Optimal spatial allocation of water resources based on Pareto ant colony algorithm

The spatial allocation of water resources is optimised using the multi-objective functions and multi-constrained conditions of the Pareto ant colony algorithm (PACA). The objective function is the highest benefit to the economy, society and the environment, while the constraints include water supply, demand and quality. The PACA is improved by limiting local pheromone scope and dynamically updating global pheromone levels. Since both strategies guide the ant towards borders of high-pheromone concentration, the new approach enhances the global search capability and convergence speed. Programming, database management and interface tools are then integrated into geographic information systems (GIS) software. The study area is located in Zhenping County, Henan Province, China, and water resource data are obtained using remote sensing (RS) and GIS technology. The improved PACA is solved in the GIS environment. Optimal spatial allocation schemes are obtained for surface, ground and transferred water and the model yields optimal spatial benefit schemes of water resources, embracing economic, social and ecological benefits. The results of improved PACA are superior to those of other intelligent optimisation algorithms, including the ant colony algorithm, multi-objective genetic algorithm and back-propagation artificial neural network. Therefore, the integration of RS, GIS and PACA can effectively optimise the large-scale, multi-objective allocation of water resources. The model also enhances the global search capability, convergence speed and result precision, and can potentially solve other optimal spatial problems with multi-objective functions.     

Optimal spatial allocation of irrigation water under uncertainty using the bilayer nested optimisation algorithm and geospatial technology

The optimal spatial allocation of irrigation water under uncertainty has become a serious concern because of irrigation water shortage and uncertain factors that affect irrigation water allocation. In this study, an optimal multi-objective model for irrigation water allocation under uncertainty is developed to maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation. The original and optimal plantation structure, irrigation mode and soil water content are acquired through geospatial technology. A bilayer nested optimisation (BLNO) algorithm is designed to produce multiple individuals of design vectors using an ant colony neural network algorithm for an outer optimisation. Meanwhile, a continuous adaptive ant colony (CAAC) algorithm is used for inner optimisation to calculate the interval values of the uncertain model. The crop distribution and irrigation mode are obtained to parameterise the planting area and the water demand of each crop and each block in the multi-objective model. This model is then solved and analysed. Compared to the optimal schemes obtained from an inexact two-stage fuzzy-stochastic programming and the CAAC, respectively, BLNO can effectively and efficiently solve the optimal spatial allocation of irrigation water under uncertainty. This method can spatially maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation using lower and upper bound maps whilst visually obtaining the exact crop type, reasonable irrigation method and precise water demand for each block and for the entire irrigated area.     

A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas

Optimizing land use allocation is a challenging task, as it involves multiple stakeholders with conflicting objectives. In addition, the solution space of the optimization grows exponentially as the size of the region and the resolution increase. This article presents a new ant colony optimization algorithm by incorporating multiple types of ants for solving complex multiple land use allocation problems. A spatial exchange mechanism is used to deal with competition between different types of land use allocation. This multi-type ant colony optimization optimal multiple land allocation (MACO-MLA) model was successfully applied to a case study in Panyu, Guangdong, China, a large region with an area of 1,454,285 cells. The proposed model took only about 25 minutes to find near-optimal solution in terms of overall suitability, compactness, and cost. Comparison indicates that MACO-MLA can yield better performances than the simulated annealing (SA) and the genetic algorithm (GA) methods. It is found that MACO-MLA has an improvement of the total utility value over SA and GA methods by 4.5% and 1.3%, respectively. The computation time of this proposed model amounts to only 2.6% and 12.3%, respectively, of that of the SA and GA methods. The experiments have demonstrated that the proposed model was an efficient and effective optimization technique for generating optimal land use patterns.     

An improved artificial bee colony algorithm for optimal land-use allocation

Land-use allocation is of great importance for rapid urban planning and natural resource management. This article presents an improved artificial bee colony (ABC) algorithm to solve the spatial optimization problem. The new approach consists of a heuristic information-based pseudorandom initialization (HIPI) method for initial solutions and pseudorandom search strategy based on a long-chain (LC) mechanism for neighborhood searches; together, these methods substantially improve the search efficiency and quality when handling spatial data in large areas. We evaluated the approach via a series of land-use allocation experiments and compared it with particle swarm optimization (PSO) and genetic algorithm (GA) methods. The experimental results show that the new approach outperforms the current methods in both computing efficiency and optimization quality.     

