本文从全球变化的研究热点--“土地利用/土地覆盖变化”的涵义及研究内容出发，概括分析了土地利用变化研究的方法-土地利用变化模型的建立，阐述了各类模型的涵义及在土地利用变化研究中的意义，并重点介绍了定量研究土地利用动态变化的几种模型-（1）土地资源数量变化模型；（2）土地资源生态背景质量变化模型；（3）土地利用程度变化模型；（4）土地利用变化区域差异模型；（5）土地利用空间变化模型；（6）土地需求量预测模型。

Human actions are altering the terrestrial environment at unprecedented rates, magnitudes, and spatial scales. Land-cover change stemming from human land uses represents a major source and a major element of global environmental change. Not only are the global-level data on land-use and land-cover change relatively poor, but we need a much better understanding of the underlying driving forces for these changes. Many forces have been proposed as significant, but single-factor explanations of land transformation have proved to be inadequate. How the human causes interact, and under what circumstances each is important, are questions needing systematic research. An international and interdisciplinary agenda is currently being developed to address these issues, through several closely-connected foci of study. A division of the world according to common situations of environment, human driving forces, and land-cover dynamics will be followed by detailed study of the processes at work within each situation. The results will form the basis for a concurrent effort to develop a global land model that can offer projections of patterns of land transformation.

变化检测技术是遥感应用领域的一个重要研究方向。本文首先对常见的变化检测方法进行了概括性介绍与优缺点评述,并分析了当前变化检测方法中存在的普遍问题;并在此基础上,展望了一种基于影像分割的变化检测方法:在仅对其中一个时相影像进行分割的基础上,建立了两时相影像间的对应图斑单元与变化判别规则,实现图斑单元间的直接比较来提取变化信息。并分析了基于分割的变化检测方法与像素级变化检测方法相比具有的优势。

Updating land cover maps from remotely sensed data in a timely manner is important for many areas of scientific research. Unfortunately, traditional classification procedures are very labor intensive and subjective because of the required human interaction. Based on the strategy of updating land cover data only for the changed area, we proposed an integrated, automated approach to update land cover maps without human interaction. The proposed method consists primarily of the following three parts: a change detection technique, a Markov Random Fields (MRFs) model, and an iterated training sample selecting procedure. In the proposed approach, remotely sensed data acquired in different seasons or from different remote sensors can be used. Meanwhile, the approach is completely unsupervised. Therefore, the methodology has a wide scope of application. A case study of Landsat data was conducted to test the performance of this method. The experimental results show that several sub-modules in this method work effectively and that reasonable classification accuracy can be achieved.

The recent release of the U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001, which represents the nation's land cover status based on a nominal date of 2001, is widely used as a baseline for national land cover conditions. To enable the updating of this land cover information in a consistent and continuous manner, a prototype method was developed to update land cover by an individual Landsat path and row. This method updates NLCD 2001 to a nominal date of 2006 by using both Landsat imagery and data from NLCD 2001 as the baseline. Pairs of Landsat scenes in the same season in 2001 and 2006 were acquired according to satellite paths and rows and normalized to allow calculation of change vectors between the two dates. Conservative thresholds based on Anderson Level I land cover classes were used to segregate the change vectors and determine areas of change and no-change. Once change areas had been identified, land cover classifications at the full NLCD resolution for 2006 areas of change were completed by sampling from NLCD 2001 in unchanged areas. Methods were developed and tested across five Landsat path/row study sites that contain several metropolitan areas including Seattle, Washington; San Diego, California; Sioux Falls, South Dakota; Jackson, Mississippi; and Manchester, New Hampshire. Results from the five study areas show that the vast majority of land cover change was captured and updated with overall land cover classification accuracies of 78.32%, 87.5%, 88.57%, 78.36%, and 83.33% for these areas. The method optimizes mapping efficiency and has the potential to provide users a flexible method to generate updated land cover at national and regional scales by using NLCD 2001 as the baseline.

