape Ecological Security Pattern In Peri-urban Areas

The structure and function of terrestrial ecosystem in peri-urban areas have long been severely damaged due to the irrational urban sprawl and unreasonable land development, which lead to serious ecological environment problems. Since 21st Century, China's urbanization and industrialization have been rapidly developed, and the contradiction between economic development and ecological protection of peri-urban areas has become more and more intensified. In order to explore a regional ecological security pattern which could effectively balance the contradiction between economic development and ecological protection in terms of space and to avoid ecological protection falling into the dilemma of blind protection and inefficient protection, it's absolutely necessary to carry out strengthen research on the theory and method for construction of landscape ecological security pattern. However, related researches in China at present haven't carried out in-depth discussion on the theories and methods of construction of landscape ecological security pattern, especially the researches on the peri-urban areas in which landscape obviously change, people fiercely contradict with land, and environmental problems highlight were very few.Longquanyi belongs to the municipal district of Chengdu, the capital city of Sichuan province. It is 12 kilometers from the center of Chengdu as the location of Chengdu Economic and Technological Development Zone and main component of Sichuan Tianfu New Area. So that, it is a typical peri-urban areas of metropolis. Its ecological security is related to not only the sustainable development of Chengdu Economic and Technological Development Zone, but also the construction of the ecological protective screen of Sichuan Tianfu New Area. With accelerated industrialization and urbanization, population rapidly increased, economic rapidly developed, urban disorderedly sprawled, and industrial area massive expanded in this district, which brought greater negative impacts on regional ecological environment, so that the ecological security problems such as industrial "three wastes" pollution, forest vegetation decline, rural landscape fragmentation, biodiversity decreased, as well as shortage of water and land resources have been more prominent. Therefore, study on the theory and method of landscape ecological security pattern construction has important theoretical value and practical significance.As such, with the landscape pattern optimization as research perspective, based on the research of landscape classification and analysis of present landscape pattern, landscape pattern change characteristics and driving factors analysis, landscape pattern change potential and dynamic simulation, as well as regional ecological security assessment change and trend prediction in Longquanyi district of Chengdu city, a model and algorithm of landscape pattern spatial optimization in view of PSO (Particle Swarm Optimization) principle has been innovatively proposed to optimize spatial layout of the several scene landscape, and attempt to obtain a landscape ecological security pattern scheme which can effectively mitigate the contradiction between economic development and ecological protection. In this way, it provides not only a feasible space management control basis for sustainable use of regional land, rational expansion of urban and healthy development of economic, but also theoretical and technological support for promoting implementation of the ecological civilization construction strategy of the local areas. Main research conclusions were shown as follows:(1) The study area could be divided into 6 kinds of landscape types based on land use/ land cover type. The analysis of regional landscape pattern showed that the spatial distribution of landscape types in the whole area was uneven with high degree of patch fragmentation. The landscape pattern was greatly influenced by human activities.Based on the data of OLI (Operational Land Imager) images and ASTER GDEM (Advanced Spaceborne Theemal Emission and Reflection Radiometer Global Digital Elevation Model), the study used the methods of RS (Remote Sensing) and GIS (Geographical Information System), such as the remote sensing image classification, ArcGIS spatial analysis and map compilation to discuss the landscape classification, and on the basis of this, the present situation of landscape pattern in the study area was analyzed? Based on landform type, land use/land cover type, the "landscape class landscape type" two stage landscape classification system has been constructed, and the study area was divided into 18 kinds of landscape types according to the classification system, among which there were 8 kinds of main landscape types including mountainous orchard landscape, plain orchard landscape, plain urban-rural residential and industrial-mining landscape, plain farmland landscape, mountainous forest landscape, hilly orchard landscape, plain transportation landscape and plain waters landscape.? Based on land use/land cover type, the study area was divided into 6 kinds of landscape types refer to related land use/land cover classification system. Orchard, urban-rural residential and industrial-mining, farmland were the dominant landscape, among which, the area, number