bluuebunny/crossref_metadata_2025 huggingface dataset:
UNDERSTANDING OF CROP LODGING INDUCED CHANGES IN SCATTERING MECHANISMS USING RADARSAT-2 AND SENTINEL-1 DERIVED METRICS
Authors: Chauhan S. , Darvishzadeh R. , Boschetti M. , Nelson A.
Published: August 2020

Abstract. Crop lodging – the bending of crop stems from the vertical – is a major yield-reducing factor in cereal crops and causes deterioration in grain quality. Accurate assessment of crop lodging is important for improving estimates of crop yield losses, informing insurance loss adjusters and influencing management decisions for subsequent seasons. The role of remote sensing data, particularly synthetic aperture radar (SAR) data has been emphasized in the recent literature for crop lodging assessment. However, the effect of lodging on SAR scattering mechanisms is still unknown. Therefore, this research aims to understand the possible change in scattering mechanisms due to lodging by investigating SAR image pairs before and after lodging. We conducted the study in 26 wheat fields in the Bonifiche Ferraresi farm, located in Jolanda di Savoia, Ferrara, Italy. We measured temporal crop biophysical (e.g. crop angle) parameters and acquired multi-incidence angle RADARSAT-2 (R-2 FQ8-27° and R-2 FQ21-41°) and Sentinel-1 (S-1 40°) images corresponding to the time of field observations. We extracted metrics of SAR scattering mechanisms from RADARSAT-2 and Sentinel-1 image pairs in different zones using the unsupervised H/α decomposition algorithm and Wishart classifier. Contrasting results were obtained at different incidence angles. Bragg surface scattering increased in the case of S-1 (6.8%), R-2 FQ8 (1.8%) while at R-2 FQ21, it decreased (8%) after lodging. The change in double bounce scattering was more prominent at low incidence angle. These observations can guide future use of SAR-based information for operational crop lodging assessment in particular, and sustainable agriculture in general.
MINING SEISMIC THERMAL ANOMALIES FROM MASSIVE SATELLITE PASSIVE MICROWAVE IMAGES
Authors: Qi Y. , Wu L. X. , Ding Y. F. , He M. , Mao W. F. , Xie B. S. , Zhou S. Y.
Published: August 2020

Abstract. Satellite passive microwave radiative signals are considered to reflect thermal radiation and energy exchange of the Earth’s surface, and the microwave brightness temperature (MBT) has been preliminary adopted for pre-earthquake thermal anomaly monitoring in recent decades. Based on the spatio-temporally weighted two-step method (STW-TSM), this paper aims to uncover the evolution characteristics of MBT anomaly prior to typical earthquakes (EQs), i.e. the Mw7.9 Wenchuan EQ in May 2008, the Nepal EQs in April and May 2015, and the Mw5.8 Yibin EQ in June 2019, and to explore and recognize their differences and commonalities. The results are summarized as: 1) significant MBT positive anomalies appeared east and southwest close to the epicenter before the Wenchuan EQ, and the east anomaly migrated northeastward along Longmenshan faults with aftershocks, then the two anomalies dissipated subsequently with the ceasing of large aftershocks (Mw > 5.5). 2) the MBT positive anomalies of Nepal EQs firstly appeared along the Himalayas and became most obvious 1 day before the main shock, and dissipated subsequently after the first shock, and that of the second shock behaved in the same spatiotemporal patten. 3) regional positive MBT anomalies appeared around the epicenter a half month before the Yibin EQ and diminished over time, and the most obvious abnormal area transferred from the central and northwest to the southwest study area. It exhibited that MBT positive anomalies prefer to appear at the mechanically relaxed zones, such as the loose Quaternary with Wenchuan EQ, the cliff peaks with Nepal EQs, and the mountains surrounding Yibin EQ’s epicenter, which can be attributed to the declining of ground surface microwave dielectric caused by stress activated P-holes during the period of seismogenic preparation. This research provides a novel insight into mining MBT anomalies associate with large earthquakes and a possibility to explore the potential mechanism of such abnormal phenomena.
INVESTIGATION OF THE GROUND MOTION NEAR THE LEANING TOWER OF BAD FRANKENHAUSEN USING SENTINEL-1 PERSISTENT SCATTERER INTERFEROMETRY
Authors: Jänichen J. , Dubois C. , Wolsza M. , Salepci N. , Schmullius C.
Published: August 2020

Abstract. Persistent Scatterer Interferometry (PSI) is a well-established technique for monitoring millimetre deformation of the Earth’s surface. The availability of free and open SAR data with a repeat cycle of 6–12 days from the Copernicus mission Sentinel-1, allows PSI to be used complementary to traditional surveying techniques. Whilst the data resolution may not allow a precise determination of the geolocation of the occurring deformation, observed deformation patterns can be analysed with auxiliary data and often show correlation with the location of geophysical processes or human activities. In this paper, we investigate the particular case of the church tower of Bad-Frankenhausen in the north of the Free State Thuringia, Germany, with PSI processing of Sentinel-1 data. Both pass directions (descending and ascending) are considered, and different motion models are tested in order to retrieve the most accurate displacement pattern around the church location. Deformation up to −6 mm/yr are observed near the church location for the period 2016–2019 in ascending direction.
STRATEGIC OPTIMIZATION OF CONVOLUTIONAL NEURAL NETWORKS FOR HYPERSPECTRAL LAND COVER CLASSIFICATION
Authors: Buehler C. , Schenkel F. , Gross W. , Schaab G. , Middelmann W.
Published: August 2020

