scholarly journals Grounding and Calving Cycle of Mertz Ice Tongue Revealed by Shallow Mertz Bank

2016 ◽  
Author(s):  
Xianwei Wang ◽  
David M. Holland ◽  
Xiao Cheng ◽  
Peng Gong
Keyword(s):  
Remote Sensing ◽  
Flow Direction ◽  
Surface Condition ◽  
Remote Sensing Data ◽  
Seafloor Topography ◽  
Ice Flow ◽  
Air Content ◽  
Time Period ◽  
Level Data ◽  
Increasing Trend

Abstract. A recent study, using remote sensing, provided some evidence that a seafloor shoal influenced the 2010 calving event of the Mertz Ice Tongue (MIT), by partially grounding the MIT several years earlier. In this paper, we propose a method to calculate firn air content (FAC) around Mertz from seafloor-touching icebergs. Our calculations indicate the FAC around Mertz region as 4.87 ± 1.31 m. We design an indirect method of using freeboard and sea level data extracted from ICESat/GLAS, FAC, and highly accurate seafloor topography to detect grounding sections of the MIT between 2002 and 2008 and analyze the process of grounding before the calving. By synthesizing remote sensing data, we point out that the grounding position was just localized northeast of the Mertz ice front close to the Mertz Bank. The grounding outlines of the tongue caused by the Mertz Bank are extracted as well, however the length is only limited in several kilometers since late 2002. From 2002 to 2008, the grounding area increased and the grounding became more pronounced. Additionally, the ice tongue could not climb over the Mertz Bank in following the upstream ice flow direction and that is why MIT rotated clockwise after late 2002. Furthermore, we demonstrate that the area-increasing trend of the MIT changed little after calving (~36 km2/a), thus allowing us to use remote sensing to estimate the elapsed time until the MIT can reground on the shoal. This time period is approximately 70 years. The calving of MIT can be repeatable because of the shallow Mertz Bank and the calving cycle of the MIT explains the cycle of sea-surface condition change around Mertz. Keywords: Mertz Ice Tongue, Firn air content, iceberg grounding, Mertz Bank, iceberg scouring, calving cycle.

scholarly journals Grounding and calving cycle of Mertz Ice Tongue revealed by shallow Mertz Bank

2016 ◽  
Vol 10 (5) ◽  
pp. 2043-2056 ◽  
Author(s):  
Xianwei Wang ◽  
David M. Holland ◽  
Xiao Cheng ◽  
Peng Gong
Keyword(s):  
Remote Sensing ◽  
Flow Direction ◽  
Remote Sensing Data ◽  
Sea Surface ◽  
Seafloor Topography ◽  
Ice Flow ◽  
Air Content ◽  
Cyclic Variations ◽  
Cyclical Process ◽  
Height Data

Abstract. A recent study, using remote sensing, provided evidence that a seafloor shoal influenced the 2010 calving event of the Mertz Ice Tongue (MIT), by partially grounding the MIT several years earlier. In this paper, we start by proposing a method to calculate firn air content (FAC) around Mertz from seafloor-touching icebergs. Our calculations indicate the FAC around Mertz region as 4.87 ± 1.31 m. We then design an indirect method of using freeboard and sea surface height data extracted from ICESat/GLAS, FAC, and relatively accurate seafloor topography to detect grounding sections of the MIT between 2002 and 2008 and analyze the process of grounding prior to the calving event. By synthesizing remote sensing data, we point out that the grounding position was localized northeast of the Mertz ice front close to the Mertz Bank. The grounding outlines of the tongue caused by the Mertz Bank are extracted as well. From 2002 to 2008, the grounding area increased and the grounding became more pronounced. Additionally, the ice tongue could not effectively climb over the Mertz Bank in following the upstream ice flow direction and that is why MIT rotated clockwise after late 2002. Furthermore, we demonstrate that the area-increasing trend of the MIT changed little after calving (∼  36 km2 a−1), thus allowing us to use remote sensing to estimate the elapsed time until the MIT can reground on and be bent by the shoal. This period is approximately 70 years. Our observations suggest that the calving of the MIT is a cyclical process controlled by the presence of the shallow Mertz Bank location and the flow rate of the tongue. This calving cycle also explains the cyclic variations in sea-surface conditions around the Mertz detected by earlier studies.

