A review of change detection techniques


Published on

abhishek bhatt
iit roorkee

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

A review of change detection techniques

  2. 2. OUTLINEThis seminar is organized into eight sections as follows:1. Background and applications of change detection techniques2. Considerations before implementing change detection3. A review of seven categories of change detection techniques4. Comparative analyses among the different techniques5. A global change analyses6. Threshold selection7. Accuracy assessment8. Summary and recommendationsReferences
  3. 3. Background• In general, change detection involves the application of multi- temporal datasets to quantitatively analyze the temporal effects• Change detection can be defined as the process of identifying differences in the state of an object or phenomenon by observing it at different times. This process is usually applied to Earth surface changes at two or more times.• understanding relationships and interactions to better manage and use resources• Change detection is useful in many applications such as land use changes, habitat fragmentation, rate of deforestation, coastal change, urban sprawl, and other cumulative changes
  4. 4. Change detection ► Two main categories of land cover changes: ▪ Conversion of land cover from one category to a different category. ▪ Modification of the condition of the land cover type within the same category (thinning of trees, selective cutting, pasture to cultivation, etc.)source; Norsk Regnesentral website
  5. 5. Applications of change detection techniques• land-use and land-cover (LULC) change• forest or vegetation change• forest mortality, defoliation and damage assessment• deforestation, regeneration and selective logging• wetland change• forest fire and fire-affected area detection• landscape change• urban change• environmental change, drought monitoring, flood monitoring, monitoring coastal marine environments, desertification, and detection of landslide areas• other applications such as crop monitoring, shifting cultivation monitoring, road segments, and change in glacier mass balance and facies.
  6. 6. Considerations before implementing change detection• Before implementing change detection analysis, the following conditions must be satisfied: i. precise registration of multi-temporal images; ii. precise radiometric and atmospheric calibration or normalization between multi-temporal images; iii. selection of the same spatial and spectral resolution images if possible
  7. 7. Good change detection research should provide the following information: i. area change and change rate ii. spatial distribution of changed types iii. Change trajectories of land-cover types iv. accuracy assessment of change detection results.
  8. 8. A review of change detection techniques• Because digital change detection is affected by spatial, spectral, radiometric and temporal constraints.• Many change detection techniques are possible to use, the selection of a suitable method or algorithm for a given research project is important, but not easy.
  9. 9. The seven change detection technique categories1. Algebra Based Approach 4. Advanced Models • image differencing • Li-Strahler Reflectance Model • image regression • Spectral Mixture Model • image ratioing • Biophysical Parameter Method • vegetation index differencing 5. GIS • change vector analysis • Integrated GIS and RS Method • GIS Approach2. Transformation 6. visual Analysis • PCA • Visual Interpretation • Tasseled Cap (KT) 7. other Change Detection Techniques • Gramm-Schmidt (GS) • Measures of spatial dependence • Chi-Square • Knowledge-based vision system • Area production method3. Classification Based • Combination of three indicators: vegetation • Post-Classification Comparison indices, land surface temperature, and • Spectral-Temporal Combined Analysis spatial structure • EM Transformation • Change curves • Unsupervised Change Detection • Generalized linear models • Hybrid Change Detection • Curve-theorem-based approach • Artificial Neural Networks (ANN) • Structure-based approach • Spatial statistics-based method
  10. 10. Category I Algebra Based Approach• The algebra category includes – image differencing, – image regression – Image ratioing – vegetation index differencing – change vector analysis (CVA)
  11. 11. Algebra based Approach……• These algorithms have a common characteristic, i.e. selecting thresholds to determine the changed areas. These methods (excluding CVA) are relatively simple, straightforward, easy to implement and interpret, but these cannot provide complete matrices of change information.• In this category, two aspects are critical for the change detection results: – selecting suitable image bands – selecting suitable thresholds
  12. 12. Image Differencing• Concept – Date 1 - Date 2 – No-change = 0 – Positive and negative values interpretable – Pick a threshold for change
  13. 13. Image Differencing 8 10 8 11240 11 10 22 Image Date 1205 210 205 54 3 1 1 1220 98 88 46 143 2 2 0 107 110 0 -168 5 9 7 10 117 0 -166 -164 97 9 8 22 98 100 205 222 Image Date 2 Difference Image =103 98 254 210 Image 1 - Image 2
  14. 14. Image Differencing• Image differencing: Pros – Simple (some say it’s the most commonly used method) – Easy to interpret – Robust• Cons: – Difference value is absolute, so same value may have different meaning – Requires atmospheric calibration
  15. 15. Image regression► Relationship between pixel values of two dates isestablished by using a regression function.► The dimension of the residuals is an indicator of wherechange occurred.► Advantage▪ Reduces impact of atmospheric, sensor and environmentaldifferences.► Drawback• Requires development of accurate regression functions.• Does not provide change matrix.