Abstract. Hyperspectral data recorded by future earth observation satellites will have up to hundreds of narrow bands that cover a wide range of the electromagnetic spectrum. The spatial resolution (around 30 meters) of such data, however, can impede the integration of the spatial domain for a classification due to spectrally mixed pixels and blurred edges in the data. Hence, the ability of performing a meaningful classification only relying on spectral information is important. In this study, a model for the spectral classification of hyperspectral data is derived by strategically optimizing a convolutional neural network (1D-CNN). The model is pre-trained and optimized on imagery of different nuts, beans, peas and dried fruits recorded with the Cubert ButterflEye X2 sensor. Subsequently, airborne hyperspectral datasets (Greding, Indian Pines and Pavia University) are used to evaluate the CNN's capability of transfer learning. For that, the datasets are classified with the pre-trained weights and, for comparison, with the same model architecture but trained from scratch with random weights. The results show substantial differences in classification accuracies (from 71.8% to 99.8% overall accuracy) throughout the used datasets, mainly caused by variations in the number of training samples, the spectral separability of the classes as well as the existence of mixed pixels for one dataset. For the dataset that is classified least accurately, the greatest improvement with pre-training is achieved (difference of 3.3% in overall accuracy compared to the non-pre-trained model). For the dataset that is classified with the highest accuracy, no significant transfer learning was observed.
SEASONAL SPECTRAL SEPARABILITY OF SELECTED GRASSES: CASE STUDY FROM THE KRKONOŠE MTS. TUNDRA ECOSYSTEM
Authors: Červená L. , Kupková L. , Potůčková M. , Lysák J.
Published: August 2020

Abstract. This paper focuses on spectral separability of closed alpine grasslands dominated with Nardus stricta and competitive grasses Calamagrostis villosa and Molinia caerulea in the relict arctic-alpine tundra located in the Krkonoše Mountains National Park, Czech Republic. The spectral data were acquired and compared at three levels: spectra of a single layer of leaves measured with the ASD FieldSpec4 Wide-Res spectroradiometer coupled with a contact probe in a laboratory (leaf level), canopy spectra measured in a field with the same spectroradiometer using the fiber optic cable with a pistol grip (canopy level), and hyperspectral image data acquired by Nano-Hyperspec® fastened to the DJI Matrice 600 Pro drone (image level). All the measurements were repeated three times during the 2019 vegetation season – in June, July and August. Using the methods of analysis of variance and Welch's (unpaired) t-test, it was proven that there were differences in the results for all three spectra sources. But in general, for each combination of species and each data source a suitable date and intervals of the spectral bands for species separation exist. The most suitable term for data acquisition in order to differentiate all the species is July. At the leaf level, the best species separability was observed in the near-infrared and shortwave infrared spectral ranges. At the canopy and image levels, the visible bands are of higher importance for discriminating the species.
SPATIO-TEMPORAL SALINITY MONITORING OF THE GHAGHARA RIVER USING LANDSAT TIME-SERIES IMAGERY AND MULTIPLE REGRESSION ANALYSIS
Authors: Gašparović M. , Singh S. K.
Published: August 2020

Abstract. Nowadays, water has become one of the most important environmental issues for our ecosystem and is facing major challenges today. During the COVID-19 pandemic, the world has understood the need for good quality of water for sanitation and hygiene. Earth observing satellites plays a critical role in near-real-time detection and monitoring of land and water change and quality. This research presents a methodology for modeling and mapping water salinity in high spatial resolution. Data for modeling were measured on the five monitoring stations (Ayodhya, Basti, Birdghat, Paliakalan, and Turtipar) along the Ghagraha River Basin in India, during the period of 28 years (1985–2013). In this research, Electrical Conductivity (EC) as water salinity parameter modeled by means of Landsat 5 satellite imagery. All available Landsat 5 imagery were acquired on the same date as the ground measurement data was utilized for the modeling. Modeling was done based on linear, 2nd and 3rd polynomial multiple regression analysis. All statistical parameters for accuracy assessment show that 3rd degree polynomial performs better EC prediction capability than 2nd degree polynomial and linear regression. The 3rd degree polynomial multiple regression model RMSE, R2, MAE, p-value were 8.682, 0.993, 6.493, 0.008, respectively. The developed algorithm provides new knowledge that can be widely applied in various environmental research mapping and monitoring like water salinity. Also, this method allows rapid detection of water pollution, which has an important impact on human health, agriculture, and the environment.
DEEP PHENOTYPING CONSIDERING TILE DRAINAGE FROM UAS-BASED MULTISPECTRAL IMAGERY BY CONVOLUTIONAL NEURAL NETWORKS
Authors: Herrero-Huerta M. , Rahmani S. R. , Rainey K. M.
Published: August 2020

