A spectral gradient difference based approach for land cover change detection

Change detection with remotely sensed imagery plays an important role in land cover mapping, process analysis and dynamic information services. Euclidean distance, correlation and other mathematic metrics between spectral curves have been used to calculate change magnitude in most change detection methods. However, many pseudo changes would also be detected because of inter-class spectral variance, which remains a significant challenge for operational remote sensing applications. In general, different land cover types have their own spectral curves characterized by typical spectral values and shapes. These spectral values are widely used for designing change detection algorithms. However, the shape of spectral curves has not yet been fully considered. This paper proposes to use spectral gradient difference (SGD) to quantitatively describe the spectral shapes and the differences in shape between two spectra. Change magnitude calculated in the new spectral gradient space is used to detect the change/no-change areas. Then, a chain model is employed to represent the SGD pattern both qualitatively and quantitatively. Finally, the land cover change types are determined by pattern matching with the knowledgebase of reference SGD patterns. The effectiveness of this SGD-based change detection approach was verified by a simulation experiment and a case study of Landsat data. The results indicated that the SGD-based approach was superior to the traditional methods.     

基于网络服务的地类遥感影像样本检索系统的设计与实现

为了有效地辅助第二次全国土地调查,对土地覆盖情况和地类遥感影像判读提供辅助,运用ASP.NET和网络服务技术,以多源遥感影像为数据源,构建了基于网络服务的地类遥感影像样本检索系统。本文重点研究了基于网络服务的地类遥感影像样本检索系统的结构、流程和功能,提出了系统的数据库表结构设计方法,为网络化遥感样本检索服务提供了解决方案。     

DMC卫星影像在海啸灾情土地覆盖类型变化分析中的应用

利用DMC卫星影像,对在2004年12月26日发生在印度洋海啸中,受灾严重的印度尼西亚亚齐特别自治区进行土地覆盖类型变化比较分析,科学客观的对灾情进行评估,从而阐述了DMC卫星影像在灾情分析及土地覆盖类型监测等方面的应用前景。     

Application of remote sensing technology for land use/land cover change analysis

Land use/land cover changes over a period of 30 years were studied using remote sensing technology in a part of Gohparu block, Shahdol district of Madhya Pradesh. Land use/ land cover maps were prepared by visual interpretation of two period remotely sensed data. Post-classification comparison technique was adopted for this purpose. The loss of vegetation cover was estimated to be 22 percent and 14 percent of the land was found to have been tranformed into wasteland between 1967 and 1996. Overall rate of change was found to be 1.8 percent per year during this period.     

Polarimetric synthetic aperture radar change detection for specific land cover types

This paper presents a supervised polarimetric synthetic aperture radar (PolSAR) change detection method applied to specific land cover types. For each pixel of a PolSAR image, its target scattering vector can be modeled as having a complex multivariate normal distribution. Based on this assumption, the joint distribution of two corresponding vectors in a pair of PolSAR images is derived. Then, a generalized likelihood ratio test statistic for the equality of two likelihood functions of such joint distribution is considered and a maximum likelihood distance measure for specific land cover types is presented. Subsequently, the Kittler and Illingworth minimum error threshold segmentation method is applied to extract the specific changed areas. Experiments on two repeat-pass Radarsat-2 fully polarimetric images of Suzhou, China, demonstrate that the proposed change detection method gives a good performance in determining the specific changed areas in PolSAR images, especially the areas that have changed to water.     

基于GIS的岳阳县土地利用变化研究

本文利用遥感和GIS技术,分析了岳阳县1993年～2002年土地利用变化及其空间分异特征。研究表明,9年间岳阳县农用地、水域呈减少趋势,建设用地、林地呈增加趋势,其中农用地的减少和林地的增加十分显著;主要转移方向包括农用地向建设用地、林地转移,水域向林地转移等;各类土地利用变化在空间上呈现明显的区域分异;城市化的迅速发展,旅游业的繁荣以及人类生态意识的提高是研究时段内土地利用变化的主要影响因子。     

Introducing landscape accuracy metric for spatial performance evaluation of land use/land cover change models

