bluuebunny/crossref_metadata_2025 huggingface dataset:
A NEURAL NETWORK REGRESSION MODEL FOR ESTIMATING MAXIMUM DAILY AIR TEMPERATURE USING LANDSAT-8 DATA
Authors: Nascetti A. , Monterisi C. , Iurilli F. , Sonnessa A.
Published: May 2022

Abstract. Urban Heat Islands (UHI) phenomenon is a pressing problem for highly industrialized areas with serious risks for public health. Weather stations guarantee long-term accurate observations of weather parameters, such Air Temperature (AT), but lack appropriate spatial coverage. Numerous studies have argued that satellite Land Surface Temperature (LST) is a relevant parameter for estimating AT maps, exploring both linear regression and Machine Learning algorithms. This study proposes a Neural Network (NN) regression model for estimating the maximum AT from Landsat-8 data. The approach has been tested in a variegated morphological region (Puglia, Italy) using a large stack of data acquired from 2018 to 2020. The algorithm uses the median values of LST and Normalized Difference Vegetation Index (NDVI) computed using different buffer radius around the location of each reference weather station (250 m, 1000 m, and 2000 m) to train the NN model with a K-fold cross-validation strategy. The reference dataset was split into three sets using a stratified sampling approach considering the different station categories: rural, High- and Low-density Urban areas respectively. The algorithm was tested with different learning rates (LR) (0.001 and 0.005). The results show that our NN model accuracy improves with the increase of the buffer radius, minimizing the difference in terms of R^2 between training and evaluation data, with an overall accuracy consistently higher than 0.84. Future research could investigate more input variables in the NN model such as morphology or climate variables and test the algorithm on larger areas.
AUTOMATIQUE TECHNIQUE FOR OBTAINING URBAN/SUBURBAN CHANGE DETECTION INFORMATION FROM HIGH-RESOLUTION SATELLITE IMAGES
Authors: Panagiotopoulou A. , Ragia L.
Published: May 2022

Abstract. Multitemporal satellite images can serve for monitoring the same geographical area on the earth and recognize potential changes that have occurred. Actually, high-resolution (HR) satellite images can provide detailed pieces of information about change detection. In the current work automatic change detection is carried out in urban and suburban areas of Crete, Greece. QuickBird images of spatial resolution 60 cm/pixel and WorldView images of resolution 30 cm/pixel, that have a relative time difference of 13 years, are utilized. The scene is classified into buildings, vegetation, water and ground as soon as multiindex representation has been applied on the images. Afterwards, automatic change detection is feasible by pixel-per-pixel comparison of the classified multi-temporal images.The water index and the vegetation index which are utilized in the current work are compared with vegetation and water detections which appear in the literature. The experimental results indicate the superiority of the particular spectral indices. Moreover, the presented methodology not only specifies if changes have arisen or not but also provides particular information regarding the types of changes. In the future, further experimentations could lead to the optimization of the proposed methodology.
INTEGRATING INSAR COHERENCE AND BACKSCATTERING FOR IDENTIFICATION OF TEMPORARY SURFACE WATER, CASE STUDY: SOUTH KALIMANTAN FLOODING, INDONESIA
Authors: Bioresita F. , Hayati N. , Ngurawan M. G. R. , Berliana M.
Published: May 2022

