Chapter 8 Change Detection

n RS & GIS to inventory and monitor natural and cultural phenomena on the surface of the Earth n Some may be static, but many are dynamic n It is important to map/quantify the changes accurately so that the processes at work can be fully understood Introduction

n State problems (study area, frequency, classification scheme) n Considerations (instrument, environment) n Change detection (data acquisition, preprocessing, change detection, change statistics) n Accuracy assessment n Products (analogue, digital) General steps

A list of digital methods for change detection n Image differencing n Cross-classification (post-classification comparison) n Image deviation n Principal component analysis n Change vector analysis

Image differencing n To see change across two time slices n A simple method: each pixel from an image is subtracted from its corresponding pixel in another image

n Two images of NDVI from Southern Africa for March of 1991 and March of n Most of the crops are in their maturity stage around this time of the year. n produced by USAID/FEWS from compositing dekadal (10 day) images into a monthly maximum value composite, where the maximum value over the three dekads in each month for each pixel is used to represent that pixel's monthly vegetation index.

Cross-classification n land-cover / land-use change analysis using a two-date images taken on the same area n raw imagery of each date classified into land-cover / land-use categories

n The cross-classification: calculating the logical AND of all possible combinations of categories on the two classified input images (change map coding) n Cross-tabulation matrix that shows the distribution of image cells between classes (change statistics)

n A Boolean image: all non-change areas are assigned a value of 0 and the change pixels are assigned a value of 1 n to identify which class on Date 1 has changed to which class on Date 2 Resulting products

Image deviation n Much larger time series data are being examined n To examine trends in environmental change or the abstraction of significant anomalies from the general trend

n it is assumed that change areas are identified by contrast to a long-term average or characteristic condition. Given such a characteristic image, the deviation of any particular time from this long-term average can then be assessed by simple differencing. n to understand change in one time slice as a deviation from its own long term mean. We will difference the March 1992 NDVI image from the long term average NDVI image for March.

Principal component analysis n variant of PCA known as the Standardized Principal Components Analysis to analyze remotely sensed data in the temporal domain. n The standardization is intended to minimize the undue influence of other extraneous factors e.g. atmospheric interference (aerosols and water vapour), changes surface illumination conditions, e.t.c..

a typical continental vegetation map of Africa, this component alone accounts for 96.7 per cent of the variance in the 60 months time series.

Component 2 accounts for only 1.97 of the entire continental scale variance. It however contains very useful information on the seasonality patterns of vegetation. it shows a strong positive NDVI anomaly pattern in band stretching from Senegal to Ethiopia in the northern hemisphere (green areas) and negative anomaly in the southern hemisphere (red to deep blue).

1. Post-classification comparison Class 1 “ AND ” Class 2 2. Image differencing  Z(i,j)) = Z 2 (i,j) - Z 1 (i,j) 3. Image deviation  Z(i,j)) = Z t (i,j) – Z mean (i,j) 4. PCA Anomalies vs. typical pattern Change detection in review

Change vector analysis In the Brazilian Amazon, the annual deforestation rate has been around 16,000 km 2, due to agricultural and, specially cattle raising activities. using three Landsat TM scenes, bands 1 to 5 and 7, for the years 1990, 1997 and 1999.

radiometric correction to normalize the reflectance values of each pixel to those pixel values of the reference image:  conversion of digital values to reflectance values (Markham and Barker 1987)  radiometric rectification (Hall et al., 1991) Tasseled Cap transform (Kauth & Thomas, 1976), which generates the components Greenness and Brightness

Greenness, associated with the amount and vigor of vegetation, and Brightness, associated with variations of soil reflectance

Class 1, mainly related to the growth of vegetation biomass Class 2, is strongly related to great losses of vegetation biomass as a result of the clear-cut of tropical forest. Class 3, mainly related to smaller losses of biomass, such as transformation of sections with regrowth or cultures to pasture.

the deforested area was 850 Km2 in the period. deforestation is 86 Km2 /year for the period between 1990 and 1997,

the deforested area, increasing to 165 Km2 /year between 1997 to 1999.

some infrastructure in place: small settlements, and access trails, and roads (which eased the penetration of humans into the forest, as well as the transformation of small lots to subsistence agriculture or the grouping of several lots for the extensive cattle raising). The Class of Change 4 (blue), defined by the decrease in gray levels of “Greenness” and “Brightness” is related to either water bodies /or burnings. The few burned down areas identified during this period are the result of the “slash and burn technique” applied on the primary forest for the expansion of family based, small scale, productive system. It is also observed that few water reservoirs were built in the region, which are normally associated with irrigation and water supply of the settlements during the dry season.

Tasseled Cap analysis (Greenness and Brightness components) in temperate and sub-tropical regions must be further analyzed and adapted to be used in tropical regions. A word of caution

National Institute for Space Research – INPE Brazil Hall, F.G.; Strebel, D.E.; Nickeson, J.E.; Goetz, S.J. (1991) Radiometric rectification: toward a common radiometric response among multidate, multisensor images. Remote Sensing of Environment, v.35, n.1, p Kauth, R. J.; Thomas, G. S. (1976) Tasseled Cap – a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. : Proceedings of the Machine Processing of Remotely Sensed Data Symposium, Purdue University, West Lafayette, Indiana, p. 4b41 – 4b51. Markham, B. L.; Barker, J. L (1987) Radiometric properties of U.S. processed Landsat MSS data Data. Remote Sensing of Environment, v.22, p Useful references for CVA

n The Kappa Index of Agreement (K) n Its values range from -1 to +1 after adjustment for chance agreement. n 1 - the two input images are in perfect agreement (no change has occurred) n -1 - the two images are completely different n 0 - If the change between the two dates occurred by chance Accuracy in detected change

n Kappa is an index of agreement between the two input images as a whole. n It also evaluates a per-category agreement between two dates:

n The per-category K can be calculated using the following formula (Rosenfield and Fitzpatrick-Lins,1986): n K = (Pii - (Pi.*P.i )/ (Pi. - Pi.*P.i ) n where: n P ii = Proportion of entire image in which category i agrees for both dates n P i. = Proportion of entire image in class i in reference image n P. i = Proportion of entire image in class i non-reference image

n You should know when and where to use which n change/no-change n change type, n accuracy in detected change n Other methods for exploration: e.g., ANNs Summary

References n Tucker, C. J. and Townshend, J. R. G. and Goff, T. E. (1985) African Land-Cover Classification Using Satellite Data. Science, 227(4685): Anyamba, A. and Eastman, J. R. (1996) Interannual Variability of NDVI over Africa and its relation to El Ni ñ o / Southern Oscillation. International Journal of Remote Sensing 17(13) :

Questions 1. Discuss applications of the methods of change detection described in this unit