Remote sensing and environmental assessment of wetland ecological degradation in the Small Sanjiang Plain, Northeast China

IntroductionThe plain marsh wetland ecosystems are sensitive to changes in the natural environment and the intensity of human activities. The Sanjiang Plain is China’s largest area of concentrated marsh wetland, the Small Sanjiang Plain is the most important component of the Sanjiang Plain. However, with the acceleration of the urbanization and development of large-scale agricultural reclamation activities in the Small Sanjiang Plain in Northeast China, the wetland has been seriously damaged. In light of this degradation this study examines the Small Sanjiang Plain.MethodsFrom the four aspects of area, structure, function, and human activities, we try to construct a wetland degradation comprehensive index (WDCI) in cold region with expert scoring methods and analytic hierarchy process (AHP), coupled with network and administrative unit. The objective was to reveal the degradation of wetlands in Northeast China over three decades at a regional scale.ResultsThe results showed that (1) the overall wetland area decreased between 1990 and 2020 by 39.26×103 hm2. Within this period a significant decrease of 336.56×103 hm2 occurred between 1990 and 200 and a significant increase of 214.62×103 hm2 occurred between 2010 and 2020. (2) In terms of structural changes, the fractal dimension (FRAC) has the same trend as the Landscape Fragmentation Index (LFI) with little change. (3) In terms of functional changes, the average above-ground biomass (AGB) increased from 1029.73 kg/hm2 to 1405.38 kg/hm2 between 1990 and 2020 in the study area. (4) In terms of human activities, the average human disturbance was 0.52, 0.46, 0.57 and 0.53 in 1990, 2000, 2010 and 2020, with the highest in 2010. (5) The composite wetland degradation index shows that the most severe wetland degradation was 49.61% in 2010 occurred between 1990 and 2020. (6) Among the severely deteriorated trajectory types in 2010–2020, mild degradation → serious degradation accounted for the largest area of 240.23×103 hm2, and the significant improvement trajectory type in 1990–2000 accounted for the largest area of 238.50×103 hm2.DiscussionIn brief, we conclude that the degradation of the Small Sanjiang Plain wetland was caused mainly by construction, overgrazing, deforestation, and farmland reclamation. This study can also provide new views for monitoring and managing wetland degradation by remote sensing in cold regions

Existence and Limit Behavior of Constraint Minimizers for a Varying Non-Local Kirchhoff-Type Energy Functional

In this paper, we study the constrained minimization problem for an energy functional which is related to a Kirchhoff-type equation. For s=1, there many articles have analyzed the limit behavior of minimizers when η>0 as b→0+ or b>0 as η→0+. When the equation involves a varying non-local term ∫R3|∇u|2dxs, we give a detailed limit behavior analysis of constrained minimizers for any positive sequence {ηk} with ηk→0+. The present paper obtains an interesting result on this topic and enriches the conclusions of previous works

Representation Model of Topological Relations between Complex Planar Objects

In order to express the details of the topological relations between complex planar objects, the classic 9-intersection model is improved and the two extended 9-intersection models that based on decomposition ideas are proposed: the one 9-intersection model method that is decomposed into simple area has its advantage of simplification, but at the cost of complicated expressions; another 9-intersection model method that is decomposed into point-set, conforming well with the classical 9-intersection model, but has relatively complex calculations. Compared the expressive abilities between the two kinds of extened 9-intersection models and the classic 9-intersection model by examples. The results show that both the two extended 9-intersection models can give more accurately expression of the topological relations between details of the sub parts in complex planar objects, the expressive ability of 9-intersection model has been expanded and improved

Self-Organizing Maps Computing on Graphic Process Unit

Abstract. Self-Organizing Maps (SOM) is a widely used artificial neural network (ANN) model. Because of its heavy computation load when the map is big and inherent parallel, there is a need to apply a parallel algorithm on it. As a SIMD parallel processor, Graphic processing unit (GPU) shows a fast growing speed than CPU. And it also provides programmability recently. In this paper, the algorithm and result of SOM computing on GPU has been given. The result shows that GPU can make SOM computing much faster than standard CPU. Some design tricks for improving the efficiency of computing has discussed. Based on the results and current trends in the development of GPU, it is reasonable to expect that graphic hardware will widely used in other ANN computing for getting high-performance.