  16. 16. Image regression
  17. 17. Image Ratioing• Concept – Date 1 / Date 2 – No-change = 1 – Values less than and greater than 1 are interpretable – Pick a threshold for change• Pros – Simple – May mitigate problems with viewing conditions, esp. sun angle• Cons – Scales change according to a single date, so same change on the ground may have different score depending on direction of change; I.e. 50/100 = .5, 100/50 = 2.0
  18. 18. Change Detection source: CCRS website, CANADAImage Difference (TM99 – TM88) Image Ratio (TM99 / TM88)
  19. 19. Change vector analysis• In n-dimensional spectral space, determine length and direction of vector between Date 1 and Date2 Date 1 Band 4• No-change = 0 length Date 2• Change direction may be interpretable• Pick a threshold for change Band 3
  20. 20. Change vector analysis ► Determines in n-dimensional spectral space, the length and direction of the vector between Date 1 and Date 2. ► Produces an intensity image and a direction image of change. The direction image can be used to classify change. ► Typically used when all changes need to be investigated. ► Advantage ▪ Works on multispectral data. • Allows designation of the type of change occurring ► Drawbacksource; Norsk Regnesentral website ▪ Shares some of the drawbacks of algebra based techniques but less severe
  21. 21. Change vector analysis
  22. 22. Category I. Algebra Based Approach Techniques Characteristics Advantages Disadvantages Examples Key factors1. Image Subtracts the first date Simple and Cannot provide Forest Identifiesdifferencing image from a second- Straight forward, a detailed change defoliation, suitable image date image, pixel by easy to interpret matrix, requires land-cover bands and pixel the results selection of Change and thresholds thresholds irrigated crops monitoring2. Image Establishes relationships Reduces impacts Requires to develop Tropical forest Develops theregression between bitemporal of the atmospheric, accurate regression change and regression images, then estimates sensor and functions for the forest function; pixel values of the environmental selected bands conversion identifies second-date image by use differences between before suitable bands of a regression function, two-date images implementing and thresholds subtracts the regressed change detection image from the first-date image3. Image Calculates the ratio of Reduces impactsof Non-normal Land-use Identifies theratioing registered images of two Sun angle, shadow distribution of the mapping image bands dates, band by band and topography result is often and thresholds criticized
  23. 23. Techniques Characteristics Advantages Disadvantages Examples Key factors4. Vegetation Produces vegetation index separately, Emphasizes random noise or Vegetation Identifies suitableIndex then subtracts the differences in the coherence noise change vegetation indexdifferencing second-date vegetation index spectral response and forest and thresholds from the first-date vegetation index of different features canopy and reduces impacts change of topographic effects Enhances and illumination.5. Change Generates two outputs: (1) the Ability to process Difficult to landscape Definesvector analysis spectral change vector describes the any number of identify land variables thresholds(CVA) direction and magnitude of change spectral bands cover change land-cover and identifies from the first to the second date; and desired and to trajectories changes change (2) the total change magnitude per produce detailed disaster trajectories pixel is computed by determining the change detection assessment Euclidean distance between end information and conifer points through n-dimensional change forest change space
  24. 24. Category II.Transformation of data sets
  25. 25. Transformations► Principal Component Analysis► Alt1: Perform PCA on data from bothdates and analyse the componentimages.► Alt2: Perform PCA separately on eachimage and subtract the second-date PCimage from that of the first date.► Advantage▪ Reduces data redundancy.► Drawback▪ Results are scene dependent and can be difficult to interpret.▪ Does not provide change matrix.
  26. 26. Kauth Thomas Transformation• Described the temporal spectral patterns derived from Landsat MSS imagery for crops. As crops grow from seed to maturity, there is a net increase in NIR and decrease in Red Reflectance. This effect varies based on soil Color• Brightness Greenness Wetness• The Brightness, Greenness, Wetness transform was first developed for use with the Landsat MSS system and called the “Tasseled Cap” transformation.• The transform is based on a set of constants applied to the image in the form of a linear algebraic formula.• Brightness – primary axis calculated as the weighted sum of reflectances of all spectral bands.• Greenness – perpendicular to the axis of the Brightness component that passes through the point of maturity of all plants• Yellow Stuff – perpendicular to both Greenness and Brightness axis representing senesced vegetation.