Abstract. Accurate and reliable information about the spatiotemporal level of surface water can be provided by Temporary Surface Water (TSW) monitoring. TSW is defined as a waterbody experiencing frequent drying phases which also correspond to surfaces frequently affected by flooding. In Indonesia, flooding is a frequent disaster, which is about 30% of the total number of disasters that occurred. In mid-January 2021, South Kalimantan Province, Indonesia was hit by a flood. The loss impact of this disaster is estimated at Rp. 1.349 trillion. Synthetic Aperture Radar (SAR), one type of remote sensing, can be used to map the spatial distribution of flood inundation. SAR has the advantage of not being constrained by day or night, weather conditions, and cloud cover or fog. Nowadays, the availability of Sentinel-1 SAR images can increase the use of SAR imagery for flood mapping. Specular reflections that occur on the smooth water surface produce a darker color in the SAR backscattering data, which makes floodwater distinguishable on dry land surfaces. However, inundation between buildings can cause changes in the backscattering value. Several studies have shown that the Interferometric SAR coherence method is a good method for mapping floods in urban areas. The main goal of this study is thus to map Temporary Surface Water (flood) of the South Kalimantan flood event in Indonesia using Sentinel-1 SAR imagery. With backscattering data, flood in a non-urban area can be mapped, but it is difficult to map flood in an urban area. Therefore, we will try to integrate InSAR coherence and SAR backscattering data in order to map flood in the whole study area. The result shows that using backscattering data, bare soil or non-urban flood inundation in the whole study area can be mapped with an overall accuracy of about 76.4%. Yet, flood in an urban area cannot be mapped only with backscattering data. The result from coherence imagery can map flood inundation in an urban area. Thus, integration from both of them can map flood inundation in the whole study area, either urban or non-urban.
SPECTRAL-SPATIAL MULTISCALE RESIDUAL NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION
Authors: He S. , Jing H. , Xue H.
Published: May 2022

Abstract. In recent years, deep neural networks (DNN) are commonly adopted for hyperspectral image (HSI) classification. As the most representative supervised DNN model, convolutional neural networks (CNNs) have outperformed most algorithms. But the main problem of CNN-based methods lies in the over-smoothing phenomenon. Meanwhile, mainstream methods usually require a large number of samples and a large amount of computation. A multi-task learning spectral-spatial multiscale residual network (SSMRN) is proposed to learn features of objects effectively. In the implementation of the SSMRN, a multiscale residual convolutional neural network (MRCNN) is proposed as spatial feature extractors and a band grouping-based bi-directional gated recurrent unit (Bi-GRU) is utilized as spectral feature extractors. To evaluate the effectiveness of the SSMRN, extensive experiments are conducted on public benchmark data sets. The proposed method can retain the detailed boundary of different objects better and yield a competitive performance compared with two state-of-the-art methods especially when the training samples are inadequate.
TRANSFER LEARNING WITH LIMITED SAMPLES FOR THE SAME SOURCE HYPERSPECTRAL REMOTE SENSING IMAGES CLASSIFICATION
Authors: Li W. , Liu Q. , Wang Y. , Li H.
Published: May 2022

Abstract. A classification method for hyperspectral datasets with a limited number of samples based on transferred convolutional neural network (CNN) is proposed. For the CNN model, a lot of labeled samples are needed for the classification of hyperspectral images, but it takes plenty of time and labor to annotate images in the experiment. In our work, CNN model and transfer learning are applied to solve this problem. By pre-training the model on the other hyperspectral dataset, the classification results of the target hyperspectral images can be effectively improved, when the number of trainable samples is limited. Three transfer approaches are chosen for classifying hyperspectral images and their performance are compared and analyzed. With the decrease of the number of samples, transfer learning has an increasing impact on the classification results of hyperspectral images. In the three transferred models, freezing the convolutional layer weights and retraining the fully connected layer weights yields the best classification performance, which reaches 77.23% in classification accuracy, when the number of samples per class is set 10. And when the number of training samples is 5, the classification accuracy growth rate reaches a maximum of 33%. The results indicate that a relatively high classification accuracy could be obtained by training only a limited number of samples with the same domain transferred parameters.
ACTIVE LEARNING ON LARGE HYPERSPECTRAL DATASETS: A PREPROCESSING METHOD
Authors: Thoreau R. , Achard V. , Risser L. , Berthelot B. , Briottet X.
Published: May 2022

Abstract. Machine learning algorithms demonstrated promising results for hyperspectral semantic segmentation. However, they strongly rely on the quality of training datasets. As far as the annotation of hyperspectral images is often expensive and time-consuming, only a few thousand pixels can be labeled. In this context, active learning algorithms select the most informative pixels to be labeled. In the machine learning community, recent active learning methods have overcome the performance of conventional algorithms but do not always scale to large remote sensing images. Therefore, we introduce in this paper a preprocessing method that allows the use of computationally intensive active learning algorithms without significant impacts on their effectiveness.
MULTIVIEW ANALYSIS OF MIXED PIXELS IN THE FRACTION AND REFLECTANCE DOMAINS FOR UNDERSTANDING SUB-PIXEL TOPOGRAPHIC STRUCTURE
Authors: Vidro Y. , Kizel F.
Published: May 2022

