Mapping of lakes in the Qinghai-Tibet Plateau from 2016 to 2021: trend and potential regularity

Lakes over the Qinghai-Tibet Plateau (TP) have large quantities and areas. As an important component of fragile plateau ecosystems, these lakes have attracted increasing attention. However, owing to the limitations of technology and methods, changes in smaller lakes on the TP have received less attention. In this study, we used Google Earth Engine (GEE) with the Analysis Ready Data (ARD) preparation framework to obtain preprocessed Sentinel-1 data covering the plateau. The D-LinkNet framework was introduced to achieve lake extraction, and the lake dataset was completed from 2016 to 2021. The lake dataset showed an area accuracy of 86.49% and Intersection over Union (IoU) of 0.72-0.99 in different regions. The findings were as follows: during the study period, the TP lakes tended to be stable after increasing, with an increase in area and number of +7.6% and +14.8%. Except for the northwest TP, the other regions show the same general trend. In particular, the Tarim Basin exhibited a lake variation pattern independent of the TP. Significantly, we found frequent lake activity in the Kunlun Mountains, Qaidam Basin, Mountain Qogir, etc. Effects of the ongoing La Niña event on the TP lakes may occur in the next few years

Multi-Source Precipitation Data Merging for Heavy Rainfall Events Based on Cokriging and Machine Learning Methods

Gridded precipitation data with a high spatiotemporal resolution are of great importance for studies in hydrology, meteorology, and agronomy. Observational data from meteorological stations cannot accurately reflect the spatiotemporal distribution and variations of precipitation over a large area. Meanwhile, radar-derived precipitation data are restricted by low accuracy in areas of complex terrain and satellite-based precipitation data by low spatial resolution. Therefore, hourly precipitation models were employed to merge data from meteorological stations, Radar, and satellites; the models used five machine learning algorithms (XGBoost, gradient boosting decision tree, random forests (RF), LightGBM, and multiple linear regression (MLR)), as well as the CoKriging method. In the north of Guangdong Province, data of four heavy rainfall events in 2018 were processed with geographic data to obtain merged hourly precipitation data. The CoKriging method secured the best prediction of spatial distribution of accumulated precipitation, followed by the tree-based machine learning (ML) algorithms, and significantly, the prediction of MLR deviated from the actual pattern. All machine learning methods showed poor performances for timepoints with little precipitation during the heavy rainfall events. The tree-based ML method showed poor performance at some timepoints when precipitation was over-related to latitude, longitude, and distance from the coast

Runoff simulation driven by multi-source satellite data based on hydrological mechanism algorithm and deep learning network

Study region: Four sub-basins of the Songhua River basin, northeast China. Study focus: Conventional runoff models typically require in-depth knowledge of the hydrological and physical processes and are costly to construct and compute. Moreover, these models predominantly rely on ground site data, where incomplete or delayed data might introduce simulation uncertainty. Therefore, it is imperative to provide a scientifically rigorous and rational approach for simulating the runoff process, effectively addressing the limitations of existing methods. Combining a long short-term memory (LSTM) network with a modified Michel soil conservation service (MMSCS) algorithm, this study proposed the LSTM-MMSCS runoff simulation scheme. New hydrological insights for the region: The LSTM-MMSCS model was constructed by adjusting and optimizing the difference characteristics of the LSTM runoff simulation by establishing regression relationships according to the MMSCS-calculated runoff depth. LSTM-MMSCS adopted the coupling method of hydrological mechanism and deep learning to establish a simulation framework with adaptive feedback and adjustment between observed and simulated data. This scheme incorporated satellite meteorological products, solving the problem of inaccuracies caused by standard models' ineffective mining of temporal series information. LSTM-MMSCS reduced overall runoff error (RMSE was reduced from 50.07 mm to 24.47 mm) and effectively alleviated the problem of peak runoff underestimation (the relative error was reduced from 30.39% to 13.39%) compared to LSTM. Using satellite meteorological data to drive LSTM-MMSCS enabled runoff change trends visualization and aids in abnormal runoff localization

Multi-Source Precipitation Data Merging for Heavy Rainfall Events Based on Cokriging and Machine Learning Methods

Gridded precipitation data with a high spatiotemporal resolution are of great importance for studies in hydrology, meteorology, and agronomy. Observational data from meteorological stations cannot accurately reflect the spatiotemporal distribution and variations of precipitation over a large area. Meanwhile, radar-derived precipitation data are restricted by low accuracy in areas of complex terrain and satellite-based precipitation data by low spatial resolution. Therefore, hourly precipitation models were employed to merge data from meteorological stations, Radar, and satellites; the models used five machine learning algorithms (XGBoost, gradient boosting decision tree, random forests (RF), LightGBM, and multiple linear regression (MLR)), as well as the CoKriging method. In the north of Guangdong Province, data of four heavy rainfall events in 2018 were processed with geographic data to obtain merged hourly precipitation data. The CoKriging method secured the best prediction of spatial distribution of accumulated precipitation, followed by the tree-based machine learning (ML) algorithms, and significantly, the prediction of MLR deviated from the actual pattern. All machine learning methods showed poor performances for timepoints with little precipitation during the heavy rainfall events. The tree-based ML method showed poor performance at some timepoints when precipitation was over-related to latitude, longitude, and distance from the coast