Soil Moisture Inversion Based on AMSR-E and MODIS Data Fusion: A Case Study of Huaihe River Basin

2014 ◽  
Vol 716-717 ◽  
pp. 1064-1067
Author(s):  
Jing Wen Xu ◽  
Yu Peng Wang ◽  
Jun Fang Zhao ◽  
Fei Yu Pu ◽  
Peng Wang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Data Fusion ◽  
River Basin ◽  
Surface Condition ◽  
Remote Sensing Data ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Sensing Data ◽  
Modis Data

In this paper, the correlation between fused data and original data, the measured soil and the precipitation data over Huaihe river basin by exploring the inversion of soil moisture from the time and space based on the method of multi-source remote sensing data fusion has been studied. In order to fuse the AMSR-E data which is all-day and all-weather and can penetrate the earth surface to some extent, with the MODIS data that can reflect the surface condition and temperature characteristics, the method of wavelet fusion was carried out in MATLAB. The conclusions of this study are listed as follows: (1) the inversion result of the fused data based on AMSE-E and MODIS is much better than a single remote sensing data inversion; (2) the fused data based on AMSE-E and MODIS is sensitive to soil moisture change trend when the seasons alternated every year, especially in the spring, summer and autumn.

scholarly journals Modeling soil erosion and deposition within a Mediterranean mountainous environment utilizing remote sensing and GIS – Wadi Tlata, Morocco

2008 ◽  
Vol 63 (1) ◽  
pp. 36-47 ◽  
Author(s):  
H. Chen ◽  
L. A. Lewis ◽  
A. El Garouani
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Soil Loss ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Erosion And Deposition ◽  
Critical Areas ◽  
Time Period ◽  
Decision Making Processes ◽  
Olive Trees

Abstract. This article presents the results of the GIS-based analysis of four Landsat and Spot images covering a fifteen year period (1987, 1994, 2000, 2002). The purpose of the study was to establish a means of rapidly determining land cover and land use changes, as well as spatial patterns of erosion and deposition, in areas with relatively poor data bases and where soil loss results primarily from nonchannelized flows. The procedure selected involved the following: establishment of land use class distribution and size for each year of observation, static estimation of soil loss, calculation of net erosion and deposition, and prioritisation of critical areas. Thus, for the targeted 123 km2 Tlata catchment of northeastern Morocco, six main land use classes could be defined (highly degraded lands, annual cereal crops fields, mixed farmlands, olive trees, reforested areas, and natural protected forest). Analysis of remote sensing data allowed establishment of the areal distribution of each land use class for each year. Soil loss was estimated using a RUSLE module integrated in a GIS framework. These static areal estimates of soil loss were then fed into a sedimentation algorithm that models downslope movement of soil loss. From the resulting spatial (flow) movements, net erosion and deposition for each time period could be estimated. The results permit, at the least, an ordinal ranking of erosion and deposition within the basin. This supports decision-making processes on prioritization of areas where interventions are needed to ameliorate or prevent land degradation.

scholarly journals R/UAStools::plotshpcreate: Create Multi-Polygon Shapefiles for Extraction of Research Plot Scale Agriculture Remote Sensing Data

2020 ◽  
Author(s):  
Steven L. Anderson ◽  
Seth C. Murray
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Remote Sensing Data ◽  
Research Field ◽  
Current Data ◽  
Specific Information ◽  
Field Orientation ◽  
Level Data ◽  
Small Plot ◽  
Aerial Systems