贵州猫跳河流域土地利用变化和土壤侵蚀(英文)

Due to the extremely poor soil cover, a low soil-forming rate, and inappropriate intensive land use, soil erosion is a serious problem in Guizhou Province, which is located in the centre of the karst areas of Southwest China. In order to bring soil erosion under control and restore environment, the Chinese Government has initiated a serious of ecological rehabilitation projects such as the Grain-for-Green Programme and Natural Forest Protection Program and brought about tremendous influences on land-use change and soil erosion in Guizhou Province. This paper explored the relationship between land use and soil erosion in the Maotiao River watershed, a typical agricultural area with severe soil erosion in central Guizhou Province. In this study, we analyzed the spatio-temporal dynamic change of land-use type in Maotiao River watershed from 1973 to 2007 using Landsat MSS image in 1973, Landsat TM data in 1990 and 2007. Soil erosion change characteristics from 1973 to 2007, and soil loss among different land-use types were examined by integrating the Revised Universal Soil Loss Equation (RUSLE) with a GIS environment. The results indicate that changes in land use within the watershed have significantly affected soil erosion. From 1973 to 1990, dry farmland and rocky desertified land significantly increased. In contrast, shrubby land, other forestland and grassland significantly decreased, which caused accelerated soil erosion in the study area. This trend was reversed from 1990 to 2007 with an increased area of land-use types for ecological use owing to the implementation of environmental protection programs. Soil erosion also significantly varied among land-use types. Erosion was most serious in dry farmland and the lightest in paddy field. Dry farmland with a gradient of 6°-25° was the major contributor to soil erosion, and conservation practices should be taken in these areas. The results of this study provide useful information for decision makers and planners to take sustainable land use management and soil conservation measures in the area.     

新疆克拉玛依市生态敏感性研究

基于地理信息系统空间分析、栅格运算与叠加方法,针对克拉玛依市土壤侵蚀、土地沙漠化、土壤盐渍化和生物多样性减少等区域自然环境演变过程中出现的生态问题,考虑油田开发这一人类活动影响因素,定量研究克拉玛依市综合生态敏感性空间特征.研究结果表明:(1)土壤侵蚀敏感性以中、高和极高敏感性为主,高和极高敏感性分布在克拉玛依区和独山...     

遗传算法和GIS结合进行空间优化决策

资源的有效利用和管理往往涉及到空间的优化配置问题。例如需要在空间上确定n个设施的最佳位置。当选址问题涉及多个目标和不同的约束性条件时,就会变得十分复杂。利用一般的brute-force搜索方法无法对涉及高维数据的问题进行求解。利用遗传算法和GIS结合来解决复杂的空间优化配置问题,具有智能的搜索方法可以大大提高空间的搜索能力。在基于进化的优化过程中,根据GIS的空间数据来计算不同解决方案 (染色体) 的适应度。针对不同的应用目的,GIS可以给出不同的适应度函数。实验表明,所提出的方法比简单的搜索方法和退火算法有更大的优越性。该方法在处理复杂的空间优化问题有更好的表现。     

Improving the performance of genetic algorithms for land-use allocation problems

Multi-objective optimization can be used to solve land-use allocation problems involving multiple conflicting objectives. In this paper, we show how genetic algorithms can be improved in order to effectively and efficiently solve multi-objective land-use allocation problems. Our focus lies on improving crossover and mutation operators of the genetic algorithms. We tested a range of different approaches either based on the literature or proposed for the first time. We applied them to a land-use allocation problem in Switzerland including two conflicting objectives: ensuring compact urban development and reducing the loss of agricultural productivity. We compared all approaches by calculating hypervolumes and by analysing the spread of the produced non-dominated fronts. Our results suggest that a combination of different mutation operators, of which at least one includes spatial heuristics, can help to find well-distributed fronts of non-dominated solutions. The tested modified crossover operators did not significantly improve the results. These findings provide a benchmark for multi-objective optimization of land-use allocation problems with promising prospectives for solving complex spatial planning problems.     

