189 resultados para Landscape pattern


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A new method of face recognition, based on Biomimetic Pattern Recognition and Multi-Weights Neuron Network, had been proposed. A model for face recognition that is based on Biomimetic Pattern Recognition had been discussed, and a new method of facial feature extraction also had been introduced. The results of experiments with BPR and K-Nearest Neighbor Rules showed that the method based on BPR can eliminate the error recognition of the samples of the types that not be trained, the correct rate is also enhanced.

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A new theoretical model of Pattern Recognition principles was proposed, which is based on "matter cognition" instead of "matter classification" in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using "optimal separating" as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR)(1). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Onmidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.

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An improved BP algorithm for pattern recognition is proposed in this paper. By a function substitution for error measure, it resolves the inconsistency of BP algorithm for pattern recognition problems, i.e. the quadratic error is not sensitive to whether the training pattern is recognized correctly or not. Trained by this new method, the computer simulation result shows that the convergence speed is increased to treble and performance of the network is better than conventional BP algorithm with momentum and adaptive step size.

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A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

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景观边界是特定时空尺度下相对均质的景观之间所存在的异质性过渡带,作为景观的一个四维组分,边界动态直接反映景观变化,与基于斑块的研究相比,明确景观边界的生态意义是理解景观过程的一个新的切入点。边界效应是景观边界最显著特征之一,也是当前生态学领域的研究热点,但大部分研究集中在生态系统及其以下尺度,对大尺度上的研究涉及内容尚少。 本文以岷江上游地区为例,利用TM影像数据、林相图、土地利用图和野外实测数据,应用RS、GIS、SPSS、Fragstats等软件的数据处理、分析、运算功能,研究了景观边界网络格局的变化,并分析了与之相关的生态过程;研究了类型水平上森林景观的边界效应以及景观尺度上林农复合景观的边界网络效应。旨在揭示岷江上游景观格局的变化趋势及其驱动因子,以及林农复合景观格局的生态效应,从而为研究区土地利用提供科学参考。研究结果如下: (一)在岷江上游地区的13种景观边界类型中,建筑用地边界和农田边界是人工边界,同时也都是清晰边界;但林地、灌木林地、草地等自然景观之间的边界并不都是模糊边界。冰雪边界是典型的随季节变化的变动型边界,其它边界在年内的变化则相对稳定。 (二)在1974-2000年期间,由于人为干扰强度的加剧,岷江上游景观边界网络结构变得更加复杂,在早期以边界长度增加为主,网络连接度变大,在后期以边界数量和结点数量增加为主,网络连接度变小;森林景观与低坡位景观类型间的边界减少,森林下线上移;农田与林地的边界在早期增加,1986年后减少,而与灌木林地的边界持续增加;基于边界特征的格局分析表明,森林景观结构变得简单化,而农田、灌木林地、草地等景观类型的结构变得更加复杂。 在景观水平上,基于边界和结点特征的格局指数与对应的基于斑块特征的格局指数具有基本一致的变化趋势,但基于边界和结点特征的指数对格局的变化更灵敏,在类型水平上,基于结点的格局指数比基于边界的格局指数具有更大的灵敏性,而且前者能够反映出后者反映不出的格局信息。 (三)研究区的农田边界共有5种类型,分别是农田与林地、灌木林地、草地、水体、建筑用地之间的边界,总长6583.4km;其中林农边界长2473.7km,占37.6%,是除灌农边界(占农田边界总长度的44.9%)之外比例最大的农田边界类型,广泛分布在岷江干流及其支流的河谷中,海拔多在1000-3500m之间。 (1)多元线性回归分析结果表明,农田生物量的边界效应深度与海拔和坡度显著相关,与其它因子相关性不大;林地生物量的边界效应深度与海拔和坡向相关性较大,与其它因子相关性不大;林农边界对农田生物量产生正面效应,对林地生物量产生负面效应,且对农田的影响面积大于对林地的影响面积;根据回归方程计算边界效应的影响面积,2000年有14532hm2林地生物量受到林农边界网络的影响,占研究区林地面积的1.2%,有16659 hm2农田生物量受到林农边界网络的影响,占研究区农田面积的22.6%,由于边界位置、长度的变化,不同年间林农边界网络的生态效应也存在差异。 除了生物量,本文还研究了林农边界网络对林地和农田土壤水分和生物多样性的影响: (2)林农边界减少林地和农田边缘的土壤水分,岷江上游有2103 hm2农田和371 hm2林地其土壤水分受到林农边界效应的影响,分别占研究区农田面积的2.3%和林地面积的0.03%。生物多样性受林农边界网络影响的农田面积为4855 hm2,占研究区农田面积的5.37%;生物多样性受林农边界网络影响的林地面积为3401 hm2,占研究区林地面积的0.29%。 上述研究从景观边界网络的角度揭示了岷江上游近三十年的景观变化特征,在印证斑块类型所反映的景观变化特征的同时,还反映出传统格局研究方法反映不出的格局信息,为景观生态学中格局与过程研究提供了新思路,也丰富了边界效应的理论和案例研究,有关结论还可为高山峡谷区土地利用格局优化和实施退耕还林还草工程提供参考依据。

