Change Detection with GRASS GIS – Comparison of images taken by different sensors

Images of American military reconnaissance satellites of the Sixties (CORONA) in combination with modern sensors (SPOT, QuickBird) were used for detection of changes in land use. The pilot area was located about 40 km northwest of Yemen’s capital Sana’a and covered approximately 100 km2. To produce comparable layers from images of distinctly different sources, the moving window technique was applied, using the diversity parameter. The resulting difference layers reveal plausible and interpretable change patterns, particularly in areas where urban sprawl occurs. The comparison of CORONA images with images taken by modern sensors proved to be an additional tool to visualize and quantify major changes in land use. The results should serve as additional basic data eg. in regional planning. The computation sequence was executed in GRASS GIS.


Introduction
GRASS GIS (http://grass.osgeo.org)with extended functionality and operability is more than a common geographic information system.It is powerful in raster data processing, offers fundamental functions in terrain-and landscape analysis with extended tools for hydrological modeling and a small functionality for remote sensing.Furthermore it can be used to process three dimensional data.This powerful functionality can be used as a frame for studies, which use GIS in combination with remote sensing tools.Change Detection is defined as: "The sensing of environmental changes that uses two or more scenes covering the same geographic area acquired over a period of time."(Glossary of Canada Centre for Remote Sensing, http://www.ccrs.nrcan.gc.ca/glossary)Aside from visual interpretation different algorithms are applied.

Essential aims of Change Detection are:
Detection and evaluation of land use changes Support the monitoring of disasters triggered by geological, meteorological or man made factors.
The use of Change Detection algorithms requires two preconditions: 1. Changes in land cover must result in changes in radiance values.
2. Changes in radiance due to land cover changes must be large with respect to radiance changes caused by other factors, such as atmospheric conditions, sun angle or vegetation phenology.
The preconditions mentioned are based on processing scenes from the same sensor type.The scenes acquisition should be done carefully because differences in radiation, precipitation and surface temperature in combination with phenological variations lead to discrepancies in reflectance properties.These sources of interference have to be extensively eliminated.
The phenological variations are reduced by using scenes taken at the same season of the year.Additionally, climate data should be available to assess the phenological stage of the vegetation.
Well-known satellite missions have been operating continuously for decades.Landsat missions for instance have been delivering images since 1972 with repetition rates of 18 days (MSS) and 16 days (Landsat 4, 5, 7), respectively.
The data preparation includes:

Radiometric calibration with atmospheric correction
The goal is to achieve high quality images with geographic precision of less than one pixel and correlation of radiometric calibration close to 1.
The applied methods of Change Detection comprise simple difference procedures and multivariate statistical routines.Change Detection can be used directly to multiband stacks or derived resp.classified layers.An overview of Change Detection methods can be found in Théau (2006), the comparison and evaluation of methods and their applicability is described in Peinado (2001).Some major definitions used in remote sensing are given below according to Théau (2006)  This technique is usually used to reduce the number of spectral components (spectral bands) to fewer principal components accounting for the most variance in the original multispectral images.Image spectral bands of two or more dates are treated as a single data set.After performing PCA, information that is common to multidate images is mapped to the first component (unchanged areas) whereas information that is unique to one of the dates is mapped to the following components (changed areas).

Composite Analysis
Supervised and unsupervised classifications are used to analyze these datasets.Classes where changes occurred are expected to present statistics significantly different from where changes did not take place.

Comparison of post-classifications
The critical step of all mentioned methods is deciding where to place the threshold for changes.Furthermore the exact nature of the changes needs a careful interpretation including the knowledge of the investigation area including ground checks.

Change Detection with GRASS GIS -Comparison of images taken by different sensors
Reconnaissance Satellite Photos -CORONA The term stands for a series of U. S. Military reconnaissance satellites (KH 1 to KH 5) which were operated between 1959 and 1972.The satellites of the CORONA series delivered panchromatic photographs of many areas of the world.
Images of the first generation were declassified at the end of the Nineties.The ground resolution of the two KH-4 systems (1963 -1972)

