bluuebunny/crossref_metadata_2025 huggingface dataset:
El registro más austral de la Fauna Aseptella-Tuberculatella / Rhipidomella-Micraphelia (Serpukhoviano tardío-Bashkiriano) en la cuenca Calingasta-Uspallata, Argentina
Authors: Sterren Andrea F. , Cisterna Gabriela A. , López-Gamundí Óscar , Vergel María M.
Published: February 2020

The Agua del Jagüel Formation crops out in the southernmost part of the Calingasta-Uspallata basin, in central western Argentina. The lower part of the unit is characterized by a glacigenic sequence with diamictites and mudstones with dropstones. In the latter, elements of the Aseptella-Tuberculatella/Rhipidomella-Micraphelia Fauna (AT/RM Fauna), such as the brachiopods Rhipidomella discreta Cisterna, Micraphelia indianae Simanauskas and Cisterna and Orbiculoidea? sp., the gastropods Murchisonia? sp., and Glabrocingulum (Stenozone)? sp., the bivalve Nuculanidae indet., rugose corals, and indeterminate fragments of nautiloids and hyolithids, have been identified. The importance of this fauna mainly resides in its paleoenvironmental and biostratigraphic implications. AT/RM Fauna is characteristic of restricted environments with relatively low concentrations of oxygen and nutrients in the seafloor, which is consistent with the glaciomarine sequences in fjord-type coasts suggested for the Agua del Jagüel Formation. The relatively low diversity of the fauna in this unit compared to that defined in the El Paso Formation, located further north in the basin, might suggest more restricted sectors for benthic colonization, related to the paleovalle’s isolation from oceanic waters. The postglacial mudstones with marine invertebrate faunas of late Serpukhovian-Bashkirian age would have been deposited in relatively restricted (palaeofjord) part of the Uspallata-Calingasta basin as well as in open shelf environments. The marine flooding over drastically different coast configurations and the availability of nutrient and oxygen in the water column would have propitiated the development of faunas with important differences in the taxonomic composition and the paleoecological structure (AT/RM and Levipustula Faunas) occurring at the same time interval. Radiometric data in Agua del Jagüel Formation and paleontological records in the glacial-postglacial sedimentary succession in the basin (marine invertebrate faunas, palynomorphs and plants) are the most important tools to adjust the timing of the postglacial transgression. This information herein presented complements the scheme proposed for the Carboniferous sequences throughout the central western of Gondwana but is not sufficient to assign a more precise age of the fauna studied within the late Serpukhovian-Bashkirian interval.
Potential mineral resources of the Chilean offshore: an overview
Authors: García Marcelo , Correa Jorge , Maksaev Víctor , Townley Brian
Published: February 2020

The seabed included in the Chilean Exclusive Economic Zone (EEZ) has proper conditions to originate and host different ore deposits, but its geological knowledge is scarce and scattered. The existent studies are very limited in terms of geochemical and mineralogical analyses. The most significant non-energetic mineral resources off Chile correspond to Fe-Mn nodules and crusts, phosphorite deposits, gold and titanium placer deposits and massive sulfide deposits. Fe-Mn nodule sites occur in abyssal plains and seamounts, and around volcanic islands, at depths from 2,890 to 4,332 m, and can contain important concentrations of Cu and Ni (up to 1.38% Cu+Ni) and Co (up to 0.53%). Co-rich Fe-Mn crust occurrences have been reported around the Rapanui and Salas y Gómez islands, with Cu+Ni contents up to 0.3%. Phosphorite sites occur in Holocene sedimentary beds of the continental shelf off Peru and northern Chile, with P2O5 average content of 22.6%. Gold placer deposits are found in beaches and channels of southern Chile and may extend offshore probably in submarine canyons. Titanium placer deposits have been evaluated in different beaches of Chile and could also be continued offshore. Platinum anomalies have been found in channels and bays of southernmost Chile. No samples of Volcanogenic Massive Sulfide (VMS) mineralization are known in the Chilean EEZ to date. However, samples of hydrothermal vents, with potential for polymetallic VMS type mineralization, have been recovered around the Rapanui Island, and several seamount and volcanic island chains indicate favorable conditions for formation and preservation of these ore-deposits. The available geological information on the seabed of the Chilean EEZ is insufficient to estimate the real potential of its non-energetic mineral resources.
SUGARCANE CLASSIFICATION OPTIMIZATION METHOD BASED ON HIGH RESOLUTION SATELLITE REMOTE SENSING IMAGE OF LOVÁSZ HINGE
Authors: He Y. N. , Zhu M. , He Y. Q. , Wu B.
Published: February 2020