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本文系统研究了沈阳城市森林的布局与结构、城市森林功能、城市森林病虫害发生与树木健康状况和城市自然资源与社会经济状况等指标对沈阳城市森林生态系统健康与管理的影响。同时一,采用2种生态系统健康评价方法对沈阳城市森林生态系统健康状况进行了评价,并提出了沈阳城市森林生态系统健康管理的对策。研究结果如下:1、截至2004年末,沈阳城市森林植被覆盖率已经达到35%,城市森林林地分布基本合理,但需要进一步加强道路林地、居住区林地和城郊大面积生态林建设。2、沈阳城市森林以乔木为主,乔灌株数比为1.7:1,乔灌的覆盖度比约为7:1。3、沈阳城市森林不同类型林地中植物组成不同。公园林地中有74个属,137个种(变种);庭院林地中有53个属,104个种(变种);居住区林地中有45个属,81个种(变种);道路林地中有43个属,94个种(变种);运河风景林地中有75个属,142个种(变种);棋盘山风景林地中有48个属,118个种(变种)。4、公园林地、庭院林地、居住区林地、道路林地和运河风景林地的Shannon一Wiener多样性指数分别为2.78、3.05、3.15、3.18和3.18,均匀度指数分别为0.56、0.66、0.72、0.70和0.64。除了棋盘山风景林地外,沈阳城市森林中栽植总量超过乔木总量5%的乔木树种有7个属,分别为李、柳树、杨树、桧柏、榆树、槐树和银杏,7种树木总量达到了全部乔木总量的82.09%;栽植总量超过灌木总量5%的灌木树种也有7个属,分别为水腊、丁香、李属,小聚、玫瑰、忍冬和连翘,7个属灌木总量达到了全部灌木总量的87.92%。5、公园林地、庭院林地、道路林地和防护林地中OBH<20cm、20cm<DBH<60cm和DBH>60cm树木的比例分别为:57.9%、40.0%、2.1%,49.2%、47.8%、3.0%,65.3%、33.1%、1.6%和64.6%、34.9%、0.5%,表明沈阳城市森林树木的规格总体上偏小。6、经样方调查和CITYgreen模型计算,沈阳城市森林的生态效益约2.0亿USD/yr.。公园林地、庭院林地和风景林地的景观指标相对较高;道路林地和居住区林地的景观效果一般;防护林地的景观效果较差。7、目前已经发现的沈阳城市森林病害约600余种,虫害约700余种,其中杨树主要病虫害39种,柳树的主要病虫害有33种,榆树和槐树的主要病虫害均为,1种。杨柳树腐烂病、光肩星天牛、天幕毛虫、桃红颈天牛和美国白蛾等是近10年来沈阳城市森林中普遍发生和造成严重危害的主要病虫害。沈阳城市森林主要树木的平均健康指数为2.68,处于一般健康状态。8、沈阳城市森林的土壤和水资源状况均不利于树木的健康生长,沈阳的社会经济发展也有待于进一步提高。9、经过生物指示物法(光肩星天牛为生物指示物)、专家权重法、公众问卷调查和对比研究,沈阳城市森林生态系统总体上处于亚健康状态。10、通过对沈阳城市森林资源、管理状况的调查研究和健康状况的评价,本文提出了沈阳城市森林生态系统健康管理的对策,包括合理规划沈阳城市森林林地布局,增加道路林地、居住区林地和城郊林地的面积和植被覆盖率;调整树木种类组成,避免单一或少数树种的大量栽植,提高生物多样性水平;保护大树和古树;增加城市森林管理资金的投入;应用先进技术,采取科学的病虫害防治和植物养护方法,促进树木的健康生长等。This project systematically studied the urban forest ecosystem health and management in Shenyang. The study explored factors, such as urban forest structure, distribution, pests, aesthetic value, ecological benefit, natural resources and socieo-economic status, that affecting the urban forest ecosystem health and management. Two methods were used to evaluate the ecosystem health. This project also proposed Shenyang's urban forest ecosystem health management strategies. The research results can be summarized as follows: 1. As of the end of 2004, urban forest coverage in Shenyang is about 35%, and is in relatively even patch distribution pattern. However, the street trees and roadside forest patches, residential block forest patches should be enhanced. 2. Trees are the major component of the Shenyang s urban forest, followed by shrubs. The quantity ratio of tree to shrub is about 1.7:1, and the coverage ratio of trees to shrub is about 7:1. 3. Species composition varies by location. There are 74 genera, 137 species (including varieties) in the public parks; 53 genera, 104 species (and var.) in the green spaces of the institution (including school), factory, and company; 45 genera, 81 species (var.) in residential blocks; 43 genera, 94 species (var.) in streets and roadside forest patches; 75 genera, 142 species (var.) in the Canal landscape forest patches; 48 genera, 118 species (var.) in the Qipan Mountain recreation forest. 