The response of soil erosion and sediment export to land-use change in four areas of Europe: The importance of landscape pattern

The response of erosion and sediment export to past land-use change has been studied in four agricultural areas of Europe. Three of these areas were subject to land abandonment or de-intensification and one to intensification of land-use practices. Erosion and sediment yield were modeled using the WaTEM/SEDEM model, which combines the RUSLE equation with a sediment routing algorithm. Spatial relationships between the RUSLE C-factor (i.e. land-use) and other erosion and sediment export-determining factors (slope, soil erodibility and distance to rivers) were investigated, as these account for non-linearity in the response of erosion and sediment export to land-use change.Erosion and sediment export have decreased enormously in the de-intensified areas, but slightly increased in the intensively cultivated area. The spatial pattern of land-use change in relation to other erosion and sediment export-determining factors appears to have a large impact on the response of soil erosion and sediment export to land-use change. That the drivers of abandonment of arable land and erosion coincide indicates that de-intensification leads to a more favourable landscape pattern with respect to reduction of erosion and sediment export. This mechanism applies not only within the study areas, but also among the European study areas where the process of intensification of some areas and de-intensification of others might result in an overall decrease of erosion and sediment yield through time.     

基于USLE和GIS的水土流失敏感性空间分析——以河北太行山区为例

以USLE方程为理论指导,遥感影像为主要数据源,基于ArcGIS 进行水土流失敏感性空间分析。选择降雨侵蚀力、土地利用类型、坡度、植被覆盖度等影响因子构建水土流失敏感性评价指标体系,在考虑不同因子影响作用大小的情况下,运用空间分析的方法按标准将水土流失敏感性分为五级;并以河北太行山区为例进行研究,利用危险性指数表征研究区水土流失敏感性大小,分析研究区在不同地理背景下的水土流失敏感区的空间分布特征。研究表明：河北太行山区的水土流失敏感性危险性指数为3.97;空间上水土流失敏感性分等级呈现明显的条带状分布;中度敏感区所占面积比例最大,为39.2%;整体水土流失敏感性中度偏重。     

广州市土地利用结构优化配置及其潜力

基于广州市土地利用结构、地区经济、生态与社会特点,结合广州城市总体规划(2010-2020),对广州市域土地利用结构进行生态经济效益-自然生态效益导向的多目标优化配置研究。结果表明：对广州市土地利用数量结构进行调整,一定时间内能够满足地区可持续发展;但是,土地仍然承担着巨大的压力,焦点是建设用地和农用地之间的数量转变,以耕地为主,也包括对未利用土地的利用;土地利用潜力度模型结果显示,通过优化土地利用结构,土地系统的协调度增大而潜力度减小：在生态经济效益目标下,潜力度分别为0.628～0.661,0.372～0.339;在自然生态效益目标下,分别为0.531～0.782,0.469～0.218,表明土地利用系统将趋于更高的协调状态,而其数量结构优化配置的潜力减小,优化各产业部门内部土地资源结构及其空间布局,将是广州市土地资源高效集约利用和进一步挖潜的主导方向。     

黄土丘陵沟壑区乔灌草植物空间优化配置——以甘肃省定西地区为例

黄土丘陵沟壑区水土流失的影响因素有气候、植被、土壤、地形等,而乔灌草植物在空间上的不合理配置是造成该区水土流失严重的主要原因之一。本文选择研究区内限制植物生长与分布的4个关键环境因子 (高程、坡度、坡向和降雨量),利用多属性决策分析(MCDA)的方法,探讨乔灌草植物在空间上的优化配置,将整个研究区划分为5类乔灌草植物空间优化配置主模式和18类乔灌草植物空间优化配置子模式;同时,应用GIS聚类分析与统计分析方法,对所划分的乔灌草植物空间优化配置模式进行空间布局分析。结果显示:农民赖以生存的经济林/农林复合配置模式和农田/庭院经济配置模式是研究区的主要配置模式,占配置模式总面积的76.02％。     

哈尔滨市土地利用与生态环境物元评价

针对区域土地利用与生态环境系统的特点,建立了土地利用与生态环境协调评价的指标体系和复合物元模型,以哈尔滨市为例,对各县级单元进行了土地利用与生态环境协调的空间差异评价。结果表明:①物元分析理论对区域土地利用与生态环境评价具有明显的适用性,评价结果显示出哈尔滨市域内土地利用与生态环境协调状况的空间差异明显;②基于复合物元模型进行评价,通过单指标信息可以找出影响区域土地利用与生态环境协调发展的关键问题,如大多数县级区域存在土地集约利用水平较低、面临土壤污染、土壤侵蚀及湿地生态功能脆弱等土地生态环境风险,符合研究区的实际情况;③只有合理制定区域差异化的土地生态环境管制措施,积极引导对土地退化等问题的防治,才能实现区域土地可持续利用、生态环境质量改善和区域可持续发展。     

