In the context of global warming, glacier changes in the Qinghai–Tibet Plateau (QTP) and its surroundings have attracted a great amount of public attention. To date, there have been many studies of ...glacier mass balance across the QTP. However, given that most of the previous studies have focused on a short observation period (2000–2015), and that long-term mass change measurements are available only for some local regions, we utilized declassified KH-9 images and 1 arc-second Shuttle Radar Topography Mission (SRTM) digital elevation models (DEMs) to provide the region-wide mass balance (from the mid-1970s to 2000) for a larger scale (including 11 sample regions) across the QTP and its surroundings. The final results indicate that the glaciers in the northwest of the QTP have shown a less negative or near-zero mass balance, ranging from −0.11 ± 0.13 m w.e. a−1 to 0.02 ± 0.10 m w.e. a−1, compared to those in the southeast part, with a mass balance range of −0.30 ± 0.12 m w.e. a−1 to −0.11 ± 0.14 m w.e. a−1. The most serious mass loss has emerged in the central-eastern Himalaya. Integrating our results with the observations after 2000 suggests that, over the past four decades (mid-1970s to the mid-2010s), the glaciers in the Himalaya, Nyainqêntanglha, and Tanggula mountains, as a whole, have exhibited accelerated mass loss, and the most significant acceleration has occurred in the eastern Nyainqêntanglha. Moreover, the Hindu Raj glaciers have shown a stable rate of continuous mass loss, while a nearly stable or slight mass gain state in the western Kunlun region can be dated back to at least as far as the mid-1970s.
•Glacier mass balances (1975–2000) in the Tibet Plateau are estimated.•Glaciers in the northwest experienced less ice loss than those in the southeast.•The Himalaya-Nyainqêntanglha glaciers showed accelerated mass loss after 2000.•The western Kunlun glaciers have been in a stable state since the mid-1970s.
Display omitted
•OPTSI is an optical image matching time series selection and inversion algorithm.•OPTSI enables decreasing the uncertainty and enhancing the spatial coverage.•The annual dune ...migration rates are extracted in NSS from Landsat 8 and Sentinel-2.•The migration directions from optical images agree with RDD from the wind data.
Sand dune migration poses a potential threat to desert infrastructure, vegetation, and atmospheric conditions. Capturing the patterns of long-term dune migration is useful for predicting probable desertification issues and wind conditions across vast desert areas. In this study, we employed optical image matching and a singular value decomposition approach to estimate the rates of dune migration in the North Sinai Sand Sea using the free Landsat 8 and Sentinel-2 archives. Our optical image matching time-series selection and inversion (OPTSI) algorithm limited the difference in the solar illumination of correlated pairs to decrease shadows and seasonal variability. We found that the maximum annual dune migration rates were 9.4 m/a and 15.9 m/a for Landsat 8 and Sentinel-2 data, respectively, and the results of time-series analysis revealed the existence of seasonal variations in dune migration controlled by wind regimes. The directions of sand movement extracted from the mean velocity solution agreed strongly with each other and with the drift directions estimated using wind data from meteorological stations. We assessed the uncertainty of each solution based on the variance of stable areas. Our results showed that the proposed inversion decreased uncertainty by up to 25% and increased the spatial coverage by up to 20%. This algorithm is also promising for the retrieval of historical time series on the ground displacements of glaciers and slow-moving landslides employing free archives that provide high-frequency images.
Multitemporal interferometric synthetic aperture radar (InSAR) is increasingly being used for Earth observations. Inaccurate estimation of the covariance matrix is considered to be the most important ...source of error in such applications. Previous studies, namely, DeSpecKS and its variants, have demonstrated their advantages in improving the estimation accuracy for distributed targets by means of statistically homogeneous pixels (SHPs). However, these methods may be unreliable for small sample sizes and sensitive to data stacks showing large time spacing due to the variability of the temporal sample. Moreover, these methods are computationally intensive. In this paper, a new algorithm named fast SHP selection (FaSHPS) is proposed to solve both problems. FaSHPS explores the confidence interval for each pixel by invoking the central limit theorem and then selects SHPs using this interval. Based on identified SHPs, two estimators with respect to the despeckling and the bias mitigation of the sample coherence are proposed to refine the elements of the InSAR covariance matrix. A series of qualitative and quantitative evaluations are presented to demonstrate the effectiveness of our method.
