Land-use and land-cover change detection in a semi-arid area of Niger using multi-temporal analysis of Landsat images

Recent studies using low-resolution satellite time series show that the Sahelian belt of West Africa is witnessing an increase in vegetation cover/biomass, called re-greening. However, detailed information on local processing and changes is rare or lacking. A multi-temporal set of Landsat images was used to produce land-cover maps for the years 2000 and 2007 in a semi-arid region of Niger, where an anomalous vegetation trend was previously detected. Several supervised classification approaches were tested: spectral classification of single Landsat data, temporal classification of normalized difference vegetation index time series from Landsat images, and two-step classification integrating both these approaches. The accuracy of the land-cover maps obtained ranges between 80% and 90% overall for the two-step classification approach. Comparison of the maps between the two years indicates a stable semi-arid region, where some change in hot spots exists despite a generally constant level of rainfall in the area during this period. In particular, the Dallol Bosso fossil valley highlights an increase in cultivated land, while a decrease in herbaceous vegetation was observed outside the valley where rangeland is the predominant natural landscape.     

Improving change vector analysis by cross-correlogram spectral matching for accurate detection of land-cover conversion

Time series of vegetation index (VI) information derived from remote sensing is important for land-cover change detection. Although traditional change vector analysis (TCVA) is an effective method for extracting land-cover change information from a time series of VI data, it has the disadvantage of being too sensitive to temporal fluctuations in VI values. The method tends to overestimate the changes and confuse the actual land-cover conversion with the land covers that have not been converted but experience significant VI changes. Cross-correlogram spectral matching (CCSM) can tell the degree of shape similarity between VI profiles and be used to detect land-cover conversion. However, this method may omit some land conversion in which the before and after land-cover types are rather similar in VI profile shape but differ significantly in absolute VI values. This article proposes a new approach that improves TCVA with an adapted use of CCSM. First, TCVA is employed for preliminary detection of land-cover changes. Second, the changes caused by temporal fluctuations of VI values are identified through the CCSM analysis and excluded to only keep the most likely land-cover conversions. Finally, classification is performed to map the different types of land-cover conversions. The improved change vector analysis (ICVA) was applied to detect land-cover conversions from 2000 to 2008, using a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced VI images for the Beijing–Tianjin–Tangshan urban agglomeration district, China. The results show that ICVA is able to detect land-cover conversion with a significantly higher accuracy (78.00%, κ?=?0.56) than TCVA (64.00%, κ?=?0.35) or CCSM (66.60%, κ?=?0.27). The proposed approach is of particular value in distinguishing actual land-cover conversion from land-cover modifications resulting from phenological changes.     

结合结构相似度的自适应多尺度SAR图像变化检测

目的 结合高斯核函数特有的性质,提出一种基于结构相似度的自适应多尺度SAR图像变化检测算法。方法 本文提出的算法包括差异图像获取、高斯多尺度分解、基于结构相似性的最优尺度选择、特征矢量构造以及模糊C均值分类。首先,通过对多时相SAR图像进行对数比运算获取差异图像,然后,利用基于图像的结构相似度估计高斯多尺度变换的最优尺度,继而在该最优尺度参数下逐像素构建变化检测特征矢量,最后通过模糊C均值聚类方法实现变化像素与未变化像素的分离,生成最终的变化检测结果图。结果 在两组真实的SAR图像数据上测试本文算法,正确检测率分别达到0.9952和0.9623,Kappa系数分别为0.8200和0.8540,相比传统算法有了较大的提高。结论 本文算法充分利用了尺度信息,对噪声的鲁棒性有所提高。实测SAR数据的实验结果表明,本文算法可以智能获取最优分解尺度,显著提高了SAR图像变化检测性能。     