Abstract. The spectral mixture analysis (SMA) plays a vital role in spectral data analysis and extraction of subpixel information. However, this technique provides only quantitative information regarding the materials’ abundance fractions within the pixel. On the other hand, the Bidirectional Reflectance Distribution Function (BRDF) indicates that sub-pixel topography affects the surface’s directional reflection to a large extent. Unfortunately, despite the high importance of the BRDF effect and the SMA in remote sensing, only very few research works addressed their mutual influence. Thus, in this work, we propose a study that addresses this mutual influence and suggests an approach for extracting sub-pixel topographic information from mixed pixels. For this purpose, we conducted two multiview imaging experiments under controlled conditions using artificial mixed surfaces. Each surface type is made of two materials and has a varying structural pattern. Then we measured the BiConical Reflectance Factor (BCRF) of each surface from various viewing zenith angles. Next, we applied spectral unmixing to estimate the abundance fraction of three endmembers (EMs) in each surface’s pattern. Finally, we tested the relationship between the sup-pixel topography and the fraction variation vs. the multiple imaging directions. The first experiment results showed that multiview spectral measurements allow the separability between surfaces combining the same materials’ composition but with different sub-pixel structural arrangements.Moreover, such separability is more accurate in the fraction space than in reflectance space. Besides, and most importantly, the second experiment revealed exciting outcomes regarding the relationship between the sub-pixel topographic feature and the variation of the EM fraction vs. the imaging viewing direction. Specifically, we showed a high correlation between the EMs’ fractions and the height of a repetitive element within the sub-pixel topography with a determination coefficient that reaches 0.89.
A HYPERSPECTRAL REMOTE SENSING FUSION TECHNOLOGY BASED ON SPECTRAL NORMALIZATION OF GF AND ZY SERIES SATELLITES
Authors: Liu S. , Li H. , Wang X. , Liu C. , Wang Y. , Huang P.
Published: May 2022

Abstract. Globalized surface coverage, environmental monitoring and other earth system science and high-quality global surface coverage monitoring applications urgently need basic hyperspectral remote sensing reflectance data to support. Spectral reflectance is the core data product of hyperspectral satellite remote sensing data, which is directly related to the application efficiency and quality of hyperspectral satellite. Due to the differences in spectral range, resolution width, central wavelength and other factors between multispectral and hyperspectral remote sensing images, the fusion processing of hyperspectral and multispectral remote sensing images is extremely difficult. In this study, the idea of normalization of physical parameters is adopted. Based on the remote sensing data of multi-source satellites such as GF5, GF1WFV and ZY3, the physical parameters of GF series satellites are fused to improve the spatial resolution of hyperspectral remote sensing images while the physical parameters of surface parameters are maintained. According to the fusion quality evaluation results of noise, information entropy, clarity, spectral angle cosine, correlation coefficient and root mean square error, the fusion results are consistent with the original numerical performance. The noise level, information entropy and clarity index of GF5 and ZY3 fusion are better. The spectral angle cosine, correlation coefficient and root mean square error index of GF5 and GF1 fusion are better. This study provides a new way and method for high-precision and quantitative processing of hyperspectral remote sensing data.
SPATIOTEMPORAL RECOVERY OF HIMAWARI-8 HOURLY AEROSOL OPTICAL DEPTH PRODUCTS VIA THE NESTED BAYESIAN MAXIMUM ENTROPY METHOD
Authors: Xia X. , Zhu Z. , Zhang T. , Wei G. , Ji Y.
Published: May 2022

