Use of multitemporal ERS-2 SAR images for identification of burned scars in south-east Asian tropical rainforest

Evaluation of an area severely affected by fires in 1998 using a multitemporal series of ERS-2 Synthetic Aperture Radar (SAR) images showed that fire induced changes of the vegetation cover strongly affected C-band radar backscatter. We investigated the changes in radar backscatter over a period of ten months in areas of interest that represented different land-cover types at a study site in East Kalimantan, Indonesia. The impact of fire was found to cause a strong decrease in backscatter (2-5 dB) for all land-cover classes while areas not affected by fire showed only slight variations in backscatter (maximum 0.5 dB). Ground and aerial evidence suggests that the marked decrease in backscatter can be attributed to the removal of the vegetation cover and subsequently higher contribution of backscatter from dry soil. After the onset of rain the radar backscatter increased to 5.5 dB in areas severely affected by fire while in unburned forests it returned to values similar to those before the drought. Burned scars could be identified visually in multitemporal principal component analysis-enhanced ERS SAR colour composites.     

Correlation Analysis between L-band SAR and ForestStem Volume in Northeast China

Because Synthetic Aperture Radar(SAR)can penetrate into forest canopy and interact with the primary stem volume contents of the trees (trunk and branch),SAR data are widely used for forest stem volume estimation.This paper investigated the correlation between SAR data and forest stem volume in Xunke,Heilongjiang using the stand-wise forest inventory data in 2003 and ALOS PALSAR data for five dates in 2007.The influences of season and polarizations on the relationship between stem volume and SAR data were studied by analyzing the scatterplots;that was followed by interpretation of the mechanisms primarily based on a forest radar backscattering model-water cloud model.The results showed that the relationship between HV polarization backscatter and stem volume is better than HH polarization,and SAR data in summer dry conditions are more correlated to stem volume than the data acquired in other conditions.The interferometric coherence with 46-day temporal baseline is negatively correlated to the stem volume.The correlation coefficients from winter coherence are higher than those from summer coherence and backscatter.The study results suggest using the interferometric coherence in winter as the best choice for forest stem volume estimation with L-band SAR data.     

Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia

In Queensland, Australia, forest areas are discriminated from non-forest by applying a threshold (∼ 12%) to Landsat-derived Foliage Projected Cover (FPC) layers (equating to ∼ 20% canopy cover), which are produced routinely for the State. However, separation of woody regrowth following agricultural clearing cannot be undertaken with confidence, and is therefore not mapped routinely by State Agencies. Using fully polarimetric C-, L- and P-band NASA AIRSAR and Landsat FPC data for forests and agricultural land near Injune, central Queensland, we corroborate that woody regrowth dominated by Brigalow (Acacia harpophylla) cannot be discriminated using either FPC or indeed C-band data alone, because the rapid attainment of a canopy cover leads to similarities in both reflectance and backscatter with remnant forest. We also show that regrowth cannot be discriminated from non-forest areas using either L-band or P-band data alone. However, mapping can be achieved by thresholding and intersecting these layers, as regrowth is unique in supporting both a high FPC (> ∼ 12%) and C-band SAR backscatter (> ~ − 18 dB at HV polarisation) and low L-band and P-band SAR backscatter (e.g. < =∼ 14 dB at L-band HH polarisation). To provide a theoretical explanation, a wave scattering model based on that of Durden et al. [Durden, S.L., Van Zyl, J.J. & Zebker, H.A. (1989). Modelling and observation of radar polarization signature of forested areas. IEEE Trans. Geoscience and Remote Sensing, 27, 290-301.] was used to demonstrate that volume scattering from leaves and small branches in the upper canopy leads to increases in C-band backscattering (particularly HV polarisations) from regrowth, which increases proportionally with FPC. By contrast, low L-band and P-band backscatter occurs because of the lack of double bounce interactions at co-polarisations (particularly HH) and volume scattering at HV polarisation from the stems and branches, respectively, when their dimensions are smaller than the wavelength. Regrowth maps generated by applying simple thresholds to both FPC and AIRSAR L-band data showed a very close correspondence with those mapped using same-date 2.5 m Hymap data and an average 73.7% overlap with those mapped through time-series comparison of Landsat-derived land cover classifications. Regrowth mapped using Landsat-derived FPC from 1995 and JER-1 SAR data from 1994-1995 also corresponded with areas identified within the time-series classification and true colour stereo photographs for the same period. The integration of Landsat FPC and L-band SAR data is therefore expected to facilitate regrowth mapping across Queensland and other regions of Australia, particularly as Japan's Advanced Land Observing System (ALOS) Phase Arrayed L-band SAR (PALSAR), to be launched in 2006, will observe at both L-band HH and HV polarisations.     