Integration of linear programming and GIS for land-use modelling

Abstract

Geographical Information Systems (GIS) are becoming basic tools for a wide variety of earth science and land-use applications. This article presents linear programming (LP) as a promising tool for spatial modelling within a GIS. Although LP is not properly a spatial technique, it may be used to optimize spatial distributions or to guide the integration of variables. An example of the use of LP in land-use planning is described, with minimizing rural unemployment as the main goal. Technical, financial and ecological constraints are established to show the influence of several limitations on achieving the optimal solution. LP makes it possible to achieve optimal land-use, where the objective is maximized and the constraints respected. LP can also be used to simulate different planning scenarios, by modifying both the objective function coefficients and the constraints. The integration of LP and GIS is presented in two phases: (i) acquisition of attribute data for the LP model, and (ii) modelling and mapping the results.     

模拟退火算法用于产生城市土地空间布局方案

本文发展了一种模拟退火算法辅助产生城市土地空间布局方案。首先建立了城市土地空间配置问题的数学模型 ,目标函数是最小化土地开发费用、最大化不同功能地块间的空间协调性。由于问题存在的诸多空间约束条件使得可行的土地利用布局方案的搜寻过程变得十分缓慢 ,采用了将一些空间约束条件结合到目标函数中的方法 ,如结合了距离约束、方向约束、相同土地利用单元的邻近约束和地块形状的紧凑约束等约束条件。应用于湖南省长沙市暮云工业区用地规划的结果表明 ,模拟退火算法是一种辅助城市土地利用规划的有用的、有潜力的优化方法     

基于生态-经济权衡的京津冀城市群土地利用优化配置

探索生态保护与经济发展的权衡关系,并利用该权衡关系协调土地利用优化配置是解决城市群经济与生态协调发展难题的重要途径,已成为目前的一个研究热点。论文针对京津冀城市群生态友好型协同发展的需求,设置生态系统服务价值最大化和经济价值最大化2种优化目标,每种目标下再设置“生态保护”“统筹兼顾”“粮食安全”和“经济发展”4种土地利用情景,采用CLUE-S模型模拟2025年京津冀城市群在不同情景下的土地利用空间配置格局,及其生态系统服务价值、经济价值在不同目标的各种情景下的变化。研究结果表明：相比2015年,2025年在不同目标不同情景下,各类土地利用面积的数量变化和空间格局均有较大不同,且生态目标和经济目标下不同情景的土地利用配置不同。生态系统服务价值在生态目标生态保护情景下最大,为14423.58亿元;而经济价值则在经济目标经济发展情景下最大,为96771.49亿元。从土地利用变化的空间分布上来看,生态用地中林地与草地的增加多出现在坝上高原和燕山与太行山山地,水体的增加则主要分布在东部沿海地区。研究认为,生态效益与经济效益间存在权衡关系,基于生态-经济权衡的土地利用优化研究结果对未来京津冀城市群生态实践工作有较大的参考价值。     

基于“多规合一”的市级国土空间优化方法——以烟台市为例

针对现阶段各规划边界交叉、空间重叠的问题,本文从提高空间价值、减少空间破碎度和协调各类空间的角度出发,在梳理现有空间优化模型及智能算法的基础上,改进多目标规划模型并适应性改造遗传算法,以此二者构建市级国土空间优化模型。以烟台市为例,设置3种情景为决策者提供方案集进行3类规划主导下的2020年国土空间优化研究,结果显示：①优化后烟台市农业、城镇和生态空间价值分别增加了23.24%、29.27%、6.30%;②不同情景下空间分布合理且较为集中,生态保护情景有利于国土空间集合连片,经济发展情景更适合于多空间协调发展。研究表明：该模型能有效地解决国土空间内容重叠问题,明显提高了国土空间价值,同时优化模型适应性强,为“多规合一”背景下市级国土空间优化提供技术支撑。     

