7. Change Detection. 7.1 Definition. 7.2 Development of Change Detection over Time. Part B 7. Jérôme Théau

Transcription

1 hange 7. hange Detection 7 Jérôme Théau This chapter is an overview of change detection techniques applied to Earth observation. Section 7. gives a short definition of change detection followed by an historical background of this process (Sect. 7.). Methods of change detection are described in Sect. 7. and are illustrated using an analysis of wildlife habitat (Sect. 7.4). Typical applications are then listed. The chapter is concluded with future directions of change detection studies (Sect. 7.). 7. Definition Development of hange Detection over Time Methods Overview hanges on Earth Surface Imagery haracteristics Regarding hanges hanges in Imagery Data Selection and Preprocessing hange Detection Methods Typical pplications Forestry griculture and Rangelands Urban Ice and Snow Ocean and oastal Probable Future Directions... 8 References... 8 Part 7 7. Definition hange detection can be defined as the process of identifying differences in the state of an object or phenomenon by observing it at different times [7.]. This process is usually applied to Earth surface changes at two or more times. The primary source of data is geographic and is usually in digital format (e.g., satellite imagery), analog format (e.g., older aerial photos), or vector format (e.g., feature maps). ncillary data (e.g., historical, economic, etc.) can also be used. 7. Development of hange Detection over Time hange detection history starts with the history of remote sensing and especially the first aerial photography taken in 89 by Gaspard Félix Tournachon, also known as Nadar. Thereafter, the development of change detection is closely associated with military technology during World Wars I and II and the strategic advantage provided by temporal information acquired by remote sensing. ivilian applications of change detection were developed following these events in the th century using mostly interpretation and analog means. However, civilian availability of data was limited until the 97s and 98s due to military classification of imagery. The development of digital change detection era really started with the launch of Landsat- (called first: Earth Resources Technology Satellite) in July 97. The regular acquisition of digital data of the Earth surface in multispectral bands allowed scientists to get relatively consistent data over time and to characterize changes over relatively large area for the

2 76 Part Geographic Information Part 7. first time. The continuity of this mission as well as the launch of numerous other ones ensured the development of change detection techniques from that time. However, the development of digital change detection techniques was limited by data processing technology capacities and followed closely the development of computer technologies. The situation evolves from the 96s when a few places in the world were equipped with expensive computers to the present when personal computers are fast and cheap enough to apply even complex algorithms and change detection techniques to satellite imagery. The computer technology also evolved from dedicated hardware to relatively userfriendly software specialized for image processing and change detection. ased on published literature, the algebra techniques such as image differencing or image ratioing were the first techniques used to characterize changes in digital imagery during the 97s [7.]. These techniques are simple and fast to perform and are still widely used today. More complex techniques were developed since then with the improvement of processing capacities but also with the development of new theoretical approaches. hange detection analysis of the Earth surface is a very active topic due to the concerns about consequences of global and local changes. This field of expertise is constantly progressing. 7. Methods Overview 7.. hanges on Earth Surface The Earth surface is changing constantly in many ways. First, the time scales, at which changes can occur, are very heterogeneous. They may vary from catastrophic events (e.g., flood) to geological events (e.g., continental drift) which correspond to a gradient between punctual and continuous changes respectively. Secondly, the spatial scales, at which changes can occur, are also very heterogeneous and may vary from local events (e.g., road construction) to global changes (e.g., change of ocean water temperature). Due to this very large spatio-temporal range, the nature and extent of changes are complex to determine because they are interrelated and interdependent at different scales (spatial, temporal). hange detection is, therefore, a challenging task. 7.. Imagery haracteristics Regarding hanges Since the development of civilian remote sensing, the Earth benefits from a continuous and increasing coverage by imagery such as: aerial photography or satellite imagery. This coverage is ensured by various sensors with various properties. First, in terms of the time scale, various temporal resolutions (i. e., revisit time) and mission continuities allow coverage of every point of the Earth from days to decades. Secondly, in terms of the spatial scale, various spatial resolutions (i. e., pixel size, scene size) allow coverage of every point of the Earth at a submeter to a kilometer resolution. Thirdly, sensors are designed to observe the Earth surface using various parts of the electromagnetic spectrum (i. e., spectral domain) at different resolutions (i. e., spectral resolution). This diversity allows the characterization of a large spectrum of Earth surface elements and change processes. However, change detection is still limited by data availability and data consistency (i. e., multisource data). 7.. hanges in Imagery hanges in imagery between two dates translate into changes in radiance. Various factors can induce changes in radiance between two dates such as changes in: sensor calibration, solar angle, atmospheric conditions, seasons, or Earth surface. The first premise of using imagery for change detection of the Earth surface is that change in the Earth surface must result in a change in radiance values. Secondly, the change in radiance due to Earth surface changes must be large compared to the change in radiance due to other factors. The challenge in change detection of the Earth surface using imagery is to minimize these other factors. This is usually performed by carefully selecting the multidate imagery and by applying preprocessing treatments Data Selection and Preprocessing The data selection is a critical step in change detection studies. The acquisition period (i. e., season, month) of multidate imagery is an important parameter to consider in image selection because it is directly related to

