Detection and monitoring of sand dune mobility in southeast Niger using multitemporal

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1 Detection and monitoring of sand dune mobility in southeast Niger using multitemporal coherence images Bodart Catherine (1) (2), Derauw Dominique (3), Ozer André (2) (1) FNRS Research Fellow - Ph.D. Student (2) Geomorphology and Remote Sensing Laboratory, Unit of Physical Geography, University of Liège (3) Centre spatial de Liège, Belgium

2 Study area Inter-dune basins in southeast Niger Large sandy plateau peppered with small deep inter-dune depressions roughly circular and elongated dips. Semi-arid climate with an average annual rainfall between 200 and 400 mm. Climatic variation Dramatic drought started since the late 1960s Ended in the late 1980s. However, the end of the drought did not lead to any landscape improvement. Reactivation of sand dune Reactivation of old dunes fixed by vegetation since the Holocene period > Drought + trampling, overgrazing, unsustainable agricultural practices, collecting wood for fuel, expanding urban areas, etc. > Desertification indicator 2

3 Coherence image Useful in many applications such as image classification Local phase correlation between two complex SAR images γ = c N N n=1 y (n) 1 y N (n) 2 y1 n=1 n=1 *(n) 2 y (n) 2 2 Two different types of temporal decorrelation factors a permittivity change or a geometry change of the scatterers between the two acquisitions In a semi-arid region, the InSar signal coherence provides a reliable detection of random change on the land surface as it is mainly driven by the position change of the scatterers within a resolution cell. Examine the potential of SAR interferometry coherence as a useful tool for the study of aeolian transport processes in south-east Niger 3

4 Data set 3 different tracks 23 SLC images ERS ½ 7 tandem ERS ½ image pairs acquired between December 95 and February 99 Envisat-ASAR :13 programmed IMS images + Landsat 7 ETM and Spot 5 high-resolution optical data 4

5 Processings InSAR processing, including georeferencement, was performed using the processor of the "Centre Spatial de Liège" Calibration Co-registration at sub-pixel level in three separated data sets corresponding to the three different ERS tracks (451, 179 and 408) Georeferenciation to a 40x40 m pixel size (Poster: D.Derauw) For each tandem pair, a coherence image and two corresponding amplitude images were produced. 5

6 Results ERS ½ tandem coherence Sand dune movements - Vegetation movements -Human activities 6

7 Interpretations Detection of mobile dunes Amplitude image + Coherence image Coherence image derived from ERS ½ SAR tandem pair of 6-7 April 96 Amplitude image -6 April 96 Color composite of coherence, amplitude -6 April 96 and amplitude -7 April 96 in RGB Landsat 7 ETM+ image 17 nov 2000 Active sand dunes (dark colours) Coherence: low Backscattering: low Land surface change in 24 hours Low rugosity Inter-dune basins (blue green colours) Coherence: low Land surface change in 24 hours Backscattering: High High rugosity (high density of vegetation) R: the coherence image G: the amplitude image of the first acquisition B: the amplitude image of the second acquisition 7

8 Interpretations Coherence image derived from ERS ½ SAR tandem pair of April 96 amplitude image -25 April 96 Color composite of coherence, amplitude -25 April 96 and amplitude -26 April 96 in RGB Landsat 7 ETM+ image 8 nov 2000 Human activities (blue green colors) Coherence: low Land surface change in 24 hours Backscattering: High High rugosity Ring of sand dune reworking linked to the urban area extension Straight line : result of anthropogenic disturbance between the two acquisitions - track trampled by livestock or human people 8

9 ERS ½ tandem coherence Distribution of sand dunes movements that occurred in a very short period of time. Mainly due to the wind that generates sand movements, even when the dune might remain static as a whole. Limited to the pixel resolution size 9

10 Results Monitoring of sand mobility Confrontation of multi-temporal tandem coherence images to monitor the dune mobility and detect the possible dune migration between acquisitions R: the coherence image December 95 G: the coherence image April 96 B: the amplitude image December 95 Progression of active sand dunes in the wind main direction of this region that is from north-east to south-west 10

11 Coherence evaluation Despite of no temporal changes occurring between the two pair acquisitions, an increase of the baseline may induce a decrease of the coherence values γ total = γ spatial x γ temporal x γ thermal Date 7/8-dec-95 25/26-avril-96 6/7-avril-96 7/8-nov-98 4/5-dec-95 22/23-avril-96 1/2-fevrier-99 Track Baseline (m) γ spatial γ spatiale = 1 - γ average B R y λ r cos 2 θ Zebker 1992 γ average / γ spatial

12 Image classification Identification of active sand zones sorted by the coherence and the amplitude image (visual threshold) Beforehand: Calibration of amplitude images Removal of spatial decorrelation Quantitative measurement of active dunes surfaces and surfaces changes between two acquisitions Identification and distribution of sensitive zones 12

13 Envisat-ASAR images Coherence from pairs with more than 35 days of temporal separation Average coherence very low for the whole area With the increase of temporal separation, the coherence decreases and the nature of changes is more difficult to interpret Little relevance for mapping and studying sand dunes dynamic by interferometric coherence 13

14 Concluding remarks Coherence imagery provides us with original and unique information compared with traditional optical and radar imagery. It is thus a useful tool for the mapping of dune activity Combined with amplitude images, coherence images derived from ERS ½ SAR tandem pair allow us to detect and locate active sand dune on the whole study area The results of the confrontation of multi-temporal tandem coherence images are innovative. The temporal progression of active sand dunes could be observed in the wind main direction. Relatively scarce SAR data suitable for tandem interferometry are available on the edge of the Sahara desert. This is the major limitation for more expanded researches. TerreSAR X/CosmoSkyMED? 14

15 We thank ESA for ERS SAR data and Envisat-ASAR data provided under the FADA project: Fight Against Desertification in West Africa