4. The Shannon-Woener indices varies in parks, in institution, factory, and company yards, in streets and roadside forest patches, in residential blocks.there are 2.78, 3.05, 3.18, 3.15, 3.18, respectively; and the evenness indices are 0.56, 0.66, 0.70, 0.72, 0.64, respectively. Besides the Qipan Mountain forest patches, trees of 7 genera, Prunus spp., Salix spp., Populus spp., Sabina spp., Ulmus spp., Robinia spp. and Ginkgo biloba are of more than 5% the total urban trees, respectively. In fact, trees from these 7 genera are about 82% of all trees in Shenyang's urban forests. In terms of shrubs, species of 7 genera, Ligustrum spp., Syringa spp., Prunus spp., Berberis spp., Rosa spp., Lonicera spp., and Forsythia spp. are more than 5% the total urban shrubs, respectively. 88% of all the shrubs in Shenyang s urban forest are from these 7 genera. 5. The diameter class of DBH<20cm, 20cm60cm of trees in parks, in institution, factory, and company yards, in in streets and roadside forests, and in protective forests are 57.9%, 40.0%, 2.1%; 49.2%, 47.8%, 3.0%; 65.3%, 33.1%, 1.6%; and 64.6%, 34.9%, 0.5%, respectively. The result indicated that the DBH is relatively small in Shenyang s urban forests. 6. Using random plots sampling and CITYgreen model calculation, the ecological benefit of Shenyang's urban is about 0.2 billion US dollars per year. The aesthetic value of parks, institution and company yards, and scenary forest patches is relative high; however, the aesthetic value of protective forest patches is low. 7. There are more than 600 types of diseases and more than 700 kinds of insects in Shenyang's urban forest. Among them, Populus spp., Salix spp., Ulmus spp. and Robinia spp., are attached mainly by 39, 33, 11, and 11, difference types of pests respectively. The rot disease of the Populus spp. and Salix spp., and the insects such as, Anoplophora glabripennis, Malacosoma neustria, Aromia bungii, and Hyphantria cunea are major pests in Shenyang s urban forest for the past 10 years. The trees health condition is relatively poor by having the average health index of 2.68 for major species. 8. The urban soil and water resource are not favorable to tree's growth in Shenyang, and so is the socieo-economic status. 9. Through using bio-indicator, professional and public evaluation, and comparision with other urban forests, it can be concluded that the status of Shenyang s urban forest is not very healthy in general. 10. To improve the urban forest ecosystem health in Shenyang, a better urban forest design need to be implemented to enhance the area and vegetation coverage of the street and roadside forests, the residential block forests, and the surburb forest Species biodiversity need to be enhanced by adjust the species composition of urban forest trees to reduce the single or several tree species populations and to plant more varieties. A better fundings for urban forest health management need to be budgeted. Advanced technologies in tree care and scientific measures to control pests need to be adopted to prove better care for plants and to keep trees grow healthfully.

