This research focused on the quantitative assessment of the surface deformation velocities and rates and their natural and man-made controlling factors at Tengiz Oilfield in Kazakhstan using the Small Baseline Subset remote sensing technique followed by 3D and 2D decompositions and cosine corrections to derive vertical and horizontal movements from line-of-sight (LOS) measurements. In the present research we applied time-series of Sentinel-1 satellite images acquired during 2018–2020. All ground deformation derivatives showed the continuous subsidence at the Tengiz oilfield with increasing velocity. 3D and 2D decompositions of LOS measurements to vertical movement showed that the Tengiz Oil Field 2018–2020 continuously subsided with the maximum annual vertical deformation velocity around 70 mm. Based on the LOS measurements, the maximum annual subsiding velocity was observed to be 60 mm. Cosine corrections of LOS measurements to vertical movement, however, revealed a maximum annual vertical deformation velocity of 77 mm. The vertical deformation confirmed typical patterns of subsidence caused by oil extraction. Detected east-west and north-south horizontal movements at the Tengiz field clearly indicated that the study area crossed by seismic faults is affected by natural tectonic processes. The overall RMSE of 3D decomposed vertical deformation in relationship to LOS measurements and cosine corrections were in the range of 10–13 mm and 6–8 mm, correspondingly. The results of the present research will support operators of oil and gas fields and also other types of infrastructure to evaluate the actual differences of InSAR ground deformation measurements against the required standards and the precision of measurements depending on the operational needs, timeframes and availability of radar imagery.
The main goals of this study are the objectbased land-cover classification of LANDSAT-8 satellite imagery of 2014 and 2015, the quantitative assessment of gross and net changes of agricultural land, built-up areas, forest, bare soil and forest between 2014 and 2015, the quantification of the Normalized Difference Vegetation Index (NDVI) rates within these land-cover classes, and the change detection analysis between the NDVIs. The achieved overall accuracies of object-based classification for the 2014 and the 2015 land-cover maps were 82 and 87 %, respectively. Therefore, the achieved accuracies were considered to be acceptable for quantified change detection analyses. For the gross areas of agricultural land, forest and built-up areas an increase was observed. The agricultural gross area was 30,911 km 2 in 2014 and 31,999 km 2 in 2015. The gross area of the built-up land increased from 12,550 to 13,548 km 2. The gross area of forest land changed from 8211 to 9175 km 2. A decrease was observed in the gross area of grassland from 28,229 to 24,925 km 2. This was primarily related to the land-cover shifts driven by agricultural activities. The gross areas of Keywords Land-cover Á Change detection Á Gross and net change Á LANDSAT-8 (OLI) Á Segmentation Á Classification & Emil Bayramov
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.