The Global Positioning System (GPS) records monsoonal precipitable water vapor (PWV) and vertical crustal displacement (VCD) due to hydrological loading, and can thus be applied jointly to diagnose ...meteorological and hydrological droughts. We have analyzed the PWV and VCD observations during 2007.0-2015.0 at 26 continuous GPS stations located in Yunnan province, China. We also obtained equivalent water height (EWH) derived from the Gravity Recovery And Climate Experiment (GRACE) and precipitation at these stations with the same period. Then, we quantified the annual variations of PWV, precipitation, EWH and VCD and provided empirical relationships between them. We found that GPS-derived PWV and VCD (positive means downward movement) are in phase with precipitation and GRACE-derived EWH, respectively. The annual signals of VCD and PWV show linearly correlated amplitudes and a two-month phase lag. Furthermore, the results indicate that PWV and VCD anomalies can also be used to explore drought, such as the heavy drought during winter/spring 2010. Our analysis results verify the capability of GPS to monitor monsoon variations and drought in Yunnan and show that a more comprehensive understanding of the characteristics of regional monsoon and drought can be achieved by integrating GPS-derived PWV and VCD with precipitation and GRACE-derived EWH.
Natural wetland ecosystems provide not only important habitats for many wildlife species, but also food for migratory and resident animals. In Shanghai, the Chongming Dongtan International Wetland, ...located at the mouth of the Yangtze River, plays an important role in maintaining both ecosystem health and ecological security of the island. Meanwhile it provides an especially important stopover and overwintering site for migratory birds, being located in the middle of the East Asian-Australasian Flyway. However, with the increase in development intensity and human activities, this wetland suffers from increasing environmental pressure. On the other hand, biological succession in the mudflat wetland makes Chongming Dongtan a rapidly developing and rare ecosystem in the world. Therefore, studying the wetland spatio-temporal change is an important precondition for analyzing the relationship between wetland evolution processes and human activities. This paper presents a novel method for analyzing land-use/cover changes (LUCC) on Chongming Dongtan wetland using multispectral satellite images. Our method mainly takes advantages of a machine learning algorithm, named the Kernel Extreme Learning Machine (K-ELM), which is applied to distinguish between different objects and extract their information from images. In the K-ELM, the kernel trick makes it more stable and accurate. The comparison between K-ELM and three other conventional classification methods indicates that the proposed K-ELM has the highest overall accuracy, especially for distinguishing between Spartina alternflora, Scirpus mariqueter, and Phragmites australis. Meanwhile, its efficiency is remarkable as well. Then a total of eight Landsat TM series images acquired from 1986 to 2013 were used for the LUCC analysis with K-ELM. According to the classification result, the change detection and spatio-temporal quantitative analysis were performed. The specific analysis of different objects are significant for learning about the historical changes to Chongming Dongtan and obtaining the evaluation rules. Generally, the rapid speed of Chongming Dongtan’s urbanization brought about great influence with respect to natural resources and the environment. Integrating the results into the ecological analysis and ecological regional planning of Dongtan could provide a reliable scientific basis for rational planning, development, and the ecological balance and regional sustainability of the wetland area.
The spatiotemporal changes of lake water resources objectively reflect not only the process of the water resources balance, but also the ecological environment changes in the lake area. In recent ...decades, climate changes and human activities have caused great impacts on the spatial distribution of the earth’s water resources and the spatiotemporal process of the surface water cycle, which has caused a series of ecological crises and environmental problems, such as the drying-up of inland lakes, the disappearance of the oasis, water shortage or flooding and water pollution. Therefore, monitoring and fully understanding the dynamic changes of lakes is of great scientific significance for grasping regional water balance, water resources management, and sustainable development of the ecological environment. In this study, we focus on using multi-source satellite data on the estimation of water volume and multi-timescale variations analysis for large scale lakes. This study combines the problems in the practical application of “African Water Action”, taking the largest lake in Africa, Lake Victoria, as the study area, and utilizes long-term serial multi-source satellite data of the past 15 years (2003–2017), including Moderate-resolution Imaging Spectroradiometer (MODIS), Jason-1/-2/-3 and Gravity Recovery and Climate Experiment (GRACE) to perform the comprehensive analysis on the water volume change estimation. Firstly, the satellite altimetry data of Jason-1/-2/-3 and MODIS imagery was used to calculate series of water level, and to extract series of water surface area, respectively. On this basis, a more accurate regression model between the area and water level variation (ΔH) was constructed. Then, the model between water volume variation (ΔV) and ΔH, derived from area-ΔH model, was applied to calculate the relative water volume of Lake Victoria. Meanwhile, terrestrial water storage (TWS) changes between 2003 and 2016, derived from GRACE data, were also used for a comparative verification of the ΔV results. The results show the long-term series change trends of ΔV and the TWS are the same. Finally, the multi-timescale analysis of water volume changes was carried out on different time scales, such as the inter-annual, inter-monthly, and variation period.
•K-ELM was utilized on spatio-temporal LUCC analysis and performed precisely.•LUCC in 23 years was demonstrated through 7 Landsat images in the Chao Lake Basin.•Frequent land cover changes have ...relationship with lake algae bloom pollution.•Farmland and village buildings suffered most in the flood in July 2020.