Abstract. In recent years, great progress has been made in the study of semantic segmentation in the field of computer vision. The accuracy of semantic segmentation has been constantly improved, and it has been widely applied in the fields of automatic driving, medical treatment and remote sensing image classification.Semantic segmentation in all kinds of neural network structure has been optimized, according to different segmentation task put forward different loss function and different optimization algorithm to improve the accuracy of classification, such as used in the classification task more softmax cross entropy loss in the sigmoid function is used in the classification task, two different loss functions have a different impact on classification results, at the same time, the training data set imbalance can also cause the precision of classification result deviation.In the task of remote sensing image classification, it is often necessary to extract and classify a variety of different land types, such as road, water system, vegetation, etc., from an image, but sometimes it is also necessary to extract one of the land types.Due to remote sensing image contains abundant spectral information, so the remote sensing image classification task is different from ordinary classification task scenarios, common softmax and sigmoid function, the number can not meet the existing remote sensing image classification task, this requires a combination of specific classification task to adjust and optimize the loss function, to adapt to the different classification task.As a major sugarcane planting province in China, guangxi plays an important role in the development of China's sugar industry. Therefore, it is of great significance to propose sugarcane planting area through high-resolution satellite remote sensing image.But because of guangxi planting condition is complicated and changeable weather condition, often appear cloudy, so in high resolution satellite remote sensing image acquisition and there is still a big challenge on extraction and classification, and on the high rate of satellite remote sensing image texture feature of sugarcane and cassava, corn and other crops of texture feature are similar, therefore in the process of classification will easy to misjudge corn, cassava as sugar cane, which led to a decline in classification accuracy.This paper combined with the extraction of sugarcane planting area based on high-resolution satellite remote sensing images by Jaccard loss of Lovasz hinge, and compared the effects of different loss functions on the accuracy of the results through experiments. Finally, it was concluded that combining Jaccard loss of Lovasz hinge could effectively reduce losses and improve the extraction accuracy of sugarcane planting area.
RESEARCH ON HIGH RESOLUTION REMOTE SENSING IMAGE CLASSIFICATION BASED ON SEGNET SEMANTIC MODEL IMPROVED BY GENETIC ALGORITHM
Authors: Xue M. , Wei B. , Yang L.
Published: February 2020

Abstract. SegNet model is an improved model of Full Convolutional Networks (FCN). Its encoder, i.e. image feature extraction, is still a convolutional neural network (CNN). Aiming at the problem that most traditional CNN training uses error back propagation algorithm (BP algorithm), which has slow convergence speed and is easy to fall into local optimum solution, this paper takes SegNet as the research object, and proposes a method of extracting partial weights by using genetic algorithm (GA) to select features of SegNet model, and to alleviate the problem that SegNet is easy to fall into local optimal solution. In the training process of SegNet model, the weight of convolution layer of SegNet model used to extract features is optimized through selection, crossover and mutation of genetic algorithm, and then the improved SegNet semantic model (GA-SegNet model) is obtained by GA. In order to verify the image classification effect of the proposed GA-SegNet model, the same high-resolution remote sensing image data are used for experiments, and the model is compared with maximum likelihood (ML), support vector machine (SVM), traditional CNN and SegNet semantic model without GA improvement. The experimental results show that the proposed GA-SegNet model has the best classification accuracy and effect, which GA overcomes the problem of premature convergence of BP random gradient descent to a certain extent, and improves the classification performance of SegNet semantic model.
ACQUISITION AND ANALYSIS OF TRAFFIC ACCIDENT SCENE INFORMATION BASED ON LASER POINT CLOUD
Authors: Zheng Y. M. , He Y. R. , Wang X. R. , Chen Q. J.
Published: February 2020

Abstract. With the rapid development of the economy, the number of vehicles in China has increased rapidly, which has also brought about frequent ills in traffic accidents. How to improve the efficiency of on-site treatment of traffic accidents, and quickly and accurately conduct accident investigation and analysis is imminent. This paper was based on the point cloud to draw the accident scene DLG, and then used the local elevation difference method to automatically extract the point cloud data of the accident vehicle, and analyzes the vehicle speed calculation, the damage area measurement and the road surface flatness, as well as constructs the overall 3D scene of the accident scene. By analyzing the DLG of accident scene, the point cloud data and the constructed 3D model, which could quickly improve the efficiency of traffic accident investigation. The application results show that the method of information collection and rapid exploration of the accident site what based on the laser point cloud not only provides a basis for traffic accident treatment, but also effectively shortens the exploration time of accident site. At the same time, it cloud relieve the traffic congestion in a certain extent with the obvious results.
THE APPLICATION OF UAV OBLIQUE PHOTOGRAMMETRY IN SMART TOURISM: A CASE STUDY OF LONGJI TERRACED SCENIC SPOT IN GUANGXI PROVINCE
Authors: Kang C. L. , Cheng Y. , Wang F. , Zong M. M. , Luo J. , Lei J. Y.
Published: February 2020

Abstract. With the application of UAV Oblique photography, rapid 3D real-time modeling based on aerial image data has become a new technology in the field of 3D modeling. In view of the current poor mapping and inability to reuse the basic surveying and mapping data in the publicity and display, dynamic planning, etc., based on the UAV photogrammetry technology that is used to rapidly and accurately collect image data of the Longji terraced scenic spot in Guilin, Guangxi, this paper develops a platform by 3D real-time modeling technology with the combination of the high-tech such as digital mapping to realize the three-dimensional navigation and smart tourism of the scenic spot. The practical results show that the application can better meet the actual needs of high precision, large scale, wide audience, strong interaction, etc., which effectively alleviates the contradiction between the protection and utilization of cultural resources in cultural tourism, expands the cultural resources audience, and provides high-tech support to the cultural tourism.
AN EFFICIENT CLUSTERING METHOD FOR DBSCAN GEOGRAPHIC SPATIO-TEMPORAL LARGE DATA WITH IMPROVED PARAMETER OPTIMIZATION
Authors: Li J. W. , Han X. Q. , Jiang J. W. , Hu Y. , Liu L.
Published: February 2020

