Object-based land cover change detection for cross-sensor images

Accurate and timely land cover change detection at regional and global scales is necessary for both natural resource management and global environmental change studies. Satellite remote sensing has been widely used in land cover change detection over the past three decades. The variety of satellites which have been launched for Earth Observation (EO) and the large volume of remotely sensed data archives acquired by different sensors provide a unique opportunity for land cover change detection. This article introduces an object-based land cover change detection approach for cross-sensor images. First, two images acquired by different sensors were stacked together and principal component analysis (PCA) was applied to the stacked data. Second, based on the Eigen values of the PCA transformation, six principal bands were selected for further image segmentation. Finally, a land cover change detection classification scheme was designed based on the land cover change patterns in the study area. An image–object classification was implemented to generate a land cover change map. The experiment was carried out using images acquired by Landsat 5 TM and IRS-P6 LISS3 over Daqing, China. The overall accuracy and kappa coefficient of the change map were 83.42% and 0.82, respectively. The results indicate that this is a promising approach to produce land cover change maps using cross-sensor images.     

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.     

An analysis of land use/cover change in Indonesia

The phenomena of land use/ change were evaluated by using a remote sensing approach in a case study in Yogyakarta, Indonesia. The index of changes, which was calculated by the superimposition of land use/ images of 1972, 1984, and land use maps of 1990, were introduced to analyse the pattern of change in the area. The results demonstrated that the pattern of land use/ change in the study area was that of paddy coverage to open/ land to settlement. The annual growth ratios of new settlements to absorb paddy coverage, mixed vegetation, and open/ land were 16 per cent, 20 per cent, and 64 per cent respectively. The larger the percentage of the paddy coverage, the higher the tendency for settlement growth to absorb the paddy coverage, and the larger the percentage of open/ land, the higher the tendency for settlement growth to absorb open/ land. Settlement growth had a high correlation with road accessibility. The highest settlement growth was distributed mostly in suburban areas between 200 and 400 m from secondary roads. Without intelligent intervention by the government and public awareness, the loss of these agricultural land cannot be stopped and will jeopardize the local and regional economy.     

Analysis of rain fall and the temperature of Coimbatore District using land use and land cover change detection by image segmentation

Image segmentation is a process has done for the classification of high resolution remote sensing images in the present research work. The segmentation results are capable of influencing the subsequent process effects. An image can be partitioned into a number of disjoint segments which is used to represent the image structures. It is found that it is more compact to represent an image and the low level and high structures can be combined. There are different types of methods to segment an image namely, threshold-based, edge-based and region-based. Region growing approach is image segmentation methods in which the neighboring pixels are examined and merged with the class region in case of no edges are detected. The iteration is done for every pixel boundary. Unlike gradient and Laplacian methods, the edges of the region are found by the region growing and it is perfectly their region. The images are determined by the LANDSAT TM satellite data. The remote sensing technique is used for collecting information about the Coimbatore district. The sensed data is a key to many diverse applications. The contribution of this work for Coimbatore district is to find the change of the Land used and Land covered in the entire region and also to find the changes in the green lands, vegetation and Land surface utilized for urban area. The neighboring regions are taken into account and the similarities are checked in the growing process. No single region is allowed to dominate the entire proceedings. A certain number of regions are allowed to grow at a time. Comparable regions will gradually combine into expanding regions. The Control of these methods may be quite complicated but efficient methods have been developed. The directions of growing pixels are easy and efficient to implement on parallel computers. The threshold-based segmentation is completely depending on the gray level images which regards the reflectivity of the featured images. It determines a threshold based on brightness of the ground objects. It is purely from the image background. But it is rapid and its uncertainty is significant. It is not convenient to process multi-spectral images.     

Trajectory analysis of land cover change in arid environment of China

Remotely sensed data have been utilized for environmental change study over the past 30 years. Large collections of remote sensing imagery have made it possible for spatio‐temporal analyses of the environment and the impact of human activities. This research attempts to develop both conceptual framework and methodological implementation for land cover change detection based on medium and high spatial resolution imagery and temporal trajectory analysis. Multi‐temporal and multi‐scale remotely sensed data have been integrated from various sources with a monitoring time frame of 30 years, including historical and state‐of‐the‐art high‐resolution satellite imagery. Based on this, spatio‐temporal patterns of environmental change, which is largely represented by changes in land cover (e.g., vegetation and water), were analysed for the given timeframe. Multi‐scale and multi‐temporal remotely sensed data, including Landsat MSS, TM, ETM and SPOT HRV, were used to detect changes in land cover in the past 30 years in Tarim River, Xinjiang, China. The study shows that by using the auto‐classification approach an overall accuracy of 85–90% with a Kappa coefficient of 0.66–0.78 was achieved for the classification of individual images. The temporal trajectory of land‐use change was established and its spatial pattern was analysed to gain a better understanding of the human impact on the fragile ecosystem of China's arid environment.     

Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods

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.     