  27. 27. Kauth Thomas Transformation Typically the first few components contain most of the information in the data so that four channels of LANDSAT MSS data or the six channels of the http://www.sjsu.edu/faculty/watkins/tassel.htm Thematic Mapper data may be reduced to just three principal components. The components higher than three are usually treated as being information less.Source; www.sjsu.edu/faculty/watkins/tassel.htm
  28. 28. Category II. Transformation Techniques Characteristics Advantages Disadvantages Examples Key factors1. Principal Assumes that multitemporal data Reduces data PCA is scene dependent, Land-cover Analyst’s skill incomponent are highly correlated and change Redundancy thus the change detection change urban identifying whichanalysis (PCA) information can be highlighted in between bands results between different expansion component best the new components. Two ways to and emphasizes dates are often difficult ,tropical forest represents the apply PCA for change detection different to interpret and label. It conversion , change and are: (1)put two or more dates of information in cannot provide a forest selecting images into a single file, then the derived complete matrix of mortality and thresholds perform PCA and analyse the components change class information forest minor component images for and requires determining defoliation change information; and thresholds to identify the (2) perform PCA separately, then changed areas subtract the second-date PC image from the corresponding PC image of the first date2. Tasselled cap The principle of this method is Reduces data Difficult to interpret and Monitoring Analyst’s skill is(KT) similar to PCA. The only difference redundancy label change forest needed in from PCA is that PCA depends on between bands information, cannot mortality , identifying the image scene, andKT and emphasizes provide a complete monitoring which transformation is independent of different change matrix; requires green biomass component best the scene. The change detection is information in determining thresholds and represents the implemented based on the three the derived to identify the changed land-use change and components: brightness, greenness components. areas. Accurate change thresholds and wetness KT is scene atmospheric calibration independent. is required
  29. 29. Techniques Characteristics Advantages Disadvantages Examples Key factors3. The GS method The association It is difficult to extract Monitoring InitialGramm– orthogonalizes spectral of transformed more than one single forest identification ofSchmidt vectors taken directly from components component related to a mortality the stable(GS) bi-temporal images, as does with scene given type of subspace of the the original KT method, characteristics change. The GS multi-date data is produces three stable allows the process relies on required components corresponding extraction of selection of spectral to multitemporal analogues information that vectors from multi-date of KT brightness, greenness would not be image typical of the and wetness, and a change accessible using type of change being component other examined techniques4. Chi- Y=(X-M)T ∑-1*(X-M) Multiple bands The assumption that a Urban Y is distributedsquare Y:digital value of change Are value of Y~0 environmen as a Chi-square image simultaneously represents a pixel of no tal random variable X:vector of the difference considered to change is not true when change with p degrees of the six digital values produce a a large portion of the of freedom ( p is between the two dates single change image is the number of M:vector of the mean image. changed. Also the bands) residual of each band change related to T:transverse of the matrix specific spectral ∑-1= inverse covariance direction not identified matrix
  30. 30. Category-IIIClassification based approach
  31. 31. Post-classification• Post-classification (delta classification) – Classify Date 1 and Date 2 separately, compare class values on pixel by pixel basis between dates• Post-classification: Pros – Avoids need for strict radiometric calibration – Favors classification scheme of user – Designates type of change occurring• Cons – Error is multiplicative from two parent maps – Changes within classes may be interesting
  32. 32. Composite Analysis• Composite Analysis – Stack Date 1 and Date 2 and run unsupervised classification on the whole stack• Composite Analysis: Pros – May extract maximum change variation – Includes reference for change, so change is anchored at starting value, unlike change vector analysis and image differencing• Cons – May be extremely difficult to interpret classes
  33. 33. Unsupervised techniques ► Objective ▪ Produce a change detection map in which changed areas are separated from unchanged ones. ► The changes sought are assumed to result in larger changes in radiance values than other factors. ► Comparison is performed directly on the spectral data. ► This results in a difference image which is analysed to separate insignificant from significant changes.source; Norsk Regnesentral website
  34. 34. Supervised techniques  Objective Generate a change detection map where changed areas are identified and the land-cover transition type can be identified.  The changes are detected and labelled using supervised classification approaches.  Main techniques: • Post-classification comparison • Multidate direct classificationsource; Norsk Regnesentral website
