张掖市土地利用/覆盖变化模拟

土地利用/覆盖变化是全球变化的主要原因,也是与可持续发展密切相关的课题。土地利用/覆盖变化模拟是预测未来土地利用/覆盖变化的重要方法。将国际上先进的CLUE-S模型应用到位于黑河中上游的张掖市,模拟该地区的土地利用/覆盖变化。模拟时段为2001～2020年。模型中将土地利用类型分为:①耕地;②林地;③草地;④水域;⑤城镇用地;⑥未利用地。用回归分析的方法,选择了对该地区土地利用/覆盖变化有重要贡献的7种驱动因子,分别为:与城市的距离、与河流的距离、与道路的距离、人口密度、海拔、坡度、坡向。模拟结果显示:到2020年,林地、草地、水域和城镇用地面积增加,耕地,未利用地面减少。     

基于对象级分类的土地覆盖动态变化及趋势分析

以广东省东莞市2005~2008年SPOT 5遥感影像为主要数据源,采用对象级分类后比较的变化检测技术,从土地覆盖类型的面积总量、相互转移等方面多层次分析了研究区域4 a土地覆盖的变化情况。在此基础上,利用马尔科夫链模型对该区域未来5 a的土地覆盖动态变化及演变趋势进行了分析和预测,为广东省土地覆盖变化研究提供典型案例分析,以达到全面把握研究区土地覆盖变化规律的目标。结果表明:研究区域的土地覆盖变化基本趋势表现为城镇建设用地总量持续增加以及耕地和园林地面积总量减少,其中城镇建设用地和农业耕地变化幅度较大,其他类型土地变化相对稳定。增加的城镇建设用地主要来源于耕地的人为减少,农业垦殖环境将趋于恶化。研究结果可为研究区合理有效地利用土地以促进土地可持续发展、推进城市化进程奠定决策上的技术基础。     

基于面向对象分类的土地利用信息提取及其时空变化研究

基于面向对象的影像分类技术与土地利用变化模型,选取处于剧烈变化环境下的东江流域为研究对象,对其1980~2008年的土地利用变化特征进行了研究。结果表明:(1)面向对象的遥感分类方法在SPOT5高分辨率遥感影像分类中具有较高的精度(总体精度达87.7%),可以有效避免"椒盐现象"发生;(2)1980~2008年东江流域的土地利用方式和空间格局发生了显著变化。耕地面积急剧减少了2 854.4km2,流失的耕地主要转化为了林地、城镇建设用地;园地面积减少了667km2,流失的园地主要转化为了林地;林地面积增加了1 988.7km2,呈波动变化;草地面积比由4.9%缩减为2.0%;水域面积先减少后增加;城镇用地呈快速增长趋势,年增长率高达186.23%。加强耕地保护和适度限制城镇用地增长对区域可持续发展至关重要。     

吴哥遗产地土地利用/土地覆盖变化遥感分析

吴哥窟是柬埔寨的象征,近年来深受严重的环境问题的困扰。利用长时间序列卫星影像,采用最大似然分类方法,提取吴哥遗产本体及周边区域近30年土地利用/土地覆盖及变化信息,并基于转换矩阵方法分析各土地类型变化规律,最后利用野外地面实测数据对分类精度进行了验证。研究表明:基于光学影像的吴哥遗产地土地利用/土地覆盖分类精度可达81.4%;遗产地周边建设用地增加迅猛;林地面积大量减少,主要转化为农业用地及草地;农业用地显著增加,来源于裸地及林地;水体和湿地变化较少;导致吴哥土地类型变化的主要驱动因素是旅游业带来的资源过度开发、森林大量砍伐,吴哥遗产的原真性、完整性和蕴含的历史与文化价值也正遭受极大威胁。     

近30 a来蒙古国乌兰巴托市城镇扩张及其驱动力分析

蒙古国自20世纪90年代政体改革以来,城镇化发展迅速,认识其区域发展特征与城镇化特点对于我国"一带一路"倡议实施及"中蒙俄经济走廊"建设具有重要意义。基于遥感影像,采用面向对象的分类方法,获取了蒙古国乌兰巴托市1990、2001、2010、2017年土地覆盖数据集,总体分类精度分别为86.00%、89.00%、91.6%、94.80%。利用转移矩阵对其土地覆盖变化信息进行了挖掘,结果表明:草地—林地、草地—建筑用地、林地—草地之间的转移占绝对优势。建筑用地增幅最大,扩张趋势显著,面积从99.87 km~2增加到了216.16 km~2,增幅达到了216.44%,扩张速率8.01 km~2/a,属于快速扩展模式。扩张方向以中北部、东北部和西部为主。其中,中北部主要是"summer house"度假房屋建设为主,东北部以传统的家—户—院的房屋为主(蒙古包与低层建筑混搭),西部以工业用地与居民用地为主。乌兰巴托市城镇用地扩张是外部社会经济发展和国家制度政策共同作用发生的,其中,土地私有化、市场经济化与人口数量是城镇用地扩张最主要的驱动因素。     