Impacts of imagery temporal frequency on land-cover change detection monitoring

An important consideration for monitoring land-cover (LC) change is the nominal temporal frequency of remote sensor data acquisitions required to adequately characterize change events. Ecosystem-specific regeneration rates are an important consideration for determining the required frequency of data collections to minimize change omission errors. Clear-cut forested areas in north central North Carolina undergo rapid colonization from pioneer (replacement) vegetation that is often difficult to differentiate spectrally from that previously present. This study compared change detection results for temporal frequencies corresponding to 3-, 7-, and 10-year time intervals for near-anniversary date Landsat 5 Thematic Mapper (TM) data acquisitions corresponding to a single path/row. Change detection was performed using an identical change vector analysis (CVA) technique for all imagery dates. Although the accuracy of the 3-year analysis was acceptable (86.3%, κ=0.55), a significant level of change omission errors resulted (51.7%). Accuracies associated with both the 7-year (43.6%, κ=0.10) and 10-year (37.2%, κ=0.05) temporal frequency analyses performed poorly, with excessive change omission errors of 84.8% and 86.3%, respectively. The average rate of LC change observed over the study area for the 13-year index period (1987-2000) was approximately 1.0% per annum. Overall results indicated that a minimum of 3-4-year temporal data acquisition frequency is required to monitor LC change events in north central North Carolina. Reductions in change omission errors could probably best be achieved by further increasing temporal data acquisition frequencies to a 1-2-year time interval.     

Landsat digital enhancements for change detection in urban environments

Comparison of Landsat digital classifications from two dates has met with limited success for change detection in urban environments. The capabilities of digital enhancements for displaying change were therefore investigated as an alternative procedure for this task. Three different color enhancements, an overlay of band 5, ratios of bands 5 and 7, and a vegetation index, were generated using 1974 and 1978 Landsat data of Hamilton, Ontario. The ratios only emphasize major changes. The band 5 overlay shows change most clearly, but the vegetation index enhancement is almost as good and in addition to showing change also emphasizes the urban boundary and the major road network. It is suggested that change enhancements could be used effectively by agencies responsible for monitoring urban development over large areas.     

A novel approach based on structural information for change detection in SAR images

Structural information, extracted by simulating the human visual system (HVS), is independent of viewing conditions and individual observers. Structural similarity (SSIM), a measure of similarity between two images, has been widely used in image quality assessment. Given the fact that the change detection techniques identify the changed area by the similarity of multi-temporal images, SSIM has significant prospect in change detection of synthetic aperture radar (SAR) images. However, the experimental results show that SSIM performs worse in change detection of multi-temporal SAR images. In this study, we first propose an advanced SSIM (ASSIM) based on a two-step assumption of extracting structural information and a visual attention measure (VAM) model. Then, we propose a novel approach based on ASSIM for change detection in SAR images. SSIM, ASSIM, and state-of-the-art methods are tested on two datasets to compare their performances in change detection of SAR images. Experimental results show that the proposed method can acquire a better difference image than SSIM and other state-of-the-art methods, and improve the accuracy of change detection in SAR images effectively.     

Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments

Remotely sensed images and processing techniques are a primary tool for mapping changes in tropical forest types important to biodiversity and environmental assessment. Detailed land cover data are lacking for most wet tropical areas that present special challenges for data collection. For this study, we utilize decision tree (DT) classifiers to map 32 land cover types of varying ecological and economic importance over an 8000 km² study area and biological corridor in Costa Rica. We assess multivariate QUEST DTs with unbiased classification rules and linear discriminant node models for integrated vegetation mapping and change detection. Predictor variables essential to accurate land cover classification were selected using importance indices statistically derived with classification trees. A set of 35 variables from SRTM-DEM terrain variables, WorldClim grids, and Landsat TM bands were assessed.

Of the techniques examined, QUEST trees were most accurate by integrating a set of 12 spectral and geospatial predictor variables for image subsets with an overall cross-validation accuracy of 93% ± 3.3%. Accuracy with spectral variables alone was low (69% ± 3.3%). A random selection of training and test set pixels for the entire landscape yielded lower classification accuracy (81%) demonstrating a positive effect of image subsets on accuracy. A post-classification change comparison between 1986 and 2001 reveals that two lowland forest types of differing tree species composition are vulnerable to agricultural conversion. Tree plantations and successional vegetation added forest cover over the 15-year time period, but sometimes replaced native forest types, reducing floristic diversity. Decision tree classifiers, capable of combining data from multiple sources, are highly adaptable for mapping and monitoring land cover changes important to biodiversity and other ecosystem services in complex wet tropical environments.     

