Your search found 5 records
1 Liu, D.; Wang, X.; Aminjafari, S.; Yang, W.; Cui, B.; Yan, S.; Zhang, Y.; Zhu, J.; Jaramillo, F. 2020. Using InSAR [Interferometric Synthetic Aperture Radar] to identify hydrological connectivity and barriers in a highly fragmented wetland. Hydrological Processes, 14p. (Online first) [doi: https://doi.org/10.1002/hyp.13899]
Wetlands ; Hydrological factors ; SAR (radar) ; Radar imagery ; Water levels ; Satellites ; Remote sensing ; Interferometry ; Barriers ; Ecosystems ; Grasslands ; Vegetation / China / Baiyangdian Wetland
(Location: IWMI HQ Call no: e-copy only Record No: H049975)
https://onlinelibrary.wiley.com/doi/epdf/10.1002/hyp.13899
https://vlibrary.iwmi.org/pdf/H049975.pdf
https://vlibrary.iwmi.org/pdf/H049975.pdf
(3.71 MB) (3.71 MB)
Hydrological connectivity is a critical determinant of wetland functions and health, especially in wetlands that have been heavily fragmented and regulated by human activities. However, investigating hydrological connectivity in these wetlands is challenging due to the costs of high-resolution and large-scale monitoring required in order to identify hydrological barriers within the wetlands. To overcome this challenge, we here propose an interferometric synthetic aperture radar (InSAR)-based methodology to map hydrologic connectivity and identify hydrological barriers in fragmented wetlands. This methodology was applied along 70 transects across the Baiyangdian, the largest freshwater wetland in northern China, using Sentinel 1A and 1B data, covering the period 2016–2019. We generated 58 interferograms providing information on relative water level changes across the transects that showed the high coherence needed for the assessment of hydrological connectivity. We mapped the permanent and conditional (temporary) barriers affecting connectivity. In total, 11% of all transects are permanently disconnected by hydrological barriers across all interferograms and 58% of the transects are conditionally disconnected. Areas covered by reed grasslands show the most undisturbed hydrological connectivity while some of these barriers are the result of ditches and channels within the wetland and low water levels during different periods of the year. This study highlights the potential of the application of Wetland InSAR to determine hydrological connectivity and location of hydrological barriers in highly fragmented wetlands, and facilitates the study of hydrological processes from large spatial scales and long-time scales using remote sensing technique.
2 Chaudhary, A.; Agarwal, N.; Sharma, R.; Ojha, S. P.; Kumar, R. 2021. Nadir altimetry vis-a-vis swath altimetry: a study in the context of SWOT mission for the Bay of Bengal. Remote Sensing of Environment, 252:112120. [doi: https://doi.org/10.1016/j.rse.2020.112120]
Satellite observation ; Altimeters ; Oceans ; Sea level ; Models ; Simulation ; SAR (radar) ; Interferometry ; Mapping / South Asia / Bay of Bengal
(Location: IWMI HQ Call no: e-copy only Record No: H050098)
(17.70 MB)
Conventional nadir looking altimeters make along track measurements on a line and mapped sea level anomaly (SLA) information is obtained using a combination of several such altimeters (Jason, SARAL, Cryosat etc.). Mapping techniques, in general, introduce a lot of uncertainties in sea level representation and sub-mesoscale variability. Surface Water and Ocean Topography (SWOT) mission, based on radar interferometry, will measure SLA along wide swath thus providing detailed ocean information. This study aims to evaluate the advantages of SWOT measurements over nadir looking altimeters by making use of SWOT-simulator tool in the Bay of Bengal (BoB) region. Although, BoB is a small basin but interestingly it is full of mesoscale and sub-mesoscale features. The study performs several sensitivity experiments to allow a comparison of gridded SLA product from SWOT with the product from a constellation of nadir altimeters. Space-time scales for mapping the SLA from SWOT were obtained by performing a series of sensitivity experiments involving different spatial resolutions and temporal sampling. Sensitivity to different type of errors on the quality of mapped SLA fields from nadir-altimeters and SWOT is also carried out. In case of SWOT, mapped SLA fields generated using correlated noise were better as compared to the maps that were generated by making an assumption that the noise is uncorrelated. It is found that gridded SLA from SWOT have less error in the eddy dominant (high variability) regions as compared to the mapped SLA field from nadir altimeters, which perform better in the regions of low SLA variability. Apart from this, the position and strength of mesoscale eddies is well resolved by SWOT-mapped SLA fields as compared to nadir-altimeter mapped fields.