Abstract. Subsurface agriculture tile lines can greatly impact plant phenotypic characteristics through spatial variation of soil moisture, plant nutrient, and plant rooting depth. Therefore, location of subsurface tile lines plays a critical role in supporting the above ground plant phentoyping and needs to be considered in plant phenotyping analysis. Unnamed Aerial Systems (UAS) imagery together with deep learning methods can develop strong relations between the vegetation spectra and soil parameters.Here, we consider the capability of deep convolutional neural networks (CNN) to evaluate crop quality based on biomass production derived from soil moisture differences by using UAS-based multispectral imagery over soybean breeding fields. Results are still being evaluated, with particular attention to the temporal and spatial resolution of the data required to apply our approach.
LAND COVER / LAND USE MONITORING FOR AGRICULTURE FEATURES CLASSIFICATION
Authors: Guliyeva S. H.
Published: August 2020

Abstract. Remote sensing applications are directed to agricultural observation and monitoring. It has been huge of scientific papers are dedicated to the research of the contribution of remote sensing for agriculture studies. There are several global challenges needed to be considered within agriculture activities. It can be embraced by the main agriculture sector facing the obstacles impacting the production and productivity of the sector. These are the following options that can be pointed out: biomass and yield estimation; vegetation vigor and drought stress monitoring; assessment of crop phenological development; crop acreage estimation and cropland mapping; and mapping of disturbances and Land Use/Land Cover changes. In this study has been undertaken the realization of satellite-based Land Use/Land Cover monitoring based on various optical satellite data. It has been used satellite images taken from satellites AZERSKY, RapidEye, Sentinel-2B and further processed for Land Use/Land Cover classification. Following the complex approach of the supervised and unsupervised classification, the methodology has been used for satellite image processing. As the main satellite imagery for monitoring crop condition were AZERSKY taken during the growing season, from May to June of 2019 year. The study area was some part of the Sheki region, which covers the central part of the southern slope of the Greater Caucasus Mountain Range within Azerbaijan Republic. In this research work satellite imagery processing and mapping has been carried out on the basis of software application of ArcGIS Pro 2.5.
EELGRASS MAPPING IN ATLANTIC CANADA USING WORLDVIEW-2 IMAGERY
Authors: Forsey D. , Leblon B. , LaRocque A. , Skinner M. , Douglas A.
Published: August 2020

Abstract. Eelgrass (Zostera marina L.) is a marine angiosperm plant that grows throughout coastal areas in Atlantic Canada. Eelgrass meadows provide numerous ecosystem services, and while they have been acknowledged as important habitats, their location, extent, and health in Atlantic Canada are poorly understood. This study examined the effectiveness of WorldView-2 optical satellite imagery to map eelgrass presence in Tabusintac Bay, New Brunswick (Canada), an estuarine lagoon with extensive eelgrass coverage. The imagery was classified using two supervised classifiers: the parametric Maximum Likelihood Classifier (MLC) and the non-parametric Random Forests (RF) classifier. While Random Forests was expected to produce higher classification accuracies, it was shown not to be much better than MLC. The overall validation accuracy was 97.6% with RF and 99.8% with MLC.
THE IMPACT OF FOREST FIRE ON FOREST COVER TYPES IN MONGOLIA
Authors: Geserbaatar N.-E. , Nasanbat E. , Lkhamjav O.
Published: August 2020

Abstract. The objective of this study was the impact of forest fire on forest cover types. This study has identified non-forest and forest area that has seven forest class are included with cedar, pine, larch, birch, birch-pine mixed, birch-larch mixed and cedar-larch mixed, additionally, remote sensing imagery is applied. In contrast, Landsat imagery has been used several classification approaches. Moreover, the current classification has developments in segmentation and object-oriented techniques offer the suitable analysis to classify satellite data. In the object-oriented classification approach, images cluster to homogenous area as forest types by suitable parameters in some level. The accuracy analysis revealed that overall accuracy showed a good accuracy of determination (86.33 percent in 2000 and 93.75 percent in 2011) with regard to identify of the forest cover and type. Furthermore, these results suggest that the Landsat TM and ETM+ data can reliable detect the forest type based upon the segmentation and object-oriented techniques. In generally, our study area is high-risky region to forest fires. It is higher influence to forest cover and tree species and other ecosystems. Overall, wildfire of impact results showed that 25239 ha of forests were changed to burnt area and 52603 ha forests were changed to grassland.
LANDSCAPE URBANIZATION AND FARMLAND REDUCTION FROM 2010 TO 2017 IN SOUTH CHINA
Authors: Hasan S. , Shi W. , Zhu X. , Abbas S.
Published: August 2020

Abstract. Land use land cover (LULC) of Guangdong, Hong Kong, and Macao (GHKM), south china, has undergone significant changes in the last few decades. This study analyze the spatio-temporal LULC changes and urban expansion during 2010–2017 using Landsat TM, ETM+, and OLI. The Landsat images were classified using support vector machine (SVM) into seven classes as forest, grassland, water, fishponds, built-up, bareland, and farmland. Several socioeconomic factors were also obtained to determine their impact on LULC. The result shows that during the studied period, massive economic development and urbanization has increased the built-up area from 8.26% (16,209.61 km2) to 10.31% (20241.77 km2) and substantial reduction in both farmland from 37.64% (73,897.77 km2) to 33.05% (64932.19 km2) and fishponds from 1.25% (2451.12 km2) 0.85% (1674.71 km2). The most dominant conversion were from farmland to built-up and to forest. Furthermore, forest cover increased to 45.02 % (88384.97 km2) in 2017 from 42.38% (83215.59 km2) in 2010 as a result of different afforestation scheme and policies in order to make Greener study area. The analysis of socioeconomic factors shows that increase in gross domestic product (GDP), total investment in fixed assets, and industrialization has led to urbanization growth on a large scale and reduction of farmland. Therefore, there is pressing need for sustainable development and protection of farmlands.
EXPLORING NASA’S HARMONIZED LANDSAT AND SENTINEL-2 (HLS) DATASET TO MONITOR DEFORESTATION IN THE AMAZON RAINFOREST
Authors: Lechler S. , Picoli M. C. A. , Soares A. R. , Sanchez A. , Chaves M. E. D. , Verstegen J.
Published: August 2020

