The second data release from the European Pulsar Timing Array IV. Search for continuous gravitational wave signals

We present the results of a search for continuous gravitational wave signals (CGWs) in the second data release (DR2) of the European Pulsar Timing Array (EPTA) collaboration. The most significant candidate event from this search has a gravitational wave frequency of 4-5 nHz. Such a signal could be generated by a supermassive black hole binary (SMBHB) in the local Universe. We present the results of a follow-up analysis of this candidate using both Bayesian and frequentist methods. The Bayesian analysis gives a Bayes factor of 4 in favor of the presence of the CGW over a common uncorrelated noise process, while the frequentist analysis estimates the p-value of the candidate to be 1%, also assuming the presence of common uncorrelated red noise. However, comparing a model that includes both a CGW and a gravitational wave background (GWB) to a GWB only, the Bayes factor in favour of the CGW model is only 0.7. Therefore, we cannot conclusively determine the origin of the observed feature, but we cannot rule it out as a CGW source. We present results of simulations that demonstrate that data containing a weak gravitational wave background can be misinterpreted as data including a CGW and vice versa, providing two plausible explanations of the EPTA DR2 data. Further investigations combining data from all PTA collaborations will be needed to reveal the true origin of this feature.Comment: 12 figures, 15 pages, to be submitte

Comparing Machine Learning Algorithms for Pixel/Object-Based Classifications of Semi-Arid Grassland in Northern China Using Multisource Medium Resolution Imageries

Knowledge of grassland classification in a timely and accurate manner is essential for grassland resource management and utilization. Although remote sensing imagery analysis technology is widely applied for land cover classification, few studies have systematically compared the performance of commonly used methods on semi-arid native grasslands in northern China. This renders the grassland classification work in this region devoid of applicable technical references. In this study, the central Xilingol (China) was selected as the study area, and the performances of four widely used machine learning algorithms for mapping semi-arid grassland under pixel-based and object-based classification methods were compared: random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and naive Bayes (NB). The features were composed of the Landsat OLI multispectral data, spectral indices, Sentinel SAR C bands, topographic, position (coordinates), geometric, and grey-level co-occurrence matrix (GLCM) texture variables. The findings demonstrated that (1) the object-based methods depicted a more realistic land cover distribution and had greater accuracy than the pixel-based methods; (2) in the pixel-based classification, RF performed the best, with OA and Kappa values of 96.32% and 0.95, respectively. In object-based classification, RF and SVM presented no statistically different predictions, with OA and Kappa exceeding 97.5% and 0.97, respectively, and both performed significantly better than other algorithms. (3) In pixel-based classification, multispectral bands, spectral indices, and geographic features significantly distinguished grassland, whereas, in object-based classification, multispectral bands, spectral indices, elevation, and position features were more prominent. Despite the fact that Sentinel 1 SAR variables were chosen as an effective variable in object-based classification, they made no significant contribution to the grassland distinction

An Assessment Framework for Grassland Ecosystem Health with Consideration of Natural Succession: A Case Study in Bayinxile, China

Grassland health assessment is the basis for formulating grassland protection policy. However, there are few assessment methods that consider the angle of natural succession for northern China’s regional native grassland with excessive human activities. The main purpose of this study is to build an assessment system for these areas from the perspective of natural succession. Besides, the minimal cumulative resistance (MCR) model was used to extract potential ecological information from the study area as a supplementary reference for the assessment results. The result for Bayinxile pasture, a typical semiarid steppe with excessive human activities located in northern China, showed that: (1) The ecological function of eastern hilly area was better than that of other regions and the western area was lowest as a whole. (2) The river was the most important ecological network in the whole grassland in that it was of vital significance in the prevention of retrogressive succession and in the linking of ecological communities. (3) The density of ecological network was closely related to the intensity of human activities, and farmland and roads had great negative influence on the connection of the grassland ecological network. We further proposed an ecological control zone and made suggestions for Bayinxile ecological management to prevent grassland degradation based on the above results. This study should provide a new perspective for grassland health assessment and sustainable development of regional grassland

Comparing the performance of machine learning algorithms for estimating aboveground biomass in typical steppe of northern China using Sentinel imageries