Abstract. Satellite-derived aerosol optical depth (AOD) is an indispensable parameter when conducting studies related to atmospheric environment, climate change, and biogeochemical cycle. However, current satellite-derived AOD products are limited in related applications due to the large proportion of missing data, and the existed methods mainly concentrate on recovering AOD from polar-orbit satellite sensors. In order to solve these issues and take full use of the preponderance of geostationary satellite sensors in high frequency observation, we propose a spatiotemporal AOD recovery framework integrating multi-time scale AOD products based on the nested Bayesian maximum entropy methodology (NBME), aimed to obtain satellite-derived AOD datasets with low data missing and high accuracy. The experiment results show that the spatial coverage of AOD datasets increases from 20.5% to 70.0%, and the R2 and RMSE of the recovered AOD against ground-based AERONET AOD are approximately 0.62 and 0.19, respectively. Moreover, the further simulated experiments indicate that the proposed method also performs better relatively when comparing with other popular recovery methods. Therefore, the proposed NBME recovery method can obtain a more convincing product both in applicable accuracy and visual quality.
DIGITAL ELEVATION MODELS FOR VEGETATION MONITORING: A CASE STUDY OF A FOREST DISTRICT IN POLAND
Authors: Becek K. , Waclawik P.
Published: May 2022

Abstract. Monitoring vegetation cover is one of the prime aims of remote sensing. Spatio-temporal trajectories of the vegetation state are key findings used by various branches of sciences for research and planning of human activities purposes. Among available vegetation monitoring methods, including forests, are multispectral and microwave indices. However, there are other ways to monitor vegetation/forest cover using surveys performed from satellite orbit. These approaches, including LiDAR and InSAR-derived digital elevation data products (further DEMs), are relatively less discussed in the literature on forest monitoring. Here, we present the results of a study of forest monitoring using the difference between two DEMs captured approximately 15 years apart. DEMs involved are the Shuttle RadarTopography Mission (SRTM) and Copernicus. The study area is located in a forestry district in the southwestern part of Poland. Spatial constraints and forest stand age, and dominant species were available from a forest stand map of the district. The study concludes that DEMs difference can be used for preliminary forest change assessment.
BUILDING EARTH OBSERVATION DATA CUBES ON AWS
Authors: Ferreira K. R. , Queiroz G. R. , Marujo R. F. B. , Costa R. W.
Published: May 2022

Abstract. Image time series analysis and machine learning methods have been widely used in recent years to extract information from big data of remote sensing images. To support image time series analysis, remote sensing images have been modeled as Earth observation (EO) data cubes. EO data cubes can be defined as a set of time series associated to spatially aligned pixels ready for analysis. This paper describes an application for building EO data cubes on the Amazon Web Service (AWS) cloud computing environment. The Data Cube Builder on AWS application is based on a serverless approach to produce EO data cubes from remote sensing images stored in AWS buckets. In this work, we present the architecture of this application and its use to produce EO data cubes for Brazil from big data of remote sensing images.
LAND COVER CHANGE DETECTION NEAR SMALL WATER BODIES BASED ON RGB UAV DATA: CASE STUDY OF THE POND BAROCH, CZECH REPUBLIC
Authors: Jech J. , Komarkova J. , Sedlak P.
Published: May 2022

Abstract. Monitoring changes of land cover near water bodies and water bodies themselves represents a part of environment protection and management. The management can be done at the global or local level. The local level requires more detailed data, which can be collected i.e. by means of aircraft or UAV. The paper describes a case study focused on the utilization of UAV-based RGB data to monitor land cover near the pond Baroch, which is located in the Czech Republic, near the city of Pardubice. The area is specific – it is a small pond accompanied by several smaller pools and connecting canals and surrounded by meadows (often watered), reeds, bushes and some trees Used data were collected by authors by in advance planned flights in August, September, October, November, and December 2021. Support Vector Machine, Maximum Likelihood, Random Trees, and Deep Learning are used as methods to process data and detect land cover changes. Manually interpreted data are used as reference data. Because of the nature of the data (only R, G, and B bands), classification into bare land, the water, vegetation, dry vegetation, and wet vegetation classes only was used. Very high heterogeneity of the observed area, availability of RGB bands only, and very high spatial resolution (1,9 cm per pixel) led to isolated cells.
ANALYSIS OF OVERESTIMATION IN HISTORICAL ICE FLOW VELOCITY MAPS IN WESTERN PACIFIC OCEAN SECTOR, ANTARCTICA
Authors: Ge S. , Cheng Y. , Li R. , Cui H. , Yu Z. , Chang T. , Luo S. , Li Z. , Li G. , Zhao A. , Yuan X. , Li Y. , Xia M. , Wang X. , Qiao G.
Published: May 2022