3 hange Detection 7. Methods Overview 77 phenology, climatic conditions, and solar angle. careful selection of multidate images is therefore needed in order to minimize the effects of these factors. In vegetation change studies (i. e., over different years), for example, summer is usually used as the target period because of the relative stability of phenology, solar angle, and climatic conditions. The acquisition interval between multidate imagery is also important to consider. s mentioned before, Earth surface changes must cause enough radiance changes to be detectable. However, the data selection is often limited by data availability and the choice is usually a compromise between the targeted period, interval of acquisition, and availability. The cost of imagery is also a limiting factor in data selection. However, a careful data selection is usually not enough to minimize radiometric heterogeneity between multidate images. First, atmospheric conditions and solar angle differences usually need additional corrections and secondly other factors such as sensor calibration or geometric distortions need to be considered. In change detection analysis, multidate images are usually compared on a pixel basis. Then, very accurate registrations need to be performed between images in order to compare pixels at the same locations. Misregistration between multidate images can cause significant errors in change interpretation [7.]. The sensitivity of change detection approaches to misregistration is variable though. The minimization of radiometric heterogeneity (due to sources other than Earth surface change) can be performed using different approaches depending on the level of correction required and the availability of atmospheric data. The techniques such as dark object subtraction, relative radiometric normalization or radiative transfer code can be used. 7.. hange Detection Methods Summarized here are the most common methods used in change detection studies [7.,, 4 7]. Most of these methods use image processing approaches applied to multidate satellite imagery. Image Differencing This simple method is widely used and consists of subtracting registered images acquired at different times, pixel by pixel and band by band Dx k ij = xk ij (t ) x k ij (t ), Raster data covering the exact same location Pixel values (e.g. digital number band a) 7 Image differencing results Image ratioing results Fig. 7. Example of image differencing and image ratioing procedures where Dxij k is the difference between pixel value x located at row i and column j, forbandk, between acquisition date (t )anddate(t )[7.]. No changes between times result in pixel values of, but if changes occurred these values should be positive or negative (Fig. 7.). However, in practice, exact image registration and perfect radiometric corrections are never obtained for multidate images. Residual differences in radiance not caused by land cover changes are still present in images. Then the challenge of this technique is to identify threshold values of change and no-change in the resulting images. Standard deviation is often used as a reference values to select these thresholds. Different normalization, histogram matching, and standardization approaches are used on multidate images to reduce scale and scene dependent effects on differencing results. The image differencing method is usually applied to single bands but can be also applied. Part 7.