A prototype method was developed to update the U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001 to a nominal date of 2006. NLCD 2001 is widely used as a baseline for national land cover and impervious cover conditions. To enable the updating of this database in an optimal manner, methods are designed to be accomplished by individual Landsat scene. Using conservative change thresholds based on land cover classes, areas of change and no-change were segregated from change vectors calculated from normalized Landsat scenes from 2001 and 2006. By sampling from NLCD 2001 impervious surface in unchanged areas, impervious surface predictions were estimated for changed areas within an urban extent defined by a companion land cover classification. Methods were developed and tested for national application across six study sites containing a variety of urban impervious surface. Results show the vast majority of impervious surface change associated with urban development was captured, with overall RMSE from 6.86 to 13.12% for these areas. Changes of urban development density were also evaluated by characterizing the categories of change by percentile for impervious surface. This prototype method provides a relatively low cost, flexible approach to generate updated impervious surface using NLCD 2001 as the baseline.

The importance of characterizing, quantifying, and monitoring land cover, land use, and their changes has been widely recognized by global and environmental change studies. Since the early 1990s, three U.S. National Land Cover Database (NLCD) products (circa 1992, 2001, and 2006) have been released as free downloads for users. The NLCD 2006 also provides land cover change products between 2001 and 2006. To continue providing updated national land cover and change datasets, a new initiative in developing NLCD 2011 is currently underway. We present a new Comprehensive Change Detection Method (CCDM) designed as a key component for the development of NLCD 2011 and the research results from two exemplar studies. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (CV, RCVMAX, dNBR, and dNDVI) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. For NLCD 2011, the improved and enhanced change products obtained from the CCDM provide critical information on location, magnitude, and direction of potential change areas and serve as a basis for further characterizing land cover changes for the nation. An accuracy assessment from the two study areas show 100% agreement between CCDM mapped no-change class with reference dataset, and 18% and 82% disagreement for the change class for WRS path/row p22r39 and p33r33, respectively. The strength of the CCDM is that the method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and anthropogenic disturbances potentially associated with land cover changes on different landscapes.

Postclassification comparison (PCC) and change vector analysis (CVA) have been widely used for land use/cover change detection using remotely sensed data. However, PCC suffers from error cumulation stemmed from an individual image classification error, while a strict requirement of radiometric consistency in remotely sensed data is a bottleneck of CVA. This letter proposes a new method named CVA in posterior probability space (CVAPS), which analyzes the posterior probability by using CVA. The CVAPS approach was applied and validated by a case study of land cover change detection in Shunyi District, Beijing, China, based on multitemporal Landsat Thematic Mapper data. Accuracies of “change/no-change” detection and “from-to” types of change were assessed. The results show that error cumulation in PCC was reduced in CVAPS. Furthermore, the main drawbacks in CVA were also alleviated effectively by using CVAPS. Therefore, CVAPS is potentially useful in land use/cover change detection.

以东营市为例 ,把基于典型相关分析的方法运用于多时相遥感影像的变化检测中。对于变化阈值的确定 ,采用了一种基于贝叶斯理论的最小错误率的方法。这种方法实质上是一种非监督分类的方法 ,即不需要地面实况数据或其它先验知识 ,直接对典型相关处理后的差值图像进行分析计算得到阈值 ,使变化检测的错误率达到最小。实验结果证明了这种方法的有效性。

通过对传统变化检测方法存在不足的分析, 引进典型相关分析的基础理论, 将不同时相的多通道遥感数据视为分组的多元随机变量, 利用典型变换进行遥感数据的多元变化检测。实验结果表明, 所提出的M变换方法用于多时相、多通道遥感影像的变化检测具有明显的优势和应用前景。