  35. 35. Post classification comparison ► Standard supervised classifiers are used to classify the two images independently. ► Changes are detected by comparing the two classified images. ► Advantage ▪ Common and intuitive. ▪ Provides change matrix. ► Drawback ▪ Critically depends on the accuracy of the classification maps. Accuracy close to the product of the two results. ▪ Does not exploit the dependence between the information from the two points in time.source; Norsk Regnesentral website
  36. 36. Post classification comparison
  37. 37. Multidate direct classification ► Two dates are combined into one multitemporal image and classified. ► Performs joint classification of the two images by using a stacked feature vector. ► Change detection is performed by considering each transition as a class, and training the classifier to recognize all classes and all transitions. ► Advantage ▪ Exploits the multitemporal information. ▪ Error rate not cumulative. ▪ Provides change matrix. ► Drawback ▪ Ground truth required also for transitions.source; Norsk Regnesentral website
  38. 38. Supervised vs. Unsupervised Supervised UnsupervisedLevel of change Change detection at Change detection at data decision level. level.detectionChange Provides explicit labeling Separates ‘change’ from of change and class ‘no change’.information transitionsChange Obtained directly from Obtained through the classified images. interpretation of thecomputation difference image.Ground truth Requires ground truth. Requires no ground truth.Spectral Multispectral. Most methods work on one spectral band.information.Data requirements Not sensitive to Sensitive to atmospheric atmospheric conditions conditions and sensor and sensor differences. differences.
  39. 39. Category III. Classification based approach Techniques Characteristics Advantages Disadvantages Examples Key factors1. Post Separately classifies multi- Minimizes Requires a great LULC change, Selects sufficientclassification temporal images into thematic impacts of amount of time and wetland training samplecomparison maps, then implements comparison atmospheric, expertise to create change data for of the classified images, pixel by sensor and classification and urban classification pixel environmental products. The final expansion differences accuracy depends on between the quality of the multitemporal classified image of images; provides a each date complete matrix of change information2. Spectral– Puts multi-temporal data into a Simple and Difficult to identify Changes in Labels thetemporal single file, then classifies the timesaving and label the change coastal zone change classescombined combined dataset and identifies and in classification classes; cannot environmentsanalysis labels the changes provide a complete and forest matrix of change change information3. EM The EM detection is a This method was Requires estimating Land-cover Estimates thedetection classification-based method using reported to the a priori joint change a priori joint an expectation maximization (EM) provide higher class probability. class probability algorithm to estimate the a priori change detection joint class probabilities at two accuracy than times. These probabilities are other change estimated directly from the images detection methods under analysis
  40. 40. Techniques Characteristics Advantages Disadvantages Examples Key factors4. Unsupervised Selects spectrally similar groups of This method Difficulty in Forest hange Identifies thechange pixels and clusters date 1 image makes use of the identifying and spectrally similardetection into primary clusters, then labels unsupervised labelling change or relatively spectrally similar groups in date 2 nature and trajectories homogeneous image into primary clusters in date automation of the units 2 image, and finally detects and change analysis identifies changes and outputs process results5. Hybrid Uses an overlay enhancement from This method Requires selection LULC change Selects suitablechange a selected image to isolate changed Excludes of thresholds to , vegetation thresholds todetection pixels, then uses Supervised unchanged implement change identify the classification. A binary change pixels from classification; and change and non- mask is constructed from the classification to somewhat complicated monitoring change areas and classification results. This change reduce to eelgrass develops accurate mask sieves out the classification identify change classifi’n output changed themes from the LULC errors trajectories maps produced for each date6. Artificial The input used to train the neural ANN is a The nature of hidden Mortality The architectureneural network is the spectral data of the nonparametric layers is poorly detection in used such as thenetworks period of change. A Supervised known; a long training Lake , land- number of hidden(ANN) backpropagation algorithm is often method and has time is required. ANN cover change, layers, and used to train the multi-layer the ability to is often sensitive to the forest change, training samples perceptron neural network model estimate the amount of training Urban hange properties of data data used. ANN based on the functions are not training samples common in image processing software