Abstract. How to establish an effective method of large data analysis of geographic space-time and quickly and accurately find the hidden value behind geographic information has become a current research focus. Researchers have found that clustering analysis methods in data mining field can well mine knowledge and information hidden in complex and massive spatio-temporal data, and density-based clustering is one of the most important clustering methods.However, the traditional DBSCAN clustering algorithm has some drawbacks which are difficult to overcome in parameter selection. For example, the two important parameters of Eps neighborhood and MinPts density need to be set artificially. If the clustering results are reasonable, the more suitable parameters can not be selected according to the guiding principles of parameter setting of traditional DBSCAN clustering algorithm. It can not produce accurate clustering results.To solve the problem of misclassification and density sparsity caused by unreasonable parameter selection in DBSCAN clustering algorithm. In this paper, a DBSCAN-based data efficient density clustering method with improved parameter optimization is proposed. Its evaluation index function (Optimal Distance) is obtained by cycling k-clustering in turn, and the optimal solution is selected. The optimal k-value in k-clustering is used to cluster samples. Through mathematical and physical analysis, we can determine the appropriate parameters of Eps and MinPts. Finally, we can get clustering results by DBSCAN clustering. Experiments show that this method can select parameters reasonably for DBSCAN clustering, which proves the superiority of the method described in this paper.
RESEARCH ON CONSTRUCTION METHOD OF SPATIO-TEMPORAL DATA MODEL BASED ON BEHAVIOR-EVENT
Authors: Li J. W. , Ma Y. , Jiang J. W. , Chen W. D. , Yu N. , Li X.
Published: February 2020

Abstract. Starting from the object-oriented idea, this paper analyses the existing event-based models and the logical relationship between behavioral cognition and events, and discusses the continuity of behavioral cognition on the time axis from the perspective of temporal and spatial cognition. A geospatial data model based on behavioral-event is proposed. The physical structure and logical structure of the model are mainly designed, and the four-dimensional model of “time, space, attribute and event” is constructed on the axis. The organic combination of the four models can well describe the internal mechanism and rules of geographical objects. The expression of data model based on behavior-event not only elaborates the basic information of geospatial objects, but also records the changes of related events caused by the changes of geographic Entities' behavior, and expresses the relationship between spatial and temporal objects before and after the changes of behavior cognition. This paper also designs an effective method to organize spatio-temporal data, so as to realize the effective management and analysis of spatio-temporal data and meet the requirements of storage, processing and mining of large spatio-temporal data.
RESEARCH ON ACCURACY EVALUATION METHOD BASED ON THE RESULT OF IMAGE CORRECTION AT 10000-SCENES CLASS
Authors: Li M. , Chen H. P. , Qiu B. , Xie W. J. , Chen Y. H.
Published: February 2020

Abstract. In the project of National Fundamental Geographic Information Database Updating, the high-qualified orthorectification to a bulk of remote sensing images are the foundations that guarantee the reliable geographic information for national economic construction and social developments. Therefore, quality control for orthorectification, as a crucial step during remote sensing, is of great significance. Basically, the image orthorectification have been achieved by automatic matching of millions of frame-referenced images. In this paper, a make-to-measure method is devised with improvement in the way of sampling and plane precision evaluation. The improved method is verified through nine 1:1million scenes across the nation. Stratified sampling is deployed according to the topographical features and locations geo-information is collected referring to the digital orthorectification model (DOM) results of geographical conditions Monitoring in 2017. The results show that the root mean square errors (RMSEs) calculated by improved methods described in this paper are highly consistent with the RMSEs that are calculated by automatic image matching, which means the improved method can adequately evaluate the nationwide accuracy of image correction and enable to provide reference and instruction for the large-scale, 10000-scenes class quality assurance of the image correction.
STUDY ON ECOLOGICAL AND ENVIRONMENTAL SUITABILITY OF TOURISM RESOURCES DEVELOPMENT IN LIJIANG RIVER BASIN OF GUILIN, CHINA
Authors: Li N. , Wang J. Y. , Wang H. Y. , Yang X. L.
Published: February 2020

Abstract. Ecology and environmental suitability are widely used in urban ecology and regional ecology. However, there is still a lack of systematic evaluation method system for the research impacts of tourism development and tourism resource development on ecology and environment in ecologically sensitive scenic spots. Taking Lijiang River Basin, an ecologically sensitive area with typical karst landforms, as an example, this paper analyses the ecological and environmental suitability of tourism development from the perspective of space. In this paper, TM image and DEM model were firstly used to extract NDVI, slope, slope aspect and other related data. Then, based on the characteristics of the study area, the data of each factor was graded and assigned with a grade value, and the spatial situation of each single factor was analyzed, and superposition was carried out at last. Meanwhile, the study area is divided into three levels for tourism development, namely, unsuitable area, sub-suitable area and suitable area.
REMOTE SENSING DETECTION AND IMPACT ANALYSIS OF TIBETAN HUMAN LANDSCAPE IN JIUZHAIGOU
Authors: Ma J. , Li D. R. , Huq M. E. , Cheng Q. M.
Published: February 2020