Although the past few decades have witnessed the great development of Synthetic Aperture Radar Interferometry (InSAR) technology in the monitoring of landslides, such applications are limited by ...geometric distortions and ambiguity of 1D Line-Of-Sight (LOS) measurements, both of which are the fundamental weakness of InSAR. Integration of multi-sensor InSAR datasets has recently shown its great potential in breaking through the two limits. In this study, 16 ascending images from the Advanced Land Observing Satellite (ALOS) and 18 descending images from the Environmental Satellite (ENVISAT) have been integrated to characterize and to detect the slow-moving landslides in Zhouqu, China between 2008 and 2010. Geometric distortions are first mapped by using the imaging geometric parameters of the used SAR data and public Digital Elevation Model (DEM) data of Zhouqu, which allow the determination of the most appropriate data assembly for a particular slope. Subsequently, deformation rates along respective LOS directions of ALOS ascending and ENVISAT descending tracks are estimated by conducting InSAR time series analysis with a Temporarily Coherent Point (TCP)-InSAR algorithm. As indicated by the geometric distortion results, 3D deformation rates of the Xieliupo slope at the east bank of the Pai-lung River are finally reconstructed by joint exploiting of the LOS deformation rates from cross-heading datasets based on the surface-parallel flow assumption. It is revealed that the synergistic results of ALOS and ENVISAT datasets provide a more comprehensive understanding and monitoring of the slow-moving landslides in Zhouqu.
Modeling PSInSAR Time Series Without Phase Unwrapping Zhang, Lei; Ding, Xiaoli; Lu, Zhong
IEEE transactions on geoscience and remote sensing,
2011-Jan., 2011, 2011-01-00, 20110101, Letnik:
49, Številka:
1
Journal Article
Recenzirano
Odprti dostop
In this paper, we propose a least-squares-based method for multitemporal synthetic aperture radar interferometry that allows one to estimate deformations without the need of phase unwrapping. The ...method utilizes a series of multimaster wrapped differential interferograms with short baselines and focuses on arcs at which there are no phase ambiguities. An outlier detector is used to identify and remove the arcs with phase ambiguities, and a pseudoinverse of the variance-covariance matrix is used as the weight matrix of the correlated observations. The deformation rates at coherent points are estimated with a least squares model constrained by reference points. The proposed approach is verified with a set of simulated data.
•High resolution glacier elevation changes (2000–2016) in the central Kunlun region.•Overall, glaciers experience moderate mass loss with a highly heterogeneous pattern.•Lake hypsometry method is ...used to extrapolate unmeasured lake water level.•The water storage of LexieWudan Lake and KekeXili Lake increase by 1.82 and 1.90 km3.•The contributions of glacier meltwater to lake growth are less than 9.9% and 11.1%.
Joint monitoring of the variations of glaciers and lakes within a basin is essential for an accurate understanding of region-wide climate change and the water cycle process. The central Kunlun-KekeXili region is an ideal experimental field due to the wide distribution of glaciers and lakes. In this study, we first investigated the glacier mass balance of 2000–2015/16 for seven major glacier clusters by utilizing high-resolution SPOT-6/7 stereo imagery and the SRTM DEM. The final results revealed an overall mass balance of −0.16 ± 0.05 m w.e./a. for the study region (with a total glacier area of 967 km2). Secondly, ICESat/GLAS altimetry data were used to quantify the water-level change of 2003–2008/09 for the two largest glacier-fed closed lakes (i.e., LexieWudan Lake and KekeXili Lake) in this region. Based on this, we applied a strategy which establishes the statistical relationship between the lake area change and the lake water-level change for 2003–2008/09 to estimate the specific water level using the corresponding lake area. We then further calculated the variation in lake water storage between 2000 and 2015. The results showed that the water storage of LexieWudan Lake and KekeXili Lake increased by 1.82 ± 0.14 km3 and 1.90 ± 0.38 km3, respectively. For each lake basin, meanwhile, the glaciers lost −0.18 ± 0.03 km3 and −0.21 ± 0.04 km3 of water, accounting for 9.9% and 11.1% of the increase in lake water storage for LexieWudan Lake and KekeXili Lake, respectively. Our results not only demonstrate that glacier meltwater has only a limited impact on the lake expansion in this region, but they also provide new evidence for the warming and wetting process of the climate in the northern part of the Qinghai–Tibet Plateau.
azaphilone pigments (MonAzPs) are very widely used as food colorants, but their biosynthetic pathway has remained poorly characterized for more than half a century. In this study, the individual ...steps of MonAzPs biosynthesis in
M7 were elucidated by a combination of targeted gene knockouts, heterologous gene expression, and
chemical and enzymatic reactions. This study describes the first rational engineering of MonAzPs biosynthesis and provides a roadmap for future pathway engineering efforts directed towards the selective production of the most valuable pigments and serves as a model for the biosynthesis of fungal azaphilones in general.