Abstract. Deforestation is a threat to biodiversity and the world’s climate. As agriculture and mining areas grow, forest loss becomes unbearable for the environment. Consequently, monitoring deforestation is crucial for decision makers to create polices. The most reliable deforestation data about the Amazon forest is generated by the Brazil’s National Institute for Space Research (INPE) through its PRODES project. This effort is labor and time intensive because it depends on visual interpretation from experts. Additionally, frequent Amazon’s atmospheric phenomena, such as clouds, difficult image analysis which induces alternative approaches such as time series analysis. One way to increase the number of images of an area consists of using images from different satellites. NASA provides the Harmonized Landsat and Sentinel-2 (HLS) dataset solving spectral dissimilarities of satellite sensors. In this paper, the possibilities of HLS for forest monitoring are explored by applying two deforestation detection methods, Break Detection for Additive Season and Trend (BFAST) monitor and Random Forest, over four different vegetation indices, NDVI, EVI, GEMI and SAVI. The SAVI index used as input for BFAST monitor performed the best in this data setup with 95.23% for deforested pixel, 53.69% for non-deforested pixels. Although the HLS data is described as analysis ready, further pre-processing can enhance the outcome of the analysis. Especially, since the cloud and cirrus cover in the Amazon causes gaps in the dataset, a best pixel method is recommended to create patched images and thus a continuous time series as input for any land cover and land use classification.
ANALYSIS OF LAND USE CHANGE IN INNER MONGOLIA REGION FROM 1978 TO 2018 BASED ON REMOTE SENSING
Authors: Qu W. , Yao Y. , Pang Z. , Lu J. , Yang K. , Li X. , Li L. , Jiang W. , Fu J. , Lei T.
Published: August 2020

Abstract. Land use change is an important theme of the research on the impact of human interaction on global change. In this paper, two phases of land use data were interpretated from remote sensing images of 1978 and 2018, and the spatial-temporal characteristics of land use change in China's Inner Mongolia Region from 1978 to 2018 were analyzed. The results indicated that grasslands and arable land are mainly distributed in the central and eastern region of Inner Mongolia, forest land are mainly distributed in the eastern region, and unused land is mainly distributed in the western region. From 1978 to 2018, the area of arable land in Inner Mongolia decreased by 9,000 km2, forest land increased by 900 km2, and the area of grassland decreased by 1,400 km2. Urban and rural, industrial mines, and residential land continued to increase with an area of 7,800 km2; and unused land increased by an area of 11,500 km2. It was indicated that after 40 years of development, land use in urban and rural areas, industrial mines, and residential areas caused by human activities in the Inner Mongolia Region has increased significantly. At the same time, the policy of returning farmland to forests to protect the environment has achieved significant results.
MIDDLE RESOLUTION REMOTE SENSING IMAGE CHANGE DETECTION BASED ON VECTOR ANALYSIS OF MIDLINE CHANGE
Authors: Ye Q. , Zhang X. , Jiang X. , Huang Q.
Published: August 2020

Abstract. The extraction and timely updating of land use /cover information is a key issue in remote sensing change detection. The change vector analysis (CVA) is a better method of change detection. However, the CVA method is the blindness of artificial choice of threshold. Moreover, the direction cosine of CVA cannot represent the unique point in change vector space and it can’t distinguish the change category effectively. In order to avoid this defect, the midline vector is added to CVA method. In this paper, we use the midline change vector analysis (MCVA) method to detect the land use /cover change in multi temporal remote sensing images. We proposed the two-step threshold method to get the optimal threshold and determine the change and the unchanged region of the difference remote sensing image. We chose Hefei city of Anhui Province as the study area, and adopted two Landsat5 TM images in 2000 and 2008 year as experiment data. We use the MCVA and two-step threshold method to achieve remote sensing change detection. In order to compare the detection accuracy between MCVA method and the traditional post classification comparison method, the paper choose the same area (178 pixels × 180 pixels) in the two images to analyse the accuracy, and compare the accuracy of MCVA method with that of the traditional post classification comparison method based on SVM. The experiment results show that the MCVA method has higher overall accuracy, lower allocation disagreement and quantity disagreement. What’s more, the overall accuracy of MCVA method can reach nearly 60%, much higher than the traditional post classification comparison method (less than 40%). And the MCVA method can effectively avoid the problem of change vector direction cosine values are not unique, and the result is much better than the traditional post classification (SVM) comparison method. It indicates that MCVA is a more effective method in land use / cover change detection for middle resolution multispectral images.
INTER-COMPARISONS AMONG PASSIVE MICROWAVE SEA ICE CONCENTRATION PRODUCTS FROM FY-3D MWRI AND AMSR2
Authors: Chen Y. , Zhao X. , Qu M. , Cheng Z. , Pang X. , Ji Q.
Published: August 2020