Abstract. The qualitative analysis of human landscape vulnerability is widely recognized, but quantitative analysis needs a lot of manpower, material resources as well as time to carry out the social observation. Based on remote sensing and spatial data, quantitative analysis of human landscape vulnerability will change this situation. This paper put forward a vulnerability evaluation model for change detection with remote sensing time series images and spatial tourism data. It is not only a quickly analysis tool which spans from the earth observation to the social observation for quantitative evaluation, but also an assistant tool for decision-making from change detection analysis to trend analysis.The vulnerability evaluation model of Jiuzhaigou Tibetan human landscape highlights the vulnerability of the whole region has increased significantly. The good structure ratios of bio-abundance are the basis of the human landscape protection, but the activities of ice and snow areas may be a natural factor for the migration of native along the tour roads in the valley from hillsides. The impact of tourism activities is far less than the natural environment. But tourism activities have inevitably affected the human landscape protection, and its negative index benefit is almost three times as much as the positive index benefit of protection measures. Especially, with the change of production and lifestyle of native brought by tourism activities, the human landscape is gradually disappearing. Further development of tourism activities will also extend the impact to the Balance Zone. It is urgent to reconstruct the current management model of human landscape.
STUDY ON THE WATER BODY EXTRACTION USING GF-1 DATA BASED ON ADABOOST INTEGRATED LEARNING ALGORITHM
Authors: Sun J. Y. , Wang G. Z. , He G. J. , Pu D. C. , Jiang W. , Li T. T. , Niu X. F.
Published: February 2020

Abstract. Surface water system is an important part of global ecosystem, and the changes in surface water may lead to disasters, such as drought, waterlog, and water-borne diseases. The rapid development of remote sensing technology has supplied better strategies for water bodies extraction and further monitoring. In this study, AdaBoost and Random Forest (RF), two typical algorithms in integrated learning, were applied to extract water bodies in Chaozhou area (mainly located in Guangzhou Province, China) based on GF-1 data, and the Decision Tree (DT) was used for comparative tests to comprehensively evaluate the performance of classification algorithms listed above for surface water body extraction. The results showed that: (1) Compared with visual interpretation, AdaBoost performed better than RF in the extraction of several typical water bodies, such as rivers, lakes and ponds Moreover, the water extraction results of the strong classifiers using AdaBoost or RF were better than the weak basic classifiers. (2) For the quantitative accuracy statistics, the overall accuracy (96.5%) and kappa coefficient (93%) using AdaBoost exceeded those using RF (5.3% and 10.6%), respectively. The classification time of AdaBoost increased by 403 seconds and 918 seconds relative to RF and DT methods. However, in terms of visual interpretation, quantitative statistical accuracy and classification time, AdaBoost algorithm was more suitable for the water body extraction. (3) For the sample proportion comparison experiment of AdaBoost, four sampling proportions (0.1%, 0.2%, 1% and 2%) were chosen and 0.1% sampling proportion reached the optimum classification accuracy (93.9%) and kappa coefficient (87.8%).
A MANAGEMENT OF REMOTE SENSING BIG DATA BASE ON STANDARD METADATA FILE AND DATABASE MANAGEMENT SYSTEM
Authors: Wu H. , Fu K.
Published: February 2020

Abstract. As a kind of information carrier which is high capacity, remarkable reliability, easy to obtain and the other features，remote sensing image data is widely used in the fields of natural resources survey, monitoring, planning, disaster prevention and the others (Huang, Jie, et al, 2008). Considering about the daily application scenario for the remote sensing image in professional departments, the demand of usage and management of remote sensing big data is about to be analysed in this paper.In this paper, by combining professional department scenario, the application of remote sensing image analysis of remote sensing data in the use and management of professional department requirements, on the premise of respect the habits, is put forward to remote sensing image metadata standard for reference index, based on remote sensing image files and database management system, large data serialization of time management methods, the method to the realization of the design the metadata standard products, as well as to the standard of metadata content indexed storage of massive remote sensing image database management.
MULTISPECTRAL REMOTE SENSING IMAGE CLASSIFICATION BASED ON QUANTUM ENTANGLEMENT
Authors: Yang F. , Zhou G. Q. , Xiao J. R. , Li Q. , Jia B. , Wang H. Y. , Gao J.
Published: February 2020