Deep Belief Network for Feature Extraction of Urban Artificial Targets

Reducing the dimension of the hyperspectral image data can directly reduce the redundancy of the data, thus improving the accuracy of hyperspectral image classification. In this paper, the deep belief network algorithm in the theory of deep learning is introduced to extract the in-depth features of the imaging spectral image data. Firstly, the original data is mapped to feature space by unsupervised learning methods through the Restricted Boltzmann Machine (RBM). Then, a deep belief network will be formed by superimposed multiple Restricted Boltzmann Machines and training the model parameters by using the greedy algorithm layer by layer. At the same time, as the objective of data dimensionality reduction is achieved, the underground feature construction of the original data will be formed. The final step is to connect the depth features of the output to the Softmax regression classifier to complete the fine-tuning (FT) of the model and the final classification. Experiments using imaging spectral data showing the in-depth features extracted by the profound belief network algorithm have better robustness and separability. It can significantly improve the classification accuracy and has a good application prospect in hyperspectral image information extraction

Change of the spatial and temporal pattern of ecological vulnerability: A case study on Cheng-Yu urban agglomeration, Southwest China

China's urban economy has developed rapidly over the decades, and the Cheng-Yu urban agglomeration has become one of China's four typical urban agglomerations, with a large population and a high level of economic development. However, the conflict between humans and the environment is becoming increasingly prominent together with economic development. In order to protect the ecological environment in urban areas, thus scientific understanding and assessment of ecological vulnerability are beneficial to establishing regional conservation measures, and serve as a key means to maintain environmental health. Based on the “Sensitivity-Resilience-Pressure” (SRP) model, this study considered remote sensing, geographic and statistical data to construct an evaluation system for regional ecological vulnerability. In addition, the coupled AHP (Analytic hierarchy process)-Entropy weighting model was proposed to obtain the weight of each evaluation indicator and analyze the spatio-temporal distribution characteristics of the ecological vulnerability of the study area during 2000–2020. The changes and the divergence pattern were depicted by the transfer matrix, dynamic degree and spatial auto-correlation. The results indicated that the ecological vulnerability of Cheng-Yu urban agglomeration is mainly mild and moderate, with an overall high distribution in Chongqing and Chengdu, while low in the central and north zone (e.g., Ziyang, Mianyang). It is consistent with the distribution of HH (High-High) and L-L (Low-Low) clusters, respectively, having a significant positive spatial correlation. In particular, the severely vulnerable area increased from 7059 km2 in 2000 to 23553 km2 in 2020, with an increased rate of 233.66 %. Combining the transfer matrix and dynamic degree, it was found that the ecological environment underwent a rapid deterioration followed by a slow recovery. This study provides a scientific reference for the ecological policy making which serves sustainable urban development

Landslide Susceptibility Assessment Model Construction Using Typical Machine Learning for the Three Gorges Reservoir Area in China

The Three Gorges Reservoir region in China is the Yangtze River Economic Zone’s natural treasure trove. Its natural environment has an important role in development. The unique and fragile ecosystem in the Yangtze River’s Three Gorges Reservoir region is prone to natural disasters, including soil erosion, landslides, debris flows, landslides, and earthquakes. Therefore, to better alleviate these threats, an accurate and comprehensive assessment of the susceptibility of this area is required. In this study, based on the collection of relevant data and existing research results, we applied machine learning models, including logistic regression (LR), the random forest model (RF), and the support vector machine (SVM) model, to analyze landslide susceptibility in the Yangtze River’s Three Gorges Reservoir region to analyze landslide events in the whole study region. The models identified five categories (i.e., topographic, geological, ecological, meteorological, and human engineering activities), with nine independent variables, influencing landslide susceptibility. The accuracy of landslide susceptibility derived from different models and raster cells was then verified by the accuracy, recall, F1-score, ROC curve, and AUC of each model. The results illustrate that the accuracy of different machine learning algorithms is ranked as SVM > RF > LR. The LR model has the lowest generalization ability. The SVM model performs well in all regions of the study area, with an AUC value of 0.9708 for the entire Three Gorges Reservoir area, indicating that the SVM model possesses a strong spatial generalization ability as well as the highest robustness and can be adapted as a real-time model for assessing regional landslide susceptibility

Response of net primary productivity of vegetation to drought: A case study of Qinba Mountainous area, China (2001–2018)

Drought can significantly affect the carbon cycle of ecosystems. The Qinba Mountains region has a high potential for developing carbon sink forestry due to its strong carbon fixation capacity. This study applied the Carnegie-Ames-Stanford approach (CASA) model calculate monthly net primary productivity (NPP) in the region from 2001 to 2018. The standardized precipitation evapotranspiration indices (SPEI1-12) at various time scales were also analyzed by the Thornthwaite method, along with the test of significance and sustainability (Sen-MK-Hurst) model, to examine the persistent characteristics of dry and wet changes in the Qinba Mountains. The results of the correlation analysis between NPP and SPEI at multiple time scales showed that regional vegetation NPP is significantly impacted by drought, with the most sensitive months being July and October. On average, there is a four-month cumulative effect of drought on NPP. About 45.49% and 19.99% of NPP in the Qinba Mountains region are extremely or heavily sensitive to drought, with grasses being the most sensitive, followed by cultivated plants, deciduous broadleaved forests, mixed forests, sparse forests, and shrublands. The study also found that 40.99% of the region is expected to become wetter and 14.9% is expected to become drier in the future. Understanding the response of NPP to drought in this region can help with the management of regional climate change for environmental regulation