4 78 Part Geographic Information Part 7. to processed data such as multidate vegetation indices or principal components. Image Ratioing This method is comparable to the image differencing method in terms of its simplicity and challenges. However, it is not as widely used. It is a ratio of registered images acquired at different times, pixel by pixel and band by band Rx k ij = xk ij (t ) x k ij (t ), Multispectral images of the exact same location Independent classifications Example of change matrix (pixel count). hange appears in bold. lass lass Total 7 7 Total Fig. 7. Example of a post-classification procedure 7 where Rx k ij is the ratio between pixel value x located at row i and column j, forbandk, betweenacquisition date (t )anddate(t )[7.]. hanges are represented by pixel values higher or lower than (Fig. 7.). Pixels with no change will have avalueofone.inpractice,forthesamereasonsasin image differencing, the challenge of this technique is in selecting threshold values between change and no change. This technique is often criticized because the nonnormal distribution of results limits the validity of threshold selection using the standard deviation of resulting pixels. Post lassification This method is also commonly referred to as Delta classification.itiswidelyusedandeasytounderstand.two images acquired at different times are independently classified and then compared. Ideally, similar thematic classes are produced for each classification. hanges between the two dates can be visualized using a change matrix indicating, for both dates, the number of pixels in each class (Fig. 7.). This matrix allows one to interpret what changes occurred for a specific class. The main advantage of this method is the minimal impacts of radiometric and geometric differences between multidate images. However, the accuracy of the final result is the product of accuracies of the two independent classifications (e.g., 64% final accuracy for two 8% independent classification accuracies). Direct Multidate lassification This method is also referred to as composite analysis, spectral-temporal combined analysis, spectraltemporal change classification, multidate clustering, or spectral change pattern analysis. Multidateimagesare combined into a single dataset on which a classification is performed (Fig. 7.). The areas of changes are expected to present different statistics (i. e., distinct classes) compared to the areas with no changes. The approach can be unsupervised or supervised and necessitates only one classification procedure. However, this method usually produces numerous classes corresponding to spectral changes within each single image but also to temporal changes between images. The interpretation of results is often complex and requires a good knowledge of the study area. ombined approaches using principal component analysis or ayesian classifier can be performed to reduce data dimensionality or the coupling between spectral and temporal change respectively.

5 hange Detection 7. Methods Overview 79 Linear Transformation This approach includes different techniques using the same theoretical basis. The principal component analysis (P) andthetasseled-captransformationsarethe most common ones. Linear transformations are often used to reduce spectral data dimensionality by creating fewer new components. The first components contain most of the variance in the data and are uncorrelated. When used for change detection purposes, linear transformations are performed on multidate images that are combined as a single dataset (Fig. 7.). fter performing a P, unchanged areas are mapped in the first component (i. e., information common to multidate images) whereas areas of changes are mapped in the last components (i. e., information unique to either one of the different dates). Usually the P is calculated from a variance/covariance matrix. However, standardized matrix (i. e., correlation matrix) is also used. The P is scene dependent and results can be hard to interpret. The challenging steps are to label changes from principal components and to select thresholds between change and no-change areas. goodknowledgeofthestudyareaisrequired. The tasseled-cap is also a linear transformation. However, unlike P, itisindependentofthescene. The new component directions are selected according to predefined spectral properties of vegetation. Four new components are computed and oriented to enhance brightness, greenness, wetness, and yellowness. Results are also difficult to interpret and change labeling is challenging. Unlike P, tasseled-captransformationfor change detection requires accurate atmospheric calibration of multidate imagery. Other transformations such as multivariate alteration detection or Gramm Schmidt transformation were also developed but used to a lesser extent. hange Vector nalysis This approach is based on the spatial representation of change in a spectral space. When a pixel undergoes a change between two dates, its position in n-dimensional spectral space is expected to change. This change is represented by a vector (Fig. 7.4) which is defined by two factors, the direction which provides information about the nature of change and the magnitude which provides information about the level of change. This approach has the advantage to process concurrently any number of spectral bands. It also provides detailed information about change. The challenging steps are to define thresholds of magnitude, discriminating between change and no change, and to H Multispectral images of the exact same location D D D E F D E E Direct multidate classification results G G a b c omponents Linear transformation results Fig. 7. Example of direct multidate classification and linear transformation procedures interpret vector direction in relation with the nature of change. This approach is often performed on transformed data using methods such as tasseled-cap. Image Regression This approach assumes that there is a linear relationship between pixel values of the same area at two different times. This implies that a majority of the pixels did not encounter changes between the two dates (Fig. 7.). regression function that best describes the relationship between pixel values of each spectral band at two dates is developed. The residuals of the regression are considered to represent the areas of changes. hanges can also be detected using the image differencing method between predicted image values at date (computedusingtheregressionfunction)andimage values at date, Dx k ij = ˆxk ij (t ) x k ij (t ), Part 7.