Performance evaluation is a critical step for land use/land cover (LULC) change modelling. It can be conducted through pixel quantity and its geographical location according to majority of current approaches. It is hence important to know to what extent spatial patterns of a given landscape are properly replicated in simulated LULC maps. Therefore, a new validation metric, named as landscape accuracy metric (LAM), is introduced by inspiration from landscape ecology. Unlike pixel quantity validation metrics, model performance is measured by LAM through quantifying spatial patterns including structure, composition and configuration attributes. The functionality of LAM was studied to assess the performance of the built-up change simulation under historical, ecological and stochastic scenarios, applying Cellular Automata Markov model. LAM is a flexible measure such that modellers can apply this metric through adding or eliminating various metrics of their interest in a selective manner and under different environmental circumstances.     

Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8–10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results.     

多源数据的土地覆被样本自动提取

随着遥感数据获取能力的不断增强,自动化程度已经成为大尺度遥感土地覆被分类面临的关键问题。然而,现有训练样本的人工选取方法成为制约土地覆被分类自动化的瓶颈。本文以河南、贵州两省为研究区,提出一种基于多源数据的土地覆被样本自动提取方法,以构建适用于大尺度的土地覆被自动分类。首先,以2010年1∶10万土地利用数据CHINALC和30 m分辨率全球土地覆被数据Globle Land30为样本数据源;然后,利用空间一致性分析及异质性分析确定样本初选区域;最后,通过样本提纯去除无效样本。结果表明:(1)应用多源数据的土地覆被样本自动提取方法获得的分类产品总体分类精度高于人工样本提取方法制作的全球土地覆被产品MCD12Q1。(2)与单源样本自动提取方法相比,应用多源数据的土地覆被样本自动提取方法,可获得更好的分类稳定性。综上,多源数据的土地覆被样本自动提取方法可在保证精度的同时,提升土地覆被分类的自动化程度。     

InSAR和地表覆盖的地表沉降驱动力分析

针对传统地表沉降监测和成因分析无法实现大规模应用的问题,该文采用一种结合InSAR和地表覆盖数据的多源数据监测和分析方法。通过对Envisat数据集和哨兵1号数据集时序处理中的配准技术进行重点攻关,获取大范围、高精度、多时相的地表沉降成果;然后结合相应年度的地表覆盖数据进行多要素数据的空间拓扑分析,确定地表沉降变化变化的驱动力。以天津市汉沽为例,开展多源InSAR数据、多时相地表覆盖数据的地表沉降监测和分析,发现大面积水域的减少、构筑物和房屋建筑的增加等反映城市建设的指标与地表沉降加剧有密切关系,并结合天津市总体规划进行验证分析。实验结果表明,基于InSAR和地表覆盖方法对于地表沉降驱动力分析的有效性,可为后续城市地表沉降灾害防治和可持续发展应用提供参考。     

Integrating the system dynamic and cellular automata models to predict land use and land cover change

Land use and land cover change (LULCC) is a widely researched topic in related studies. A number of models have been established to simulate LULCC patterns. However, the integration of the system dynamic (SD) and the cellular automata (CA) model have been rarely employed in LULCC simulations, although it allows for combining the advantages of each approach and therefore improving the simulation accuracy. In this study, we integrated an SD model and a CA model to predict LULCC under three future development scenarios in Northern Shanxi province of China, a typical agro-pastoral transitional zone. The results indicated that our integrated approach represented the impacts of natural and socioeconomic factors on LULCC well, and could accurately simulate the magnitude and spatial pattern of LULCC. The modeling scenarios illustrated that different development pathways would lead to various LULCC patterns. This study demonstrated the advantages of the integration approach for simulating LULCC and suggests that LULCC is affected to a large degree by natural and socioeconomic factors.     