Soil moisture limitations on monitoring boreal forest regrowth using spaceborne L-band SAR data

A study was carried out to investigate the utility of L-band SAR data for estimating aboveground biomass in sites with low levels of vegetation regrowth. Data to estimate biomass were collected from 59 sites located in fire-disturbed black spruce forests in interior Alaska. PALSAR L-band data (HH and HV polarizations) collected on two dates in the summer/fall of 2007 and one date in the summer of 2009 were used. Significant linear correlations were found between the log of aboveground biomass (range of 0.02 to 22.2 t ha^-1) and σ° (L-HH) and σ° (L-HV) for the data collected on each of the three dates, with the highest correlation found using the L-HV data collected when soil moisture was highest. Soil moisture, however, did change the correlations between L-band σ° and aboveground biomass, and the analyses suggest that the influence of soil moisture is biomass dependent. The results indicate that to use L-band SAR data for mapping aboveground biomass and monitoring forest regrowth will require development of approaches to account for the influence that variations in soil moisture have on L-band microwave backscatter, which can be particularly strong when low levels of aboveground biomass occur.     

JERS-1/SAR backscatter and its relationship with biomass of regenerating forests

It has been hypothesized that regenerating tropical forests are large atmospheric carbon sinks. Accurate estimates of the location, extent and biomass of regenerating tropical forests are needed in order to quantify their contribution to global carbon budgets. Synthetic Aperture Radar (SAR) data are independent of near-constant tropical cloud cover and have proved useful for locating and mapping the extent of regenerating tropical forests. To estimate the biomass of regenerating tropical forests we need to determine the nature and strength of the relationship between radar backscatter and biomass for different types of regenerating forest. To further investigate this, two extreme forms of regenerating forest were considered; they were block-logged (clear-cut) forest in the Tapajós area of Pará State, Brazil and selectively-logged forest in Southern Cameroon. Biomass was estimated alometrically for 15 plots in Tapajós and 34 plots in Cameroon and was related to L-band backscatter derived from the JERS-1 SAR. The relationship between backscatter and biomass was strong for the Tapajós study area and weak for the Cameroonian study area. It was concluded that there is potential for the use of JERS-1/SAR to locate, map and estimate biomass for young regenerating forests following block-logging rather than selective-logging.     

Properties of X-, C- and L-band repeat-pass interferometric SAR coherence in Mediterranean pine forests affected by fires

Synthetic Aperture Radar (SAR) data has been investigated to determine the relationship between burn severity and interferometric coherence at three sites affected by forest fires in a hilly Mediterranean environment. Repeat-pass SAR images were available from the TerraSAR-X, ERS-1/2, Envisat ASAR and ALOS PALSAR sensors. Coherence was related to measurements of burn severity (Composite Burn Index) and remote sensing estimates expressed by the differenced normalized burn ratio (dNBR) index. In addition, the effects of topography and weather on coherence estimates were assessed. The analysis for a given range of local incidence angle showed that the co-polarized coherence increases with the increase of burn severity at X- and C-band whereas cross-polarized coherence was practically insensitive to burn severity. Higher sensitivity to burn severity was found at L-band for both co- and cross-polarized channels. The association strength between coherence and burn severity was strongest for images acquired under stable, dry environmental conditions. When the local incidence angle is accounted for the determination coefficients increased from 0.6 to 0.9 for X- and C-band. At L-band the local incidence angle had less influence on the association strength to burn severity.     