6 8 Part Geographic Information Part 7. and a and b Multispectral images of the exact same location and a and b Raster data covering the exact same location (e.g. digital number band a) Pixel values band b 8 9 Pixel values band a hange vector for the grey pixel selected above Fig. 7.4 Example and principle of the change vector procedure where ˆx k ij (t )isthepredictedvalueofpixelx at date (t ), located at row i and column j,forbandk [7.]. This method has the advantage of reducing the impact of radiometric heterogeneity (i. e., atmosphere, sun angle, sensor calibration) between multidate images. However, the challenging steps are to select an appropriate regression function and to define thresholds between change and no change areas. Multitemporal Spectral Mixture nalysis The spectral mixture analysis is based on the premise that a pixel reflectance value can be computed from individual values of its composing elements (i. e., endmembers) weighted by their respective proportions n DN c = F i DN i,c + E c i= with the constraints n F i = and F i, i= where DN c is the digital number of the pixel value in channel c, F i is the fraction of end member i; DN i,c is Pixel values time y Pixel values time x Scatterplot representing a theoretical situation without any changes in pixel values between the two dates Pixel values time y Pixel values time x Scatterplot representing a realistic situation with various changes in pixel values between the two dates. However, a linear relationship is still present Fig. 7. Example and principle of the image regression procedure the digital number value of end member i in channel c, n is the number of end members and E c is the error of the estimate for channel c [7.8]. This case assumes a linear mixing of these components. This method allows retrieving subpixel information (i. e., surface proportions of end-members) and can be used for change detection purposes by performing separate analysis and comparing results at different dates (Fig. 7.6). The advantage of this method is to provide precise and repeatable results. The challenging step of this approach is to select suitable end-members. ombined pproaches The previous techniques represent the most common approaches used for change detection purposes. They can be used individually, but are often combined together or with other image processing techniques to provide more accurate results. Numerous combinations can be used and they will not be described here. Some

7 hange Detection 7. Methods Overview 8 hange detection procedure Image selection Image registration - Sensor selected: Landsat thematic mapper and multispectral scanner (MSS) - Targeted years: 998, 988, Targeted periods: end of ugust (minimize phenological effects) - Limitations: cloud free scene and Landsat MSS availability Image master (e.g. 998) Example: Mapping changes in caribou habitat Relative image registration Image to correct (e.g. 988) Part 7. Radiometric and atmospheric corrections Image master Radiometric normalization of scenes using statistical selection of pseudo-invariant features ommon area used for correction a) b) Image to correct efore correction fter correction Multitemporal spectral mixture analysis Mathematical representation of a mixed pixel digital number (DN) in channel c Multitemporal analyses DN c Mixed pixel =(DN cx Fracion) Lichen +(DN cx Fraction) anopy +(DN cx Fraction) Shadow +Error c Spectral Mixture nalysis provides for each pixel: Lichen fraction, anopy fraction, and Shadow fraction c) d) e) Lichen map 978 Lichen map 988 Lichen map 998 % lichen hange detection km h) Increased % lichen i) Decreased % lichen Image differencing results for lichen fractions between 978 and 998 f) g) For more details see: Théau and Duguay (4) Mapping Lichen Habitat hanges inside the Summer Range of the George River aribou Herd (Québec-Labrador, anada) using Landsat Imagery ( ). Rangifer. 4: -. Fig. 7.6 Example of a change detection procedure. ase study of mapping changes in caribou habitat using multitemporal spectral mixture analysis

