Glacial Lake Detection from GaoFen-2 Multispectral Imagery Using an Integrated Nonlocal Active Contour Approach: A Case Study of the Altai Mountains, Northern Xinjiang Province

Due to recent global climate change, glacial lake outburst floods (GLOFs) have become a serious problem in many high mountain areas. Accurately and rapidly mapping glacial lakes is the basis of other glacial lake studies that are associated with water resources management, flood hazard assessment, and climate change. Most glacial lake detection studies have mainly used medium to coarse resolution images, whose application is limited to large lakes. Because small glacial lakes are abundant and because changes in these lakes are small and occur around the lake shores, fine-resolution satellite imagery is required for adequate assessments. In addition, the existing detection methods are mainly based on simply applying a threshold on various normalized difference water indices (NDWIs); this cannot give appropriate results for glacial lakes that have a wide range of turbidity, mineral, and chlorophyll content. In the present study, we propose a region-dependent framework to overcome the spectral heterogeneity of glacial lake areas using a nonlocal active contour model that is integrated with the NDWI. As the first trial, the glacial lakes were detected using high-resolution GaoFen-2 multispectral imagery in the test site of Altai Mountains (northern Xinjiang Province). The validation of the results was carried out using the manually digitized lake boundaries. The average probabilities of false positives P F P and false negatives P F N were found to be 0.0106 and 0.0039, respectively. After taking into consideration the spectral features of the water and making slight NDWI threshold adjustments, this method can also be used for lake detection in any glaciated environment elsewhere in the world

High-Frequency Glacial Lake Mapping Using Time Series of Sentinel-1A/1B SAR Imagery: An Assessment for the Southeastern Tibetan Plateau

Glacial lakes are an important component of the cryosphere in the Tibetan Plateau. In response to climate warming, they threaten the downstream lives, ecological environment, and public infrastructures through outburst floods within a short time. Although most of the efforts have been made toward extracting glacial lake outlines and detect their changes with remotely sensed images, the temporal frequency and spatial resolution of glacial lake datasets are generally not fine enough to reflect the detailed processes of glacial lake dynamics, especially for potentially dangerous glacial lakes with high-frequency variability. By using full time-series Sentinel-1A/1B imagery over a year, this study presents a new systematic method to extract the glacial lake outlines that have a fast variability in the southeastern Tibetan Plateau with a time interval of six days. Our approach was based on a level-set segmentation, combined with a median pixel composition of synthetic aperture radar (SAR) backscattering coefficients stacked as a regularization term, to robustly estimate the lake extent across the observed time range. The mapping results were validated against manually digitized lake outlines derived from Gaofen-2 panchromatic multi-spectral (GF-2 PMS) imagery, with an overall accuracy and kappa coefficient of 96.54% and 0.95, respectively. In comparison with results from classical supervised support vector machine (SVM) and unsupervised Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) methods, the proposed method proved to be much more robust and effective at detecting glacial lakes with irregular boundaries that have similar backscattering as the surroundings. This study also demonstrated the feasibility of time-series Sentinel-1A/1B SAR data in the continuous monitoring of glacial lake outline dynamics

Characterization of Kyagar Glacier and Lake Outburst Floods in 2018 Based on Time-Series Sentinel-1A Data

Early recognition of glacial lake outburst floods (GLOFs) is required for timely and cost-effective remedial efforts to be implemented. Although the formation of ice-dammed lakes is known to begin as a pond or river that was blocked by ice from the glacier terminus, the relationship between glacier dynamics and lake development is not well understood. Using a time-series of Sentinel-1A synthetic aperture radar (SAR) data acquired just before and after the lake outburst event in 2018, information is presented on the dynamic characteristics of Kyagar Glacier and its ice-dammed lake. Glacier velocity data derived from interferometry show that the glacier tongue experienced an accelerated advance (maximum velocity of 20 cm/day) just one month before the lake outburst, and a decreased velocity (maximum of 13 cm/day) afterward. Interferometric and backscattering properties of this region provide valuable insight into the diverse glaciated environment. Furthermore, daily temperature and total precipitation data derived from the ECMWF re-analysis (ERA)Interim highlight the importance of the sustained high-temperature driving force, supporting empirical observations from previous studies. The spatial and temporal resolution offered by the Sentinel-1A data allows variations in the glacier surface motion and lake evolution to be detected, meaning that the interaction mechanism between the glacial lake and the associated glacier can be explored. Although the glacier surge provided the boundary conditions favorable for lake formation, the short-term high temperatures and precipitation caused the melting of ice dams and also a rapid increase in the amount of water stored, which accelerated the potential for a lake outburst