Abstract. High accuracy reconstruction of historical ice flow velocity fields is crucial in mass balance research of the Antarctic Ice Sheet by utilizing the input-output approach. A historical flow velocity of the Western Pacific Ocean sector region of East Antarctica from 1963 to 1989 was mapped and then corrected for its velocity overestimation. In this study, we analyzed the spatial distribution of the corrected values, and further assessed the relationship between the corrected values and related factors including timespan of image pairs, ice flow velocity, the spatial acceleration of ice flow velocity, and surface slope. The results indicate that the corrected ice flow velocity points are mostly dispersed between a buffer 25 km upstream and a buffer 25 km downstream the grounding line, with the largest mean value emerging in the region between the grounding line and its 25 km downstream. The corrected values exhibit linear correlation with three conditions: 1) when the ice flow velocity range is 0 – 1586 m/y, 2) spatial acceleration is 0 – 69 (m/y)/km, and 3) the slope is 0 – 1.95 degrees and the R2 is higher than 0.7. However, the correlation between timespan and corrected values is not obvious. The corrected values for the floating region have a greater linear correlation with all three factors than the inland region. Ice flow velocity, spatial acceleration, and surface slope all have an influence on the distribution of the corrected values of the reconstructed historical ice flow velocity maps, and may further affect the assessment of the mass balance of the Antarctic Ice Sheet.
IMPROVED ON SNOW COVER EXTRACTION IN MOUNTAINOUS AREAS BASED ON MULTI-FACTOR NDSI DYNAMIC THRESHOLD
Authors: Ma Y. , Zhang Y.
Published: May 2022

Abstract. Snow cover is one of the most active elements of the cryosphere and plays an important role in the surface radiation budget and water balance. Optical satellite remote sensing has become an important tool for snow identification and monitoring. The Sentinel-2 A/B satellite has become an important data source for snow cover extraction because of its high radiation resolution, which reduces the problem of snow/ice saturation in remote sensing observations. The normalized differential snow cover index (NDSI) and the snow cover index considering the forest cover area (NDFSI) are important methods for snow cover extraction, but due to the strong spatial heterogeneity and fast change speed of snow cover in mountainous areas, using the classical fixed threshold method to extract snow cover will result in a large omission error. In this paper, a dynamic threshold method is constructed by synthesizing the effects of the type of snow cover period, aspect and snow underlying land coverage type on the snow cover NDSI/NDFSI. Compared with the high-resolution GF-2 snow map results, the dynamic threshold method has higher accuracy in extracting snow cover, and the overall classification accuracy, omission error, commission error and Kappa coefficient are 97.70%, 0.20%, 11.17% and 0.93 respectively. The dynamic threshold method is used to extract snow cover in a snow cover period in the Babao River Basin. The snow cover rate in the basin fluctuates greatly with time, and the spatial distribution of snow cover is uneven, with more snow cover in mountainous areas and rapid changes in snow cover in river valleys.
BREAK OUT OF A-76 ICEBERG AND RECENT DYNAMIC CHANGES OF ITS ENCOLSURE RIFTS IN RONNE ICE SHELF, ANTARCTICA
Authors: Zhao A. , Cheng Y. , Lv D. , Xia M. , Li R. , An L. , Liu S. , Tian Y.
Published: May 2022

Abstract. Monitoring the stability of the Ronne Ice Shelf, particularly the calving event, is an integral and important part of the study of Antarctic ice sheet mass balance and sea-level rise. This paper presents the results of the analysis of the world’s largest iceberg (A-76 iceberg), which was formed during a calving event in the Ronne Ice Shelf (RIS) on May 13, 2020, and subsequently broke into three fragments (A-76A, A-76B, and A-76C icebergs). The iceberg development cycle, up to this point, including its formation, separation and drift, was observed and analyzed. The detailed development process of rifts associated with the detachment of the A-76 iceberg in front of RIS before calving was analyzed using remote sensing data from multiple sources (ERS, RADARSAT-1, ALOS PALSAR, Landsat-7, and Landsat-8). In addition, based on a total of 66 Sentinel-1 A/B Synthetic Aperture Radar (SAR) images acquired between May 13, 2021 and March 11, 2022, a multi-scale segmentation approach was applied to continuously track the drift path of A-76 icebergs, A-76A, A-76B, and A-76C. We calculated the average drift velocity of these icebergs and found that A-76C iceberg drifted the fastest, followed by A-76A, and A-76B, from May 30, 2021, to March 11, 2022. Future tracking of other iceberg parameters, such as area, thickness, freeboard, and volume, could help assess the melting rates of the icebergs.
IDENTIFYING TIME PATTERNS AT THE FIELD SCALE FOR RETRIEVING SUPERFICIAL SOIL MOISTURE ON AN AGRICULTURAL AREA WITH A CHANGE DETECTION METHOD: A PRELIMINARY ANALYSIS
Authors: Graldi G. , Vitti A.
Published: May 2022