Abstract. Aiming at the problems of low accuracy and slow speed in the current remote sensing image classification algorithm,In order to improve remote sensing image classification, a quantum entanglement algorithm is proposed.The model transforms the classification process of remote sensing image into a random self-organization process of quantum particles in the state configuration space. The state configuration formed by entanglement of quantum particles evolves with time and finally converges to an average probability distribution.Taking Kunming city of Yunnan province as the research area, this paper compares the classification method in this paper with the traditional remote sensing classification method by using the 02C image data of yuanyuan1.Compared with other classification methods, the classification accuracy of this paper meets the requirements.
LAND COVER CLASSIFICATION USING HIGH RESOLUTION SATELLITE IMAGE BASED ON DEEP LEARNING
Authors: Zhu M. , Wu B. , He Y. N. , He Y. Q.
Published: February 2020

Abstract. In the coming era of big data, the high resolution satellite image plays an important role in providing a rich source of information for a variety of applications. Land cover classification is a major field of remote sensing application. The main task of land cover classification is to divide the pixels or regions in remote sensing imagery into several categories according to application requirements. Recently, machine interpretation methods including artificial neural network and decision tree are developing rapidly with certain fruits achieved. Compared with traditional methods, deep learning is completely data-driven, which can automatically find the best ways to extract land cover features through high resolution satellite image. This study presents a detailed investigation of convolutional neural networks for the classification of complex land cover classes using high resolution satellite image. The main contributions of this paper are as follows: (1) Aiming at the uneven spatial distribution of surface coverage, we study the training errors caused by this uneven distribution. An improved SMOTE algorithm is designed for automatic processing the task of sample augmentation. Through experimental verification, the improver algorithm can increase 2–5% classification accuracy by the same network structure. (2) The main representations of the network are also shared between the edge loss reinforced structures and semantic segmentation, which means that the CNN simultaneously achieves semantic segmentation by edge detection. (3) We use Beijing-2 satellite (BJ-2) remote sensing data for training and evaluation with Integrated Model, and the total accuracy reaches 89.6%.
EVOLUTION ANALYSIS OF WATERCOURSE AND SHOALS OF THE XUNJIANG RIVER IN WUZHOU BASED ON TIME-SERIES REMOTE SENSING DATA
Authors: Du Y. L. , Lan G. W. , Hua Y. P. , Chen J. J. , Fan D. L. , Pan Z. G.
Published: February 2020

Abstract. Wuzhou reach of Xunjiang River is the most valuable waterway in Guangxi. The research on the evolution of Xunjiang River in Wuzhou is of great practical significance in the aspects of river regulation and water conservancy construction. Based on Landsat images of normal water level in 1997, 2001, 2005, 2009, 2014 and 2018, this paper firstly conducts a quantitative analysis of evolution of the watercourse and shoals in Wuzhou section of Xunjiang River through using RS and GIS. Then the relationships between the changes of river course and sandbars of the research area and the annual rainfall and vegetation coverage of the upstream region are analyzed to determine the causes of the changes of watercourse and shoals of Xunjiang River in Wuzhou. The results show that the riverway contracted with a contraction amount of about 9.93 km2 from 1997 to 2005, and the expansion volume of sandbars was about 12.21 km2. From 2005 to 2014, the watercourse approximately expanded 12.33 km2, and the shoals shrank about 12.49 km2. From 2014 to 2018, the river course was a contraction trend, reducing about 1.57 km2 in area; the sandbars showed an expanding trend, with an expanding amount of about 0.82 km2. The changes of river channel and shoals are mainly influenced by the incoming water and silt from the upper reaches and are related to the annual rainfall and vegetation coverage of the upstream area. The correlation coefficient between watercourse area and upstream annual rainfall is 0.26, and that between riverway area and upstream vegetation coverage is 0.78. However, the correlations between the area of shoals and the annual rainfall and vegetation coverage of the upstream region are contrary to that of the river channel. The correlation coefficients between the area of shoals and the annual rainfall and vegetation coverage of the upstream district are −0.28 and −0.83 respectively.
SPATIAL AND TEMPORAL CHANGE ANALYSIS OF URBAN HEAT ISLAND EFFECT IN WUHAN CITY
Authors: Huang M. H. , Chen J. J.
Published: February 2020