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LUCC是全球变化研究的核心主题之一,也是社会经济可持续发展的关键问题。改革开放后四川的社会经济发展非常快,在各种因素的驱动下,土地利用/覆盖发生了深刻变化。目前四川省缺乏基于实际调查数据的、全域性的、具有连续时间序列的LUCC和驱动力分析及土地可持续利用研究成果,这对我们从全局上把握全省土地利用现状、发展变化趋势,利用土地政策参与宏观调控,实现长期可持续发展目标,建设资源节约型、环境友好型社会极为不利。本研究针对这一问题,选取全川八大土地利用类型作为研究对象,研究了全省1996年到2006年的土地利用/覆盖格局和变化情况,分析了不同尺度的驱动因素,对全省农用地和建设用地的集约利用状况、潜力进行了分析评价,并提出相应的对策措施。 1.1996年-2006年10年来整个省域的土地利用/覆盖格局变化。 (1)1996年-2006年全省的土地利用/覆盖格局 1996年,全省是一个以农用地为主的土地利用/覆盖格局,林地和牧草地属于优势覆盖类型(合占69.17%),居民点及工矿用地和交通用地合占只有3%左右。 2000年的LUCC格局较为明显的特点是耕地所占比重下降0.4个百分点,水域和未利用土地所占比重有所下降,牧草地保持不变,其余地类所占比重有所上升。 与2000年相比,2004年林草地的优势格局进一步得到强化(合占比重达到70.23%)。耕地面积占幅员面积的比重下降0.83个百分点,略有下降的有未利用土地、水域和牧草地。值得关注的是在“退耕还林还草”的大背景下,牧草地占幅员面积的比重下降0.04个百分点。 到2006年,仍为林草地为主导优势的格局,二者合占上升0.15%。在城市化快速推进的背景下,居民点及工矿用地中的城市用地和建制镇用地占比重超过15%,农村居民点占比重降至76%。交通用地中农村道路占比重降至57.8%,公路用地占比重升至37.5%。五个地貌区的土地利用/覆盖格局与全省的变化基本一致。值得关注的是盆西平原区的交通用地上升幅度和盆地丘陵区的未利用土地的开发利用力度明显大于其它地貌区。 (2)1996-2006年10年间土地利用/覆盖格局的变化 1996-2000年4年间,耕地、水域和未利用地三个地类下降,年均减少0.75、0.19和0.32个百分点。其中耕地年均减少49229.0公顷,约一半流向林地,13.77%流向园地,约20%流向建设用地。另外5个地类面积增长,增长绝对量最大的是林地,年均增长40063.7公顷,交通用地增幅最大,4年年均增长达1.95%。 2001-2004年是西部大开发逐步推进、“退耕还林还草”项目全面展开和土地整理深入实施的关键期,LUCC更为深刻。耕地、未利用地、水域和牧草地四个地类面积下降,其余地类按增长幅度依次是园地、交通用地、居民点及工矿用地和林地。耕地加速下降,年均降幅达到1.59%,其减少去向主要是林地(占66.75%)和园地(占19.84%),其增加来源主要是未利用地、园地和水域。交通用地的增幅最大,为3.96%,其增加主要来源于耕地、未利用土地和林地,分别占49.96%、16.63%和13.09%。居民点及工矿用地增长幅度为3.12%。 从1996年到2006年的10年间,耕地、未利用地、水域和牧草地下降幅度分别为10.36%、3.61%、1.34%和0.26%。园地增幅达23.61%。绝对面积增长最大的则是林地,达630733.3公顷。交通用地和居民点及工矿用地增幅也较大,分别为15.00%和9.31%。 10年间年均总变化量为310326.6公顷,2000年-2004年之间变化最大(为356865.8公顷),高于平均变化量,而1996-2000年间和2004-2006年间都小于平均变化量。 (3)10年间不同地貌区的LUCC变化 盆西平原区的特点是园地大幅上升达77%,居民点及工矿用地和交通用地也大幅上升,耕地、未利用地下降幅度大,该区耕地、水域、未利用地的减少强度和园地、居民点及工矿用地、交通用地的上升强度均居五区第一;盆地丘陵区的特点是牧草地下降幅度大,为-36.89%,交通用地、园地和林地上升幅度较大,该区耕地减少、未利用地减少、林地增加、居民点及工矿用地和交通用地增加的变化强度均居五区相应地类增减的第二位;盆周山地区的特点是耕地减少较多,交通用地和园地增长较大,该区林地变化强度居各区第一位,牧草地和水域变化强度居各区第二位,耕地、居民点及工矿用地和未利用地居各区第三位;川西南山地区的特点是园地、耕地、交通用地和居民点及工矿用地变化幅度大,另外四个地类变化较小。