This paper introduces a new orthogonal transformation, the multivariate alteration detection (MAD) transformation, based on an established multivariate statistical technique canonical correlation analysis. The theory for canonical correlation analysis is sketched and a result necessary for the definition of the MAD transformation is proven. As opposed to traditional univariate change detection schemes our scheme transforms two sets of multivariate observations (e.g. two multispectral satellite images covering the same geographical area acquired at different points in time) into a difference between two linear combinations of the original variables explaining maximal change (i.e. the difference explaining maximal variance) in all variables simultaneously. The MAD transformation is invariant to linear scaling. The MAD transformation can be used iteratively. First, it can be used to detect outliers (such as drop-outs) or noise and in a second iteration, it can be used to perform the actual change detection after appropriate action on outliers or noise. Also, if an analyst has additional information such as geographical position of certain changes of interest that show up in certain bands only, our method can be applied to any spatial and/or spectral subset of the full data set to direct the analysis in any desired manner. In order to obtain a spatially more coherent representation of the detected change as obtained from the MAD analysis, post-processing by means of a minimum/maximum autocorrelation factor (MAF) transformation of the MAD variates can be performed. Whereas the traditionally used principal component (PC) transformation optimizes the data variance in each new component the MAF transformation optimizes the autocorrelation represented by each component. This post-processing introduces a new spatial element into our change detection scheme which is highly relevant for image data. Two case studies using multispectral SPOT HRV data from 5 February 1987 and 12 February 1989 covering coffee and pineapple plantations in central Kenya, and Landsat TM data from 6 June 1986 and 27 June 1988 covering a forested region in northern Sweden show the usefulness of these new concepts. Because of their ability to detect change in many channels simultaneously, the MAD transformation and the MAF post-processing are expected to be even more useful when applied to image data with more bands.

This paper describes new extensions to the previously published multivariate alteration detection (MAD) method for change detection in bi-temporal, multi- and hypervariate data such as remote sensing imagery. Much like boosting methods often applied in data mining work, the iteratively reweighted (IR) MAD method in a series of iterations places increasing focus on "difficult" observations, here observations whose change status over time is uncertain. The MAD method is based on the established technique of canonical correlation analysis: for the multivariate data acquired at two points in time and covering the same geographical region, we calculate the canonical variates and subtract them from each other. These orthogonal differences contain maximum information on joint change in all variables (spectral bands). The change detected in this fashion is invariant to separate linear (affine) transformations in the originally measured variables at the two points in time, such as 1) changes in gain and offset in the measuring device used to acquire the data, 2) data normalization or calibration schemes that are linear (affine) in the gray values of the original variables, or 3) orthogonal or other affine transformations, such as principal component (PC) or maximum autocorrelation factor (MAF) transformations. The IR-MAD method first calculates ordinary canonical and original MAD variates. In the following iterations we apply different weights to the observations, large weights being assigned to observations that show little change, i.e., for which the sum of squared, standardized MAD variates is small, and small weights being assigned to observations for which the sum is large. Like the original MAD method, the iterative extension is invariant to linear (affine) transformations of the original variables. To stabilize solutions to the (IR-)MAD problem, some form of regularization may be needed. This is especially useful for work on hyperspectral data. This paper describes ordinary two-set canonical correlation analysis, the MAD transformation, the iterative extension, and three regularization schemes. A simple case with real Landsat Thematic Mapper (TM) data at one point in time and (partly) constructed data at the other point in time that demonstrates the superiority of the iterative scheme over the original MAD method is shown. Also, examples with SPOT High Resolution Visible data from an agricultural region in Kenya, and hyperspectral airborne HyMap data from a small rural area in southeastern Germany are given. The latter case demonstrates the need for regularization

Threshold is a popular tool for segmenting gray level images.In this paper, a new optimal threshold selection method based on the maximum entroy principle is proposed.Experimental results show that the approach is correct and effectual,and it can be applicable,even when histogram bimodality is unclear.

One of the main problems related to unsupervised change detection methods based on the difference image lies in the lack of efficient automatic techniques for discriminating between changed and unchanged pixels in the difference image. Such discrimination is usually performed by using empirical strategies or manual trial-and-error procedures, which affect both the accuracy and the reliability of the change-detection process. To overcome such drawbacks, in this paper, we propose two automatic techniques (based on the Bayes theory) for the analysis of the difference image. One allows an automatic selection of the decision threshold that minimizes the overall change detection error probability under the assumption that pixels in the difference image are independent of one another. The other analyzes the difference image by considering the spatial-contextual information included in the neighborhood of each pixel. In particular, an approach based on Markov Random Fields (MRF's) that exploits interpixel class dependency contexts is presented. Both proposed techniques require the knowledge of the statistical distributions of the changed and unchanged pixels in the difference image. To perform an unsupervised estimation of the statistical terms that characterize these distributions, we propose an iterative method based on the Expectation-Maximization (EM) algorithm. Experimental results confirm the effectiveness of both proposed techniques.

