Temporal Classification and Change Detection March 20, 2000 Temporal Classification and Change Detection FR 4262

25. Temporal Classification and Change Detection March 20, 2000 IKONOS Imagery May 6 August 29 September 14 Rosemount Research & Outreach Center FR 4262

Multitemporal Landsat 5 imagery April Multitemporal Landsat 5 imagery May June July Inter-temporal covariance provides separability not available in single date imagery Numerous studies show improved classification accuracy with two or more dates of imagery over single date classifications

25. Temporal Classification and Change Detection March 20, 2000 February Multitemporal IKONOS Imagery April June August September Cloquet Forestry Center FR 4262

August, full-resolution image 25. Temporal Classification and Change Detection August, full-resolution image March 20, 2000 FR 4262

25. Temporal Classification and Change Detection March 20, 2000 May FR 4262

25. Temporal Classification and Change Detection March 20, 2000 May FR 4262

25. Temporal Classification and Change Detection March 20, 2000 September FR 4262

25. Temporal Classification and Change Detection March 20, 2000 September FR 4262

Use of Temporal Information in Classification 25. Temporal Classification and Change Detection March 20, 2000 Use of Temporal Information in Classification Basic rationale W A O Time 1 Optimal time for W vs. A W A O Time 2 Optimal time for W vs. O W A O T1 + T2 All 3 classes are accurately classified Inter-temporal covariance provides separability not available in single date imagery FR 4262

Multitemporal Data and Classification Accuracy 25. Temporal Classification and Change Detection March 20, 2000 Multitemporal Data and Classification Accuracy Numerous studies with multitemporal data have shown that two or more dates are frequently better than a single date, especially if the single date is not at the optimal time Classification Accuracy (%) Acquisition Period FR 4262

Monitoring Vegetation Dynamics with MODIS NDVI 25. Temporal Classification and Change Detection March 20, 2000 Monitoring Vegetation Dynamics with MODIS NDVI Movie of Minnesota "Green Up" April – November 2006 FR 4262

25. Temporal Classification and Change Detection March 20, 2000 Temporal Profile Model (a second approach for using temporal information) Several parameters can be derived from a profile model fit to several dates of data and used as features in image classification Parameters are related to important biological-ecological characteristics Reduces number of features to be classified 4 dates with 5 spectral bands (= 20 features) can be reduced to 4 or 5 features Increases classification accuracy FR 4262

Temporal Profile Parameters 25. Temporal Classification and Change Detection March 20, 2000 Temporal Profile Parameters start of green-up rate of growth maximum “greenness” date of maximum duration of greenness rate of senescence end of growing season total seasonal accumulation (area under the curve) Time “Greenness” (NDVI) 3 4 6 2 5 8 1 7 FR 4262

Examples of Temporal Profiles for several Minnesota cover types 25. Temporal Classification and Change Detection March 20, 2000 Examples of Temporal Profiles for several Minnesota cover types FR 4262

25. Temporal Classification and Change Detection March 20, 2000 Example Images of Temporal Profile Metrics Start Date (1) Rate of Growth (2) Maximum (3) Time of Peak (4) Duration (5) Senescence Rate (6) AVHRR NDVI, 1998 FR 4262

25. Temporal Classification and Change Detection March 20, 2000 Garden City, Kansas 1972 1988 FR 4262

25. Temporal Classification and Change Detection March 20, 2000 1975 Rondonia, Brazil 1982 1992 FR 4262

25. Temporal Classification and Change Detection March 20, 2000 1975 Twin Cities Landsat Images 1981 1986 1991 1998 2002 Currently, change detection, monitoring and updating rely primarily on two techniques: image-to-image comparisons and post classification, or map-to-map, comparisons (Green et al, 1994). Image-to-image comparison methods apply various algorithms directly to multiple dates of satellite imagery (Ridd and Liu, 1998) to generate “change” vs. “no-change” maps. Map-to-map methods are considered “post classification” comparisons because they involve comparing two separate classifications of satellite data to produce “from-to” maps based on classification differences in the input maps (Yuan et al., 1998). Although subject to error propagation, map-to-map comparisons have several advantages over image-to-image comparisons. A multi-date series of land cover classifications can be used for many applications other than change detection, and the classifications enable deriving “from-to” change information. FR 4262

General Steps for Digital Change Detection 25. Temporal Classification and Change Detection March 20, 2000 General Steps for Digital Change Detection Define the problem and select appropriate land cover classification system Obtain appropriate imagery considering spatial, spectral-radiometric and temporal resolution atmospheric, illumination, seasonal, moisture, … conditions Preprocess imagery Geometric registration of multi-date images Radiometric correction or normalization (depending on the classification approach) Select and apply appropriate change detection algorithm FR 4262

Classification of Image Differences 25. Temporal Classification and Change Detection March 20, 2000 Date 1 imagery Classification of Image Differences Date 2 imagery minus Subtract one date of imagery from another to produce a “difference” image which is then classified Difference images “Change” map Image classification FR 4262

Classification of Image Differences 25. Temporal Classification and Change Detection March 20, 2000 Classification of Image Differences Advantages Efficient way to detect change Requires only one classification Disadvantages “From-to” change information is not available Requires careful definition of “change - no change” threshold differences in DN values due to other factors such as phenology, sun angle, atmosphere or sensors differences are not “real” changes Requires acquisition of comparable imagery and careful radiometric calibration such that where there are no changes in land cover the images are near identical (i.e., difference equals zero) FR 4262

Comparison of Classifications 25. Temporal Classification and Change Detection March 20, 2000 Comparison of Classifications Date 1 imagery Classification of Date 1 Two dates are classified separately Date 2 imagery Classification of Date 2 FR 4262

Comparison of Classifications 25. Temporal Classification and Change Detection March 20, 2000 Comparison of Classifications Date 1 imagery Classification of Date 1 Two dates are classified separately Classification of Date 1 minus Date 2 imagery Classification of Date 2 Classification map of Date 2 is then subtracted from the map of Date 1 “Change” map FR 4262

Comparison of Classifications 25. Temporal Classification and Change Detection March 20, 2000 Comparison of Classifications Advantages provides “from - to” change class information next base year is already completed Disadvantages accuracy of change map depends on the accuracy of the individual classifications requires two classifications FR 4262

25. Temporal Classification and Change Detection March 20, 2000 Summary Multitemporal data, typically at different times of the year, can be used to increase classification accuracy and specificity but does require acquiring and processing additional dates of data Data acquired over different years can be used to detect and classify changes in land cover and use Pre-classification vs. Post-classification change detection (from-to info). FR 4262