8 8 Part Geographic Information Part 7.4 of them include the combination of vegetation indices and image differencing, change vector analysis and principal component analysis, direct multidate classification and principal component analysis, multitemporal spectral analysis and image differencing, or image enhancement and post-classification. Example of hange Detection nalysis (Mapping hanges) The George River aribou Herd (GRH), located in northeastern anada, increased from about in the 9s to about 7 head in the 99s. This has led to an over-utilization of summer habitat, resulting in degradation of the vegetation cover. This degradation has had a direct impact on health problems observed in the caribou (Rangifer tarandus)populationoverthelast few years and may also have contributed to the recent decline of the GRH (44 head in ). Lichen habitats are good indicators of caribou herd activity because of their sensitivity to overgrazing and overtrampling, their widespread distribution over northern territories, and their influence on herd nutrition. The herd range covers a very large territory which is not easily accessible. s a result, field studies over the whole territory are limited and aerial surveys cannot be conducted frequently. Satellite imagery offers the synoptic view and temporal resolution necessary for mapping and monitoring caribou habitat. In this example, a change detection approach using Landsat imagery was used. The procedure was based on spectral mixture analysis and produced maps showing the lichen proportion inside each pixel. The procedure was applied to multidate imagery to monitor the spatiotemporal evolution of the lichen resource over the past three decades and gave new information about the habitat used by the herd in the past, which was very useful to better understand population dynamics. Figure 7.6 summarizes the approach used in this study and illustrates the steps typical of a change detection procedure. 7.4 Typical pplications The Earth surface is changing constantly in many ways. hanges occur at various spatial and temporal scales in numerous environments. hange detection techniques are employed for different purposes such as research, management, or business [7.,9 9]. Monitoring changes using GIS and remote sensing is therefore used in a wide field of applications. nonexhaustive list of key applications is presented here Forestry Deforestation (e.g., clear cut mapping, regeneration assessment) Fire monitoring (e.g., delineation, severity, detection, regeneration) Logging planning (e.g., infrastructures, inventory, biomass) Herbivory (e.g., insect defoliation, grazing) Habitat fragmentation (e.g., landcover changes, heterogeneity, ecological integrity) 7.4. griculture and Rangelands rop monitoring (e.g., growing, biomass) Invasive species (e.g., detection, distribution) Soil moisture condition (e.g., drought, flood, landslides) Desertification assessment (e.g., bare ground exposure, wind erosion) 7.4. Urban Urban sprawl (e.g., urban mapping) Transportation and infrastructure planning (e.g., landcover use) Georisk (e.g., Earthquakes, volcanoes, subtle deformation) Ice and Snow Navigation route (e.g., sea ice motion) Infrastructure protection (e.g., flooding monitoring) Glacier and ice sheet monitoring (e.g., motion, melting) Permafrost monitoring (e.g., surface temperature, tree line) 7.4. Ocean and oastal Water quality (e.g., temperature, productivity) quaculture (e.g., productivity) Intertidal zone monitoring (e.g., erosion, vegetation mapping) Oil spill (e.g., detection, oil movement)