Abstract. Passive microwave (PM) sensors on satellite can monitor sea ice distribution with their strengths of daylight- and weather-independent observations. Microwave Radiation Imager (MWRI) sensor aboard on the Chinese FengYun-3D (FY-3D) satellites was launched in 2017 and provides continuous observation for Arctic sea ice since then. In this study, sea ice concentration (SIC) product is derived from brightness temperature (TB) data of MWRI, based on an Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie points algorithm. Our product is inter-compared with a published MWRI SIC product by the Enhanced NASA Team (NT2) algorithm, and three Advanced Microwave Scanning Radiometer 2 (AMSR2) SIC products by the ASI, Bootstrap (BST) and NT2 algorithm. Results show that MWRI SIC are generally higher than AMSR2 SIC and the median of monthly SIC differences are larger in summer. Regional analysis indicates that the smaller differences between AMSR2 SIC and MWRI-ASI SIC occur in the higher SIC areas, and the biases are within ±5% in the Beaufort Sea, Chukchi Sea, East Siberian Sea, Canadian Archipelago Sea and Central Arctic Sea. There is the smallest SIC difference in the Central Arctic Sea with the biases of −0.77%, −0.60%, and 0.19% for AMSR2-ASI, AMSR2-BST and AMSR2-NT2, respectively. The trends of MWRI and AMSR2 sea ice extent and sea ice area are consistent with correlation coefficients all greater than 0.997. Besides, mean SIC, sea ice extent and sea ice area of MWRI-ASI are closer to those of AMSR2 than those of MWRI-NT2.
CHARACTERIZATION OF THE FROST HEAVE DEFORMATIONS IN HIGH LATITUDE AND DEEP SEASONALLY FROZEN SOIL OF INNER MONGOLIA WITH SENTINEL-1 INSAR OBSERVATIONS
Authors: Qu T. , Zhang H. , Niu F. , Shi X. , Li Z.
Published: August 2020

Abstract. Global warming in recent years led to significant permafrost degradation worldwide. Accurate monitoring and comprehensive characterization of the deformation process in seasonally frozen soil is of great importance for constructions in cold regions in China. This work concentrates on the deformation laws and spatio-temporal characteristics of frost heave in high latitude and seasonally frozen soil of Inner Mongolia with time series InSAR observations. With 101 Sentinel-1 descending scenes that covered more than three freeze-thaw cycles for the study area along Shiwei-Labudalin Highway, this study realized the characterization of the frost heave deformations in seasonally frozen soil, and the analysis of the influencing factors of the hydrothermal process with the help of temperature and moisture data from in-situ monitoring. Time series InSAR observations show that most parts of the highway show obvious deformation with a displacement rate of around 30–60 mm/yr. Especially, the deformation evolution in this seasonally frozen soil region changes with seasons, demonstrating a notable annual cyclical characteristic and seasonal activity. Moreover, time series in-situ monitoring data in deep underground boreholes not only further explains the deformation kinematics from InSAR observations, but also provide a better supplement for a more comprehensive mechanism understanding of frost heave deformations.
URBAN ECOLOGICAL SPACE CHANGES OF 338 PREFECTURE-LEVEL CITIES IN CHINA FROM 2016 TO 2017 WITH HIGH-PRECISION URBAN BOUNDARY AND LAND COVER DATA
Authors: Ning X. , Wang H. , Liu Y. , Hao M. , Dong Q. , Xu W. , Cai X. , Fu M. , Liu Y. , Dong W.
Published: August 2020

Abstract. Urban ecological space is a significant factor for sustainable urban development and ecological civilization construction. Traditional urban ecological space analysis mainly used medium-resolution image data at large scales and used high-resolution images at a typical urban scale. Few studies focused on the high-precision ecological space analysis at a national urban scale. In this study, high-precision urban boundary and land cover data were utilized to analyze the urban ecological space change and its reason from 2016 to 2017. Ecological space was extracted and merged from high-precision land cover data, which came from the National Geoinformation Survey data of China. Results showed that in 2017, the total urban ecological space area of 338 prefecture-level cities in China was 8514.2 square kilometers, accounting for 22.4% of the total urban area, which was far below the threshold of 40% for evaluating the urban green coverage in China. Urban ecological space of 184 cities declined. There were four principal reasons for the decrease of urban ecological space. First, the green space supporting the buildings was occupied. Second, the green landscape space near rivers and lakes was occupied. Third, blocks of woodland were occupied. Fourth, the water area was occupied. The reduced urban ecological space was mainly changed into construction sites, structures, and buildings. Urban ecological space of China was seriously insufficient, and it was heavily occupied. Adequate ecological space should be preserved not only in new urban development areas but also in the old urban areas to ensure people a more comfortable living environment.
A BIBLIOMETRIC STUDY OF GRAPHIC VARIABLES USED ON TACTILE MAPS
Authors: Araújo N. S. , Delazari L. S. , Fernandes V. O. , Júnior M. A.
Published: August 2020