Bisphosphoglycerate mutase predicts myocardial dysfunction and adverse outcome in sepsis: an observational cohort study

Abstract Background Sepsis not only causes inflammation, but also damages the heart and increases the risk of death. The glycolytic pathway plays a crucial role in the pathogenesis of sepsis-induced cardiac injury. This study aims to investigate the value of bisphosphoglycerate mutase (BPGM), an intermediate in the glycolytic pathway, in evaluating cardiac injury in septic patients and predicting poor prognosis in sepsis. Methods This prospective study included 85 patients with sepsis. Serum BPGM was measured at the time of enrollment, and the patients were divided into a BPGM-positive group (n = 35) and a BPGM-negative group (n = 50) according to their serum BPGM levels. Baseline clinical and echocardiographic parameters, and clinical outcomes were analyzed and compared between the two groups. Kaplan–Meier analysis was used to compare the 28-day survival rate between BPGM-negative and BPGM-positive patients. Multivariate logistic regression analysis was conducted to explore the independent risk factors for 28-day mortality in septic patients. The predictive value of serum BPGM for sepsis-induced myocardial injury and poor prognosis in sepsis was evaluated using receiver operating characteristic (ROC)curve analysis. Result The serum level of BPGM was significantly higher in patients who died within 28 days compared to survivors (p < 0.001). Kaplan–Meier analysis showed that serum BPGM-positive sepsis patients had a significantly shorter 28-day survival time (p < 0.001). Multivariate logistic regression analysis showed that serum BPGM (OR = 9.853, 95%CI 1.844–52.655, p = 0.007) and left ventricular ejection fraction-simpson(LVEF-S) (OR = 0.032, 95% CI 0.002–0.43, p = 0.009) were independent risk factors for 28-day mortality in sepsis patients. Furthermore, BPGM levels was negatively correlated with LVEF-S (p = 0.005) and positively correlated with the myocardial performance (Tei) index (p < 0.001) in sepsis patients. ROC curve analysis showed that serum BPGM was a good predictor of septic myocardial injury and 28-day mortality in sepsis patients. Conclusion The level of BPGM in the serum of sepsis patients can serve as a monitoring indicator for myocardial injury, with its high level indicating the occurrence of secondary myocardial injury events and adverse outcomes in sepsis patients

Abnormal open-hole natural gamma ray (GR) log in Baikouquan Formation of Xiazijie Fan-delta, Mahu Depression, Junggar Basin, China

Based on large amounts of cores, open-hole conventional logs and mineral components analysis, abnormal natural gamma ray (GR) log showing high values in conglomerates and low values in fine-grained sediments, are described and explained in Baikouquan Formation of Xiazijie Fan-delta, Mahu Depression, Junggar Basin. After observing cores, normalizing the GR log and correcting depth errors between both, the GR log values of individual grain-sized lithology are extracted and counted. When grain-size decreases, the average GR values of different sized grains increase generally. The GR values of conglomerates are mostly between 50 and 80 API, while the values of fine-grains are mainly between 70 and 100 API. However, abnormal GR log features exist in the cores and wells of Baikouquan Formation prevalently. A great deal of high radioactive intermediate-acid volcanic minerals, such as volcanic tuff, felsite, andesite, granite, rhyolite, et al., distribute widely in the conglomerates, which results in abnormal high GR values in conglomeratic intervals. Low radioactive quartz components exit widely in high percentage in mudstones, which is the primary mineral explanation for the abnormal low GR values in reddish-brown siltstones and sandstones intervals

Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms

In the dwindling natural mangrove today, mangrove reforestation projects are conducted worldwide to prevent further losses. Due to monoculture and the low survival rate of artificial mangroves, it is necessary to pay attention to mapping and monitoring them dynamically. Remote sensing techniques have been widely used to map mangrove forests due to their capacity for large-scale, accurate, efficient, and repetitive monitoring. This study evaluated the capability of a 0.5-m Pléiades-1 in classifying artificial mangrove species using both pixel-based and object-based classification schemes. For comparison, three machine learning algorithms—decision tree (DT), support vector machine (SVM), and random forest (RF)—were used as the classifiers in the pixel-based and object-based classification procedure. The results showed that both the pixel-based and object-based approaches could recognize the major discriminations between the four major artificial mangrove species. However, the object-based method had a better overall accuracy than the pixel-based method on average. For pixel-based image analysis, SVM produced the highest overall accuracy (79.63%); for object-based image analysis, RF could achieve the highest overall accuracy (82.40%), and it was also the best machine learning algorithm for classifying artificial mangroves. The patches produced by object-based image analysis approaches presented a more generalized appearance and could contiguously depict mangrove species communities. When the same machine learning algorithms were compared by McNemar’s test, a statistically significant difference in overall classification accuracy between the pixel-based and object-based classifications only existed in the RF algorithm. Regarding species, monoculture and dominant mangrove species Sonneratia apetala group 1 (SA1) as well as partly mixed and regular shape mangrove species Hibiscus tiliaceus (HT) could well be identified. However, for complex and easily-confused mangrove species Sonneratia apetala group 2 (SA2) and other occasionally presented mangroves species (OT), only major distributions could be extracted, with an accuracy of about two-thirds. This study demonstrated that more than 80% of artificial mangroves species distribution could be mapped