辽河口国家级自然保护区湿地时空演变遥感评估

以1980年、1990年、2000年、2010年和2020年Landsat遥感影像作为数据源,结合野外实测数据和Google Earth的高分影像,采用面向对象的决策树分类方法,得到1980～2020年辽河口国家级自然保护区土地覆盖变化情况。结合土地利用转移矩阵、景观格局分析法以及等扇方位分析法,研究近40 a辽河口国家级自然保护区内的人工类型用地时空动态演变特征。结果表明：1980～2020年间,研究区内自然湿地减少了270.12 km2,主要转化为耕地、油井、建设用地及交通用地等人工土地覆盖类型。由于受人类活动干扰较大,研究区内景观趋于破碎化、均衡化,景观异质性降低。近40 a来,研究区内人工土地覆盖类型主要沿北西北方向扩张。国家政策和经济发展对辽河口湿地的演变过程影响极大,农田开垦、城镇建设、油田开发和海水养殖等人类活动是自然湿地演变的主要驱动力。     

近20年来腾格里沙漠南缘土地利用/土地覆盖变化及其成因探讨——以甘肃省古浪县冰草湾地区为例

以甘肃省古浪县冰草湾地区1991、2000和2009年3期Landsat TM影像为数据源,采用基于对象的影像分类方法提取研究区各时期的土地利用/土地覆盖信息,在此基础上分析了研究区土地利用结构变化、土地利用类型转换关系和导致土地利用变化的驱动力因素。结果表明:采用基于对象的影像分类方法精度较高,可以达到准确提取土地利用/土地覆盖信息的目的;1991~2009年,冰草湾地区农田和居民用地的面积持续增加,并向沙漠和盐碱地方向扩张,呈现"人进沙退"的格局;2000~2009年各种土地利用类型之间的相互转换速度明显低于1991~2000年,土地利用状况呈现趋于稳定的趋势。造成土地利用/土地覆盖变化的主要驱动力是引黄灌溉和生态移民工程的实施。     

利用Landsat影像构建河西绿洲土地利用信息图谱——以张掖市甘州区为例

基于地学信息图谱理论,结合GIS和RS技术,利用河西绿洲甘州区1975年、1987年、1999年和2009年四期Landsat影像作为土地利用信息提取的基础空间数据,构建了一系列土地利用信息图谱,通过图谱来分析研究区的土地利用时空变化规律。结果表明:1975年～2009年河西绿洲甘州区的土地利用发生了显著变化,耕地、建设用地和未利用地呈现出持续的增加态势,其中耕地主要来源于草地、林地和未利用地的转化;林地、草地和水域面积呈减小趋势,主要表现为草地退化、开垦为耕地。     

基于CBERS CCD数据的鹿洼煤矿塌陷区LUCC检测

本文基于多时相中巴地球资源卫星CBERS CCD数据,以鹿洼煤矿塌陷区为例,利用混合像元分解技术开展土地利用/覆盖变化LUCC检测.分别选取研究区域的2002年、2006年和2010年3个时相的CBERSCCD图像作为遥感数据源,将粒子群优化算法引入到端元提取中,基于线性光谱混合模型提取鹿洼煤矿塌陷区水体、建筑用地、农田和土壤4类地物信息,并对结果进行统计分析.LUCC检测结果表明,2002年至2010年间,鹿洼煤矿塌陷地面积逐年增加,造成大面积农田积水,导致无法进行作物耕种.最后,结合当地政府采取的塌陷地治理措施分析了土地利用变化情况.     

洪泽湖区土地利用/覆盖变化分析

利用1973年、1984年和2006年3期Landsat卫星遥感图像,分别对校正处理后的3期图像进行缨帽变换,并利用亮度、绿度和湿度这3个分量的差值运算进行信息复合。然后利用变化向量分析法确定复合图像中的变化和非变化像元之间的变化强度阈值。最后利用阈值分割和掩膜技术,对3期图像进行土地利用/覆盖变化监测。研究表明,近33年来洪泽湖区土地利用结构发生根本性改变:天然湿地面积大幅度缩减,1973年～1984年间减少了200.55km2,1984年～2006年间减少了357.04km2,主要表现为敞水区和湿地植被面积的减少,养殖场、农田面积显著增加,城镇用地略有增加。本文研究表明基于缨帽变换并结合差值运算方法和变化向量分析法实现土地利用/覆盖变化监测的可行性。     