Abstract. Tactile maps are accessible for visually impaired people. Therefore, studies on the use of graphic variables in tactile cartography are necessary. A search for papers related to the keyword "tactile map" was conducted in the Scopus database and in the annals of the International Cartographic Conference, between the years 2009 and 2019, with the objective of identifying in the international literature the topics concerning to graphic variables, especially those related to color code systems. Among the full manuscripts found in the Scopus and International Cartographic Association databases, the countries with the largest number of publications in the tactile cartography field were Brazil, United States, Greece, Spain and Germany. The main topics related to tactile maps in the last 10 years were: visual impairment, navigation and wayfinding, perception, universal design, among others. Some of the papers suggest the representation of colors for colorblind people, but there is a gap regarding the use of color codes as graphic variables on tactile maps.
Retention time of lakes in the Larsemann Hills oasis, East Antarctica
Authors: Shevnina Elena , Kourzeneva Ekaterina , Dvornikov Yury , Fedorova Irina
Published: August 2020

Abstract. The study gives first estimates of water transport scale for five lakes located in the Larsemann Hills oasis (69º23' S, 76º20' E) in the East Antarctica. We estimated the lake retention time (LRT) as a ratio of the lake volume to the income and outcome terms of a lake water balance equation. The LRT was evaluated for lakes of epiglacial and land-locked types, and it was assumed that these lakes are monomictic with water exchange existing during a warm season only. We used hydrological observations collected in 4 seasonal field campaigns to evaluate the LRT from the outcome and income terms of the water balance equation. For the epiglacial lakes Progress/LH57 and Nella/Scandrett/LH72, the LRT was estimated of 12–13 and 4–5 years, respectively. For the land-locked lakes Stepped/LH68, Sara Tarn/LH71 and Reid/LH70, our results show a big difference in the LRT calculated from the outcome and income components of the water balance equation. The LRT for these lakes vary depending on the methods and errors inherent to them. We suggested to rely on the estimations from the outcome surface runoff since they are based on the hydrological measurements with better quality. Lake Stepped/LH68 exchange water within less then 1.5 years. Lake Sara Tarn/LH71 and Lake Reid/LH70 are the endorheic ponds with the water exchange through mostly evaporation, their LRT was estimated as 21–22 years and from 8–9 years, respectively. To improve the estimates of the LRT, the hydrological observations are needed to monitor the lakes and streams during the warm season with the uniform observational program.
Future wintertime meridional wind trends through the lens of subseasonal teleconnections
Authors: Sandler Dor , Harnik Nili
Published: August 2020

Abstract. Large-scale atmospheric circulation is expected to change considerably in the upcoming decades, and with it the interaction between Rossby waves and the jet stream. A common feature of midlatitude wintertime variability is upper-tropospheric quasi-stationary number 5 wave packets, which often propagate zonally along the jet. These are collectively referred to as the circumglobal teleconnection pattern (CTP). Their likeness seemingly emerges as a robust signal in future meridional wind trend projections in the Northern Hemisphere, which take the form of a zonal wave encompassing the midlatitudes. We attempt to elucidate this link across timescales (daily, monthly, and climatological), focusing on wave propagation in the jet waveguide in reanalysis and a 36-member ensemble of CMIP5 models. Using empirical orthogonal function (EOF) analysis on 300 hPa subseasonal V anomalies, we first establish the ensemble's skill in capturing the pattern. Then, by investigating EOF phase space, we characterize the CTP's behavior in present-day climatology and how it is projected to change. Under RCP8.5 forcing, most models develop a gradual preference for monthly-mean waves with certain longitudinal phases. The ensemble is thus divided into subgroups based on region of increased wave activity. For each model, this region corresponds to a more pronounced local trend, which helps explain the ensemble projection spread. Additionally, in two test-case models, this coincides with an increasing number of preferably phased wave packets at the synoptic scale. Some signs suggest that differences in CTP dynamics might stem from mean flow interaction, while no evidence was found for the role of tropical diabatic forcing. Thus, we conclude that this climate change response, seemingly a single large-scale wave, is actually comprised of several regional effects which are related to shifts in CTP phase distributions. The strong dynamical disagreement in the ensemble then manifests as significantly different circulation trends, which in turn might affect projected local temperature and precipitation patterns.
Multi-lidar wind resource mapping in complex terrain
Authors: Menke Robert , Vasiljević Nikola , Wagner Johannes , Oncley Steven P. , Mann Jakob
Published: August 2020

Abstract. Scanning Doppler lidars have great potential for reducing uncertainty of wind resource estimation in complex terrain. Due to their scanning capabilities, they can measure at multiple locations over large areas. We demonstrate this ability with dual-Doppler lidar measurements of flow over two parallel ridges. The data have been collected using two pairs of scanning lidars operated in a dual-Doppler mode during the Perdigão 2017 measurement campaign. There the scanning lidars mapped the flow 80 m above ground level along two ridges, which are considered favorable for wind turbine siting. The measurements are validated with sonic wind measurements at each ridge. By analyzing the collected data, we found that wind speeds are on average 10 % higher over the southwest ridge compared to the northeast ridge. At the southwest ridge, the data show, for approach flow normal to the ridge, a change of 20 % in wind speed along the ridge. Fine differences like these are difficult to reproduce with computational flow models, as we demonstrate by comparing the lidar measurements with Weather Research and Forecasting large-eddy simulation (WRF-LES) results. For the measurement period, we have simulated the flow over the site using WRF-LES to compare how well the model can capture wind resources along the ridges. We used two model configurations. In the first configuration, surface drag is based purely on aerodynamic roughness, whereas in the second configuration forest canopy drag is also considered. We found that simulated winds are underestimated in WRF-LES runs with forest drag due to an unrealistic forest distribution on the ridge tops. The correlation of simulated and observed winds is, however, improved when the forest parameterization is applied. WRF-LES results without forest drag overestimated the wind resources over the southwest and northeast ridges by 6.5 % and 4.5 %, respectively. Overall, this study demonstrates the ability of scanning lidars to map wind resources in complex terrain.
Características de pacientes internadas em maternidade de referência para gravidez de alto risco
Authors: Cunha Alfredo de Almeida , Toscano Cristiane Gonçalves Escobar , Nascimento Ângela Carolina , Defanti Marcela Lopes , Baptista Penélope
Published: August 2020