Unsupervised change detection using spectral features and a texture difference measure for VHR remote-sensing images

This article proposes an unsupervised change-detection method using spectral and texture information for very-high-resolution (VHR) remote-sensing images. First, a new local-similarity-based texture difference measure (LSTDM) is defined using a grey-level co-occurrence matrix. A mathematical analysis shows that LSTDM is robust with respect to noise and spectral similarity. Second, the difference image is generated by integrating the spectral and texture features. Then, the unsupervised change-detection problem in VHR remote-sensing images is formulated as minimizing an energy function related with changed and unchanged classes in the difference image. A modified expectation-maximization-based active contour model (EMCVM) is applied to the difference image to separate the changed and unchanged regions. Finally, two different experiments are performed with SPOT-5 images and compared with state-of-the-art unsupervised change-detection methods to evaluate the effectiveness of the proposed method. The results indicate that the proposed method can sufficiently increase the robustness with respect to noise and spectral similarity and obtain the highest accuracy among the methods addressed in this article.     

Object-oriented change detection method based on adaptive multi-method combination for remote-sensing images

In this study, we propose a novel object-oriented change detection method for remote-sensing images. First, the Gabor texture and Markov random field texture are extracted based on the remote-sensing images, and an initial pixel-level change detection result is produced. Second, in order to reduce the influence of feature uncertainty on the change detection results, the weights of different features are calculated by the Relief algorithm based on the initial pixel-level change detection result, and several difference images are fused to obtain a single comprehensive difference image. Third, different pixel-level change detection results are obtained using diverse change detection methods. The two-temporal images are then stacked and segmented, and to ensure change detection method separability, the weighted object change probability is obtained by fusing five different object change probabilities, which are calculated from the pixel-level change detection results. Finally, the objects are labelled as the class with a higher weighted object change probability. Our experimental results showed that the accuracy of change detection results obtained using the weighted object change probability was higher than that of the change detection results produced using the independent object change probability.     

Power spectral based detection of brain activation from fMR images

An efficient method for detecting activation on single and multiple epoch functional MRI (fMRI) data based on power spectral density of time-series and hidden Markov model is presented. Conventional methods of analysis of fMRI data are generally based on time-domain correlation analysis concentrating mainly on the multiple epoch data and generally do not provide good results for single epoch data. The main focus of this study is the analysis of single epoch data, constrained by certain experiments such as pain response, sleep, administration of pharmacological agents, which can only have a single or very few stimulus cycles. Further, our method obviates the need to exclusively model the hemodynamic response function and correctly identifies the voxels with delayed activation. We demonstrate the efficacy of our method in detecting brain activation by using both synthetic and real fMRI data.     

Gradual land cover change detection based on multitemporal fraction images

This study proposes a new approach to change detection in remote sensing multi-temporal image data. Rather than allocating pixels to one of two disjoint classes (change, no-change) which is the approach most commonly found in the literature, we propose in this study to define change in terms of degrees of membership to the class change. The methodology aims to model images depicting the natural environment more realistically, taking into account that changes tend to occur in a continuum rather than being sharply distinguished. To this end, a sub-pixel approach is implemented to help detect degrees of change in every pixel. Three experiments employing the proposed approach using synthetic and real image data are reported and their results discussed.     

Objects based change detection in a pair of gray-level images

The goal of the presented change detection algorithm is to extract objects that appear in only one of two input images. A typical application is surveillance, where a scene is captured at different times of the day or even on different days. In this paper we assume that there may be a significant noise or illumination differences between the input images. For example, one image may be captured in daylight while the other was captured during night with an infrared device. By using a connectivity analysis along gray-level technique, we extract significant blobs from both images. All the extracted blobs are candidates to be classified as changes or part of a change. Then, the candidate blobs from both images are matched. A blob from one image that does not satisfy the matching criteria with its corresponding blob from the other image is considered as an object of change. The algorithm was found to be reliable, fast, accurate, and robust even under extreme changes in illumination and some distortion of the images. The performance of the algorithm is demonstrated using real images. The worst-case time complexity of the algorithm is almost linear in the image size. Therefore, it is suitable for real-time applications.     

Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data

In this article, Landsat TM images acquired during the same season from both 1984 and 1997 were analysed for urban built-up land change detection in Beijing, China, where great changes have taken place during the recent decades. To reduce the spectral confusion between urban 'built-up' and rural 'non built-up' land cover categories, we propose a new structural method based on road density combined with spectral bands for change detection. The road density represents one type of structural information while the multiple Landsat TM bands represent spectral information. Road density maps for both dates were produced using a gradient direction profile analysis (GDPA) algorithm and then integrated with spectral bands. Results from the spectral-structural postclassification comparison (SSPCC) and spectral-structural image differencing (SSID) methods were evaluated and compared with spectral-only change detection methods. The proposed SSPCC method greatly reduced spectral confusion and increased the accuracy of land cover classification compared with spectral classification, which in turn improved the change detection results. This article also shows that the SSID change detection result complemented spectral band differencing by detecting areas with greater structural changes, some of which were missed, by spectral band differencing.     

The potential use of high-resolution Landsat satellite data for detecting land-cover change in the Greater Horn of Africa

To assess the potential of high-resolution satellite data for land-cover monitoring in the Greater Horn of Africa, we used a regular sampling grid of 170 sites (each measuring 20 km?×?20 km) located at the confluence of the latitudes and meridians across the study area. For each of these sites, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) satellite data were acquired for the years 1990 and 2000. A dot grid visual interpretation was used to assess land-cover change between the two dates in each of the sites. With only two acquisition dates, we found that these data were insufficient for accurately determining land-cover change and degradation in arid areas where non-woody biomass dominates. We were nevertheless able to detect land-cover modifications in three areas: increases in agriculture on the coastal plain near Mogadishu, increases in agriculture at the western fringes of our study area where there is higher rainfall, and finally a reduction in woodlands and shrublands in areas close to refugee camps on the Somali–Kenya border.     

Accelerated genetic algorithm based on search-space decomposition for change detection in remote sensing images

Detecting change areas among two or more remote sensing images is a key technique in remote sensing. It usually consists of generating and analyzing a difference image thus to produce a change map. Analyzing the difference image to obtain the change map is essentially a binary classification problem, and can be solved by optimization algorithms. This paper proposes an accelerated genetic algorithm based on search-space decomposition (SD-aGA) for change detection in remote sensing images. Firstly, the BM3D algorithm is used to preprocess the remote sensing image to enhance useful information and suppress noises. The difference image is then obtained using the logarithmic ratio method. Secondly, after saliency detection, fuzzy c-means algorithm is conducted on the salient region detected in the difference image to identify the changed, unchanged and undetermined pixels. Only those undetermined pixels are considered by the optimization algorithm, which reduces the search space significantly. Inspired by the idea of the divide-and-conquer strategy, the difference image is decomposed into sub-blocks with a method similar to down-sampling, where only those undetermined pixels are analyzed and optimized by SD-aGA in parallel. The category labels of the undetermined pixels in each sub-block are optimized according to an improved objective function with neighborhood information. Finally the decision results of the category labels of all the pixels in the sub-blocks are remapped to their original positions in the difference image and then merged globally. Decision fusion is conducted on each pixel based on the decision results in the local neighborhood to produce the final change map. The proposed method is tested on six diverse remote sensing image benchmark datasets and compared against six state-of-the-art methods. Segmentations on the synthetic image and natural image corrupted by different noise are also carried out for comparison. Results demonstrate the excellent performance of the proposed SD-aGA on handling noises and detecting the changed areas accurately. In particular, compared with the traditional genetic algorithm, SD-aGA can obtain a much higher degree of detection accuracy with much less computational time.     

Unsupervised change detection based on robust chi-squared transform for bitemporal remotely sensed images

Chi-squared transform (CST)-based methods are simple and effective methods for detecting changes in remotely sensed images that have been registered and aligned. The methods operate directly on information stored in the difference image. However, the estimated mean and covariance matrix of the Gaussian distribution that describes the unchanged pixels can be biased when the changed pixels (outliers) are also included. To overcome this issue, we propose a pixel-based unsupervised change detection method that gives robust estimates of these parameters. The method is iterative but requires only a small number of iterations. In addition, we also design an algorithm to automatically search for the optimal threshold that is needed for classifying changed versus unchanged pixels. This algorithm finds the optimal threshold where the mean and covariance matrix of the change detection result most agree with those statistics obtained from the above-mentioned robust algorithm. We refer to our change detection method as the robust CST (RCST) method. The proposed method has been evaluated on two image data-sets and compared with four state-of-the-art methods. The effectiveness of RCST is confirmed by its low overall errors (OE) and high kappa coefficients on both data-sets.     