AbstractAgricultural researchers are embracing remote sensing tools to phenotype and monitor agriculture crops. Specifically, large quantities of data are now being collected on small plot research studies using Unoccupied Aerial Systems (UAS, aka drones), ground systems, or other technologies but, data processing and analysis lags behind. One major contributor to current data processing bottlenecks has been the lack of publicly available software tools tailored towards remote sensing of small plots, and usability for researchers inexperienced in remote sensing. To address these needs we created plot shapefile maker (R/UAS::plotshpcreate), an open source R function which rapidly creates ESRI polygon shapefiles to the desired dimensions of individual agriculture research plots areas of interest and associates plot specific information. Plotshpcreate was developed to utilize inputs containing experimental design, field orientation, and plot dimensions for easily creating a multi-polygon shapefile of an entire small plot experiment. Output shapefiles are based on the user inputs geolocation of the front left corner of the research field ensuring accurate overlay of polygons often without manual user adjustment. The output shapefile is useful in GIS software to extract plot level data tracing back to the unique IDs of the experimental plots. Plotshpcreate is available on GitHub (https://github.com/andersst91/UAStools).

scholarly journals An Ontology-Based Framework for Complex Urban Object Recognition through Integrating Visual Features and Interpretable Semantics

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xiao Xie ◽  
Xiran Zhou ◽  
Jingzhong Li ◽  
Weijiang Dai
Keyword(s):  
Remote Sensing ◽  
Object Recognition ◽  
Probabilistic Models ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Low Level ◽  
Level Data ◽  
Major Disadvantage ◽  
High Level

Although previous works have proposed sophisticatedly probabilistic models that has strong capability of extracting features from remote sensing data (e.g., convolutional neural networks, CNN), the efforts that focus on exploring the human’s semantics on the object to be recognized are required more explorations. Moreover, interpretability of feature extraction becomes a major disadvantage of the state-of-the-art CNN. Especially for the complex urban objects, which varies in geometrical shapes, functional structures, environmental contexts, etc, due to the heterogeneity between low-level data features and high-level semantics, the features derived from remote sensing data alone are limited to facilitate an accurate recognition. In this paper, we present an ontology-based methodology framework for enabling object recognition through rules extracted from the high-level semantics, rather than unexplainable features extracted from a CNN. Firstly, we semantically organize the descriptions and definitions of the object as semantics (RDF-triple rules) through our developed domain ontology. Secondly, we exploit semantic web rule language to propose an encoder model for decomposing the RDF-triple rules based on a multilayer strategy. Then, we map the low-level data features, which are defined from optical satellite image and LiDAR height, to the decomposed parts of RDF-triple rules. Eventually, we apply a probabilistic belief network (PBN) to probabilistically represent the relationships between low-level data features and high-level semantics, as well as a modified TanH function is used to optimize the recognition result. The experimental results on lacking of the training process based on data samples show that our proposed approach can reach an accurate recognition with high-level semantics. This work is conducive to the development of complex urban object recognition toward the fields including multilayer learning algorithms and knowledge graph-based relational reinforcement learning.

scholarly journals Measuring subaqueous progradation of the Wax Lake Delta with a model of flow direction divergence

2018 ◽  
Vol 6 (4) ◽  
pp. 1155-1168 ◽  
Author(s):  
John B. Shaw ◽  
Justin D. Estep ◽  
Amanda R. Whaling ◽  
Kelly M. Sanks ◽  
Douglas A. Edmonds
Keyword(s):  
Remote Sensing ◽  
Flow Direction ◽  
Remote Sensing Data ◽  
Land Area ◽  
Constant Rate ◽  
Diagnostic Signature ◽  
Wax Lake Delta ◽  
Coastal Louisiana ◽  
Direction Field ◽  
Distributary Channel