Introdução. A gravidez pode ser classificada como de alto ou baixo risco. A primeira, embora de menor prevalência (10 a 20%), responde pela maioria (mais de 50%) dos desfechos infelizes do ciclo gravídico-puerperal. Por essa razão, as portadoras de gravidez de alto risco devem ser encaminhadas para serviços de referência. Objetivo. Avaliar as características de pacientes atendidas em maternidade de referência para gravidez de alto risco. Métodos. A população estudada consta de pacientes participantes do plano de saúde privado do Exército Brasileiro, mantido pela contribuição dos militares, potencialmente moradoras em todo o território nacional e que foram encaminhadas para maternidade de referência para gravidez de alto risco no Hospital Central do Exército no Rio de Janeiro no período de 1 de abril a 31 de dezembro de 2018. O desenho do estudo foi descritivo, com avaliação da frequência das características das pacientes. Coletaram-se os dados dos prontuários médicos e construiu-se a base de dados a com o programa Epi Info, versão 7, que serviu ainda para a análise estatística. O estudo é parte do projeto de pesquisa aprovado pelo Comitê de Ética em Pesquisa sob número de Certificado de Apresentação para Apreciação Ética (CAAE) 71271617.0.0000.5275. Resultados. Observaram-se 335 pacientes. A idade materna variou de 16 a 45 anos, com média de 27 que, quando estratificada, revelou 27 adolescentes (8,06%), 253 adultas (75,52%) e 55 pacientes que engravidaram tardiamente (16,42%). A idade gestacional por ocasião da internação variou de 24 a 41 semanas, sendo 22 nascimentos prematuros (6,57%) e 313 gestações a termo (93,43%). A paridade variou de 0 (57,31%) a pelo menos uma parturição. O antecedente de parto cesáreo foi observado como 0 (82,28%) e o antecedente de cesariana foi 0 (75,52%), com as demais tendo pelo menos uma cesariana prévia. A data da última menstruação só foi conhecida em 57,31%. A prevalência de gravidez de alto risco foi de 15,22%, assim distribuída em 51 casos: diabetes em 14 casos, hipertensão crônica em 13, hipotireoidismo em 10, pré-eclâmpsia em seis, doença hematológica em dois, VDRL positivo em dois, HIV/AIDS em dois e infecção do trato urinário. Conclusão. As características diferem do Sistema Único de Saúde pela baixa prevalência de adolescentes e maior prevalência de pacientes que engravidaram tardiamente. A alta prevalência de antecedente de cesariana é característica preocupante quanto ao tipo de parto. Apesar de estarmos lidando com pacientes de sistema de saúde privado, supostamente com melhor nível educacional, houve alta prevalência de ignorância da data da última menstruação, falha a ser corrigida pela ultrassonografia. Não se observaram gestações com idade gestacional superior a 41 semanas pela conduta de antecipar o parto (indução ou cesariana) a partir dessa data. A prevalência de gravidez de alto risco foi compatível com a missão do serviço.
O psicólogo orientado pelo construcionismo social e o palhaço: aproximações possíveis.
Authors: Rezende Paula Cristina Medeiros , Souza André Lemos
Published: August 2020

Apoiados na experiência do primeiro autor de formar-se palhaço hospitalar e psicólogo clínico, o presente trabalho se propõe a compreender as relações entre essas duas dimensões sociais. Para tal, foi elaborado um conjunto de descrições teóricas que, aproximadas, deram visibilidade às conexões entre ser palhaço e ser psicólogo que se orienta pelo movimento construcionista social. Essas descrições resgatam os contextos históricos com os quais, tanto a palhaçaria quanto o construcionismo social se inserem. Após a contextualização histórica, é enfatizada a formação de ambos, com ênfase nas posturas que adotam diante das relações. Ademais, foram incluídos na análise trechos dos diários de bordo do percurso de formação do primeiro autor enquanto palhaço que atua no hospital. No diálogo estabelecido, as interseções encontradas realçam que ambos apresentam, na interação com o outro: uma postura curiosa e investigativa; são abertos aos significados compartilhados; mantêm o foco do encontro conversacional para a potência e manifestam interesse genuíno pelas narrativas alternativas.
Estresse e satisfação de pais com o atendimento em unidade de terapia intensiva neonatal
Authors: Taurisano Aila Alves Alvarenga , Enumo Sônia Regina Fiorim , Prebianchi Helena Bazanelli , Andrade André Luiz Monezi
Published: August 2020