Monitoring aboveground biomass (AGB) is crucial for assessing, managing, and utilizing grassland ecosystems. While the technical form of combining remote sensing and machine learning algorithms is widely used to estimate AGB at a regional scale, few studies have assessed and compared the performance of popular algorithms on the typical steppe in northern China. In this study, the northern Xilinhot, a representative area of typical steppe in China, was selected as the study area to compare the performance of six widely used machine learning algorithms for AGB estimation, namely stepwise linear regression (SLR), partial least square regression (PLS), principal component regression (PCR), random forest (RF), support vector machines (SVM), and k-nearest neighbors (KNN). Additionally, the study explored the modeling capability of multisource variables from Sentinel imagery and auxiliary data. The results showed that (1) considering the aspects of prediction accuracy, noise resistance, ease of operation, and transferability, the SLR algorithm is more suitable for estimating typical steppe AGB in northern China at the Sentinel scale. (2) Vegetation Indices (VI) play a significant role in the development of selected models, with significant contributions from both traditional and soil-adjusted indices. (3) Sentinel C-band synthetic aperture radar (SAR) is unsuitable for modeling typical steppe AGB. (4) Among the selected environmental factors, only clay content and soil pH are significantly linearly correlated with AGB, while elevation, precipitation, temperature, soil pH, and sand content are advantageous for RF prediction. This study can provide important technical references for the research on AGB in typical steppe in northern China

Spatial-Temporal Characteristics and Driving Forces of Aboveground Biomass in Desert Steppes of Inner Mongolia, China in the Past 20 Years

The desert steppe serves as a transitional zone between grasslands and deserts, and long-term monitoring of aboveground biomass (AGB) in the desert steppe is essential for understanding grassland changes. While AGB observation techniques based on multisource remote-sensing data and machine-learning algorithms have been widely applied, research on monitoring methods specifically for the desert steppe remains limited. In this study, we focused on the desert steppe of Inner Mongolia, China, as the study area and used field sampling data, MODIS data, MODIS-based vegetation indices (VI), and environmental factors (topography, climate, and soil) to compare the performance of four commonly used machine-learning algorithms: multiple linear regression (MLR), partial least-squares regression (PLS), random forest (RF), and support vector machine (SVM) in AGB estimation. Based on the optimal model, the spatial–temporal characteristics of AGB from 2000 to 2020 were calculated, and the driving forces of climate change and human activities on AGB changes were quantitatively analyzed using the random forest algorithm. The results are as follows: (1) RF demonstrated outstanding performance in terms of prediction accuracy and model robustness, making it suitable for AGB estimation in the desert steppe of Inner Mongolia; (2) VI contributed the most to the model, and no significant difference was found between soil-adjusted VIs and traditional VIs. Elevation, slope, precipitation, and temperature all had positive effects on the model; (3) from 2000 to 2020, the multiyear average AGB in the study area was 58.34 g/m2, exhibiting a gradually increasing distribution pattern from the inner region to the outer region (from north to south); (4) from 2000 to 2020, the proportions of grassland with AGB slightly and significantly increasing trend in the study area were 87.08% and 5.13%, respectively, while the proportions of grassland with AGB slightly and significantly decreasing trend were 7.76% and 0.05%, respectively; and (5) over the past 20 years, climate change, particularly precipitation, has been the primary driving force behind AGB changes of the study area. This research holds reference value for improving desert steppe monitoring capabilities and the rational planning of grassland resources

Identifying Buildings with Ramp Entrances Using Convolutional Neural Networks

The Americans with Disabilities Act (ADA) is a civil rights law that was signed into law in 1992 by President George H.W. Bush. The law requires wheelchair access be made available for buildings built after 1992. Buildings under the law include retail stores, hotels, banks and most other public buildings. However, there are a large percentage of buildings built before 1992 that are not wheelchair accessible. In addition, ADA does not require the location of ramp to be at the front of the building. This is an inconvenience for individuals who use wheelchairs to access a building, as a) the building may not have a ramp or b) they may have to roll around the building to where the ramp may be located. Hence, in this paper, we describe a prototype artificial intelligent system, which takes the input of a building image, and produces the output prediction for whether the building has a ramp. The system uses a deep learning technique, convolution neural network (CNN) to classify building images. We evaluated our method on a sample dataset of building images that we collected and building images from online sources. Training and validation accuracies were very high, 98.9 and 95.6 percentages respectively