The surface temperature-vegetation index space for land cover and land-cover change analysis

Most previous applications of coarse scale remote sensing data for land-cover mapping and land-cover change analysis were based on multi-temporal Normalized Difference Vegetation Index (NDVI) data. Recent empirical studies have documented that the combination of measurements of thermal infrared radiation (e.g., land brightness temperature, Ts) and vegetation indices (VI) improves the mapping and monitoring of land cover at broad scales. We investigate the biophysical justification for such a combination, using 10 years of Advanced Very High Resolution Radiometer (AVHRR) global area coverage ( GAC) data over the African continent. First, we review recent findings on the biophysical interpretation of the TS-VI space. Second, we analyse the seasonal time trajectories of different biomes in the TS-NDVI space. Third, we measure the relative role of multi-temporal NDVI and Ts data in the discrimination of land cover classes for land-cover mapping. Fourth, we analyse trajectories of land-cover change in the TS-NDVI space for study sites in three different environments. We illustrate the usefulness of the ratio between T_s and VI as an index to perform measurements in the Tj-NDVI space.     

The use of temporal metrics for land cover change detection at coarse spatial scales

Successful land cover change analysis requires selection of an appropriate set of variables for measuring and characterizing change. Coarse spatial resolution satellite sensors offer the advantage of frequent coverage of large areas and this facilitates the monitoring of surface processes. Fine spatial resolution satellite sensors provide reliable land cover information on a local basis. This work examines the ability of several temporal change metrics to detect land cover change in sub-Saharan Africa using remote sensing data collected at a coarse spatial resolution over 16 test sites for which fine spatial resolution data are available. We model change in the fine-resolution data as a function of the coarse spatial resolution metrics without regard to the type of change. Results indicate that coarse spatial resolution temporal metrics (i) relate in a statistically significant way to aggregate changes in land cover, (ii) relate more strongly to fine spatial resolution change metrics when including a measure of surface temperature instead of a vegetation index alone, and (iii) are most effective as land cover change indicators when various metrics are combined in multivariate models.     

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.     

Ensemble of support vector machines for land cover classification

This letter presents the results of two different ensemble approaches to increase the accuracy of land cover classification using support vector machines. Finite ensemble approaches, based on boosting and bagging and infinite ensemble created by embedding the infinite hypothesis in the kernel of support vector machines, are discussed. Results suggest that the infinite ensemble approach provides a significant increase in the classification accuracy in comparison to the radial basis function kernel‐based support vector machines. While using finite ensemble approaches, bagging works well and provides a comparable performance to the infinite ensemble approach, whereas boosting decreases the performance of support vector machines. Comparison in terms of computational cost suggests that finite ensemble approaches require a large processing time in comparison to the infinite ensemble approach.     

基于国产卫星数据的深圳城市化进程中土地利用/覆盖变化探讨

土地利用/覆盖变化是全球变化中的重要组成部分,城市化进程将导致大规模的土地利用/覆盖变化.文中首先分别对1999年、2006年、2010年的CBERS和HJ-1B数据进行几何校正、拼接裁剪、分类等处理,生成土地利用/覆盖分类图,然后分别计算求得深圳市1999年到2006年和2006年到2010年的土地利用/覆盖变化转移矩阵.在此基础上,研究深圳市从1999年到2010年期间土地利用/覆盖变化的空间过程.结果表明:深圳市在快速城市化进程中发生了大规模的土地利用/覆盖变化,大量的草地、耕地、未利用土地转化为城镇用地,草地和林地之间部分结构相互转化调整.同时,10年来深圳市土地利用/覆盖变化区域差异明显,伴随着宝安和龙岗两区城市化进程加快,关外土地利用/覆盖变化强度逐渐加强,而关内逐渐减弱.在深圳城市化进程中,城镇用地重心呈现出向北部扩展的趋势.     

NDVI-based land-cover change detection using harmonic analysis

This study presents a normalized difference vegetation index (NDVI)-based land-cover change detection method based on harmonic analysis. Multi-temporal NDVI data show seasonal variation characteristics in the time domain. A harmonic model represents the characterization of the temporal variability in a data set over a local region corresponding to a pixel through its harmonic components. In this research, annual land-cover change detection is performed by tracking the temporal dynamics through analysing harmonic components. A simple but effective noise reduction process is also proposed to provide the necessary high-quality data stream for the multi-temporal NDVI analysis based on the statistics of the observed oscillations. The proposed algorithm was tested and evaluated with the multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI time series of the MYD13Q1, 16 day L3 global 250 m SIN grid (v005) VI data set. The results indicate that the proposed algorithm provides a computationally inexpensive automatic method to monitor vegetation conditions and long-term land-cover change over large regions. The method described here is particularly useful for monitoring changes in well-established deciduous forests with developed canopies.     

