scholarly journals Landslide Monitoring Using Multi-Temporal SAR Interferometry with Advanced Persistent Scatterers Identification Methods and Super High-Spatial Resolution TerraSAR-X Images

2018 ◽  
Vol 10 (6) ◽  
pp. 921 ◽  
Author(s):  
Feng Zhao ◽  
Jordi Mallorqui ◽  
Rubén Iglesias ◽  
Josep Gili ◽  
Jordi Corominas
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Sar Interferometry ◽  
Landslide Monitoring ◽  
Identification Methods ◽  
Persistent Scatterers ◽  
Multi Temporal

Classification of multi-temporal spectral indices for crop type mapping: a case study in Coalville, UK

2018 ◽  
Vol 156 (1) ◽  
pp. 24-36 ◽  
Author(s):  
Y. Palchowdhuri ◽  
R. Valcarce-Diñeiro ◽  
P. King ◽  
M. Sanabria-Soto
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Reference Data ◽  
High Spatial Resolution ◽  
Classification Algorithms ◽  
Type Mapping ◽  
Crop Type ◽  
Multi Temporal ◽  
Very High

AbstractRemote sensing (RS) offers an efficient and reliable means to map features on Earth. Crop type mapping using RS at various temporal and spatial resolutions plays an important role spanning from environmental to economical. The main objective of the current study was to evaluate the significance of optical data in a multi-temporal crop type classification-based on very high spatial resolution and high spatial resolution imagery. With this aim, three images from WorldView-3 and Sentinel-2 were acquired over Coalville (UK) between April and July 2016. Three vegetation indices (VIs); the normalized difference vegetation index, the green normalized difference vegetation index and soil adjusted vegetation index were generated using red, green and near-infrared spectral bands; then a supervised classification was performed using ground reference data collected from field surveys, Random forest (RF) and decision tree (DT) classification algorithms. Accuracy assessment was undertaken by comparing the classified output with the reference data. An overall accuracy of 91% and κ coefficient of 0·90 were estimated using the combination of RF and DT classification algorithms. Therefore, it can be concluded that integrating very high- and high-resolution imagery with different VIs can be implemented effectively to produce large-scale crop maps even with a limited temporal-dataset.

scholarly journals Complex Surface Displacements above the Storage Cavern Field at Epe, NW-Germany, Observed by Multi-Temporal SAR-Interferometry

2020 ◽  
Vol 12 (20) ◽  
pp. 3348
Author(s):  
Markus Even ◽  
Malte Westerhaus ◽  
Verena Simon
Keyword(s):  
Ground Truth ◽  
Complex Surface ◽  
Sar Interferometry ◽  
Storage Site ◽  
Geophysical Modeling ◽  
The North ◽  
Persistent Scatterers ◽  
Surface Displacements ◽  
Multi Temporal ◽  
Pressure Changes

The storage cavern field at Epe has been brined out of a salt deposit belonging to the lower Rhine salt flat, which extends under the surface of the North German lowlands and part of the Netherlands. Cavern convergence and operational pressure changes cause surface displacements that have been studied for this work with the help of SAR interferometry (InSAR) using distributed and persistent scatterers. Vertical and East-West movements have been determined based on Sentinel-1 data from ascending and descending orbit. Simple geophysical modeling is used to support InSAR processing and helps to interpret the observations. In particular, an approach is presented that allows to relate the deposit pressures with the observed surface displacements. Seasonal movements occurring over a fen situated over the western part of the storage site further complicate the analysis. Findings are validated with ground truth from levelling and groundwater level measurements.

Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring

2008 ◽  
Vol 112 (6) ◽  
pp. 2729-2740 ◽  
Author(s):  
Michael A. Wulder ◽  
Joanne C. White ◽  
Nicholas C. Coops ◽  
Christopher R. Butson
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Temporal Analysis ◽  
Multi Temporal ◽  
High Spatial Resolution Imagery

scholarly journals A Deep Fusion uNet for Mapping Forests at Tree Species Levels with Multi-Temporal High Spatial Resolution Satellite Imagery

2021 ◽  
Vol 13 (18) ◽  
pp. 3613
Author(s):  
Ying Guo ◽  
Zengyuan Li ◽  
Erxue Chen ◽  
Xu Zhang ◽  
Lei Zhao ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Tree Species ◽  
High Spatial Resolution ◽  
Forest Type ◽  
Species Level ◽  
Forest Inventories ◽  
Forest Classification ◽  
Plantation Species ◽  
Multi Temporal ◽  
Artificial Intelligence Technology

It is critical to acquire the information of forest type at the tree species level due to its strong links with various quantitative and qualitative indicators in forest inventories. The efficiency of deep-learning classification models for high spatial resolution (HSR) remote sensing image has been demonstrated with the ongoing development of artificial intelligence technology. However, due to limited statistical separability and complicated circumstances, completely automatic and highly accurate forest type mapping at the tree species level remains a challenge. To deal with the problem, a novel deep fusion uNet model was developed to improve the performance of forest classification refined at the dominant tree species level by combining the beneficial phenological characteristics of the multi-temporal imagery and the powerful features of the deep uNet model. The proposed model was built on a two-branch deep fusion architecture with the deep Res-uNet model functioning as its backbone. Quantitative assessments of China’s Gaofen-2 (GF-2) HSR satellite data revealed that the suggested model delivered a competitive performance in the Wangyedian forest farm, with an overall classification accuracy (OA) of 93.30% and a Kappa coefficient of 0.9229. The studies also yielded good results in the mapping of plantation species such as the Chinese pine and the Larix principis.