9 hange Detection References 8 7. Probable Future Directions In the past decades we observed a constant increase of remotely sensed data availability. The launch of numerous satellite sensors as well as the reduction of product costs can explain this trend. The same evolution is expected in the future. The access to constantly growing archive contents also represents a potential for the development of more change detection studies in the future. Long-term missions such as Landsat, SPOT (Satellite pour l Observation de la Terre), VHRR (dvanced Very High Resolution Radiometer) provide continuous data for more than years now. lthough radiometric heterogeneity between sensors represents serious limitation in time series analysis, these data are still very useful for long term change studies. These data are particularly suitable in the development of temporal trajectory analysis which usually involves the temporal study of indicators (e.g., vegetation indices, surface temperature) on a global scale. Moreover, as mentioned before in Sect. 7., thedevelopment of change detection techniques are closely linked with the development of computer technologies and data processing capacities. In the future, these fields will still evolve in parallel and new developments in change detection are expected with the development of computer technologies. Developments and applications of new image processing methods and geospatial analysis are also expected in the next decades. rtificial intelligence systems as well as knowledge-based expert systems and machine learning algorithms represent new alternatives in change detection studies [7.4]. These techniques have gained considerable attention in the past few years and are expected to increase in change detection approaches in the future. One of the main advantages of these techniques is that they allow the integration of existing knowledge and nonspectral information of the scene content (e.g., socio-economic data, shape, and size data). With the increasing interest in using integrated approaches such as coupled human environment systems, these developments look promising. The recent integration of change detection and spatial analysis modules in most GIS software also represents a big step towards integrated tools in the study of changes on the Earth surface. This integration also includes an improvement of compatibility between image processing software and GIS software. More developments are expected in the future which will provide new tools for integrating multisource data more easily (e.g., digital imagery, hard maps, historical information, vector data). Part 7 References 7.. Singh: Digital change detection techniques using remotely-sensed data, Int. J. Remote Sens., 989 (989) 7. R.S. Lunetta,.D. Elvidge: Remote Sensing hange Detection: Environmental Monitoring Methods and pplications (nn rbor Press, helsea 998) 7. H.Q. Wang, E.. Ellis: Image misregistration error in change measurements, Photogram. Eng. Remote Sens. 7, 7 44() 7.4 P. oppin, I. Jonckheere, K. Nackaerts,. Muys, E. Lambin: Digital change detection methods in ecosystem monitoring: review, Int. J. Remote Sens., 6 96(4) 7. D. Lu, P. Mausel, E. rondízios, E. Moran: hange detection techniques, Int. J. Remote Sens.,6 47 (4) 7.6 J.-F. Mas: Monitoring land-cover changes: comparison of change detection techniques, Int. J. Remote Sens., 9 (999) 7.7 D.. Mouat, G.G. Mahin, J. Lancaster: Remote sensing techniques in the analysis of change detection, Geocarto Int., 9 49(99) 7.8 J.. dams, M.O. Smith,.R. Gillespie: Simple models for complex natural surfaces: strategy for the hyperspectral era of remote sensing, Proc. Int. Geosci. Remote Sens. Symp. (IGRSS 89)/th an. Symp. Remote Sens., Vol. (989) pp E.. Ellis, H. Wang, H.S. Xiao, K. Peng, X.P. LIu, S.. Li, H. Ouyang, X. heng, L.Z. Yang: Measuring long-term ecological changes in densely populated landscapes using current and historical high resolution imagery, Remote Sens. Environ., (6) 7. R.H. Fraser, I. Olthof, D. Pouliot: Monitoring land cover change and ecological integrity in anada s national parks, Remote Sens. Environ.,97 49 (9) 7. R.E. Kennedy, P.. Townsend, J.E. Gross, W.. ohen, P. olstad, Y.Q. Wang, P. dams: Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects, Remote Sens. Environ., 8 96 (9)

10 84 Part Geographic Information Part 7 7. D. Massonnet, K.L. Feigl: Radar interferometry and its application to changes in the earth s surface, Rev. Geophys. 6, 44 (998) 7. anada entre for Remote Sensing: gc.ca/index_e.php (last accessed February ) 7.4 Diversitas: Integrating biodiversity science for human well-being, accessed February ) 7. ES: Observing the Earth, index.html(last accessed February ) 7.6 Global hange Master Directory: gov/index.html(last accessed February ) 7.7 IGP: International Geosphere-iosphere Programme, (last accessed February ) 7.8 IHDP: International Human Dimensions Programme on Global Environmental hange, org/(last accessed February ) 7.9 WRP: World limate Research Programme, (last accessed February )