3 Sarkar, T.; Karunakalage, Anuradha; Kannaujiya, S.; Chaganti, C. 2022. Quantification of groundwater storage variation in Himalayan & Peninsular river basins correlating with land deformation effects observed at different Indian cities. Contributions to Geophysics and Geodesy, 52(1):1-56. [doi: https://doi.org/10.31577/congeo.2022.52.1.1]
Groundwater ; Water storage ; River basins ; Observation ; Towns ; Global positioning systems ; SAR (radar) ; Precipitation ; Drought ; Rain ; Aquifers ; Time series analysis ; Models / India
(Location: IWMI HQ Call no: e-copy only Record No: H051083)
(16.80 MB) (16.8 MB)
Groundwater is a significant resource that supports almost one-fifth population globally, but has been is diminishing at an alarming rate in recent years. To delve into this objective more thoroughly, we calculated interannual (2002–2020) GWS (per grid) distribution using GRACE & GRACE-FO (CSR-M, JPL-M and SH) Level 3 RL06 datasets in seven Indian river basins and found comparatively higher negative trends (-20.10 ± 1.81 to -8.60 ± 1.52 mm/yr) in Basin 1–4 than in Basin 5–7 (-7.11 ± 0.64 to -0.76 ± 0.47 mm/yr). After comparing the Groundwater Storage (GWS) results with the CHIRPS (Climate Hazards Group Infrared Precipitation with Stations) derived SPI (Standardized Precipitation Index) drought index, we found that GWS exhausts analogously in the same period (2005–2020) when SPI values show improvement (~ 1.89–2), indicating towards wet condition. Subsequently, the GWSA time series is decomposed using the STL (Seasonal Trend Decomposition) (LOESS Regression) approach to monitor long-term groundwater fluctuation. The long term GWS rate (mm/yr) derived from three GRACE & GRACE-FO solutions vary from -20.3 ± 5.52 to -13.19 ± 3.28 and the GWS mass rate (km3 /yr) lie in range of -15.17 ± 4.18 to -1.67 ± 0.49 for basins 1–3. Simultaneously, in basin 4–7 the GWS rate observed is -8.56 ± 8.03 to -0.58 ± 7.04 mm/yr, and the GWS mass rate differs by -1.71 ± 0.64 to -0.26 ± 3.19 km3 /yr. The deseasonalized GWS estimation (2002–2020) states that Himalayan River basins 1,2,3 exhibit high GWS mass loss (-260 to -35.12 km3 ), with Basin 2 being the highest (-260 km3 ). Whereas the Peninsular River basin 4,6,7 gives moderate mass loss value from -26.72 to -23.58 km3 . And in River basin 5, the GWS mass loss observed is the lowest, with a value of -8 km3 . Accordingly, GPS (Global Positioning System) and SAR (Synthetic Aperture Radar) data are considered to examine the land deformation as an effect due to GWS mass loss. The GPS data acquired from two IGS stations, IISC Bengaluru and LCK3 Lucknow, negatively correlates with GWS change, and the values are ~ -0.90 to ~-0.21 and ~-0.7 to -0.4, respectively. Consequently, correlation between GWS mass rate (km3 /yr) and the SAR (Sentinel-1A, SBAS) data procured from Chandigarh, Delhi, Mehsana, Lucknow, Kolkata and Bengaluru shows ~ 72 – 48% positively correlated area (PCA). The vertical velocity ranges within ~ -94 to -25 mm/yr estimated from PCA. There is an increase in population (estimated 2008–2014) in Basin 1 & 2. Likewise, the correlation coefficient ( ) between GWS change and the irrigational area is positive in all seven basins indicating significant depletion in GWS due to an uncalled hike in population or irrigational land use. Similarly, the positive linear regression (R 2 ) in Basins 1–3 also indicates high depletion in GWS. But basins 4–7 observe negative linear regression even after increasing population, which implies a control on the irrigational land use, unable to determine the GWS change at local scale and heterogeneous aquifer distribution. Therefore, if such unsystematic groundwater storage variation is not controlled on time, then very soon in the future, India might reach a deadlock state of water shortage.