scholarly journals A SPIRAL-BASED DOWNSCALING METHOD FOR GENERATING 30 M TIME SERIES IMAGE DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 817-820
Author(s):  
B. Liu ◽  
J. Chen ◽  
H. Xing ◽  
H. Wu ◽  
J. Zhang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Image Data ◽  
Resolution Time ◽  
Simulated Experiment ◽  
Land Cover Data ◽  
Multi Temporal

The spatial detail and updating frequency of land cover data are important factors influencing land surface dynamic monitoring applications in high spatial resolution scale. However, the fragmentized patches and seasonal variable of some land cover types (e. g. small crop field, wetland) make it labor-intensive and difficult in the generation of land cover data. Utilizing the high spatial resolution multi-temporal image data is a possible solution. Unfortunately, the spatial and temporal resolution of available remote sensing data like Landsat or MODIS datasets can hardly satisfy the minimum mapping unit and frequency of current land cover mapping / updating at the same time. The generation of high resolution time series may be a compromise to cover the shortage in land cover updating process. One of popular way is to downscale multi-temporal MODIS data with other high spatial resolution auxiliary data like Landsat. But the usual manner of downscaling pixel based on a window may lead to the underdetermined problem in heterogeneous area, result in the uncertainty of some high spatial resolution pixels. Therefore, the downscaled multi-temporal data can hardly reach high spatial resolution as Landsat data. <br><br> A spiral based method was introduced to downscale low spatial and high temporal resolution image data to high spatial and high temporal resolution image data. By the way of searching the similar pixels around the adjacent region based on the spiral, the pixel set was made up in the adjacent region pixel by pixel. The underdetermined problem is prevented to a large extent from solving the linear system when adopting the pixel set constructed. With the help of ordinary least squares, the method inverted the endmember values of linear system. The high spatial resolution image was reconstructed on the basis of high spatial resolution class map and the endmember values band by band. Then, the high spatial resolution time series was formed with these high spatial resolution images image by image. <br><br> Simulated experiment and remote sensing image downscaling experiment were conducted. In simulated experiment, the 30 meters class map dataset Globeland30 was adopted to investigate the effect on avoid the underdetermined problem in downscaling procedure and a comparison between spiral and window was conducted. Further, the MODIS NDVI and Landsat image data was adopted to generate the 30m time series NDVI in remote sensing image downscaling experiment. Simulated experiment results showed that the proposed method had a robust performance in downscaling pixel in heterogeneous region and indicated that it was superior to the traditional window-based methods. The high resolution time series generated may be a benefit to the mapping and updating of land cover data.

scholarly journals MULTI-TEMPORAL SAR INTERFEROMETRY FOR LANDSLIDE MONITORING

2016 ◽  
Vol XLI-B8 ◽  
pp. 55-58
Author(s):  
R. Dwivedi ◽  
A. B. Narayan ◽  
A. Tiwari ◽  
O. Dikshit ◽  
A. K. Singh
Keyword(s):  
Surface Deformation ◽  
Deformation Monitoring ◽  
Sar Interferometry ◽  
Line Of Sight ◽  
Landslide Monitoring ◽  
Deformation Pattern ◽  
Mean Velocity ◽  
Study Region ◽  
Persistent Scatterer ◽  
Multi Temporal

In the past few years, SAR Interferometry specially InSAR and D-InSAR were extensively used for deformation monitoring related applications. Due to temporal and spatial decorrelation in dense vegetated areas, effectiveness of InSAR and D-InSAR observations were always under scrutiny. Multi-temporal InSAR methods are developed in recent times to retrieve the deformation signal from pixels with different scattering characteristics. Presently, two classes of multi-temporal InSAR algorithms are available- Persistent Scatterer (PS) and Small Baseline (SB) methods. This paper discusses the Stanford Method for Persistent Scatterer (StaMPS) based PS-InSAR and the Small Baselines Subset (SBAS) techniques to estimate the surface deformation in Tehri dam reservoir region in Uttarkhand, India. Both PS-InSAR and SBAS approaches used sixteen ENVISAT ASAR C-Band images for generating single master and multiple master interferograms stack respectively and their StaMPS processing resulted in time series 1D-Line of Sight (LOS) mean velocity maps which are indicative of deformation in terms of movement towards and away from the satellites. From 1D LOS velocity maps, localization of landslide is evident along the reservoir rim area which was also investigated in the previous studies. Both PS-InSAR and SBAS effectively extract measurement pixels in the study region, and the general results provided by both approaches show a similar deformation pattern along the Tehri reservoir region. Further, we conclude that StaMPS based PS-InSAR method performs better in terms of extracting more number of measurement pixels and in the estimation of mean Line of Sight (LOS) velocity as compared to SBAS method. It is also proposed to take up a few major landslides area in Uttarakhand for slope stability assessment.

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