Abstract. China has experienced rapid urbanization and rapid development of economy in the past decades, resulting in severe damage to the urban ecological environment, causing changes in the urban thermal environment and triggering the urban heat island effect. Moreover, the heat island effect has become a hot topic for scholars. The urban heat island effect refers to the phenomenon that the urban surface temperature is significantly higher than that of surrounding suburbs due to the interaction of man-made and natural. The city is considered to be the largest man-made ecosystem. Its heat island effect will not only change the growth habit of urban vegetation， but also affect the outer environment of urban buildings, it further influences human life and has a great negative impact on human health. Therefore, the study of the spatial-temporal variation characteristics of urban heat island effect and its influencing factors can provide data support for the environmental quality control and urban planning of local government departments. Based on the surface temperature remote sensing product data, we studied the spatial distribution characteristics of urban heat island effect in Wuhan from 2001 to 2013, by calculating the temperature difference between the highest and lowest temperatures and the average interval method for heat island classification. We conducted a trend analysis of vegetation cover from 2001 to 2013 initially explore the effects of vegetation cover n heat island effect. The results showed that: (1) From 2001 to 2013, the intensity of heat island in Wuhan was strong in the city center, weaker surrounding city center and the weakest in the suburbs; From 2001 to 2011, the intensity of heat island in Wuhan city was significantly weaken, among which Huangpi, Xinzhou, Jiangxia, Hannan and Caidian district were weaken, and the urban heat island effect of the city center was enhanced; From 2011 to 2013, the intensity of heat island in Wuhan city presented an increasing trend, among which Huangpi district, Xinzhou district and Caidian district were the most obvious, and the urban heat island effect was slightly weaken. (2) Between 2001 and 2013, the vegetation cover in Huangpi district and Xinzhou district increased significantly, and the vegetation cover in the downtown, Jiangxia district and Dongxihu district decreased significantly, corresponding to the urban heat island effect of Wuhan increased volatility. Our results showed that the spatial distribution of urban heat island effect in Wuhan city fluctuated with time during the study period, and the vegetation cover had a significant influence on it.
SPATIAL AND TEMPORAL ANALYSIS ON THE EVOLUTION CHARACTERISTICS OF LAND SURFACE TEMPERATURE IN THE PEARL RIVER DELTA REGION
Authors: Huang Q. Y. , Jing J. L.
Published: February 2020

Abstract. With the rapid development of social economy and the continuous progress of urbanization in the Pearl River Delta region, the urban surface temperature had a significant change, which seriously affects people's normal life. The MOD11B3 LST_Day_6km product and other auxiliary data were selected as data sources, and the temperature-standard deviation method, the profile method and the coefficient method were used to analyze the spatial and temporal evolution characteristics of land surface temperature in the Pearl River Delta Region from 2000–2015. The research results showed that: (1) the maximum surface temperature from 2000 to 2015 in June, July and August showed an upward trend, while the minimum temperature showed a fluctuation downward trend, and the average temperature changed gently. (2) the surface temperature had a spatial pattern of low on all sides, and high in the middle. The high temperature zone were mainly distributed in Guangzhou, Dongguan, Foshan and Shenzhen cities and the surrounding areas. And the high temperature areas were more concentrated and showed a spreading direction of East and South. (3) The profile analysis showed that the surface temperature had a double-peak feature along the profile line, and the temperature decreased with the distance farther away from the center of the high temperature zone. (4) The correlation coefficient between land surface temperature and elevation, vegetation index were −0.551 and −0.734 respectively, both passing p < 0.01 significance test, indicating that the surface temperature had a significant negative correlation relationship with elevation and vegetation index. Therefore, strengthen urban greening is of great significance to the healthy development of the city.
HOLE-FILLING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD DATA
Authors: Liang P. , Zhou G. Q. , Lu Y. L. , Zhou X. , Song B.
Published: February 2020

Abstract. Due to the influence of the occlusion of objects or the complexity of the measured terrain in the scanning process of airborne lidar, the point cloud data inevitably appears holes after filtering and other processing. The incomplete data will inevitably have an impact on the quality of the reconstructed digital elevation model, so how to repair the incomplete point cloud data has become an urgent problem to be solved. To solve the problem of hole repair in point cloud data, a hole repair algorithm based on improved moving least square method is proposed in this paper by studying existing hole repair algorithms. Firstly, the algorithm extracts the boundary of the point cloud based on the triangular mesh model. Then we use k-nearest neighbor search to obtain the k-nearest neighbor points of the boundary point. Finally, according to the boundary point and its k-nearest neighbor point, the improved moving least squares method is used to fit the hole surface to realize the hole repair. Combined with C++ and MATLAB language, the feasibility of the algorithm is tested by specific application examples. The experimental results show that the algorithm can effectively repair the point cloud data holes, and the repairing precision is high. The filled hole area can be smoothly connected with the boundary.
TEMPORAL AND SPATIAL CHANGES OF DROUGHT IN BEIJING-TIANJIN-HEBEI REGION BASED ON REMOTE SENSING TECHNOLOGY
Authors: Lin X. C. , Chen J. J. , Lou P. Q.
Published: February 2020