基于GIS的岷江上游流域土地利用／覆被变化研究

以岷江上游流域为对象,选取3期9景TM/+ETM遥感影像,通过多步骤最大似然监督分类、变化检测,结合空间动态分析测算模型,分析近20年土地利用/覆被变化情况。结果表明:从整个流域分析,林地面积减少,主要转化为未利用地、建设用地和耕地;未利用地在前8年以每年3.7%、后8年以每年0.4%的速度增加;建设用地在1994—2002年以每年34%的速度增加,到2002—2010年增长速度减缓;耕地总面积减少54 431hm2;从县域分析,1994—2002年间,松潘和黑水县大面积林地转为未利用地;2002—2010年间,松潘县未利用地转为林地和建设用地,茂县和汶川县未利用地面积大幅增加。该研究结论不仅为国土资源管理部门优化土地利用结构提供依据,亦为当地政府实现生态资源可持续发展提供数据支撑。     

Stylized environments and ABMs: educational tools for examining the causes and consequences of land use/land cover change

A challenge in land change science is to assess the causes and consequences of LULC change and associated pattern–process relations. Increasingly, land change organizations are examining land use at local to global scales for historical, contemporary and future periods through scenarios that assess population–environment interactions. Spatial analytical tools in GIScience are being used to link people and environment and to search for the distal and proximate factors that affect local to global land use patterns. Spatial simulation models that rely upon complexity theory as the framework and agent-based models as the analytical approach offer the capability to inform through experimentation about land issues important to science and society. Using a stylized landscape where a selected set of key social, geographical and ecological elements are spatially organized, we describe how land dynamics can be examined through agent-based models as educational tools that are useful in the classroom, boardroom and public forums.     

基于时空域密度异常的土地利用/土地覆盖短期变化检测

论文分析了时间序列遥感影像中土地利用/土地覆盖短期变化的特点及其时空异常特征, 认为和环境、物候等因素造成的影像变化相比, 由人为活动引起的土地利用/土地覆盖变化具有典型的时间和空间异常特征, 并提出了基于密度异常的土地利用短期变化检测方法。研究工作选取珠江口地区1—5月作物生长期间的3个时间序列Radarsat雷达影像进行试验, 在影像分割的基础上, 构建了基于对象的特征变化矢量, 并将密度异常检测算法(DBAD)扩展到变化矢量的N维特征空间上, 运用随机搜索策略确定检测参数, 对Radarsat时间序列变化矢量中的“小模式”事件进行了检测。检测结果认为, 密度异常检测算法检测的是变化矢量在特征空间的密度分布, 与变化矢量的强度和方向无关, 因此能在时间序列影像中分离出由典型的、正常的作物生长或农事活动引起的影像光谱或回波变化, 进而识别出由人为活动或突发事件导致的土地利用/土地覆盖变化, 这是通常的图像差值等方法难以做到的。进一步的抽样检测说明, 密度异常检测方法对新增建设用地的检测准确率最高(>88%);林地地表覆盖相对稳定, 检测误差也很低(8%);农用地和养殖水面的异常变化检测误差在11%—22%之间;较大的检测误差主要集中在建设用地、农用地和未利用地之间的转换(16%—25%);此外, 养殖水面的检测误差主要集中在河流沿岸及水面变化较大的养殖区域。影像分割结果特别是一些线状分割图斑以及混合地类图斑对误差也有一定的影响。     

四川省及成都地区土地利用/覆被变化建库与对比研究

为探讨近年四川省部分地区严重旱涝灾害天气成因,利用遥感(RS)、地理信息系统(G IS)和全球定位系统(GPS)技术建立了四川省及成都地区2000~2004年土地利用/覆被变化数据库,并对四川省及成都地区2000~2004年土地利用/覆被变化信息进行对比研究,对四川省部分地区与成都地区近年气候差异成因进行探讨。     