  41. 41. Category IV. Advanced models Techniques Characteristics Advantages Disadvantages Examples Key factors1. Li–Strahler The Li–Strahler canopy model This method combines This method Mapping Develops thereflectance is used to estimate each conifer the techniques of digital requires a large and Stand crownmodel stand crown cover for two dates image processing of number of field monitoring cover images of imageries separately. remotely sensed data Measurement data. conifer and identifies Comparison of the stand crown with traditional sampling It is complex and mortality the crown covers for two dates is and field observation not available in characteristics conducted to produce the methods. It provides commercial image of vegetation change detection results statistical results and processing types maps showing the software. It is only geometric distribution of suitable for changed patterns vegetation change2. Spectral Uses spectral mixture analysis to The fractions have This method is Land-cover Identifiesmixture derive fraction images. biophysical meanings, regarded as an change, suitablemodel Endmembers are selected from representing the areal advanced image seasonal endmembers; training areas on the image or proportion of each processing vegetation defines suitable from spectra of materials endmember within the analysis and is patterns and thresholds for occurring in the study area or pixel. The results are somewhat Vegetation each land- from a relevant spectral library. stable, accurate and complex change cover Changes are detected by repeatable using TM class based on comparing the ‘before’ and data fractions ‘after’ fraction images of each end member. The quantitative changes can be measured by classifying images based on the endmember fractions
  42. 42. Category V. GIS based approachTechniques Characteristics Advantages Disadvantages Examples Key factors3. Incorporates image data and Allows access of Different data LULC The accuracy ofIntegrated GIS data, such as the ancillary data to quality from and different dataGIS and overlay of GIS layers aid interpretation various sources urban sources and theirremote directly on image data; and analysis and often degrades the sprawl registrationSensing moves results of image has the ability to results of LULC accuraciesmethod processing into GIS system directly update change detection between the for land-use thematic images further analysis information in GIS4. GIS Integrates past and current This method Different GIS data Urban The accuracy ofapproach maps of land use with allows with different change different data topographic and geological incorporation of geometric And sources and their data. The image overlaying aerial accuracy and landscape registration and binary masking photographic data classification change accuracies techniques are useful in of current and past system degrades between the revealing quantitatively the land-use data with the quality of thematic images. change dynamics in each other map data results category
  43. 43. Category VI. Visual analysis Techniques Characteristics Advantages Disadvantages Examples Key factors1. Visual One band (or VI) from date1 Human experience Cannot provide Land-use Analyst’sinterpretation image as red, the same band and knowledge are detailed change change, skill and (or VI) from date2 image as useful during visual information. forest familiarit green, and the same band (or interpretation. Two or The results change , y with VI) from date3 image as blue three dates of images depend on the monitoring the study if available. Visually can be analysed at analyst’s skill in selectively area interprets the colour one time. The analyst image logged composite to identify the can incorporate interpretation. areas and changed areas. An alternative texture, shape, size Time- land cover is to implement on-screen and patterns consuming and change digitizing of changed areas intovisual difficulty in using visual interpretation interpretation to updating the based on overlaid images of make a decision on results diff. dates the LULC change
  44. 44. Category VII. Other change detection techniques1. Measures of spatial dependence (Henebry 1993)2. Knowledge-based vision system (Wang 1993)3. Area production method (Hussin et al. 1994)4. Combination of three indicators: vegetation indices, land surfacetemperature, and spatial structure (Lambin and Strahler 1994b)5. Change curves (Lawrence and Ripple 1999)6. Generalized linear models (Morisette et al. 1999)7. Curve-theorem-based approach (Yue et al. 2002)8. Structure-based approach (Zhang et al. 2002)9. Spatial statistics-based method (Read and Lam 2002)
  45. 45. Factors to consider when choosing a method► Objective of the change detection?▪ Monitor/identify specific changes▪ More efficient mapping at T2▪ Improved quality of mapping at T2► What type of change information to extract?▪ Spectral changes▪ Land cover transitions▪ Shape changes▪ Changes in long temporal series► What type of changes to be considered?▪ Land use and land cover change▪ Forest and vegetation change▪ Wetland change▪ Urban change▪ Environmental change
  46. 46. Factors to consider…► Expected amount of changes► Available data at date 1 and date 2 • Remote sensing data • Temporal, spatial and spectral characteristics. • Differences in characteristics btw. date 1 and date 2. • Classified maps • Ground truth► Environmental considerations • Atmospheric conditions • Soil moisture conditions • Phenological states► Accuracy requirements
  47. 47. Comparing the Different Techniques Two types of change detection either detect binary change/non-change, or the detailed “from-to” change between different classes. Different change detection techniques are often tested and compared based on an accuracy assessment or qualitative assessment. no single method is suitable for all cases. A combination of two change detection techniques can improve the change detection results (image differencing/PCA, NDVI/PCA, PCA/CVA). The most common change detection methods: image differencing, PCA, CVA, and post-classification comparison.