Monitoring Roadbed Stability in Permafrost Area of Qinghai–Tibet Railway by MT-InSAR Technology

Permafrost areas pose a threat to the safe operation of linear projects such as the Qinghai–Tibet railway due to the repeated alternating effects of frost heaving and thawing settlement of frozen soil in permafrost area. Time series InSAR technology can effectively obtain ground deformation information with an accuracy of up to millimeters. Therefore, it is of great significance to use time series InSAR technology to monitor the deformation of the permafrost section of the Qinghai–Tibet railway. This study uses multi-time InSAR (MT-InSAR) technology to monitor the deformation of the whole section of the Qinghai–Tibet railway, detect the uneven settlement of the railway roadbed in space, and detect the seasonal changes in the roadbed in the time domain. At the same time, the local deformation sections over the years are compared and discussed. The time series deformation monitoring results of the permafrost section Sentinel-1 data in 2020 show that the length of the railway roadbed from Tanggula station to Za’gya Zangbo station (TZ) section is approximately 620 m, the deformation of the east and west sides is uneven, and the average annual deformation difference is 60.68 mm/a. The impact of frozen soil in WangKun station to Budongquan station (WB) section on railway roadbed shows the distribution characteristics of high in the middle and low at both ends, and the maximum annual average settlement can reach −158.46 mm/a. This study shows that the deformation of permafrost varies with different ground layers. The impact of human activities on frozen soil deformation is less than that of topography and hydrothermal conditions. At the same time, the study determined that compared with other sections, the roadbed deformation of TZ and WB sections is more obvious

Large-Area Landslides Monitoring Using Advanced Multi-Temporal InSAR Technique over the Giant Panda Habitat, Sichuan, China

The region near Dujiangyan City and Wenchuan County, Sichuan China, including significant giant panda habitats, was severely impacted by the Wenchuan earthquake. Large-area landslides occurred and seriously threatened the lives of people and giant pandas. In this paper, we report the development of an enhanced multi-temporal interferometric synthetic aperture radar (MTInSAR) methodology to monitor potential post-seismic landslides by analyzing coherent scatterers (CS) and distributed scatterers (DS) points extracted from multi-temporal l-band ALOS/PALSAR data in an integrated manner. Through the integration of phase optimization and mitigation of the orbit and topography-related phase errors, surface deformations in the study area were derived: the rates in the line of sight (LOS) direction ranged from −7 to 1.5 cm/a. Dozens of potential landslides, distributed mainly along the Minjiang River, Longmenshan Fault, and in other the high-altitude areas were detected. These findings matched the distribution of previous landslides. InSAR-derived results demonstrated that some previous landslides were still active; many unstable slopes have developed, and there are significant probabilities of future massive failures. The impact of landslides on the giant panda habitat, however ranged from low to moderate, would continue to be a concern for conservationists for some time in the future

Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network

The timely detection and mapping of surface water bodies from Polarimetric Synthetic Aperture Radar (PolSAR) images are of great significance for emergency management and post-disaster restoration tasks. Though various methods have been proposed in previous years, there are still some inherent flaws. Thus, this paper proposes a new surface water extraction method based on superpixels and Graph Convolutional Networks (GCN). First, the PolSAR images are segmented to generate superpixels as the basic unit of classification, and the graph structure data are established according to their connection to superpixels. Then, the features of each superpixel are extracted. Finally, a GCN is used to classify each superpixel unit using node features and their relationships. This study conducted experiments on a sudden flooding event due to heavy rain and a lake in the city. Detailed verification was carried out. Compared to traditional methods, the recall was improved by 3% while maintaining almost 100% accuracy in complex flood areas. The results show that the proposed method of surface water extraction from PolSAR images has great advantages, acquiring higher accuracy and better boundary adherence in cases of fewer samples. This paper also illustrates the advantage of using GCN to mine the contextual information of classification objects

Using Persistent Scatterer Interferometry for Post-Earthquake Landslide Susceptibility Mapping in Jiuzhaigou