Abstract. Remotely sensed flow patterns can reveal the location of the subaqueous distal tip of a distributary channel on a prograding river delta. Morphodynamic feedbacks produce distributary channels that become shallower over their final reaches before the unchannelized foreset slopes basinward. The flow direction field over this morphology tends to diverge and then converge, providing a diagnostic signature that can be captured in flow or remote sensing data. A total of 21 measurements from the Wax Lake Delta (WLD) in coastal Louisiana and 317 measurements from numerically simulated deltas show that the transition from divergence to convergence occurs in a distribution that is centered just downstream of the channel tip, on average 132 m in the case of the WLD. These data validate an inverse model for remotely estimating subaqueous channel tip location. We apply this model to 33 images of the WLD between its initiation in 1974 and 2016. We find that six of the primary channels grew at rates of 60–80 m yr−1, while the remaining channel grew at 116 m yr−1. We also show that the subaqueous delta planform grew at a constant rate (1.72 km2 yr−1). Subaerial land area initially grew at the same rate but slowed after about 1999. We explain this behavior as a gradual decoupling of channel tip progradation and island aggradation that may be common in maturing deltas.

scholarly journals ESTIMATION OF FOREST BIOMASS BASED ON MULITI-SOURCE REMOTE SENSING DATA SET – A CASE STUDY OF SHANGRI-LA COUNTY

2018 ◽  
Vol IV-3 ◽  
pp. 77-81
Author(s):  
Wanwan Feng ◽  
Leiguang Wang ◽  
Junfeng Xie ◽  
Cairong Yue ◽  
Yalan Zheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Research Area ◽  
Laser Spot ◽  
Data Set ◽  
Important Indicator ◽  
Level Data ◽  
Forest Distribution ◽  
And Function

Forest biomass is an important indicator for the structure and function of forest ecosystems, and an accurate assessment of forest biomass is crucial for understanding ecosystem changes. Remote sensing has been widely used for inversion of biomass. However, in mature or over-mature forest areas, spectral saturation is prone to occur. Based on existing research, this paper synthesizes domestic high resolution satellites, ZY3-01 satellites, and GLAS14-level data from space-borne Lidar system, and other data set. Extracting texture and elevation features respectively, for the inversion of forest biomass. This experiment takes Shangri-La as the research area. Firstly, the biomass in the laser spot was calculated based on GLAS data and other auxiliary data, DEM, the second type inventory of forest resources data and the Shangri-La vector boundary data. Then, the regression model was established, that is, the relationship between the texture factors of ZY3-01 and biomass in the laser spot. Finally, by using this model and the forest distribution map in Shangri-La, the biomass of the whole area is obtained, which is 1.3972&amp;thinsp;&amp;times;&amp;thinsp;10<sup>8</sup>t.

scholarly journals Innovative Feasibility Study for the Reclamation of the Cascajo Wetlands in Peru Utilizing Sustainable Technologies

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1097
Author(s):  
Keiji Jindo ◽  
Marino S. Morikawa Sakura
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Flood Control ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Condition ◽  
Oxygen Demand ◽  
Remote Sensing Data ◽  
Sustainable Technologies ◽  
Highly Correlated

Wetlands are an important feature for our society that provides versatile benefits, such as habitat for diverse wildlife, shoreline erosion protection, flood control, and mitigation of climate change through capture and storage of carbon. The aim of this work was to assess the application of nanotechnologies for the restoration of the water quality in the Cascajo Wetlands, Peru, where the water quality was deteriorated. Ceramic-based bio-filters (CBBFs) were used to reduce and buffer the contamination rates of pollutants, whereas micro-nano bubbles (MNBs) were applied to increase the dissolved oxygen and release free radicals in water. Additionally, bio-fence was implemented to prevent water intrusion from the ocean. Remote sensing data through the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) was used to monitor the water surface condition. With treatment of CBBFs and MNBs for 13 months, we observed reduction in the chemical oxygen demand (COD), biological oxygen demand (BOD), total nitrogen (TN), and total phosphate (TP) in the water body, showing removal percentages of 98.5%, 97.5%, 98.1%, 98.5%, and 94.6%, respectively, in comparison with values before starting the implementation. The trends of NDVI and EVI over seasons are not completely aligned with the results taken from the wetlands treated with MNBs, CBBFs and bio-fence. While TN was highly correlated with the empirical value of TN based on remote sensing, no correlation was observed between COD and empirical COD. The use of eco-friendly techniques has performed efficiently to remove the pollutant.