Generating Salt-Affected Irrigated Cropland Map in an Arid and Semi-Arid Region Using Multi-Sensor Remote Sensing Data

Soil salinization is a widespread environmental hazard and a major abiotic constraint affecting global food production and threatening food security. Salt-affected cropland is widely distributed in China, and the problem of salinization in the Hetao Irrigation District (HID) in the Inner Mongolia Autonomous Region is particularly prominent. The salt-affected soil in Inner Mongolia is 1.75 million hectares, accounting for 14.8% of the total land. Therefore, mapping saline cropland in the irrigation district of Inner Mongolia could evaluate the impacts of cropland soil salinization on the environment and food security. This study hypothesized that a reasonably accurate regional map of salt-affected cropland would result from a ground sampling approach based on PlanetScope images and the methodology developed by Sentinel multi-sensor images employing the machine learning algorithm in the cloud computing platform. Thus, a model was developed to create the salt-affected cropland map of HID in 2021 based on the modified cropland base map, valid saline and non-saline samples through consistency testing, and various spectral parameters, such as reflectance bands, published salinity indices, vegetation indices, and texture information. Additionally, multi-sensor data of Sentinel from dry and wet seasons were used to determine the best solution for mapping saline cropland. The results imply that combining the Sentinel-1 and Sentinel-2 data could map the soil salinity in HID during the dry season with reasonable accuracy and close to real time. Then, the indicators derived from the confusion matrix were used to validate the established model. As a result, the combined dataset, which included reflectance bands, spectral indices, vertical transmit–vertical receive (VV) and vertical transmit–horizontal receive (VH) polarization, and texture information, outperformed the highest overall accuracy at 0.8938, while the F1 scores for saline cropland and non-saline cropland are 0.8687 and 0.9109, respectively. According to the analyses conducted for this study, salt-affected cropland can be detected more accurately during the dry season by using just Sentinel images from March to April. The findings of this study provide a clear explanation of the efficiency and standardization of salt-affected cropland mapping in arid and semi-arid regions, with significant potential for applicability outside the current study area

Response of plant traits of Stipa breviflora to grazing intensity and fluctuation in annual precipitation in a desert steppe, northern China

As a grassland type distributed in the desert steppe with warmer climate in the steppe region of central Asia, Stipa breviflora steppe has the following characteristics, i.e. transition and vulnerability. Therefore, it is susceptible to global climate changes and anthropogenic disturbances. The resistance and resilience of a plant community to the disturbances or climate changes mainly depend on plant functional traits of the dominant species. Yet there are few studies focus on the interaction between annual precipitation and grazing pressure on the variation of plant traits of desert steppe. We investigated the functional traits of S. breviflora under contrasting annual precipitations (wet and dry years) and different long-term grazing intensities (no grazing, light grazing, moderate grazing, MG, and heavy grazing, HG), and revealed the influence of grazing and the precipitation on the variability of functional traits of S. breviflora. Moreover, we discussed the vulnerable grassland type's adaptation mechanism to grazing disturbances and precipitation changes, and its grazing adaptation strategy under different annual rainfall precipitation conditions. Plant height, coverage, above-ground biomass, leaf length, single leaf area of S. breviflora, were sensitive to grazing interference and precipitation, while leaf dry matter content, specific leaf area, and leaf nitrogen content were inert traits. S. breviflora's resistance and resilience in response to grazing disturbance strongly depended on rainfall conditions. In the dry year, the functional traits of S. breviflora showed higher plasticity in responses to grazing treatment, in which moderate interference was favorable for the compensatory growth of S. breviflora. The functional traits of S. breviflora were not sensitive to the drastic changes of precipitation in grazing exclusion plot for many years. It was concluded that grazing in dry year had an adverse effect on S. breviflora desert steppe, and that light grazing or banning grazing in dry years is a better management practice for the desert steppe. (C) 2020 The Authors. Published by Elsevier B.V