Earthquakes cause a huge number of landslides and alter the regional landslide risk distribution. As a result, after a significant earthquake, the landslide susceptibility maps (LSMs) must be updated. The study goal was to create seismic landslide susceptibility maps containing landslide causative variables which are adaptable to great changes in susceptibility after the Jiuzhaigou earthquake (MS 7.0) and to perform a rapid update of the LSM after the earthquake by means of the distributed scatterer interferometric synthetic aperture radar (DS-InSAR) technique. We selected the territory of Jiuzhaigou County (southwestern China) as the study region. Jiuzhaigou is a world-renowned natural heritage and tourist area of great human and ecological value. For landslide susceptibility mapping, we examined the applicability of three models (logistic regression, support vector machine, and random forest) for landslide susceptibility mapping and offered a strategy for updating seismic landslide susceptibility maps using DS-InSAR. First, using logistic regression, support vector machine, and random forest techniques, susceptibility models of seismic landslides were built for Jiuzhaigou based on twelve contributing variables. Second, we obtained the best model parameters by means of a Bayesian network and network search, while using five-fold cross-validation to validate the optimized model. According to the receiver operating characteristic curve (ROC), the SVM model and RF model had excellent prediction capability and strong robustness over large areas compared with the LR models. Third, the surface deformation in Jiuzhaigou was calculated using DS-InSAR technology, and the deformation data were adopted to update the landslide susceptibility model using the correction matrix. The correction of deformation data resulted in a susceptibility class transition in 4.87 percent of the research region. According to practical examples, this method of correcting LSMs for the continuous monitoring of surface deformation (DS-InSAR) was effective. Finally, we analyze the reasons for the change in the revised LSM and point out the help of ecological restoration in reducing landslide susceptibility. The results show that the integration of InSAR continuous monitoring not only improved the performance of the LSM model but also adapted it to track the evolution of future landslide susceptibility, including seismic and human activities

Number and nest-site selection of breeding black-necked cranes over the past 40 years in the Longbao Wetland Nature Reserve, Qinghai, China

Black-necked crane (Grus nigricollis, BNC), facing serious threats from human activities and habitat variations, is an endangered species classified as vulnerable under the revised IUCN Red List. In this article, we investigated and analyzed the population and nesting microhabitat of BNCs in the Longbao National Nature Reserve (NNR) from 1978 to 2016, and found the number of BNCs increased from 24 in 1978 to 216 in 2016. This establishment of the Longbao NNR represented the activities of protecting endangered animal species are effective. However, the land cover classification results of Landsat images showed that the marsh wetland, which was the BNC’s primary habitat, decreased during 1978–2016, while artificial buildings increased, which affected the habitat of BNCs. The increase in average temperature over the past 40 years has also had an impact on the number of BNCs. BNCs preferred to nest in marsh wetlands or on islands with open water or star-like distributions through observation. The results of the principal component analysis showed that the nearest distance between nests and habitat type were the primary factors influencing nesting site selection. To protect BNC, we suggest decreasing wetland fragmentation, reducing habitat degradation and providing an undisturbed habitat

Remote Sensing Extraction of Agricultural Land in Shandong Province, China, from 2016 to 2020 Based on Google Earth Engine

Timely and effective access to agricultural land-change information is of great significance for the government when formulating agricultural policies. Due to the vast area of Shandong Province, the current research on agricultural land use in Shandong Province is very limited. The classification accuracy of the current classification methods also needs to be improved. In this paper, with the support of the Google Earth Engine (GEE) platform and based on Landsat 8 time series image data, a multiple machine learning algorithm was used to obtain the spatial variation distribution information of agricultural land in Shandong Province from 2016 to 2020. Firstly, a high-quality cloud-free synthetic Landsat 8 image dataset for Shandong Province from 2016 to 2020 was obtained using GEE. Secondly, the thematic index series was calculated to obtain the phenological characteristics of agricultural land, and the time periods with significant differences in terms of water, agricultural land, artificial surface, woodland and bare land were selected for classification. Feature information, such as texture features, spectral features and terrain features, was constructed, and the random forest method was used to select and optimize the features. Thirdly, the random forest, gradient boosting tree, decision tree and ensemble learning algorithms were used for classification, and the accuracy of the four classifiers was compared. The information on agricultural land changes was extracted and the causes were analyzed. The results show the following: (1) the multi-spatial index time series method is more accurate than the single thematic index time series when obtaining phenological characteristics; (2) the ensemble learning method is more accurate than the single classifier. The overall classification accuracy of the five agricultural land-extraction results in Shandong Province obtained by the ensemble learning method was above 0.9; (3) the annual decrease in agricultural land in Shandong Province from 2016 to 2020 was related to the increase in artificial land-surface area and urbanization rate