Human settlements are guided by the proximity or availability of a natural resource such as river or lake basins containing set of streams. The harmonious development of human activity and natural conditions along watershed areas needs close attention and in-depth study. In this paper, the urban agglomerations and ecological spaces in the Yangtze River Delta, China, the Chao Lake Basin and its surrounding watershed ecosystem is taken as research subject for its serious environmental degradation problems during social and economic development. This paper adopted an effective machine learning algorithm (kernel-ELM) to extract land use and land /cover information, and to analyze the land use/cover pattern evolution rules of the Chao Lake Basin with long term Landsat imagery. Subsequent studies were then carried out to demonstrate the flood-affected area and its ecological impact in the basin in 2020, to reveal the occupation on land cover types. The results indicate Conclusions are drawn from the experiment results: (1) There has been significant change in cultivated land, forest land and construction land out of six key land cover types with dynamic degree of −10.17%, 4.61, 67.04% respectively. (2) Algae bloom pollution was extracted from pattern classification results and it was up to 15% of the total water area by the year 2018. (3) The occupation on land use/cover types of the flood was revealed. The results prove effective application of remote sensing technology in environmental analysis and planning for data-driven evaluation of governing policy. This work serves as a scientific basis for environmental management and regional planning in the Chao Lake Basin and can be served as a basis and a reference for evaluating an ecological policy and its impact for other economic developing watershed human settlements with ecological issues.
Hydrological changes, which are affected by climate variability and human activities, pose challenges to the sustainable management and conservation of water resources. As such, it is important to ...improve our understanding of how climate and anthropogenic activities impact land water storage. Previous studies using Gravity Recovery and Climate Experiment (GRACE) satellite data have struggled to effectively separate the impacts of climate variability and human activities on water storage. Here, we propose a statistical model that simulates natural dynamically driven changes in water storage to reconstruct climate-driven water storage anomalies at basin scales and discuss the possibility of differentiating climate- and human-driven water storage anomalies. The prediction and reconstruction performance of this method shows optimal robustness. Compared with water storage anomalies calculated using land surface models, our reconstruction results are closer to GRACE observations, especially in humid basins with few human interventions. Mismatches are found in basins with intensive human interventions. Isolated human-driven water storage anomalies in four representative basins correlate well with anthropogenic water usage in each basin. The results of this study provide a method to effectively separate and monitor climate- and human-driven water storage anomalies, thereby providing information for better protection and utilization of water resources.
•A statistical model reconstructing climate-driven water storage anomalies (CWSAs) is proposed.•Seasonal signal of CWSA is reconstructed using statistical model for the first time.•Reconstructed CWSAs agree well with GRACE observations in most river basins.•Human-driven water storage anomalies (HWSAs) are isolated and verified.•The developed approach provides a new means for identifying CWSA and HWSA from GRACE.
The registration of 3-D point clouds is an important procedure during the terrestrial laser scanning data processing. Recently, due to their high flexibility and the powerful mathematical model, a ...large amount of least-squares-based (LSs-based) methods are proposed and widely applied to estimate the transformation parameters of 3-D point clouds registration. In these LSs-based methods some based on the generalized Gauss-Markov model do not correct the influence of random errors on source 3-D point clouds. Although there are other methods based on the errors-in-variables (EIV) model, they are inapplicable for transformation problems with large rotation angles and arbitrary scale ratio. In addition, the gross errors are usually ignored in previous studies on 3-D point clouds registration, which, however, exists commonly and could distort the registration severely. Aiming to avoid the influence of gross errors and extend its application, an advanced outlier detected total least-squares (OD-TLS) method is proposed in this paper. Based on the generalized EIV model OD-TLS performs a seven-parameter 3-D similarity transformation with large rotation angles and arbitrary scale ratio. The random errors of both source and target 3-D point clouds are considered. Furthermore, outliers are detected and removed automatically by combining the data snooping method with total least-squares (TLS) estimation. In order to indicate the benefits of OD-TLS, comparative experiments with the LS3D and weighted total least squares (WTLS) on synthetic and real-world scanned 3-D point clouds were performed. The experimental results show OD-TLS not only enhances the registration accuracy but also increases its robustness.
Spectral unmixing has trigged considerable attention to both multispectral and hyperspectral data processing. Among different kinds of spectral unmixing models, the linear mixture model (LMM) has ...been widely applied with its simplicity in light scattering mechanisms and flexibility in different scenarios. Recently, a variety of optimized LMMs have been developed to enhance the decomposing ability. However, two major challenges still exist: endmember variability and global optimum solution with the inequality constraint. In this study, aiming to avoid the influence of endmember variability and to extend the application of LMM, a novel inequality-constrained weighted linear mixture model (IWLMM) is proposed. Based on the errors-in-variables (EIV) model and the theory of weighted total least squares (WTLS), the issue of endmember variability and stochastic errors of both endmembers and mixed pixels are eliminated. With the non-negativity constraint, a brief optimal algorithm to solve the IWLMM is generated as well. In order to indicate the advantages of proposed IWLMM, comparative experiments with the conventional fully constrained least squares (FCLS), three currently proposed LMM-based models (perturbed LMM (PLMM), extended LMM (ELMM) and augmented LMM (ALMM)) and three physics-based nonlinear mixture models (bilinear-Fan model (BFM), generalized bilinear model (GBM) and polynomial post-nonlinear model (PPNM)) on four synthetic and real scenarios with hyperspectral and multispectral datasets were performed. The experimental results suggest the IWLMM outperforms the other seven linear and nonlinear unmixing models after adding the correction for endmember variability by modeling the perturbation and scaling factors of spectral features simultaneously. It has an impressive ability for decomposing and reconstructing pixels.
•Perturbation and scaling factors are considered to decrease endmember variability.•The new algorithm avoids the limitation of initialization and parameter setting.•A global optimal solution can be obtained by the new developed solution algorithm.•Impressive unmixing and reconstruction performance can be generated by the IWLMM.
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