<p>基于2007年SPOT5数据、2011年ALOS数据、2012年WorldView数据和2013年QuickBird数据,提取新街台格庙矿区开发初期的土地利用变化信息; 通过对变化信息的分析,掌握矿区内土地利用变化和转化情况,进而分析其变化规律及驱动力。研究结果表明: 耕地在2007&mdash;2013年间呈现高速增加的趋势,其驱动力来自于居民对矿区开发中征地补偿的预期,居民点、水域和设施农用地的增加也是该驱动力的直接体现; 林地和交通用地面积的增加主要集中在2007&mdash;2011年,前者缘于治沙造林工程,后者缘于新恩铁路的修建和兰嘎一级公路的改道; 工矿用地面积的持续增加归结于工业园区的建设和天然气资源的加速开采,随着矿区开发进入建设阶段,工矿用地仍将保持增长的态势; 草地作为矿区内唯一净减少的土地利用类型,其大面积被侵占是利益驱使而导致的; 而荒地的变化更多体现在草地转化为其他地类的中间过程。</p>

以2001年和2011年2期TM图像为数据源,采用分类后比较法提取了10 a间天津市蓟县土地利用动态变化信息; 利用空间分析算法生成地形起伏度和坡度2个地形因子,分析了不同地形特征上的土地利用类型分布及变化特征; 从类型转换和动态度2个方面定量分析土地利用变化情况,并分析土地利用变化与地形起伏度和坡度的相关关系以及土地利用变化的影响因素。结果表明: 地形地貌对土地利用的动态变化有显著的影响,在微缓起伏地形上,居民地增加最多,其次是水域,而林地减少最多; 在低起伏和中起伏地形上,居民地增加最多; 在山地起伏地形上,居民地有所增加; 在高山起伏地形上,只有林地和未利用地有少量变化。该结果可以为天津市蓟县的生态保护以及半山区县土地利用规划提供科学依据。

<p>针对多光谱图像的变化检测问题，提出了一种基于选权迭代估计（iterative estimation with weight selection，IEWS）与非监督分类（unsupervised classification，UC）的多光谱图像变化检测方法。借鉴IEWS的思想，并以类似于迭代加权多元变化检测（iteratively reweighted multivariate alteration detection，IRMAD）的迭代模式进行回归估计，得到初步的变化检测结果； 并通过对初始变化信息的UC处理，以及对不同类别的IEWS，得到最终的变化检测结果。利用该方法对TM图像进行了实验，结果表明： 所得到的变化信息在空间位置上同该区域相应时间段内土地利用/覆盖的变化情况具有很好的一致性； 同时与多元变化检测及IRMAD方法变化检测的结果相比较，表明该方法对相对较小的变化信息具有更好的变化检测能力。</p>

以Landsat TM/ETM⁺图像为主要数据源，提取了江苏省盐城滨海湿地土地利用/覆盖信息；基于单窗算法反演了地表温度，综合分析了1992—2009年间湿地土地利用/覆盖变化（LUCC）及其对地表温度的影响。结果表明：在1992—2009年的17 a间，江苏省滨海湿地最主要的LUCC类型是人类主导型，且以自然类型向人工类型转化最为显著；互花米草、碱蓬和芦苇等自然景观总面积减少了385.52 km²，养殖场、盐田及建筑用地等人工景观总面积增加了376.97 km²；人为干扰是湿地LUCC最主要的驱动因素。在LUCC的影响下，湿地地表温度也有显著波动，多数湿地类型都有不同层次的升温。

<p>基于MODIS数据，采用基于像元尺度的趋势分析和稳定性分析方法，深入分析了河北坝上地区近13 a的植被变化特征。研究表明：空间分布上，坝上全区地表植被覆盖从西向东依次渐好，各分区域的季节变化特征具有明显的差异性；时间上，近13 a以来坝上大部分地区植被覆盖有改善的趋势，其中得到改善的区域占坝上总面积的51.35%，基本不变的区域占25.68%；同时，因坝上地区生态环境比较脆弱，植被生长易受自然和人为因素的干扰，植被覆盖在时间序列上的稳定性较差，坝上全区每年的NDVI最大值合成数据的变异系数<em>Cv</em>平均值达到14.85%，<em>Cv</em>&lt;10%的区域仅占坝上总面积的15.3%。</p>