ALOS/PALSAR InSAR数学模型研究

日本ALOS卫星携带的相控阵型L波段合成孔径雷达(PALSAR),因其较长的波长使得相同时间间隔内地面具有较高的相干性,因而极具InSAR应用潜力。本文首先介绍ALOS PALSAR,进而详细分析该数据在InSAR数学模型(包括距离向频谱、干涉临界基线距、模糊高度、差分相位对形变的敏感度)中的特点,并与常见的ERS SAR数据进行比较。     

Decomposition of polarimetric synthetic aperture radar backscatter from upland and flooded forests

The goal of this research was to decompose polarimetric Synthetic Aperture Radar (SAR) imagery of upland and flooded forests into three backscatter types: single reflection, double reflection, and cross-polarized backscatter. We used a decomposition method that exploits the covariance matrix of backscatter terms. First we applied this method to SAR imagery of dihedral and trihedral corner reflectors positioned on a smooth, dry lake bed, and verified that it accurately isolated the different backscatter types. We then applied the method to decompose multi-frequency Jet Propulsion Laboratory (JPL) airborne SAR (AIRSAR) backscatter from upland and flooded forests to explain scattering components in SAR imagery from forested surfaces. For upland ponderosa pine forest in California, as SAR wavelength increased from C-band to P-band, scattering with an odd number of reflections decreased and scattering with an even number of reflections increased. There was no obvious trend with wavelength for cross-polarized scattering. For a bald cypress-tupelo floodplain forest in Georgia, scattering with an odd number of reflections dominated at C-band. Scattering power with an even number of reflections from the flooded forest was strong at L-band and strongest at P-band. Cross-polarized scattering may not be a major component of total backscatter at all three wavelengths. Various forest structural classes and land cover types were readily distinguishable in the imagery derived by the decomposition method. More importantly, the decomposition method provided a means of unraveling complex interactions between radar signals and vegetated surfaces in terms of scattering mechanisms from targets. The decomposed scattering components were additions to the traditional HH and V V backscatter. One cautionary note: the method was not well suited to targets with low backscatter and a low signal-to-noise ratio.     

Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia

To evaluate the use of multi-frequency, polarimetric Synthetic Aperture Radar (SAR) data for quantifying the above ground biomass (AGB) of open forests and woodlands, NASA JPL AIRSAR (POLSAR) data were acquired over a 37 × 60 km area west of Injune, central Queensland, Australia. From field measurements recorded within 32 50 × 50 m plots, AGB was estimated by applying species-specific allometric equations to stand measurements. AGB was then scaled-up to the larger area using relationships established with Light Detection and Ranging (LiDAR) data acquired over 150 (10 columns, 15 rows) 500 × 150 m cells (or Primary Sampling Units, PSUs) spaced 4 × 4 km apart in the north- and east-west directions. Large-scale (1 : 4000) stereo aerial photographs were also acquired for each PSU to assess species composition. Based on the LiDAR extrapolations, the median AGB for the PSU grid was 82 Mg ha^− 1 (maximum 164 Mg ha^− 1), with the higher levels associated with forests containing a high proportion of Angophora and Callitris species. Empirical relationships between AGB and SAR backscatter confirmed that C-, L- and P-band saturated at different levels and revealed a greater strength in the relationship at higher incidence angles and a larger dynamic range and consistency of relationships at HV polarizations. A higher level of saturation (above ∼50 Mg ha^− 1) was observed at C-band HV compared to that reported for closed forests which was attributable to a link between foliage projected cover (FPC) and AGB. The study concludes that L-band HV backscatter data acquired at incidence angles approaching or exceeding 45° are best suited for estimating the AGB up to the saturation level of ∼80-85 Mg ha^− 1. For regional mapping of biomass below the level of saturation, the use of the Japanese Space Exploration Agency (JAXA) Advanced Land Observing Satellite (ALOS) Phase Arrayed L-band SAR (PALSAR) is advocated.     

Geo-referencing of continental-scale JERS-1 SAR mosaics based on matching homologous features with a digital elevation model: theory and practice

An effective method for a posteriori ortho-rectification of continental-scale synthetic aperture radar (SAR) mosaics using a digital elevation model (DEM) has been developed. The method is based on homologous feature matching between the DEM and a simulated SAR image. The simulated image is derived from the radar-viewing geometry, topographic information and contextual information provided by the Shuttle Radar Topography Mission (SRTM), shorelines and water bodies database (SWBD) and GeoCover Landsat mosaics. Two large L-band SAR mosaics (the global boreal forest mapping (GBFM) Siberia mosaic and the global rain forest mapping (GRFM) Africa mosaic), assembled from the Japanese Earth Resources Satellite-1 (JERS-1) data, were accurately geo-referenced and ortho-rectified. The GRFM Africa mosaic was also radiometrically corrected for topographic effects. The accurate co-registration with the DEM allows for improved classification methods based on the combination of SAR backscatter with terrain features. Comparison of the revised GBFM and GRFM mosaics with a forthcoming set of continental-scale mosaics assembled from the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data will offer a unique possibility for change detection studies over the Tropical and Boreal forest zones with a temporal spacing of some 10 years.     