完善土地整理 发掘土地潜力

土地整理就是在土地利用总体规划控制下 ,对现有土地利用结构、土地利用空间、区位进行再配置 ,充分挖掘土地有效利用面积 ,以实现土地利用的社会、经济、生态效益最大化以及土地的持续利用 ,它是社会经济发展由外延型向集约型转变的一个具体体现。一、土地整理的模式第一 ,以“集中”为主的整理模式。以乡镇为基本单位 ,对零星分散和规模布局不合理的农村小城镇、乡村居民点、农村工业点等统一规划。引导农民住宅向城镇集中 ,引导工业向工业园区集中 ,推进耕地向规模化经营集中。农村居民向小城镇集中 ,就是在小城镇辖区内集体经济组织之间…     

Fuzzy expert system for land reallocation in land consolidation

One of the most important steps of land consolidation projects is land reallocation studies. In Turkey, reallocation studies carried out in the scope of land consolidation projects are made according to farmer preferences (interviews). In addition to interview-based land reallocation model, mathematical models have been used in the previous optimization studies for reallocation procedure. Recently, fuzzy logic method, which is capable of modeling human mindset and used when other forms of mathematical models cannot be developed, has also been applied to the field of geomatic engineering, as well as in other engineering branches.This study examined the applicability of a fuzzy logic method at the reallocation stage of land consolidation study, where development of an accurate mathematical model was not possible. The results obtained from the fuzzy logic-based land reallocation model were compared with those obtained from the interview-based land reallocation model. Farmers were surveyed to determine which land reallocation model they preferred. The results indicate that 80.5% of the participant landholdings were satisfied with the fuzzy logic-based reallocation land model, while 50% were with the interview-based land reallocation model.     

Classification accuracy and trend assessments of land cover- land use changes from principal components of land satellite images

The paper evaluated the accuracy of classifying Land Cover-Land Use (LCLU) types and assessed the trends of their changes from Principal Components (PC) of Land satellite (Landsat) images. The accuracy of the image classification of LCLU was evaluated using the confusion matrices and assessed with cross-referencing of samples of LCLU types interpreted and classified from System Pour l’Observation de la Terre (SPOT) images and topographical map. LCLU changes were detected, quantified, and statistically analysed. The interpretation error of the composite image of Landsat Enhanced Thematic Mapper Plus (Landsat ETM⁺) (2006) was high compared with that from the PC image of Landsat ETM⁺ (2006). From 1986 – 2006 the area covered by settlements increased by 0.8% (230,380.00 km²), agricultural land decreased by 7.5% (1009.40 km²), vegetation cover decreased by 0.9% (114.00 km²) while waterbody increased by 0.2% (25.91 km²). Also, from 1986 – 2006 the average annual rates of change in the area of settlements was 6.7%. Agricultural land and bare land showed fluctuations of change rates from 6.7% and 5.0% annually in 1986 and 2006 respectively. The quantitative evidences of LCLU changes revealed the growth of settlements. The conversions of land from agriculture to urban land represent the most significant land cover changes. The rate of change was as high as 4.8% for settlements while agricultural lands were converted at 5.0% per year. The Principal Component Analysis (PCA) of the Landsat images and supervised classification method used made it possible to classify and determine the area of LCLU classes from the set of Landsat images without prior depiction and delimitation of individual LCLU type. It permitted the measurement of area of each LCLU class at a high accuracy level and kept the level of error relatively constant. The PCA analysis in this study affirms the previous research findings. Future research works should focus on the use of remotely sensed images with high temporal and spatial resolutions such as Quick Bird and SPOT 6 to develop effective and accurate LCLU change mapping and monitoring at the local scale.

The PCA technique has been used quite widely to study changes in land cover and land use in many ‘developed’ countries but much still needs to be done in developing and undeveloped countries where land cover and land use change is poorly mapped and knowledge of such changes is very important for planning development of the country.     

Connotative land degradation mapping: A knowledge-based approach to land degradation assessment

Land degradation mapping is a problem-solving task that aims to provide information for allocating budgets and materials to counter the deterioration of land resources. Typically, it entails the implementation of a set of indicators in a GIS to appraise the severity of land degradation across a territory. Nevertheless, the selection of these indicators has proved to be challenging in practice and often this selection reflects one particular and thus limited perspective of land degradation. Because land degradation is intrinsically complex and involves decisions by many agencies and individuals, land degradation mapping should be used as a learning tool through which managers, experts and stakeholders can re-examine their views within a wider semantic context. In this paper, we introduce an analytical framework, called Connotative Land Degradation Mapping, which aims to depict the meaning of a multiplicity of interacting drivers and effects The CLDM entails the implementation of (1) geographic information systems and multicriteria decision analysis (GIS-MCDA), and (2) geo-visualization. The approach is illustrated through a case study of two urban watersheds in central Mexico. Results showed that the main land degradation drivers in the study area were related to natural processes, which were exacerbated by human activities. The output of the CLDM enabled a better communication of the land degradation issues and concerns in a way relevant for policymakers.     