of patches, diversity index, patch shape index of orchard landscape were 29864.16 hm2,772 blocks,0.8614, and 1.6968 respectively, and that of urban-rural residential and industrial-mining landscape were 9356.20 hm2,421 blocks, 0.9987,1.5887 respectively, what's more, that of farmland landscape were 8006.15 hm2, 931 blocks,0.9997,1.5709 respectively. The spatial distribution of landscape types in the whole area was uneven with high degree of patch fragmentation and greatly influenced by human.? ISODATA (Iterative Self-Organizing Data Analysis Technique) remote sensing image unsupervised classification method could distinguish small scale landform types such as the valley and shallow hill, which can ensure the continuity and gradual changes of surface morphology. Compared with C5.0 (C5.0 decision tree classification) and MLC (Maximum Likelihood Classification), QUEST (Quick Unbiased Efficient Statistical Tree) showed the higher overall classification accuracy. Kappa coefficient, average user accuracy, and average mapping accuracy of classification. Moreover, the average misclassification error and average omission error showed the order as, QUEST<C5.0<MLC, indicating that the QUEST decision tree classification method is the best way in classifying land use/ cover type in the study area. The results here suggest that the integrated application of ISODATA remote sensing image supervised classification method, QUEST decision tree classification and GIS spatial analysis can take multiple landscape ecological classification indexes for landscape type classification, which could be popularized and applicated in landscape classification in the medium scale.(2) The main landscape types in the study area were frequently transferred based on the analysis of landscape pattern change characteristics and driving factors in 1992?2014. The main driving factors of the change in landscape pattern were population situation, scientific level and economic development.Based on the data of TM (Thematic Mapper)/OLI remote sensing image, ASTER GDEM, meteorological, soil and other related socio-economic data, combined with the landscape pattern change characteristics, natural ecological environment status and related research results, landscape pattern change driving factor index system has been constructed, and spatial regression model was used for analyzing landscape pattern change driving factors of the study area.? Landscape area of transportation, orchard, urban-rural residential and industrial-mining had increased by 329%,184%,125%, respectively, and landscape area of farmland, forest and waters had decreased by 59.94%,67.85%,41% during 1992?2014 years. Landscape pattern experienced a great conversion process. The transformation of farmland to orchard landscape, forest to orchard landscape and farmland to urban-rural residential and industrial-mining landscape were significantly apparent.? Driving factors of the landscape pattern change were greatly influenced by time scale. Specifically speaking, driving factors of the same landscape pattern change would show different changes over time, while the influence of one driving factor of the landscape pattern change would also change over time.? In the study area, landscape pattern change of farmland, orchard, transportation and waters were mainly affected by the human driving factors, among which farmland landscape pattern was mainly affected by population situation, orchard landscape pattern was mainly affected by population situation, scientific and technological level, and economic development, along with the transportation landscape pattern was mainly affected by economic development while the waters pattern was mainly affected by economic development and population situation. On the contrary, landscape pattern change of forest, urban-rural residential and industrial-mining were mainly affected by the natural driving factors, in which forest landscape pattern was mainly affected by such driving factors as the topography and soil, urban-rural residential and industrial-mining landscape pattern was mainly affected by the topography driving factors.? In a word, compared with the influence of the natural driving factors, the influence of human driving factors on land landscape pattern change was greater, in which population situation, scientific and technological level, and economic development factors were the main driving factors for the landscape pattern change in the study area.(3) The dynamic simulation of landscape change in 2014?2028 showed that, the intensity of landscape change in the study area will gradually weaken, and that different degrees of transformation occurred among different landscape types, mainly in the directions of "forest to orchard, orchard to urban-rural residential and industrial-mining, orchard to farmland".With the support of ArcGIS and IDRISI Selva software, based on the data of remote sensing images of TM/OLI, ASTER GDEM as well as rainfall, organic matter content and other landscape change driving factor, ANN (Artificial Neural Networks)-Markov-CA (Cellular Automaton) composite model was established to analyze potential of landscape pattern change in 1992-2014 and landscape pattern change trend of the study area in 2021 and 2028.? The forecast accuracy rate of landscape pattern change potential was not necessarily increased with the increase of the number of driving factors. According to the forecast accuracy rate, appropriate combination of driving factors were adopted to simulate landscape pattern change potential.? Besides, the simulation effect of Ann-Markov-CA model was generally better than that of MCE (Multi Criteria Evaluation)-Markov-CA, Markov-CA and Logistics-Markov-CA models Commonly used in the existing researches, the Kappa coefficient of Ann-Markov-CA model reached the 0.41?0.60 accuracy requirements which showed a certain degree of credibility, so the Ann-Markov-CA model could achieve a better simulation of the landscape changes in the study area.? In 2014?2028, most of the landscapes will maintain the change trend in 2000? 