Short contributions

Studies of ERS-1 synthetic aperture radar (SAR) imagery have shown that fire scars in Alaskan forests are significantly brighter (3–6 dB) than surrounding unburned forest. The signature varies seasonally and changes as vegetation re-establishes on the site over longer time periods (>5years). Additionally, it is known that soil water content typically increases following forest fires due to changes in evapotranspiration rates and melting of the permafrost.

The objective of this study was to understand the relation between soil water content and the ERS-1 SAR signature at fire-disturbed sites. To accomplish this objective, we compared soil water in six burned black spruce (Picea mariana (Mill.) B.S.P.) forest sites in interior Alaska to ERS-1 SAR backscalter measurements. The six sites are of various age since burn. Soil water was periodically measured at each site during the summer of 1992 and at one site in 1993 and 1994 when the ERS-1 imaging radar was scheduled to pass overhead. Results indicate that a positive linear relation exists between soil water content and the SAR backscatter coefficient in young burns ( < ～4years). Older burns do not show this relation, a result of vegetation establishment following the burn. This interaction between soil moisture condition and ERS-1 SAR backscatter shows great potential for measuring soil water content and monitoring seasonal variations in soil water content in black spruce sites recently disturbed by wildfire.     

Analysis of ERS SAR coherence images acquired over vegetated areas and urban features

Synthetic Aperture Radar (SAR) coherence images were analysed over vegetated areas and urban features. Coherence images were formed from interferometric SAR data acquired 1 day or 35 days apart by two European Remote sensing Satellites (ERS). Forested areas are discriminated very well from cultivated fields using 1-day SAR coherence data taken in the winter when the temperatures were below freezing. This is because fields under these conditions decorrelate much less than forest. Open sandy fields gave high coherence for both winter and summer acquisitions. All vegetated areas experienced a strong temporal decorrelation over a 35-day period. This is mainly due to changing wind, precipitation and temperature conditions, but could also be due to vegetation growth or man-made changes. Many urban objects were found to decorrelate slowly with time, regardless of changing weather conditions.     

Radar modelling of coniferous forest using a tree growth model

The use of a tree growth model to provide statistical information about the microwave scattering components of boreal-type forests (in this case, Scots pine and Norwegian spruce), as an alternative to data obtained through intensive fieldwork, is described. The total backscatter from six test stands at C- and L-band frequency for three polarization combinations (HH, VV and HV) was predicted. Differences between measured C- and L-band data from a polarimetric airborne Synthetic Aperture Radar (EMISAR) and simulated backscatter values compare favourably with previous studies, with like- and cross-polarization differences generally less than 2.5 dB. Modelled backscatter values were consistently less than those observed. A likely explanation for such a discrepancy is the unrealistic manner in which the model incorporates the spatial distribution of tree needles.     

Mapping arctic landfast ice extent using L-band synthetic aperture radar interferometry

In recent years methods have been developed to extract the seaward landfast ice edge from series of remote sensing images, with most of them relying on incoherent change detection in optical, infrared, or radar amplitude imagery. While such approaches provide valuable results, some still lack the required level of robustness and all lack the ability to fully automate the detection and mapping of landfast ice over large areas and long time spans. This paper introduces an alternative approach to mapping landfast ice extent that is based on coherent processing of interferometric L-band Synthetic Aperture Radar (SAR) data. The approach is based on a combined interpretation of interferometric phase pattern and interferometric coherence images to extract the extent and stability of landfast ice. Due to the low complexity of the base imagery used for landfast ice extraction, significant improvements in automation and reduction of required manual interactions by operators can be achieved. A performance analysis shows that L-band interferometric SAR (InSAR) data enable the mapping of landfast ice with high robustness and accuracy for a wide range of environmental conditions.     

Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use

In the context of reducing emissions from deforestation and forest degradation (REDD) and the international effort to reduce anthropogenic greenhouse gas emissions, a reliable assessment of aboveground forest biomass is a major requirement. Especially in tropical forests which store huge amounts of carbon, a precise quantification of aboveground biomass is of high relevance for REDD activities. This study investigates the potential of X- and L-band SAR data to estimate aboveground biomass (AGB) in intact and degraded tropical forests in Central Kalimantan, Borneo, Indonesia. Based on forest inventory data, aboveground biomass was first estimated using LiDAR data. These results were then used to calibrate SAR backscatter images and to upscale the biomass estimates across large areas and ecosystems. This upscaling approach not only provided aboveground biomass estimates over the whole biomass range from woody regrowth to mature pristine forest but also revealed a spatial variation due to varying growth condition within specific forest types. Single and combined frequencies, as well as mono- and multi-temporal TerraSAR-X and ALOS PALSAR biomass estimation models were analyzed for the development of accurate biomass estimations. Regarding the single frequency analysis overall ALOS PALSAR backscatter is more sensitive to AGB than TerraSAR-X, especially in the higher biomass range (> 100 t/ha). However, ALOS PALSAR results were less accurate in low biomass ranges due to a higher variance. The multi-temporal L- and X-band combined model achieved the best result and was therefore tested for its temporal and spatial transferability. The achieved accuracy for this model using nearly 400 independent validation points was r² = 0.53 with an RMSE of 79 t/ha. The model is valid up to 307 t/ha with an accuracy requirement of 50 t/ha and up to 614 t/ha with an accuracy requirement of 100 t/ha in flat terrain. The results demonstrate that direct biomass measurements based on the synergistic use of L- and X-band SAR can provide large-scale AGB estimations for tropical forests. In the context of REDD monitoring the results can be used for the assessment of the spatial distribution of the biomass, also indicating trends in high biomass ranges and the characterization of the spatial patterns in different forest types.     

Deformation mapping in three dimensions for underground mining using InSAR – Southern highland coalfield in New South Wales,Australia

This article presents 3D surface deformation mapping results derived from satellite synthetic aperture radar (SAR) data acquired over underground coal mines. Both ENVISAT Advanced Synthetic Aperture Radar (ASAR) and Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data were used in this study. The quality of the 3D deformation mapping results due to underground mining is mainly limited by two factors. (1) Differential interferometric synthetic aperture radar (DInSAR) is less sensitive to displacement along the north–south direction in the case of the current SAR satellite configurations. (2) The mining-induced displacement is continuous and nonlinear; and the accuracy of the 3D DInSAR measurement is severely affected by the similar but non-identical temporal overlaps of the InSAR pairs. The simulation and real data analyzes have shown that it would be more practical to use DInSAR pairs with the assumption of negligible northing displacement to derive the displacements in the easting and vertical directions. The northing displacement could then be estimated from the residuals. This limitation could be overcome in the future with the launch of more radar satellites, which would provide better viewing geometry.     

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Siberia's boreal forests represent an economically and ecologically precious resource, a significant part of which is not monitored on a regular basis. Synthetic aperture radars (SARs), with their sensitivity to forest biomass, offer mapping capabilities that could provide valuable up-to-date information, for example about fire damage or logging activity. The European Commission SIBERIA project had the aim of mapping an area of approximately 1 million km² in Siberia using SAR data from two satellite sources: the tandem mission of the European Remote Sensing Satellites ERS-1/2 and the Japanese Earth Resource Satellite JERS-1. Mosaics of ERS tandem interferometric coherence and JERS backscattering coefficient show the wealth of information contained in these data but they also show large differences in radar response between neighbouring images. To create one homogeneous forest map, adaptive methods which are able to account for brightness changes due to environmental effects were required. In this paper an adaptive empirical model to determine growing stock volume classes using the ERS tandem coherence and the JERS backscatter data is described. For growing stock volume classes up to 80 m³/ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m.     