An artificial immune network approach to multi-sensor land use/land cover classification

An optimized artificial immune network-based classification model, namely OPTINC, was developed for remote sensing-based land use/land cover (LULC) classification. Major improvements of OPTINC compared to a typical immune network-based classification model (aiNet) include (1) preservation of the best antibodies of each land cover class from the antibody population suppression, which ensures that each land cover class is represented by at least one antibody; (2) mutation rates being self-adaptive according to the model performance between training generations, which improves the model convergence; and (3) incorporation of both Euclidean distance and spectral angle mapping distance to measure affinity between two feature vectors using a genetic algorithm-based optimization, which helps the model to better discriminate LULC classes with similar characteristics. OPTINC was evaluated using two sites with different remote sensing data: a residential area in Denver, CO with high-spatial resolution QuickBird image and LiDAR data, and a suburban area in Monticello, UT with HyMap hyperspectral imagery. A decision tree, a multilayer feed-forward back-propagation neural network, and aiNet were also tested for comparison. Classification accuracy, local homogeneity of classified images, and model sensitivity to training sample size were examined. OPTINC outperformed the other models with higher accuracy and more spatially cohesive land cover classes with limited salt-and-pepper noise. OPTINC was relatively less sensitive to training sample size than the neural network, followed by the decision tree.     

MERIS potential for land applications

The Medium Resolution Imaging Spectrometer (MERIS), to be flown on the Envisat platform, contributes to the effort made by space agencies to generate Earth observation data that are responsive to the needs of the users. Although optimized for oceanic applications, this instrument should also be useful for a range of terrestrial investigations. This paper reviews the relevance of a suite of features specific to MERIS (e.g. fine radiometric and spectral resolution, programmability, the availability of an onboard calibration system) for terrestrial applications. Scientific challenges related to scale issues, the definition of appropriate algorithms for the optimal exploitation of these data, the opportunity for synergistic studies and the comparison of MERIS data with other data collected by the Advanced Very High Resolution Radiometer (AVHRR) and other precursor or future instruments are reviewed. The urgent need for an intensive and sustained research and development programme to define and validate a panoply of highlevel products optimized for terrestrial applications is stressed.     

Developing land administration in Cambodia

Presently, Cambodia struggles with an ineffective land administration. The enormity of land problems and the insecurity of tenure is a concrete obstacle, for up to 90% of the population. The government has no means of land management through the existing land register at its disposal. The creation of a clear land policy and a land management system are seen as crucial steps towards restoring law and order. In terms of land register performance, a training program is recommended as a short-term solution to improvement. The systematic registration is presented as a long-term solution to the clarification of the situation on land and to the introduction of the security of tenure. The developed method for systematic land registration in rural Cambodia consists of six parts: public information, adjudication, demarcation, surveying, documentation and appeal. The area by area, parcel by parcel and one parcel–one visit principles are applied. The 2 years' test results are encouraging. The method works well, problems are rare and the desire for secured land titles, among the landholders, is high. The main hindrance is represented by the ambiguous legislation. The estimated cost of the first registration with the method is about 15 US$ per parcel including aerial photography, orthophoto production, systematic registration and title issurance. The evaluation demonstrated that the method is capable of facilitating the general objectives of land registration. It strongly promotes the strategic goals of the Finnish development co-operation.     

Identifying land cover variability distinct from land cover change: Cheatgrass in the Great Basin

An understanding of land use/land cover change at local, regional, and global scales is important in an increasingly human-dominated biosphere. Here, we report on an under-appreciated complexity in the analysis of land cover change important in arid and semi-arid environments. In these environments, some land cover types show a high degree of inter-annual variability in productivity. In this study, we show that ecosystems dominated by non-native cheatgrass (Bromus tectorum) show an inter-annual amplified response to rainfall distinct from native shrub/bunch grass in the Great Basin, US. This response is apparent in time series of Landsat and Advanced Very High Resolution Radiometer (AVHRR) that encompass enough time to include years with high and low rainfall. Based on areas showing a similar amplified response elsewhere in the Great Basin, 20,000 km², or 7% of land cover, are currently dominated by cheatgrass. Inter-annual patterns, like the high variability seen in cheatgrass-dominated areas, should be considered for more accurate land cover classification. Land cover change science should be aware that high inter-annual variability is inherent in annual dominated ecosystems and does not necessarily correspond to active land cover change.     

A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

This study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data.