通过软硬变化检测识别冬小麦

提出一种软硬变化检测的作物识别方法 SHLUCD(Soft and Hard Land Use/Cover Change Detection Method)。该方法利用多期遥感影像能够有效表达作物的生长物候特征,以达到在离散变化区(即纯净像元区,包括完全转换成作物的突变区域和非作物区域)和连续变化区(即渐变区,混合像元区,是部分转化为作物的区域)准确进行作物的识别。在北京市选择一个研究区,以冬小麦为研究对象,选用2011年10月6日(播种期)和2012年4月16日(拔节期)两期环境减灾1号卫星影像,分别采用硬变化检测方法 HLUCD(Hard Land Use/Cover Change Detection Method)、软变化检测方法 SLUCD(Soft Land Use/Cover Change Detection Method)和SHLUCD进行冬小麦的识别。实验结果表明:在不同尺度窗口下,SHLUCD较传统方法表现出较明显的优势,具有更低的均方根误差RMSE(SHLUCD为[0.14,0.07],HLUCD为[0.15,0.07],SLUCD为[0.16,0.08])和偏差bias(SHLUCD为-0.0008,HLUCD为-0.007,SLUCD为0.014)和更高的决定系数R2(SHLUCD为[0.68,0.86],HLUCD为[0.62,0.86],SLUCD为[0.60,0.86])。针对冬小麦突变区域、冬小麦渐变区域和非冬小麦区域分别进行评价,表明SHLUCD识别精度接近各区最佳的识别方法,进一步验证了SHLUCD的灵活性和适用性。SHLUCD方法在离散变化区能够通过土地覆盖类型状态变化来有效地识别出冬小麦,在连续变化区可识别出土地覆盖的状态变化程度定量表达冬小麦的丰度,是其他作物多时相遥感变化检测的前期实验基础。     

地表覆盖成果质量的模糊综合评价模型与方法

本文针对地理国情监测地表覆盖成果质量合理评价问题,应用多因素模糊综合评价理论与方法,设计了一套基于模糊综合评价法的地理国情监测地表覆盖成果质量评价模型与方法,并选取我国北方27个县级测区的地理国情监测地表覆盖成果质量检查数据进行了试验验证。试验结果表明,相比最小值法、加权平均法等常规质量评价方法,基于模糊综合评价法的成果质量评价结果更为客观、合理,可为自然资源统一调查监测中的地表覆盖成果等新型测绘地理信息成果质量评价提供技术参考。     

高精度地表覆盖数据优化分割的土地利用分类

针对土地利用类型多样、特征易混淆和高分辨率遥感影像信息海量、人工提取费时费力等问题,该文以北京二号卫星影像为数据源,采用高精度地表覆盖数据优化分割的面向对象分析方法、无地表覆盖数据辅助分类的面向对象分析方法,运用朴素贝叶斯、CART决策树、随机森林和K最邻近分类器,开展武功县土地利用分类,并对分类结果进行精度评估.结果 表明:①与无地表覆盖数据辅助分类方法相比,高精度地表覆盖数据优化分割的面向对象分类方法,在精度方面有较大的提升,其分类总体精度提高18.73％,Kappa系数提高0.21;②随机森林对于土地类型多样的影像对象具有较好的识别能力,获得较高的总体精度(95.3％)和Kappa系数(0.94).研究表明一种利用高精度地表覆盖数据优化影像分割的土地利用分类方法具有更好的可行性和鲁棒性.     

基于多时像TM/ETM+影像的兖州市土地利用/覆盖变化

本文利用多时相Landsat TM/ETM+影像分析了兖州市1998年和2002年的土地利用/覆盖变化。综合考虑波段间相关系数和OIF指数,选择最佳波段组合进行图像解译,并在此基础上运用最大似然分类器(MLC)和支持向量机(SVM)的分类方法对遥感影像进行分类。进而利用SVM分类结果进行土地利用遥感动态监测,获取兖州市土地利用/覆盖变化信息,并与社会经济统计资料的统计结果进行比较。最后提取TM/ETM+影像的RDVI,基于线性混合像元分解模型分析了植被覆盖的变化。结果表明,基于多时相TM/ETM+影像分析的土地利用/覆盖变化与实际统计数据较吻合,适合动态监测土地利用变化,且精度较高。     

地理国情地表覆盖的城市建设用地扩张分析——以兰州新区为例

针对传统的土地利用变化研究方法无法实现大规模在建中的建设用地动态监测的问题,该文提出了一种新的动态监测方法。结合多时相遥感影像、规划数据和外业调查数据,能够快速、低成本地提取城市建设用地。选取合适的扩展指数构建综合扩展程度指数模型,便于对建设用地变化时序特征做出评价。定量分析建设用地变化时序特征与规划用地的对比情况,明确了城市建设用地的建设进度和符合度。结果表明,城市建设用地处于高速扩展阶段,建设现状和规划情况基本相符;该方法是对地理国情监测工作的一种新的探索。