  48. 48. Global change analyses and image resolution For change detection at high or moderate spatial resolution: use Landsat TM, SPOT, or radar. For change detection at the continental or global scale, use coarse resolution data such as MODIS and AVHRR. AVHRR has daily availability at low cost; it is the best source of data for large area change detection. NDVI and land surface temperatures derived from MODIS or AVHRR thermal bands are especially useful in large area change detection.
  49. 49. Threshold Selection Many change detection algorithms require threshold selection to determine whether a pixel has changed. Thresholds can be adjusted manually until the resulting image is satisfactory, or they can be selected statistically using a suitable standard deviation from a class mean. Both are highly subjective methods. Other methods exist for improving the change detection results, such as using fuzzy set and fuzzy membership functions to replace the thresholds. However, threshold selection is simple and intuitive, so it is still the most extensively applied method for detecting binary change/no-change information.
  50. 50. Accuracy Assessment Accuracy assessments are important for understanding the change detection results and using these results in decision-making. However, they are difficult to do because reliable temporal field-based datasets are often problematic to collect. The error matrix is the most common method for accuracy assessment. To properly generate one, the following factors must be considered: 1. ground truth data collection, 2. classification scheme, 3. sampling scheme, 4. spatial autocorrelation, and 5. sample size and sample unit.
  51. 51. Summary and Recommendations The binary change/no-change threshold techniques all have difficulties in distinguishing true changed areas from the detected change areas. Single- band image differencing and PCA are the recommended methods. Classification-based change detection methods can avoid such problems, but requires more effort to implement. Post-classification comparison is a suitable method when sufficient training data is available. When multi-source data is available, GIS techniques can be helpful. Advanced techniques such as LSMA, ANN, or a combination of change detection methods can produce higher quality change detection results.
  52. 52. References• MACLEOD, R. D., and CONGALTON, R. G.,A quantitative comparison of change detection algorithms for monitoring eelgrass from remotely sensed data. Photogrammetric Engineering and Remote Sensing, vol. 64, pp 207–216. 1998,• LAMBIN, E. F., and STRAHLER, A. H., 1994b, Indicators of land-cover change for change vector analysis in multitemporal space at coarse spatial scales. International Journal of Remote Sensing, 15, 2099–2119.• LAMBIN, E. F., and STRAHLER, A. H., 1994a, Change-vector analysis in multitemporal space: a tool to detect and categorize land-cover change processes using high temporal resolution satellite data. Remote Sensing of Environment, 48, 231–244.• GREEN, K., KEMPKA, D., and LACKEY, L., 1994, Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric Engineering and Remote Sensing, 60, 331–337.• DEER, P. J., 1995, Digital change detection techniques: civilian and military applications. International Symposium on Spectral Sensing Research 1995 Report (Greenbelt, MD: Goddard Space Flight Center), http://ltpwww.gsfc.nasa.gov/ISSSR- 95/digitalc.htm• WANG, F., 1993, A knowledge-based vision system for detecting land change at urban fringes. IEEE Transactions on Geoscience and Remote Sensing, 31, 136–145• HUSSIN, Y. A., DE GIER, A., and HARGYONO, 1994, Forest cover change detection analysis using remote sensing: a test for the spatially resolved area production model. Fifth European Conference and Exhibition on Geographic Information Systems, EGIS’94 Proceedings, Paris, France, 29 March 29–1 April 1994 (Utrecht: EGIS Foundation), vol. II, pp. 1825–1834