2014, that in general the area of urban-rural residential and industrial-mining, transportation landscape continued to increase while the area of farmland and forest landscape continued to shrink, but the intensity of landscape change gradually weakened, and various landscape types were all translated with different degrees, and "forest to orchard, orchard to urban-rural residential and industrial-mining, orchard to farmland" will be main conversion type.? The simulation showed a decreasing trend of farmland and forest due to greater probability of transfer (72.92%,64.8%) in the past 14 years (2000-2014) and the future 14 years (2014?2028). Farmland and forest were taken as the stable supply source for other landscape. It is suggested that a guiding plan is required to protect farmland and forest landscape. It is the most significant to maintain regional ecological balance and promote the interaction between ecological construction and economic development.(4) The ecological security evaluation results of the study area in 2000?2014 showed that, the status of ecological security in eastern mountain area was better than that in the western dam area in study area. On the base of above, the prediction results show that, the ecological security level will slowly increase in 2015?2018, but the ecological security level of the most areas will be low and grim situation of ecological security will not be changed fundamentally.With the support of GIS technology, based on the regional ecological security evaluation index system constructed by P-S-R (Pressure-State-Response) model and the index data which was directly or indirectly obtained by utilizing TM/ETM (Enhanced Thematic Mapper)+/OLI remote sensing image, ASTER GDEM data, meteorological data, soil data and related social-economic data, comprehensive evaluation method was adopted to assess the ecological security space status of the study area in 2000-2011 and 2013-2014. On this basis, RBF (Radial Basis Function) neural network model and GIS spatial analysis method were employed in this study so as to predict the ecological security change trend of the study area in 2015-2028.? In 2000-2014, compared with the west dam regions, the east mountainous showed a better situation of ecological security. However, ecological security index has fluctuated in a downward trend in both regions, with the ecological security showing signs of erosion trend. Besides, the whole regions were almost in five ecological security levels, such as middle warning, warning, risk, sensitivity and criticality. Furthermore, the regions in which ecological security level were lower than sensitivity account for 76.2% of the total, thus indicating that ecological security level was low in most regions.? Average years of the mean absolute error and root mean square error in RBF neural network model were less than 0.05, which indicated that ecological security change forecast could be easily realized by RBF neural network model in the study area.? In 2015-2028, the proportion of the regions below the sensitive security level will rise up to 85.6%, for which there would not be fundamental changes for the grim situation of regional ecological security. In addition, regions of ecological security higher level which will be in general level, sensitive level and critical level, as well as regions with lower levels of ecological security which will be in severe warning level, middle warning level, would experience a sharp decrease. Nevertheless, the regions in the middle level will increase substantially. Most of the regions will be in early warning and risk ecological security statuses, thereby indicating the low ecological security level. In terms of the whole regions, they will fail to get rid of ecological security threat.? It is suggested that some important measures which can be taken to completely get rid of the dilemma of ecological risk threat were required to reasonable controlling population size, strictly protecting arable land red line, maintaining moderate urbanization growth rate, scientifically allocating land for urban and industrial areas, increasing the intensity of vegetation restoration and ecological protection and accelerating the depth adjustment of regional industrial structure.? PSO landscape pattern spatial optimization model and algorithm was used to establish the ecological security pattern of the study area. The result showed that, among the 3 scenario schemes, the economic development scenario scheme will make the ecological benefits of negative growth because of pursuing the maximization of