Abstract. Drought is an extremely complex natural disaster phenomenon. Sustained drought will lead to the aggravation of water shortage, food production reduction, land desertification and ecological crisis, which will have a great impact on social and economic development, industrial and agricultural production and ecological environment. In recent years, human activities have intensified, the global climate has been warming, and the frequency and intensity of extreme events such as drought have been continuously strengthened, which has been widely concerned by scholars at home and abroad. The Beijing-Tianjin-Hebei region, as the political and cultural center of China, is seriously affected by the drought, which has a certain impact on the social economy of the region. Therefore, it is of great practical significance to carry out remote sensing drought monitoring and understand the temporal and spatial characteristics of drought. Based on MOD13A2 and MOD11A2 in MODIS products, this paper constructed the NDVI-Ts feature space to obtain the Temperature Vegetation Dryness Index (TVDI) in Beijing-Tianjin-Hebei region, and analyzed the spatial and temporal variation characteristics of drought in Beijing-Tianjin-Hebei region from 2001 to 2014 with TVDI as the drought index. The results showed that: (1) the NDVI-Ts feature space in the Beijing-Tianjin-Hebei region showed that when the value of NDVI rose from 0 to 1, the maximum temperature of the surface gradually decreased, while the minimum temperature gradually rose. Both of them had a linear relationship with NDVI, and their spatial shape was similar to a triangle, indicating that the study of drought in Beijing-Tianjin-Hebei was effective by using this spatial feature; (2) the Beijing-Tianjin-Hebei region experienced drought every year in the period of 2001–2014, and the drought was more serious in 2001, 2004, 2008, 2009, 2011 and 2012; (3) The drought in the Beijing-Tianjin-Hebei region was widespread, and the severely arid regions were mainly concentrated in the southern and central regions. In terms of drought intensity, the most frequent occurrences in the period of 2001–2014 were Xingtai, Hengshui, Shijiazhuang, and Handan, and the annual drought in 14 years; the least number of occurrences was Chengde, a total of 4 times; the number of occurrences was second only to Chengde, Tangshan, Zhangjiakou, Qinhuangdao, a total of 7 times. Our results indicated that the Beijing-Tianjin-Hebei region was prone to drought and the drought degree was severe.
THE IMPLEMENTATION OF HESITANT FUZZY SPATIAL CO-LOCATION PATTERN MINING ALGORITHM BASED ON PYTHON
Authors: Liu Z. W. , Wei B. , Kang C. L. , Jiang J. W.
Published: February 2020

Abstract. As one of the important research directions in the spatial data mining, spatial co-location pattern mining aimed at finding the spatial features whose the instances are frequent co-locate in neighbouring domain. With the introduction of fuzzy sets into traditional spatial co-location pattern mining, the research on fuzzy spatial co-location pattern mining has been deepened continuously, which extends traditional spatial co-location pattern mining to deal with fuzzy spatial objects and discover their laws of spatial symbiosis. In this paper, the operation principle of a classical join-based algorithm for mining spatial co-location patterns is briefly described firstly. Then, combining with the definition of classical participation rate and participation degree, a novel hesitant fuzzy spatial co-location pattern mining algorithm is proposed based on the establishment of the hesitant fuzzy participation rate and hesitant fuzzy participation formula according to the characteristics in fusion of hesitant fuzzy set theory, the score function and spatial co-location pattern mining. Finally, the proposed algorithm is written and implemented based on Python language, which uses a NumPy system to the expansion of the open source numerical calculation. The Python program of the proposed algorithm includes the method of computing hesitant fuzzy membership based on score function, the implementation of generating k-order candidate patterns, k-order frequent patterns and k-order table instances. A hesitant fuzzy spatial co-location pattern mining experiment is carried out and the experimental results show that the proposed and implemented algorithm is effective and feasible.
TEMPORAL AND SPATIAL VARIATION OF SOIL MOISTURE IN CHINA BASED ON SMAP DATA
Authors: Lu X. J. , Huang Y. H. , Yan H. B. , Wu C. L. , Luo L. , Zhou B.
Published: February 2020

Abstract. Soil moisture is one of the most important indexes that affect crop growth and meteorological change. Widespread monitoring soil moisture is the forerunner of formulating better soil and water management measure and disaster mitigation strategies. Soil moisture (0–10cm) of SMAP L3 from April 2015 to April 2018 was cited in this paper. The accuracy of SMAP L3 was compared and evaluated by real-time products of CLDAS-V2.0 from CMA Land Data Assimilation system. The results show that SMAP L3 can reflect the objective regular of soil moisture. In addition, the spatial and temporal variation characteristics of soil moisture (0–10cm) at SMAP L3 are also analyzed, which show that the soil moisture is higher in the south than in the north and it is higher in the east than in the west of China. Moreover, the seasonal characteristics are as follows: The soil moisture is the highest in summer, and the second in autumn, while it is lower in winter and spring. Finally, based on the study of temperature and precipitation data from meteorological stations in China, it is found that there is a positive correlation between soil moisture and precipitation in south China, with a correlation coefficient of 0.5027. There is a negative correlation between soil moisture and precipitation in Northeast and northwest China and in Qinghai-Tibet region. What’s more, soil moisture has a positive correlation with temperature, with an average correlation coefficient of 0.6813; the highest correlation coefficient is 0.7752 in Northeast China.
DEBLURRING STUDY OF DMSP/OLS NIGHTTIME LIGHT DATA BY RTSVD
Authors: Ren C. , Yu Z. W. , Deng K. Y. , Pan Y. L.
Published: February 2020

Abstract. DMSP/OLS, as the earliest Nighttime light remote sensing data, has great application value and can greatly improve the data quality by solving the blurring problem existing in the data. The blur reason is analyzed, and a new algorithm of regularization truncated singular value decomposition (RTSVD) combining with Pct image luminescence frequency filtering is proposed, which can effectively eliminate the blurring phenomenon and retain the real information of the image. Firstly, considering that the luminescence frequency of the light source pixel must be higher than that of the non-light source pixel, the luminescence frequency of the pixel in the Pct image is used to exclude the non-light source pixel in the average light image, and then the truncation parameter of the regularized truncation singular value decomposition (RTSVD) is obtained by using the L curve, so as to decompose and recombine the image. The experiments show that the regularized truncation singular value decomposition method combined with Pct image luminescence frequency filtering can remove the blurring phenomenon on the basis of preserving the image information.
CHINA FOREST COVER EXTRACTION BASED ON GOOGLE EARTH ENGINE
Authors: Guo Y. T. , Zhang X. M. , Long T. F. , Jiao W. L. , He G. J. , Yin R. Y. , Dong Y. Y.
Published: February 2020