4 Bekele, Tilaye Worku; Haile, Alemseged Tamiru; Trigg, M. A.; Walsh, C. L. 2022. Evaluating a new method of remote sensing for flood mapping in the urban and peri-urban areas: applied to Addis Ababa and the Akaki Catchment in Ethiopia. Natural Hazards Research, 2(2):97-110. [doi: https://doi.org/10.1016/j.nhres.2022.03.001]
Flooding ; Mapping ; Remote sensing ; Urban areas ; Periurban areas ; Catchment areas ; Satellite imagery ; Polarization ; SAR (radar) ; Datasets ; Land use ; Land cover / Ethiopia / Addis Ababa / Akaki Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H051312)
https://www.sciencedirect.com/science/article/pii/S2666592122000130/pdfft?md5=33390119e761bbcbf93233450d6d72df&pid=1-s2.0-S2666592122000130-main.pdf
https://vlibrary.iwmi.org/pdf/H051312.pdf
https://vlibrary.iwmi.org/pdf/H051312.pdf
(7.90 MB) (7.90 MB)
The Sentinel-1 SAR dataset provides the opportunity to monitor floods at unprecedentedly high spatial and temporal resolutions. However, the accuracy of the flood maps can be affected by the image polarization, the flood detection method used, and the reference data. This research compared change detection and histogram thresholding methods using co-polarization (VV) and cross-polarization (VH) images for flood mapping in the Akaki catchment, Ethiopia, where Addis Ababa city is located. Reference data for the accuracy assessment were collected on the satellite overpass date. A new method, Root of Normalized Image Difference (RNID), has been developed for change detection. Multi-temporal flood maps using the best performing method and image polarization were generated from April to November of 2017–2020. Better accuracy was observed when using the RNID method on the VH polarization image with an overall accuracy of 95% and a kappa coefficient of 0.86. Results showed that flooding in the Akaki commonly begins in May and recedes in November, but flooding was most frequent and widespread from June to September. Irrigated land and built-up area accounted for 1057 ha and 544 ha of the inundated area, respectively. Several major roads in the study area were also affected by the floods during this period. Our findings indicate that the S-1 images were very useful for flood inundation mapping, the new change detection method (RNID) performed better in urban and peri-urban flood mapping, but the accuracy of the flood map significantly varied with the flood detection method and the image polarization.
5 Bunting, P.; Rosenqvist, A.; Hilarides, L.; Lucas, R. M.; Thomas, N.; Tadono, T.; Worthington, T. A.; Spalding, M.; Murray, N. J.; Rebelo, Lisa-Maria. 2022. Global mangrove extent change 1996–2020: Global Mangrove Watch version 3.0. Remote Sensing, 14(15):3657. (Special issue: Advances in Remote Sensing of Land-Sea Ecosystems) [doi: https://doi.org/10.3390/rs14153657]
Mangroves ; Ecosystems ; Datasets ; Coastal erosion ; Time series analysis ; Estimation ; Landsat ; Satellite imagery ; SAR (radar) ; Observation ; Mapping
(Location: IWMI HQ Call no: e-copy only Record No: H051368)
https://www.mdpi.com/2072-4292/14/15/3657/pdf?version=1660028312
https://vlibrary.iwmi.org/pdf/H051368.pdf
https://vlibrary.iwmi.org/pdf/H051368.pdf
(12.00 MB) (12.0 MB)
Mangroves are a globally important ecosystem that provides a wide range of ecosystem system services, such as carbon capture and storage, coastal protection and fisheries enhancement. Mangroves have significantly reduced in global extent over the last 50 years, primarily as a result of deforestation caused by the expansion of agriculture and aquaculture in coastal environments. However, a limited number of studies have attempted to estimate changes in global mangrove extent, particularly into the 1990s, despite much of the loss in mangrove extent occurring pre-2000. This study has used L-band Synthetic Aperture Radar (SAR) global mosaic datasets from the Japan Aerospace Exploration Agency (JAXA) for 11 epochs from 1996 to 2020 to develop a long-term time-series of global mangrove extent and change. The study used a map-to-image approach to change detection where the baseline map (GMW v2.5) was updated using thresholding and a contextual mangrove change mask. This approach was applied between all image-date pairs producing 10 maps for each epoch, which were summarised to produce the global mangrove time-series. The resulting mangrove extent maps had an estimated accuracy of 87.4% (95th conf. int.: 86.2–88.6%), although the accuracies of the individual gain and loss change classes were lower at 58.1% (52.4–63.9%) and 60.6% (56.1–64.8%), respectively. Sources of error included misregistration in the SAR mosaic datasets, which could only be partially corrected for, but also confusion in fragmented areas of mangroves, such as around aquaculture ponds. Overall, 152,604 km2 (133,996–176,910) of mangroves were identified for 1996, with this decreasing by -5245 km2 (-13,587–1444) resulting in a total extent of 147,359 km2 (127,925–168,895) in 2020, and representing an estimated loss of 3.4% over the 24-year time period. The Global Mangrove Watch Version 3.0 represents the most comprehensive record of global mangrove change achieved to date and is expected to support a wide range of activities, including the ongoing monitoring of the global coastal environment, defining and assessments of progress toward conservation targets, protected area planning and risk assessments of mangrove ecosystems worldwide.
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