Introdução. O estresse dos pais devido a internação de um filho em Unidade de Terapia Intensiva Neonatal (UTIN) pode ter consequências físicas e emocionais importantes nos cuidadores. Objetivos. Analisar as relações entre o estresse dos pais e seus níveis de satisfação com o atendimento. Método. A amostra foi composta por 32 pais que tiveram seus filhos internados em UTIN. Os cuidadores preencheram um questionário sociodemográfico além dos instrumentos PSS: NICU e EMPATHIC-N. Os dados foram analisados a partir de estatística descritiva e inferencial. Resultados. Observou-se um estresse moderado a partir do instrumento PSS:NICU, especialmente nos domínios “Alteração no papel de pai/mãe” e “Número total de experiências” (p < 0,05). Também se detectou correlação positiva entre a idade dos pais e o nível geral de estresse (r = 0,34, p < 0,05). A satisfação dos pais apresentou níveis elevados em todos os domínios. Conclusão. Os dados indicaram que quanto maior a satisfação na avaliação das equipes, menor o estresse dos pais no ambiente da UTIN.
Artificial intelligence with deep learning algorithms to model relationships between total tree height and diameter at breast height
Authors: Ercanli İlker
Published: August 2020

Aim of Study: As an innovative prediction technique, Artificial Intelligence technique based on a Deep Learning Algorithm (DLA) with various numbers of neurons and hidden layer alternatives were trained and evaluated to predict the relationships between total tree height (TTH) and diameter at breast height (DBH) with nonlinear least squared (NLS) regression models and nonlinear mixed effect (NLME) regression models.Area of Study: The data of this study were measured from even-aged, pure Turkish Pine (Pinus brutia Ten.) stands in the Kestel Forests located in the Bursa region of northwestern Turkey.Material and Methods: 1132 pairs of TTH-DBH measurements from 132 sample plots were used for modeling relationships between TTH, DBH, and stand attributes such as dominant height (Ho) and diameter (Do).Main Results: The combination of 100 # neurons and 8 # hidden layer in DLA resulted in the best predictive total height prediction values with Average Absolute Error (0.4188), max. Average Absolute Error (3.7598), Root Mean Squared Error (0.6942), Root Mean Squared error % (5.2164), Akaike Information Criteria (-345.4465), Bayesian Information Criterion (-330.836), the average Bias (0.0288) and the average Bias % (0.2166), and fitting abilities with r (0.9842) and Fit Index (0.9684). Also, the results of equivalence tests showed that the DLA technique successfully predicted the TTH in the validation dataset.Research highlights: These superior fitting scores coupled with the validation results in TTH predictions suggested that deep learning network models should be considered an alternative to the traditional nonlinear regression techniques and should be given importance as an innovative prediction technique.Keywords: Prediction; artificial intelligence; deep learning algorithms; number of neurons; hidden layer alternatives.Abbreviations: TTH (total tree height), DBH (diameter at breast height), OLS (ordinary least squares), NLME (nonlinear mixed effect), AIT (Artificial Intelligence Techniques), ANN (Artificial Neural Network), DLA (Deep Learning Algorithm), GPU (Graphical Processing Units), NLS (nonlinear least squared), RMSE (root mean squared error), AIC (Akaike information criteria), BIC (Bayesian information criterion), FI (fit index), AAE (average absolute error), BLUP (best linear unbiased predictor), TOST (two one-sided test method).
Predictors of Practitioners’ Use of Recommended Early Childhood Intervention Practices
Authors: Dunst Carl J. , Bruder Mary Beth , Maude Susan P. , Schnurr Melissa , Van Polen Angela , Clark Gloria Frolek , Winslow Amanda , Gethmann Dee
Published: August 2020

Findings from research syntheses of adult learning and in-service training studies identified the importance of professional development as a factor influencing practitioner use of recommended and evidence-based intervention practices. These relationships were used to test the hypothesis that practice-specific evidence-based capacity-building professional development would be related to early childhood practitioners’ reported use of recommended early childhood intervention practices. The participants were practitioners working with birth to 3-year-old, 3- to 5-year-old, or birth to 5-year-old children with identified disabilities, developmental delays, or at-risk conditions in home-based or center-based programs or both. The predictors included three practitioner background variables (e.g., years of professional experience) and three professional development variables (e.g., evidence-based professional development practices). Results indicated that the three professional development practice variables accounted for significant amounts of variance in the practitioners’ reported use of 10 different practices beyond that accounted for by the three background variables. The findings highlight the importance of evidence-based capacity-building professional development as a factor influencing practitioners’ judgments of their use of recommended practices.
How do cells adapt to oxygen availability – the role of hypoxia-inducible factor HIF-1
Authors: Sygitowicz Grażyna , Sitkiewicz Dariusz
Published: August 2020

Thanks to the works of this year`s Nobel Laureates, we know much more about how different oxygen levels regulate fundamental
physiological and pathophysiological processes. Variable oxygen availability requires the activation of multiple adaptation processes
from cells. Inhibition of the degradation of α subunit of the hypoxia-inducible factor (HIF-1) is a key reaction of cells to hypoxic conditions.
These conditions lead to generation of the active transcription factor – dimer HIF-1α/β, which activates the expression of plenty
of genes. HIF-1 is then accumulated in the nucleus and binded to DNA in hypoxia-regulated genes. The products of these genes are
involved in generation of new blood vessels (VEGF), erytropoesis process (EPO) and in energy metabolism in mitochondria from oxidative
phosphorylation to aerobic glycolysis (LDH, phosphoglycero kinase, aldolase and GLUT1).