Variability of Landsat MSS spectral responses of forests in relation to stand and site characteristics

Abstract

The use of field measures of slope angle, slope aspect, cover type, crown size and crown density is evaluated in appraising the variability of Landsat Multispectral Scanner (MSS) spectral responses for 182 sample sites within Crater Lake National Park, Oregon. Multiple linear regression models indicate that 73, 72, 71 and 57 percent of the variation in the mean response of MSS bands 4, 5, 6 and 7, respectively, was explained by the environmental variables entered into the models. In general, crown size and crown density are less important in altering spectral response than terrain orientation. This type of analysis is useful in guiding field work for remote sensing studies into areas that are environmentally diverse and which are, therefore, capable of significantly altering the spectral response of cover types.     

The compression of a sequence of satellite images based on change detection

A 'loss-effective' compression method which based on the change detection of raw image data is proposed for dealing with a sequence of satellite images. The average compression ratio we gained, compared with some typical satellite image formats, is about 2:1 to 3 :1. This sounds not so impressive when compared with the most current compression techniques which used in multimedia processing. However, some information will be lost in those methods, while our approach is information-loss effective, which is crucial for further satellite image analysis. Moreover, the framework can be combined with different compression algorithms to obtain different trade-offs between the compression ratio and the computation time. Experimental results based on real satellite images are included. Finally, other issues including the further optimization of the methods and some other possible applications of the method are discussed.     

Classifying successional forests using Landsat spectral properties and ecological characteristics in eastern Amazônia

Secondary forests may become increasingly important as temporary reservoirs of genetic diversity, stocks of carbon and nutrients, and moderators of hydrologic cycles in the Amazon Basin as agricultural lands are abandoned and often later cleared again for agriculture. We studied a municipality in northeastern Pará, Brazil, that has been settled for over a century and where numerous cycles of slash and burn agriculture have occurred. The forests were grouped into young (3-6 years), intermediate (10-20 years), advanced (40-70 years), and mature successional stages using 1999 Landsat 7 ETM imagery. Supervised classification of the imagery showed that these forest classes occupied 22%, 13%, 9%, and 6% of the area, respectively. Although this area underwent widespread deforestation many decades ago, forest of some type covers about 50% of the area. Row crops, tree crops, and pastures cover 8%, 20%, and 22%, respectively. The best separation among land covers appeared in a plot of NDVI versus band 5 reflectance. The same groupings of successional forests were derived independently from indices of similarity among tree species composition. Measured distributions of tree height and diameter also covaried with these successional classes, with the young forests having nearly uniform distributions, whereas multiple height and diameter classes were present in the advanced successional forests. Biomass accumulated more slowly in this secondary forest chronosequence than has been reported for other areas, which explains why the 70-year-old forests here were still distinguishable from mature forests using spectral properties. Rates of forest regrowth may vary across regions due to differences in edaphic, climatic, and historical land-use factors, thus rendering most relationships among spectral properties and forest age site-specific. Successional status, as characterized by species composition, biomass, and distributions of heights and diameters, may be superior to stand age as a means of stratifying these forests for characterization of spectral properties.     

Automated masking of cloud and cloud shadow for forest change analysis using Landsat images

Accurate masking of cloud and cloud shadow is a prerequisite for reliable mapping of land surface attributes. Cloud contamination is particularly a problem for land cover change analysis, because unflagged clouds may be mapped as false changes, and the level of such false changes can be comparable to or many times more than that of actual changes, even for images with small percentages of cloud cover. Here we develop an algorithm for automatically flagging clouds and their shadows in Landsat images. This algorithm uses clear view forest pixels as a reference to define cloud boundaries for separating cloud from clear view surfaces in a spectral-temperature space. Shadow locations are predicted according to cloud height estimates and sun illumination geometry, and actual shadow pixels are identified by searching the darkest pixels surrounding the predicted shadow locations. This algorithm produced omission errors of around 1% for the cloud class, although the errors were higher for an image that had very low cloud cover and one acquired in a semiarid environment. While higher values were reported for other error measures, most of the errors were found around the edges of detected clouds and shadows, and many were due to difficulties in flagging thin clouds and the shadow cast by them, both by the developed algorithm and by the image analyst in deriving the reference data. We concluded that this algorithm is especially suitable for forest change analysis, because the commission and omission errors of the derived masks are not likely to significantly bias change analysis results.