Mapping land cover types in the Amazon Basin using 1 km JERS-1 mosaic

During the Global Rain Forest Mapping (GRFM) project, the JERS-1 SAR (Synthetic Aperture Radar) satellite acquired wall-to-wall image coverage of the humid tropical forests of the world. The rationale for the project was to demonstrate the application of spaceborne L-band radar in tropical forest studies. In particular, the use of orbital radar data for mapping land cover types, estimating the area of floodplains, and monitoring deforestation and forest regeneration were of primary importance. In this paper we examine the information content of the JERS-1 SAR data for mapping land cover types in the Amazon basin. More than 1500 high-resolution (12.5 m pixel spacing) images acquired during the low flood period of the Amazon river were resampled to 100 m resolution and mosaicked into a seamless image of about 8 million km², including the entire Amazon basin. This image was used in a classifier to generate a 1 km resolution land cover map. The inputs to the classifier were 1 km resolution mean backscatter and seven first-order texture measures derived from the 100 m data by using a 10 x 10 independent sampling window. The classification approach included two interdependent stages. First, a supervised maximum a posteriori Baysian approach classified the mean backscatter image into five general cover categories: terra firme forest (including secondary forest), savanna, inundated vegetation, open deforested areas and open water. A hierarchical decision rule based on texture measures was then applied to attempt further discrimination of known subcategories of vegetation types based on taxonomic information and woody biomass levels. True distributions of the general categories were identified from the RADAMBRASIL project vegetation maps and several field studies. Training and validation test sites were chosen from the JERS-1 image by consulting the RADAM vegetation maps. After several iterations and combining land cover types, 14 vegetation classes were successfully separated at the 1 km scale. The accuracy of the classification methodology was estimated to be 78% when using the validation sites. The results were also verified by comparison with the RADAM- and AVHRR-based 1 km resolution land cover maps.     

Analysis of L-band SAR backscatter and coherence for delineation of land-use/land-cover

In this study, we investigated the potential improvement of land-use/land-cover (LU/LC) classification using multidate backscatter intensity as well as interferometric coherence images derived from Advanced Land Observing Satellite phased array L-band synthetic aperture radar data. Four interferometric synthetic aperture radar data pairs in horizontal–horizontal polarizations were processed to obtain backscatter intensity and coherence images. From the analysis of these images, it was observed that backscatter values alone are not sufficient to separate certain LU/LC classes, e.g. forest and mining areas, due to similarities in the associated scattering mechanisms producing similar backscatter values. However, the temporal coherence values from these LU/LC features were found to be distinctly different. Supervised classifications using maximum-likelihood distance were performed with various combinations of data (three-date backscatter intensity and two-date backscatter intensity with corresponding coherence data) to generate LU/LC maps of the study area. The comparison of classification accuracies obtained for different combinations of data indicates that the classification accuracy is improved by adding coherence information to the backscatter intensity data compared to using the multidate backscatter intensity data alone. Thus, the analysis of backscatter intensity along with coherence is a better alternative than using backscatter intensity alone to improve the accuracy in LU/LC classification.     

Surface displacement of the Mw 7 Machaze earthquake (Mozambique): Complementary use of multiband InSAR and radar amplitude image correlation with elastic modelling

In this paper we investigate the surface displacement related to the 2006 Machaze earthquake using Synthetic Aperture Radar Interferometry (InSAR) and sub-pixel correlation (SPC) of radar amplitude images. We focus on surface displacement measurement during three stages of the seismic cycle. First, we examined the co-seismic stage, using an Advanced SAR (ASAR) sensor onboard the Envisat satellite. Then we investigated the post-seismic stage using the Phase Array L-band SAR sensor (PALSAR) onboard the ALOS satellite. Lastly, we focussed on the inter-seismic stage, prior to the earthquake by analysing the L-band JERS-1 SAR data. The high degree of signal decorrelation in the C-band co-seismic interferogram hinders a correct positioning of the surface rupture and correct phase unwrapping. The post-seismic L-band interferograms reveal a time-constant surface displacement, causing subsidence of the surface at a ∼ 5 cm/yr rate. This phenomenon continued to affect the close rupture field for at least two years following the earthquake and intrinsically reveals a candidate seismogenic fault trace that we use as a proxy for an inversion against an elastic dislocation model. Prior to the earthquake, the JERS interferograms do not indicate any traces of pre-seismic slip on the seismogenic fault. Therefore, slip after the earthquake is post-seismic, and it was triggered by the Machaze earthquake. This feature represents a prominent post-seismic slip event rarely observed in such a geodynamic context.