Abstract. A preliminary analysis based on the application of a change detection method for remote sensed soil moisture retrieval at high resolution is presented. Sentinel-1 SAR images are used for studying agricultural areas in Spain, where in situ soil moisture data are available through the International Soil Moisture Network. The total backscattered SAR signal is modelled as the sum of vegetation and soil contributions. At first, the relationship between soil moisture and the co-polarized band of Sentinel-1 was analyzed for all the measurement stations of the area, and the ones with stronger relation were selected. Time series analyses were then conducted at the field scale for studying the interactions between some SAR parameters and the in situ data. The two polarizations and the polarization ratio were analyzed with respect to in situ soil moisture observations and precipitation data in order to identify homogeneous time domains in which the method can be applied in a consistent manner. Analyses show that the main driver of wide range SAR signal variations is the presence of precipitation events. Moreover, SAR coherence and polarization rate manifest specific behaviors that can be exploited either for deepening the knowledge on the role of model parameters and identifying suitable time and space extends in which operate separate estimations of vegetation, soil moisture and soil roughness parameters. Identification and isolation of precipitation driven patterns, as long as the selection of homogeneous time spans and space regions is the basis for improving the capability of satellite based soil moisture retrieval models.
DYNAMIC MONITORING OF ECOLOGICAL ENVIRONMENT QUALITY IN GANSU PROVINCE SUPPORTED BY GOOGLE EARTH ENGINE
Authors: Jin W. , Shi Y.
Published: May 2022

Abstract. With the rapid development of remote sensing technology, this technology has been widely used in the field of ecological environment monitoring. The Remote Sensing Ecological Index (RSEI) is based on remote sensing data and integrates the four factors of greenness, dryness, wetness, and heat. This study took Gansu Province as the research object and calculated its remote sensing ecological index in 2000, 2010 and 2020. The results showed: (1) In general, the average RSEI increased from 0.2665 to 0.3299, reflecting the positive trend of ecological environment quality in Gansu Province. (2) The ecological environment grades in Gansu Province were mainly poor and bad grades, which accounted for about 78% in 2000 and dropped to 65% in 2020. (3) In terms of space, the RSEI index showed a decreasing characteristic from southeast to northwest. The areas with better ecological quality were the Longnan Mountains and the Gannan Plateau, while the areas with poorer ecological quality are the Hexi Corridor. (4) From 2000 to 2010, the ecological environment quality improved slightly, and the area with slight improvement accounted for 30.17%. From 2010 to 2020, the ecological environment was relatively stable, and the stable area accounted for 71.15%. This study supplements the blank of ecological environment quality evaluation in Gansu Province and aims to provide a reference for ecological environment protection and sustainable development in Gansu Province.
A COMBINED COLOR AND WAVE-BASED APPROACH TO SATELLITE DERIVED BATHYMETRY USING DEEP LEARNING
Authors: Al Najar M. , El Bennioui Y. , Thoumyre G. , Almar R. , Bergsma E. W. J. , Benshila R. , Delvit J.-M. , Wilson D. G.
Published: May 2022