The coherence of radar echoes is a fundamental observable in interferometric synthetic aperture radar (InSAR) measurements. It provides a quantitative measure of the scattering properties of imaged ...surfaces and therefore is widely applied to study the physical processes of the Earth. However, unfortunately, the estimated coherence values are often biased due to various reasons such as radar signal nonstationarity and the bias in the estimators used. In this paper, we focus on multitemporal InSAR coherence estimation and present a hybrid approach that mitigates effectively the errors in the estimation. The proposed approach is almost completely self-adaptive and workable for both Gaussian and non-Gaussian SAR scenes. Moreover, the bias of the sample coherence can be mitigated with even only several samples included for a given pixel. Therefore, it is a more pragmatic method for accurate coherence estimation and can be applied actually. Different data sets are used to test the proposed method and demonstrate its advantages.
•A MT-ViT model is presented to interpret deformation based on single interferogram.•The performance of the model is evaluated with both simulated and real InSAR datasets.•The model is advantageous ...as demonstrated with experiments.
Many geological hazards are associated with ground deformations. Prompt and accurate detection and interpretation of ground deformation is therefore vital to geohazard mitigation. Multitemporal Interferometric Synthetic Aperture Radar (MT-InSAR) is an effective geodetic technique for monitoring ground deformation. However, accurate computation and interpretation of deformation using InSAR are often hindered by various errors and a lack of expert knowledge. We present a new advanced deep learning model based on a multi-task vision transformer (MT-ViT) to automatically detect, locate, and interpret deformation using single interferograms. To address the issue of limited training data in InSAR applications, the proposed model utilizes pre-trained weights from optical images and transfers them to a simulated InSAR dataset. Then real interferograms are used to fine-tune the weights in the network. An overall loss function is designed, which considers the classification and localization losses in the model. The effectiveness of the proposed model is demonstrated using both simulated and real InSAR datasets that contain either coseismic or volcanic deformation. The experimental results from the model are also compared with the state-of-the-art convolutional neural network (CNN) based techniques. The results show significant improvement in both the accuracy of the results and the computational efficiency over the CNN-based approaches. The MT-ViT model achieved 99.4 % classification accuracy, 54.1 % mean intersection over union (IOU), and 0.9 km localization accuracy. A comprehensive evaluation of the hyperparameters in training the MT-ViT model was carried out, which will inform future research in this direction. The research results highlight the promising capabilities of MT-ViT in near real-time deformation monitoring and automated deformation interpretation.
This study investigated the performances of different techniques, including random forest (RF), support vector machine (SVM), maximum entropy (maxENT), gradient-boosting machine (GBM), and logistic ...regression (LR), for landslide susceptibility mapping (LSM) in the rugged terrain of northern Pakistan. Initially, a landslide inventory of 200 samples was produced along with an additional 200 samples indicating nonlandslide areas and divided into training (70%) and validation (30%) groups using a stratified loop-based random sampling approach. Then, a geospatial database of 12 possible landslide influencing factors (LIFs) was generated, including elevation, slope, aspect, topographic wetness index (TWI), topographic position index (TPI), distance to drainage, distance to fault, distance to road, normalized difference vegetation index (NDVI), rainfall, land cover/land use (LCLU), and a geological map of the study area. None of the LIFs were redundant for the modeling, as indicated by the multicollinearity test (tolerance > 0.1) and information gain ratio (IGR > 0). We extended the evaluation measures of each algorithm from area-under-the-curve (AUC) analysis to the calculation of performance overall (POA) with the help of precision, recall, F1 score, accuracy (ACC), and Matthew’s correlation coefficient (MCC). The results showed that the SVM was the most promising model (AUC = 0.969, POA = 2669) for the LSM, followed by RF (AUC = 0.967, POA = 2656), GBM (AUC = 0.967, POA = 2623), maxENT (AUC = 0.872, POA = 1761), and LR (AUC = 0.836, POA = 1299). It is important to note that the SVM, RF, and GBM were the top performers, with almost similar accuracy. Thus, each of these could be equally effective for LSM and can be used for risk reduction and mitigation measures in the rugged terrain of Pakistan and other regions with similar topography.
PDF