economic benefits, and it will be not consistent with the more potential possibility of the forest area increasing. The ecological protection scenario scheme will make the economic benefits of negative growth because of pursuing the maximization of ecological benefits, and it will be not consistent with the more potential possibility of expansion of urban-rural residential and industrial-mining. Economy, ecology and comprehensive benefits in the overall consideration scenario scheme will all have been improved, and the overall consideration scenario scheme will be consistent with the potential of regional landscape change and actual situation of economic development and ecological construction, so it will be the most ideal landscape ecological security pattern in the study area.Based on the research results of landscape classification and analysis of present landscape pattern, landscape pattern change characteristics and driving factors analysis, landscape pattern change potential and dynamic simulation, regional ecological security assessment and change trend prediction, landscape suitability evaluation, landscape quantity optimization, as well as OLI remote sensing image, ASTER GDEM data, meteorological data, soil data and related social-economic data, landscape pattern spatial optimization model and algorithm which were based on PSO were established to construct the landscape ecological security pattern through optimizing landscape spatial layout of economic development, ecological protection and overall consideration scenario.? The landscape pattern spatial optimization model and algorithm based on PSO was the effective method of landscape pattern optimization. The model and algorithm could efficiently simulate landscape distribution using particle space positions, conduct spatial pattern optimization through coupling of optimization results of constrained optimization model of landscape quantity, as well as related policies, economic and social factors, and realize landscape space layout optimization based on high resolution raster image from the theories. The model and algorithm could be promoted in the construction of landscape ecological security pattern on the regional level, however, it's necessary to select the appropriate grid map resolution according to the research scope and scale in the application.? The dominant landscapes in economic development scenario were orchard, urban-rural residential and industrial-mining in target year (2021 and 2028). The landscape pattern showed that farmland, urban-rural residential and industrial-mining dominated the western flatland region, while eastern mountainous area was mainly dominated by orchard. The dominant landscapes in ecological protection scenario were forest, urban-rural residential and industrial-mining. The landscape pattern showed that the western flatland region was mainly dominated by farmland, orchard, urban-rural residential and industrial-mining, while the eastern mountainous area was dominated by forest. The dominant landscapes in overall consideration scenario were forest, orchard, urban-rural residential and industrial-mining. The landscape pattern showed that farmland, urban-rural residential and industrial-mining dominated the western flatland region, while eastern mountainous area was dominated by forest and orchard.? Compared with other scenarios, the landscape pattern optimization scheme of overall consideration scenario could be the largest potential possibility in the future, since the scenario scheme was consistent with the potential of regional landscape change and actual situation of economic development and ecological construction, as well as the economic, ecological and comprehensive benefits here would be the most optimized and promoted, implying the most ideal landscape ecological security pattern for the study area in target year.In summary, on the basis of TM/ETM+/OLI remote sensing image, ASTER GDEM data, meteorological data, soil data as well as related social-economic data, this study carried out systematic study on the basic theories and methods of construction of landscape ecological security pattern including landscape classification and present landscape pattern, landscape pattern change characteristics and driving factors, landscape pattern change potential and dynamic simulation, regional ecological security level and change trend. In results, the landscape pattern optimization model and algorithm based on PSO principle have been proposed to realize the optimization of landscape spatial layout in different situation, and successfully construct an landscape ecological security pattern which could effectively balance the contradiction relationship between economic development and ecological protection, and further enhance the systematicness of discussion on basic theory and innovativeness of application on method of landscape ecological security pattern construction. This study not only established a theoretical foundation and provided a method reference for programming planning of ecological construction and environmental protection, land use, urban space and constructing spatial strategy of ecological civilization, but also was of important practical significance to promote sustainable use of regional land, rational expansion of urban, healthy development of economic.