Radiometric rotation technique to monitor degradation and regeneration features in Brazilian

semi-arid region.

J. R. Santos1; F. D. Maldonado1; C. Quintano Pastor2; A. Fernandez Manso3

1INPE – Instituto Nacional de Pesquisas Espaciais Av. dos Astronautas, 1758 CEP.: 12.227-010 São José dos Campos – SP., Brasil

Phone: +55-12-3456427; Fax: +55-12-3456449 Email: {jroberto,francis}@dsr.inpe.br

2Universidad de Valladolid E. U. Politécnica; Francisco Mendizábal, 1 47014 - Valladolid, España Email: menchu@tele.uva.es

3Universidad de León

Ingeniería Agraria; Avd. Astorga, sn. 24400 Ponferrada, España Email: alfonso@unileon.es

Abstract - The objective is to present a technique on “Radiometric rotation controlled by a no-change axis (RCEN)”, using a multi-temporal comparison between ETM+/LANDSAT-7 and CCD/CBERS-2 images. The area under study is located in the central of Brazilian semi-arid region, whose landscape is dominated by steppe savanna (caatinga). The result of RCEN technique is an change detection-image, whose continuous image (floating-point) was transformed into a thematic image through the procedure of slicing and labelling. This allowed the spectral discrimination in five thematic classes: two related to degradation, two referring to regeneration and one of no-change. A map of changes 2001/2004 was generated, illustrating the distribution of these thematic categories mentioned at the caatinga zone , which is under a strong impact due to agriculture activities for subsistence, besides pastureland for goats in the community fields.

Key words: change detection, arid lands, land use, degradation, CBERS-2, monitoring.

I. INTRODUCTION

Satellite remote sensing data are powerful tools for the characterization and monitoring of land use/land cover changes. As a consequence, techniques for change detection are more improved and applied to the extraction of information from landscape changes, including those methods which use the vegetation index images [1, 2], analysis by principal components [3, 4], change vector analysis [5] and more recently, the radiometric controlled rotation technique [6]. Certain factors, such as differences of atmospheric conditions and sun inclination angle at the time of sensor data acquisition, may influence on the result of detection techniques, and it is necessary to minimize these effects. The use of images from the same season, for instance, reduce the differences of the illumination angle from the scene, minimizing phenological variations of vegetation covered

areas, showing with more details the dynamics of land use. Within this context of new approaches, the objective is to show the applicability of the technique “Radiometric rotation controlled by a no-change axis” (RCEN) by multi-temporal analysis of an ETM+/LANDSAT and a CCD/CBERS-2 image, showing its´ potential to monitor the degradation and recovery of vegetation cover in the semi-arid region.

II. STUDY AREA

The selected area with 190 Km2 is located in the central of Brazilian semi-arid region (S 7°54’50” to 8°10’14” and W 39°43’12” to 39°59’26”), whose landscape is dominated by bush and tree-like vegetation of steppe savanna type (caatinga). This region presents a strong sun irradiation, intensive evapotranspiration with average temperatures around 27º. There is a great interannual rainfall variability (~600mm), with 7 to 8 months of dry season.

III. METODOLOGICAL APPROACH

For the analysis of landscape dynamics of the Brazilian semi-arid, we used ETM+/LANDSAT (Sept.10th, 2001) and CCD/CBERS-2 (Aug. 31th, 2004), both corresponding to the red spectral band (0.63-0.69 µm). A description of CBERS-2 data can be found at <http://www.cbers.inpe.br/pt/ programas/cbers1-2_cameras.htm>. For image processing we used software ERDAS Imagine 8.2 and MATLAB 6.0 for the operational development of the RCEN approach.

The digital processing of these two images was accomplished in four steps, namely: geometric correction; spatial adjustment; application of RCEN technique with slicing and labelling of the change detection image generated; accuracy analysis of the change detection map. At the first step, the geometric correction images was done using the resample technique by nearest neighbor, whose positioning

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error was less than 0.3 pixel. At the second step, ETM+/LANDSAT and CCD/CBERS-2 images were converted from an integer number to floating point. This allows degradation of spatial resolution, but keeping the radiometric information, allowing that all the numeric space for detection could be used by the algorithm of detection technique. Afterwards the adjustment of distinct spatial resolutions was done (30m of ETM+ and 20m of CCD) for a basis resolution of 60m, using integer conversion values. After these initial phases, an algorithm of radiometric rotation controlled by a no-change axis (RCEN) was applied to multi-sensor images.