Abstract. Forest cover rate is the principal indice to reflect the forest acount of a nation and region. In view of the difficulty of accurately calculating large-scale forest area by traditional statistical survey methods, it is proposed to extract China forest area based on Google Earth Engine platform. Trained by the enough samples selected through the Google Earth software, there are nine different random forest classifiers applicable to their corresponding zones. Using Landsat 8 surface reflectance data of 2018 year and the modified forest partition map, China forest cover is generated on the Google Earth Engine platform. The accuracy of China's forest coverage achieves 89.08%, while the accuracy of Global Forest Change datasets of Maryland university and Japan’s ALOS Forest/Non-Forest forest product reach 87.78% and 84.57%. Besides, the precision of tropical/subtropical forest, temperate coniferous forest as well as nonforest region are 83.25%, 87.94% and 97.83%, higher than those of other’s accuracy. Our results show that by means of the random forest algorithm and enough samples, tropical and subtropical broadleaf forest, temperate coniferous forest and nonforest partition can be extracted more accurately. Through the computation of forest cover, our result shows that China has a area of 220.42 million hectare in 2018.
Globalization and Corporate Governance Mechanism, Engine of Change
Authors: Coleman Martha , Wu Mengyun , Baidoo Justices Mark
Published: February 2020

This study is undertaking to investigate the effect of the various dimensions of globalization on corporate governance sub-indices of listed companies in Nigeria and Ghana. The study used a panel data of nonfinancial companies listed on Nigeria and Ghana stock exchange for the period 2012 – 2016 with total observation of 510. Using KOF Globalization Index which entails composite index measuring economic, social and political dimension of globalization. It was discovered that all the dimensions of globalization influence the corporate governance mechanisms. The impact of globalization on sectors of the economies clearly shows that West African countries have been greatly influenced by globalization for resource utilization and improvement in living standard. Although poverty still seems to be high, it is of no doubt that globalization has sharpened the economic efficiency and sustainable growth and financial stability base on continuous improvement on corporate governance mechanisms for the achievement of better economic stability.
The Interaction of Financial Leverage and Firm's Operational Efficiency
Authors: Coleman Martha , Baidoo Justices Mark
Published: February 2020

The study examines the effect of financial leverage on operational efficiency. This study used a panel data of 102 non-financial companies listed on Nigeria and Ghana stock exchange for the period 2012 – 2016. It was revealed that there is an S-curve relationship between financial leverage and working capital management and cash holding. Also there is a significant moderating effect on the relationship between internationalization and dividend payout. This implies that firms that are involved in international activities are highly concerned with having borrowing as part of their capital structure than solely operating with owners’ capital which might be the case of most domestic firms. Relationship between financial leverage and working capital management and cash holding is considered to be non-monotonic which gives management insight of choosing the right leverage management policy to achieve desired goal.
CÓMO DESENMASCARAR A UN FORMALISTA
Authors: Atienza Manuel
Published: February 2020

El formalismo no es la única dolencia que aqueja al Derecho o, mejor, a quienes tienen la función de interpretarlo y aplicarlo. Pero es quizás la más perniciosa en los países latinos por el carácter de enfermedad endémica que ha adquirido en nuestra cultura jurídica. Es cierto, por otro lado, que no todo lo que se llama a veces "formalismo" es igualmente rechazable; e incluso hay un formalismo -un significado de esa expresión- que no lo es en absoluto. El Derecho es, en buena medida, un fenómeno de autoridad; en el mismo, las razones formales (que apelan a la autoridad y no al contenido) tienen una importancia fundamental, lo que no ocurre, por ejemplo, en la moral. Por eso, los jueces no deben dialogar, como a veces se dice, de tú a tú con la Constitución y con las leyes, sino que deben reconocer la autoridad de la una y de las otras; en el caso del juez constitucional, el sometimiento a la Constitución debe ir unido a la deferencia hacia el legislador como principio interpretativo fundamental. Si las cosas no fueran así, no habría Estado de Derecho, simplemente porque el imperio de la ley habría pasado a ser imperio o gobierno de los jueces.
Not all Humans
Authors: Sharp Hasana ,   International Association for Environmental Philosophy
Published: February 2020

Earth scientists have declared that we are living in “the Anthropocene,” but radical critics object to the implicit attribution of responsibility for climate disruption to all of humanity. They are right to object. Yet, in effort to implicate their preferred villains, their revised narratives often paint an overly narrow picture. Sharing the impulse of radical critics to tell a more precise and political story about how we arrived where we are today, this paper wagers that collective action is more effectively mobilized when we identify multiple agencies and diverse historical processes as sites in need of urgent intervention.