scholarly journals Application of Remote Sensing and Geoinformatics Techniques in Erosion Mapping and Groundwater Management in the River Amba Watershed, Central Nigeria

2021 ◽  
pp. 59-65
Author(s):  
Aliyu Itari Abdullahi ◽  
Nuhu Degree Umar
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Groundwater Management ◽  
Rainfall Intensity ◽  
Flow Direction ◽  
Remote Sensing Data ◽  
Geological Mapping ◽  
Aerial Photographs ◽  
Sensing Data ◽  
Geoinformatics Techniques

This research integrated easy-to-handle remote sensing data and geoinformatics techniques for erosion mapping and groundwater management in the River Amba watershed, central Nigeria. It is aimed at: (a) the determination of the erosion-prone areas and (b) the estimation of the groundwater potential contamination risk under current and future anthropogenic activities. Rainfall intensity was evaluated from monthly rainfall data (2001 - 2011) from the station located within the River Amba Watershed. Digital Elevation Model (DEM) for the terrain was created using the 3D Analyst tool (Surfer 14) and was used to determine the flow direction and lineament features in each raster cells. Remote sensing data (aerial photographs and LANDSAT imagery) were used to develop a land-use map, while geological mapping was used to determine the local geology of the watershed area. The contributions of the various factors to the erosion hazardous areas are: elevation 31.49 %, land use 21 %, slope 14 %, geology 12.52 %, rainfall intensity 10.5 % and flow accumulation 10.5 %. The combined influences of these factors to erosion susceptibility as either: very high, high, moderate, low, and very low with the south-western part characterized as high while other parts of the study area moderate to very low erosion vulnerability. The groundwater level is shallow (4.0 –28.5 m) and discharges through the Amba river and many springs. These springs along with boreholes and wells supply drinking water to Lafia and the environs.

scholarly journals Generalized sliding law applied to the surge dynamics of Shisper Glacier and constrained by timeseries correlation of optical satellite images

2021 ◽  
Author(s):  
Flavien Beaud ◽  
Saif Aati ◽  
Ian Delaney ◽  
Surendra Adhikari ◽  
Jean-Philippe Avouac
Keyword(s):  
Remote Sensing ◽  
Temporal Resolution ◽  
Remote Sensing Data ◽  
Surface Velocity ◽  
Landsat 8 ◽  
Ice Flow ◽  
Fast Ice ◽  
Spatio Temporal ◽  
Optical Satellite Images ◽  
Basal Shear

Abstract. Understanding fast ice flow is key to assess the future of glaciers. Fast ice flow is controlled by sliding at the bed, yet that sliding is poorly understood. A growing number of studies show that the relationship between sliding and basal shear stress transitions from an initially rate-strengthening behavior to a rate-independent or rate-weakening behavior. Studies that have tested a glacier sliding law with data remain rare. Surging glaciers, as we show in this study, can be used as a natural laboratory to inform sliding laws because a single glacier shows extreme velocity variations at a sub-annual timescale. The present study has two parts: (1) we introduce a new workflow to produce velocity maps with a high spatio-temporal resolution from remote sensing data combining Sentinel-2 and Landsat 8 and use the results to describe the recent surge of Shisper glacier, and (2) we present a generalized sliding law and provide a first-order assessment of the sliding-law parameters using the remote sensing dataset. The quality and spatio-temporal resolution of the velocity timeseries allow us to identify a gradual amplification of spring speed-up velocities in the two years leading up to the surge that started by the end of 2017. We also find that surface velocity patterns during the surge can be decomposed in three main phases, and each phase appears to be associated with hydraulic changes. Using this dataset, we are able to constrain the sliding law parameter range necessary to encompass the sliding behavior of Shisper glacier, before and during the surge. We document a transition from rate-strengthening to rate-independent or rate-weakening behavior. A range of parameters is probably necessary to describe sliding at a single glacier. The approach used in this study could be applied to many other sites in order to better constrain glacier sliding in various climatic and geographic settings.

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