Method
CORONA images are an essential source of information in particular for those decades where other high resolution images are missing.This applies to the sixties of the last century when only military reconnaissance satellites were operating.However, only Corona images are available for this decade since 1996 (http://edc.usgs.gov/guides/disp1.html).
The methods of Change Detection mentioned above are based on scenes taken by the same sensor type at different dates.The method described in this paper is based on the image differencing method.Scenes are compared that were taken by different sensors.For this, the steps for the preparation and harmonization of the image information are very important.These working steps comprise the geometric correction of the CORONA image, the transformation of the RGB channels of the modern satellite data into one panchromatic channel and the resample process into the pixel resolution of the CORONA image.Then the subsequent moving window algorithm can be applied.The computation sequence ends with the subtraction step (Fig. 1).
The core of the computation sequence uses the moving window technique.This technique is offered by the GRASS raster module r.neighbors (http://grass.osgeo.org).The command r.neighbors can be run with different parameters.Basically two groups of parameters exist.The first group comprises the statistical parameters.The second group comprises parameters commonly used in landscape analysis (McGarigal & Marks 1995).These two parameters are the diversity and the interspersion.Diversity is defined as the number of different values within the neighborhood.The computation with parameters of the second group leads to results which calculate pixelwise diversity as dimensionless value.Therefore the comparison between images taken with different sensors is possible as outlined now.
For each pixel the number of different neighborhood pixel values has to be identified and stored as a new value.Therefore the size of the moving window is to be considered as sensible value with strong influence on the result layers ( In addition to the diversity parameter the entropy formula (Eq. 1) is used in the computation.The Shannon Diversity Index (SHDI) is computed in our own application written in Fortran 90 and the results are dumped as absolute values.The SHDI is based on the information theory and is also called as Negentropy (Palm 1985).It presents the amount of information The result layers are intersected by subtraction.Sources of error originating from clouds or shadows can be masked.Therefore results of supervised or unsupervised classifications can be used because such classes normally have a good delineation from other classes in the multivariate space.

Area of investigation and data input layer
The test site is located north west of Yemen's capital Sana'a and comprises an area of 10 x 10 km.In this arid to semi-arid climate zone, an ancient cultivated land with deficiencies of water occurs.The test site is composed of a cuesta landscape with altitudes between 2500 and 3000 m a.s.l. with wide-stretched valleys and a network of wadis (Fig. 3).Farming within the test site is characterized by extensive irrigation using groundwater from wells.On a limited scale run-off water is used, too.Arable land mainly is located in the valleys and on

Change Detection with GRASS GIS -Comparison of images taken by different sensors
man-made terraces located on the pediments in front of the escarpments and on dip slopes.Aside from land use such as arable farming, various other categories of land use can be found (Fig. 4).
Due to the long term technical cooperation between the geological surveys of the Republic of Yemen and Germany, there are satellite images available in the Federal Institute for Geosciences and Natural Resources (BGR).For this study SPOT data (http://www.spot.com)and GoogleEarth-QuickBird data (http://earth.google.com)were chosen for the comparison with CORONA images (Tab.2).

Results
The first results contain the computed diversity layers derived from the panchromatic images.The diversity/heterogeneity is quantified at date A (Fig. 5) and date B (Fig. 6 -7).Areas with low and high diversity can be delineated and combined with land cover classes.The two entropy layers of QuickBird (Fig. 7) and SPOT (Fig. 6) data show identical patterns in heterogeneity.This is confirmed by the correlation of 0.84 (Tab.4).In contrast the comparison of diversity between the CORONA image and the modern scenes shows no correlation (Tab.4 Change Detection -State of the art Change Detection is a group of methods commonly used in remote sensing.Because of the repetitive coverage of earth orbiting satellites at short intervals and consistent image Change Detection with GRASS GIS -Comparison of images taken by different sensors quality, methods of Change Detection have become part of environmental observation systems (Lunetta & Elvidge 1999; Owe 2007).

Fig 2 :
Fig 2: Inclusion of neighborhood size in the moving window

Change
Detection with GRASS GIS -Comparison of images taken by different sensors as highly divers.The visible patterns with high entropy coincide with the border areas of Wadis.This can be explained by intensive human activities and changes in land use.Parts with low entropy comprise areas covered by clouds or shadows, areas on the higher part of the plateaus as well as barren land.

Fig. 8 :Fig. 10 :
Fig. 8: CORONA, QuickBird image and entropy difference layer of Shibam Grosse et al. (2005) 3 m.The photographs are 30 $ each and can be ordered under http://edcsns17.cr.usgs.gov/EarthExplorer.CORONA photos are used in various research projects.One application is the derivation of elevation models because many scenes provide stereoscopic records(Schmidt et al. 2001).Grosse et al. (2005)used CORONA images for the visual interpretation of thermokarst processes.Another area of application comprises the preparation and support of archeological excavations(Goossens et al. 2002).In geological mapping CORONA images are required where other high resolution images are missing.Lorenz (2004) completed the mapping of Paleozoic stratums in Russian Arctic with CORONA images. ).