该区减少的牧草地占全省牧草地减少的97.91%,变化强度居各个地貌区的第一位,园地相对变化强度居五区的第二位;川西北高山高原区的特点是耕地大幅下降、园地大幅上升,交通用地升幅也较大,其余地类变化不大。值得注意的是,该区牧草地和水域面积增加,与全省该地类的变化相反。其余地类的相对变化强度均是五个地貌区中最小的。 用变化强度分值考量变化强度,盆西平原区的变化强度最大,盆地丘陵区和盆周山地区的变化强度相当,川西北高山高原区的变化强度则要小得多。 (4)1996年及2006年全省土地利用/覆盖格局的景观生态学分析 全省是以自然景观占优势(占约70%)、农业景观为补充、建设用地景观居于从属地位的土地利用景观格局。景观多样性和均匀度不高。到2006年,全省总的景观格局并无大的改变。总体情况是随着时间的推移和人类活动的加强,区内景观优势度上升、多样性和均匀度变小。但斑块数减少,斑块面积和斑块孔隙度有所增大。斑块的形状指数和分维数均有所下降,表明受人为干扰有加剧的趋势。反映景观格局结构的破碎度指数有轻微下降。景观指数的变化表明全省土地利用有缓慢集中、规模聚集的趋势。 (5)三大生态建设工程对土地利用/覆盖变化的影响 1996-2006年间LUCC与三大生态建设工程实施的耦合分析,发现退耕工程对耕地、林地、牧草地等地类覆盖变化的影响最大,天保工程次之,长防工程最小。 2.四川省LUCC驱动力分析 (1)总体分析: 从整体上分析,人为因素对区域整体LUCC的影响从1996年的63.32%增加到2006年的66.99%,变得日益强烈。同时人为因素影响强度表现出明显的区域差异,地势平缓、经济区位条件好的区域其人为影响强度明显较高。 政策体制转变下的经济高速增长、快速的城市化、工业化过程和生态建设是四川省LUCC宏观尺度的驱动因素。区域的LUCC主要受到了由内向外(从城市到乡村)和由外向内(从山顶向平地)两种作用力的共同推动。局部尺度上,如距离交通线、水利线、中心城市的远近,地形凸起、大型独立项目落址、重污染项目的阻隔等,甚至一些乡规民俗等因素也会成为LUCC的驱动影响因素。在较小的尺度上,人类个体行为选择对LUCC的影响也是存在的。   根据驱动因子的特性作者将其划分为驱变、阻变、良性、惰性因子等类型。 (2)分地貌区的驱动因子分析 各地貌区都存在城市化、工业化、生态工程实施、自然灾害等驱动因子,但主次不一。对于盆西平原和盆地丘陵区,城市化、工业化是前两位的因子,而对另外三个地貌区,生态工程实施和产业结构调整则成为第第一、二位的驱动因子。 (3)分地类的驱动因子分析(以坡耕地为例) 分坡度的耕地变化分析发现,耕地减少主要集中在2°以下的平地、15°-25°和25°以上三个坡度级,是其它坡度级耕地减幅的三倍左右。这表明耕地减少受城市化进程和“退耕还林还草”工程驱动影响尤为巨大。 3.土地利用格局优化、集约利用评价和可持续利用及对策研究 (1)土地利用格局优化的战略选择及调整预测 土地利用格局调整的战略是农业生产用地、建设用地和生态及其他用地占幅员的比重分别稳定在13%、7%和80%左右,重点是三大类别内部二级和三级地类的合理调整。 (2)全省土地集约利用评价 全省农用地利用集约度为0.46,总体上集约度不高,处于较适度利用阶段。建设用地利用集约度为0.38,处于较适度利用阶段。集约利用提升空间较大。 农用地的潜力主要在于加强土地保育、完善利用制度、提高单产。城市建设用地的包括存量潜力、强度潜力、结构潜力,空间很大。农村居民点整理潜力可以逐步挖掘。 (3)新增建设用地集约利用的统筹安排 据测算,到2020年,四川省城市建设用地需求量在463850-492360hm2之间,城镇各业新增建设用地规模为361276.79hm2,占用耕地200565.94 hm2。2004-2020年间四川省农村居民点整理潜力33.86万hm2。农村居民点建设用地需求量为70.57万公顷。 (4)土地集约利用措施与坡耕地可持续利用战略 提出了土地集约利用的措施。在对坡耕地生态系统结构与功能分析的基础上,提出坡耕地可持续利用战略与生态恢复战略,并从技术和政策层面提出了坡耕地合理利用和生态退耕的措施和建议。 