Change vector analysis: A technique for the multispectral monitoring of land cover and condition

Change vector analysis (CVA) is a robust approach for detecting and characterizing radiometric change in multispectral remote sensing data sets. CVA is reviewed as a useful technique to: (1) process the full dimensionality of multispectral/multi-layer data so as to ensure detection of all change present in the data; (2) extract and exploit the 'components' of change in multispectral data; and (3) facilitate composition and analysis of change images. Examples drawn from various projects are included throughout this methodological discussion, in order to illustrate the CVA approach and suggest its potential utility.     

Spatial pattern analysis of land cover change trajectories in Tarim Basin,northwest China

This study attempts to develop a methodology to quantify spatial patterns of land cover change using landscape metrics. First, multitemporal land cover types are derived based on a unified land cover classification scheme and from the classification of multitemporal remotely sensed imagery. Categorical land cover change trajectories are then established and reclassified according to the nature and driving forces of the change. Finally, spatial pattern metrics of the land cover change trajectory classes are computed and their relationships to human activities and environmental factors are analysed. A case study in the middle reach of Tarim River in the arid zone of China from 1973 to 2000 shows that during the 30‐year study period, the natural force is dominant in environmental change, although the human impact through altering water resources and surface materials has increased dramatically in recent years. The human‐induced change trajectories generally show lower normalized landscape shape index (NLSI), interspersion and juxtaposition index (IJI) and area‐weighted mean patch fractal dimension (FARC_AM), indicating greater aggregation, less association with others and simpler and larger patches in shape, respectively. The results suggest that spatial pattern metrics of land cover change trajectories can provide a good quantitative measurement for better understanding of the spatio‐temporal pattern of land cover change due to different causes.     

Bias in land cover change estimates due to misregistration

Land cover change may be overestimated due to positional error in multi-temporal images. To assess the potential magnitude of this bias, we introduced random positional error to identical classified images and then subtracted them. False land cover change ranged from less than 5% for a 5-class AVHRR classification, to more than 33% for a 20-class Landsat TM classification. The potential for false change was higher with more classes. However, false change could not be reliably estimated simply by number of classes, since false change varied significantly by simulation trial when class size remained constant. Registration model root mean squared (rms) error may underestimate the actual image co-registration asccuracy. In simulations with 5 to 50 ground control locations, the mean model rms error was always less than the actual population rms error. The model rms error was especially unreliable when small sample sizes were used to develop second order rectification models. We introduce a bootstrap resampling method to estimate false land cover change due to positional error. Although the bootstrap estimates were unbiased, the precision of the estimates may be too low to be of practical value in some land cover change applications.     

Application of knowledge for automated land cover change monitoring

This paper outlines an approach for updating baseline land cover datasets. Knowledge about land cover, as used during manual mapping, is combined with simple remote sensing analyses to determine land cover change direction. The philosophy is to treat reflectance data as one source of information about land cover features. Applying expert knowledge with reflectance and biogeographical data allows generic solutions to the problem. The approach is demonstrated in areas of semi-natural vegetation and shown to differentiate ecologically subtle but spectrally similar land cover classes. Further, the advantages of manual mapping techniques and of high resolution remotely sensed imagery are combined. This approach is suitable for incorporation into automated approaches: it makes no assumption about the distribution of land cover features, can be applied to different remotely sensed data and is not classification specific. It has been incorporated into SYMOLAC, an expert system for monitoring land cover change.     

Object-based land cover classification using airborne LiDAR

Light Detection and Ranging (LiDAR) provides high resolution horizontal and vertical spatial point cloud data, and is increasingly being used in a number of applications and disciplines, which have concentrated on the exploit and manipulation of the data using mainly its three dimensional nature. LiDAR information potential is made even greater though, with its consideration of intensity.Elevation and intensity airborne LiDAR data are used in this study in order to classify forest and ground types quickly and efficiently without the need for manipulating multispectral image files, using a supervised object-orientated approach. LiDAR has the advantage of being able to create elevation surfaces that are in 3D, while also having information on LiDAR intensity values, thus it is a spatial and spectral segmentation tool. This classification method also uses point distribution frequency criteria to differentiate between land cover types. Classifications were performed using two methods, one that included the influence of the ground in heavily vegetated areas, and the other which eliminated the ground points before classification. The classification of three meanders of the Garonne and Allier rivers in France has demonstrated overall classification accuracies of 95% and 94% for the methods including and excluding the ground influence respectively. Five types of riparian forest were classified with accuracies between 66 and 98%. These forest types included planted and natural forest stands of different ages. Classifications of short vegetation and bare earth also produced high accuracies averaging above 90%.     

Characterizing land cover dynamics using multi-temporal imagery

An analysis of land cover changes was performed using a time-series of five SPOT HRV images for an area of the State of Rond°onia (western Brazilian Amazon) from 1986 to 1992. The total deforested area and the fraction of land abandoned to secondary vegetation were determined by means of image classification and Geographical Information System (GIS) techniques. Areas deforested by 1986 were traced throughout the period to estimate the fraction of land remaining continuously in the secondary vegetation category, possibly forming older secondary vegetation.