  53. 53. References………• LAWRENCE, R. L., and RIPPLE, W. J., 1999, Calculating change curves for multitemporal satellite imagery: Mount St. Helens 1980–1995. Remote Sensing of Environment, 67, 309–319• MORISETTE, J. T., KHORRAM, S., and MACE, T., 1999, Land-cover change detectionenhanced with generalized linear models. International Journal of Remote Sensing, 20,2703–2721• YUE, T. X., CHEN, S. P., XU, B., LIU, Q. S., LI, H. G., LIU, G. H., and YE, Q. H., 2002, A curve-theorem based approach for change detection and its application to Yellow River Delta. International Journal of Remote Sensing, 23, 2283–2292.• ZHANG, Q., WANG, J., PENG, X., GONG, P., and SHI, P., 2002, Urban build-up land change detection with road density and spectral information from multitemporal Landsat TM data. International Journal of Remote Sensing, 23, 3057–3078• READ, J. M., and LAM, N. S.-N., 2002, Spatial methods for characterizing land cover and detecting land over changes for the tropics. International Journal of Remote Sensing, 23, 2457–2474.• JENSEN, J. R., and TOLL, D. L., 1982, Detecting residential land use development at the urban fringe. Photogrammetric Engineering and Remote Sensing, 48, 629–643.• CHAVEZ, P. S. JR, and MACKINNON, D. J., 1994, Automatic detection of vegetation changes in the southwestern United States using remotely sensed images. Photogrammetric Engineering and Remote Sensing, 60, 571–583.• PILON, P. G., HOWARTH, P. J., BULLOCK, R. A., and ADENIYI, P. O., 1988, An enhanced classification approach to change detection in semi-arid environments. Photogram- metric Engineering and Remote Sensing, 54, 1709–1716.
  54. 54. References……….• FUNG, T., and LEDREW, E., 1987, The application of principal component analysis to change detection. Photogrammetric Engineering and Remote Sensing, 53, 1649–1658.• FUNG, T., 1990, An assessment of TM imagery for land-cover change detection. IEEE Transactions on Geoscience and Remote Sensing, 28, 681–684.• GONG, P., 1993, Change detection using principal component analysis and fuzzy set theory. Canadian Journal of Remote Sensing, 19, 22–29.• FOODY, G. M., 2001, Monitoring the magnitude of land-cover change around the southern limits of the Sahara. Photogrammetric Engineering and Remote Sensing, 67, 841–847• ABUELGASIM, A. A., ROSS, W. D., GOPAL, S., and WOODCOCK, C. E., 1999, Change detection using adaptive fuzzy neural networks: environmental damage assessment after the Gulf War. Remote Sensing of Environment, 70, 208–223.• DAI, X. L., and KHORRAM, S., 1999, Remotely sensed change detection based on artificial neural networks. Photogrammetric Engineering and Remote Sensing, 65, 1187– 1194.• GOPAL, S., and WOODCOCK, C. E., 1996, Remote sensing of forest change using artificial neural networks. IEEE Transactions on Geoscience and Remote Sensing, 34, 398– 403• GOPAL, S., and WOODCOCK, C. E., 1999, Artificial neural networks for detecting forest change. In Information Processing for Remote Sensing, edited by C. H. Chen (Singapore: World Scientific Publishing Co.), pp. 225–236.
  55. 55. References……….• WOODCOCK, C. E., MACOMBER, S. A., PAX-LENNEY, M., and COHEN, W. B., 2001 Monitoring large areas for forest change using Landsat: generalization across space, time and Landsat sensors. Remote Sensing of Environment, 78, 194–203.• LIU, X., and LATHROP, R. G. JR, 2002, Urban change detection based on an artificial neural network. International Journal of Remote Sensing, 23, 2513–2518.• ROGAN, J., FRANKLIN, J., and ROBERTS, D. A., 2002, A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery. Remote Sensing of Environment, 80, 143–156.• KRESSLER, F., and STEINNOCHER, K., 1996, Change detection in urban areas using satellite data and spectral mixture analysis. International Archives of Photogrammetry and Remote Sensing, 31, 379–383• ADAMS, J. B., SABOL, D., KAPOS, V., FILHO, R. A., ROBERTS, D. A., SMITH, M. O., and GILLESPIE, A. R., 1995, Classification of multispectral images based on fractions of endmembers: application to land-cover change in the Brazilian Amazoˆ n. Remote Sensing of Environment, 52, 137–154.• PETIT, C. C., and LAMBIN, E. F., 2001, Integration of multi-source remote sensing data for land cover change detection. International Journal of Geographical Information Science, 15, 785–803.• LO, C. P., and SHIPMAN, R. L., 1990, A GIS approach to land-use change dynamics detection. Photogrammetric Engineering and Remote Sensing, 56, 1483–1491