基于遥感的山区县域土地覆被变化的驱动力因子和生态环境效应分析--以重庆市忠县为例

在对重庆市忠县区域TM遥感数据进行波谱信息特征实验分析的基础上，建立了土地覆被信息提取的波段组合方案；通过人机交互目视解译和野外数据检验工作，实现利用遥感影像数据对重要生态安全指标因子之一——土地覆被因子的数据提取，并用GIS技术对其时空变化的驱动力因子和生态环境效应进行分析，效果和效率令人非常满意。这不仅在一定程度上解决了中尺度山区县域的土地覆被数据提取与地学分析的技术和方法难题，而且揭示了忠县土地覆被的时空分异特征。这是用常规与传统的技术方法与研究手段难以实现的，也为山区县域生态环境退化评价和生态恢复研究提供了一种新的方法尝试。通过GIS空间分析，揭示了忠县土地覆被和水土流失的时空分异特征。忠县土地覆被动态变化的生态环境效应表现为耕地与林地在“制高地”上(海拔500m以上)的大互换，在平川坝地地区建设用地大量占用优质耕地、林地等土地覆被类型；水土流失发生的坡度分异不明显，主要分布在200～800m间，与坡耕旱地分布密切相关。从总体上，这种变化使区域生态环境系统的功能发生退化。分析发现，忠县土地覆被变化的驱动力主要有：(1)人口压力和经济发展落后是生态环境系统安全状态恶化的社会经济背景因素；(2)对自然资源不合理开发利用是生态环境恶化的直接人为因素；(3)脆弱的生态环境是生态环境恶化的自然环境背景因素。因此，必须采取包括生态移民、生态建设与生态保护等在内的忠县区域生态安全综合调控措施。     

Land use optimization at small watershed scale on the Loess Plateau

At present, land use optimization at small watershed scale is the key measure to control soil erosion, restore the eco-environment and improve the farmers’ living standard on the Loess Plateau, China. Based on the land use survey maps of 1966, 1988, 1997, 2003 and the digital topographic map of 1984 in Yangou watershed, and assisted by spatial techniques of GIS, the basic characteristics and driving forces of land use change in Yangou watershed are analyzed. According to the summarization of land-use optimization characteristics since 1997, and with the help of continuous monitoring data for years and farmer investigation data, this paper appraises eco-environmental benefits, economic benefits and sustainability of Yangou watershed. We have used sediment reduction benefits, coverage ratio of permanent vegetation, per capita food production and per capita income of farmers as indices. The results show that Yangou watershed project has successfully controlled the soil and water loss and the farmers’ living standard has been improved markedly by reasonable adjustment to land use structure. The benefit of sediment reduction is higher than 80% and the coverage ratio of permanent vegetation reaches 61.03%. In 2006, the per capita income increased by 1493 yuan compared with the year 1998.The successful measures and experiences of Yangou watershed are worth promoting on the Loess Plateau.     

发展潜力视角的海岸带土地利用模式——以杭州湾南岸为例

基于海岸带土地利用的困境与本质解读,集成模糊综合评价、网络层次分析与GIS空间分析等方法构建海岸带土地发展潜力评价体系,依据土地发展潜力评价内容辨析海岸带土地利用模式的要素逻辑,诠释了海岸带土地利用类型优化、土地利用空间结构重组、空间管控策略之关联,提出了杭州湾南岸经济—生态协同型土地利用模式。研究发现：① 科学地测度经济发达区的海岸带经济与生态关系是评估海岸带土地效益的关键,采用反比例关系衡量土地生态与经济发展关系构建土地发展潜力评价体系的方法,可以有效地发现经济与生态的区域矛盾问题,为解决海岸带土地开发向生态敏感区拓展问题提供方法支持。② 杭州湾南岸极高潜力区和高潜力区占57.07%,中、低潜力区占42.93%,具有较高的发展潜力,呈南强北弱的分布格局。随着人工化地类面积逐渐趋少,自然化地类渐增,土地发展潜力分区呈现等级递减的规律。③ 凝练出经济—生态协同型海岸带土地利用模式是杭州湾南岸以经济发展为导向的海岸带土地利用的实践趋势与基本模式,构建该模式须遵循“稳定经济开发、严控生态保育、调控双向功能”为主线的海岸带土地利用类型优化、“陆海系统并行,多功能组团”为核心的土地利用空间结构重组方向、“生活—生产—生态”分段式协同管控的空间治理三重系统的逻辑体系。