The RCEN is a linear transform, which produces a rotation of the orthogonal axis of radiometric attributes of images (red band in different dates), in relation to the axis formed by the straight line of regression of those points labelled in the field survey as no-change [6]. This axis describes the correlation between pixels of no-change in the images of first and second date. In this technique the rotation angle (θ) is defined as the tangent arc of the inclination coefficient of the straight in the linear regression of no-change pixels previously identified on the terrain. The detection image is generated using the algorithm of a simple rotation, based on the formulation:

XDet = - X1 sen θ + X2 cos θ (1) where, XDet = image of change detection (2a component), X1 = image of certain spectral band at the first data, X2 = image of same spectral band at the second data, θ = rotation angle

The result of this technique is a change-detection image derived from data integration of both sensors mentioned, whose continuous image (floating-point numbers) was transformed into a thematic image. This was done by means of a technique of graphic computation which includes the calculation of thresholds between classes in a certain spatial attributes, the division of radiometric range of the image and finally, the labelling of classes with four thresholds. This allows the stratification of spectral attributes in five thematic classes: two related to degradation, two referring to regeneration and one of no-change, whose borders and respective legend are based on the knowledge of the interpreter, and on the different levels and patterns of changes of the study area. In slicing configuration, the maximum mode value of frequencies distribution of pixels represents the center of the no-change class between dates. From this mode value, the landscape changes are established, when there are 1 and 2 distance standard deviations. This is done taking account a symmetry restriction between thresholds of both degradation and regeneration classes. From this point a map of changes 2001/2004 was generated, illustrating the distribution of the five thematic categories mentioned at the caatinga landscape, which is suffering a low disturbance derived from the seasonal subsistence agriculture (beans, maize, cassava), but the extensive cattle raising activities (mainly goats). Field survey

was done in order to label and analyze the thematic trustworthiness of land cover patterns. Such information was obtained by forest inventory and also, the observation samples (with simple phyto-physiognomic descriptions), appropriately geo-referenced using GPS II Plus-Garmin. The type of vegetation recovery or degradation was defined through a combined analysis of floristic composition and of the vertical vegetation structure of sampled sites, recording the occurrence of scars of land use, as well as the degree and actuation time of human occupation process.

IV. RESULTS AND DISCUSSION

At Figure 1 the dispersion of radiometric values derived from the images combination from the first date (Sept., 10th 2001) of ETM+ and the second date (Aug., 31th 2004) of CBERS-2 is presented. At this figure one observes the regression line that corresponds to the no-change pixels of vegetation cover, as well as the rotation angle of θ = 27° needed to make the radiometric rotation of the pair of scenes, obtaining the change detection image. Applying the RCEN technique, the linear transformation is done on the radiometric spatial attributes, rotating the Cartesian axis in anti-hour sense, until axis “x” becomes parallel with the no-change axis from the angle found.

Figure 1. Scatterplot of radiometric values between ETM+/LANDSAT e CCD/CBERS-2 images (red spectral band) of 2001 and 2004, respectively.

At Figure 2 the change detection image for the timeframe

2001/2004 is presented, which results from the RCEN technique, whose thematic stratification shows recovery and degradation areas at different intensity levels, besides those areas of no-change. Considering that the area investigated is 190 km2 large, one observes that in the timeframe studied, only 1% of the area suffered under strong degradation, originated from clear-cut of caatinga for plantations or cattle

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raising. From the analysis of historical data, one finds out that there is a stabilization of the productive area occupied, because 93% of this area is inserted in the category of no-change, including similar variations between the percentages of moderate degradation and moderate recovery aspects (~3%). During the last few years this area did not suffer abrupt changes at the policy for production expansion, where land occupation is done on the traditional way, based on agriculture at the floodplains along intermittent rivers, besides pastureland for goats in the community fields. Around 85% of the area is occupied by arboreal and/or bush physiognomies of

caatinga. The accuracy of the change detection map, generated from the RCEN technique, is being investigated [7], reaching a Kappa value of 0.7 for a comparative analysis with TM/LANDSAT data (from 1984 and 2001). This thematic performance can be considered as similar to those obtained also for this new investigation period of 2001 (ETM+/LANDSAT) and 2004 (CCD/CBERS-2), supported by sampling points in the field survey done in 2002, since this area did not suffer significant changes in the land occupation, as mentioned.

Figure 2. Images of the ETM+/LANDSAT and CCD/CBERS-2 (red spectral band), with change detection map obtained by RCEN procedure.

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V. CONCLUSIONS

The use of RCEN technique to process multitemporal or multi-sensor images, reduces the previous need of more complex traditional corrections (data calibration for radiance and apparent reflectance, besides the atmospheric correction) simplifying the operational time of processing and allowing the obtainment of adequate results for change detection. It allows the treatment of multi-sensor images, with appropriate spectral and spatial compatibilities, whose data can be applied directly to cartography of recovery and degradation of semi-arid landscape and the evaluation of its’ intensity levels.

ACKNOWLEDGMENT

The authors acknowledge CNPq (grants 300677/91-0 and DTI-380233/2004-5).

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