LUCC is one of the key questions of global change and sustainable development of society. After the opening and reform of China, the society and economy of Sichuan Province developed very fast ,the land-use/cover changed very strong droved by many factors .But nowadays we have no constant spatial-temporal study and driving force analysis about the whole province based on investigation. And it is lack of land sustainable utilization study based on correlative study. So we choose all the land resource in Sichuan, combine RS and GIS and field investigation, and take statistic-mathematic means and system analysis, to study the LUCC patterns and different scale driving force of different physiognomy regions, land cover types and periods; to analyze the current situation and potential of land resource intensive utilization, and gave out corresponding measurements. We found that forest and grassland are the dominant cover types of Sichuan provincial land –use/cover pattern, and becoming more and more stronger from 1996 to 2006,the natural landscape is the metric and occupy 70%,the diversity and evenness index are not high; the totally change quantity from 2000 to 2004 is the biggest; cultivated land especially steep cultivated land ,garden plot, forestry land ,settlement and industry land and traffic land changed relative stronger; among five physiognomy regions ,the changing intensity of PEN XI PING YUAN QU is the biggest, CHUAN XI BEI GAO SHAN GAO QU is smallest; under the background of policy system changing, the fast developing of economy, fast urbanization and industrialization and ecology construction are the macro-scale driving force of Sichuan provincial LUCC; to compare the impacts of “TUI GENG GONG CHENG” on LUCC especially to cultivated land ,forestry land and grassland is strongest, “TIAN BAO GONG CHENG ” is stronger,“ HANG FANG GONG CHENG” is smallest; the intensive utilization level of farmland and construction land of whole province is relative moderation, there is huge potential to excavate and fulfill the increasing demand of construction land;we must take synthetic measurements to accelerate the sustainable utilization of land resource, including administrative, economical ,technological and ecological policies.