Abstract. Knowledge of the evolution of the littoral zone over time is paramount for coastal science and coastal zone management. However, traditional bathymetric surveys using echo-sounding techniques are unsuitable for large-scale applications due to a variety of constraints. On the other hand, remote sensing data such as satellite imagery allow for the development and application of bathymetry inversion models on a large scale. Deep learning is a growing field of artificial intelligence that allows for the automatic construction of models from data and has been successfully used for various Earth Observation and model inversion applications. In this work, we develop and apply a deep learning-based depth inversion model combining wave kinematics and water color information from Sentinel-2 satellite imagery. We present two different satellite image processing methods to augment wave kinematics and color information as inputs to the proposed deep learning-based models. We show competitive results with a state-of-the-art physical inversion method for satellite derived bathymetry, Satellite to Shores (S2Shores), demonstrating a promising direction for the use of deep learning models in Satellite Derived Bathymetry (SDB) and Earth observation in general.
SPATIAL DOWNSCALING OF SMAP SOIL MOISTURE USING THE MODIS AND SRTM OBSERVATIONS
Authors: Mohite J. D. , Sawant S. A. , Pandit A. , Pappula S.
Published: May 2022

Abstract. The main objective of this study is the spatial downscaling of Soil Moisture Active Passive (SMAP) soil moisture (36 km) using the Moderate Resolution Imaging Spectroradiometer (MODIS) and Shuttle Radar Topography Mission (SRTM) products. The study was conducted over India during the post-monsoon (i.e., Rabi) season Daily SMAP soil moisture (SM) data was composited to 3 days to cover the entire study area. MODIS data for the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Albedo, and Land Surface Temperature (LST) were similarly obtained by constructing a three day composite. SMAP soil moisture was used as a dependent variable, whereas, MODIS NDVI, NDWI, Albedo, LST, and SRTM elevation were used as independent variables in a regression analysis for downscaling of SMAP soil moisture. The coefficient of determination (R2) was used to evaluate the performance of multi-variate linear regression (MLR), support vector regression (SVR), and random forest regression (RFR). Each method was used to test the performance of monthly and seasonal models. RFR outperformed MLR and SVR for monthly and seasonal models. Furthermore, a comparison of monthly and seasonal models revealed that the model created on Jan. data performed best (R2=0.80), while R2 of 0.73, 0.61, 0.75, and 0.76 were attained using RFR for seasonal, Dec., Feb., and Mar. models, respectively. In addition, in-situ soil moisture data was used to validate downscaled soil moisture (1 km). Comparison between downscaled soil moisture and in-situ soil moisture showed good agreement with a difference ranging between −9.3 to 7.4 %.
PREDICTING VEGETATION ATTRIBUTES WITH NEURAL NETWORKS AND SENTINEL-1 & 2
Authors: Muro J. , Linstädter A. , Männer F. A. , Schwarz L.-M. , Hoffmann J. , Dubovyk O.
Published: May 2022

Abstract. Evidence suggests that plant traits, plant functional diversity, and species diversity are linked to ecosystem functions to different extents. However, these relationships are sometimes inconsistent because of the presence of environmental gradients (e.g. climate, topography, land use) and scale mismatches between sampling units and landscape processes. Relationships between satellite data and vegetation parameters seem to be also case-specific, which hinders the creation of generalizable models. We have built predictive models of structural parameters and species composition across a broad range of climatic and topoedaphic conditions and management practices across grasslands and forests in Germany. For that, we use Sentinel multitemporal imagery and neural networks. Our models manage to explain 50% of the data variability for structural parameters, show high stability, and can generalize well across environmental gradients and sites. We also found that prediction models of biodiversity parameters show lower predictive capabilities. Spatially continuous models of grassland and forest attributes provide vital information on ecosystem functions at landscape scale. Thus, they can contribute to studying the feedback mechanisms between biodiversity, ecosystem functions, and land management at the scales to which ecological processes occur.
EFFECT OF TEXTURAL FEATURES FOR LANDCOVER CLASSIFICATION OF UAV MULTISPECTRAL IMAGERY OF A SALT MARSH RESTORATION SITE
Authors: Norris G. S. , Leblon B. , LaRocque A. , Barbeau M. A. , Hanson A. R.
Published: May 2022

Abstract. Salt marshes are intertidal ecosystems valuable for services including coastal protection and carbon sequestration. Restoration of salt marshes is popular in this era of climate change and sea-level rise, especially in areas where marshes have been historically altered, including in the Bay of Fundy. Salt marsh restoration involves landcover change through time as a community of halophytic vegetation develops in the study area. Restoration sites are difficult to survey using traditional on-foot methods, and developing remote sensing methods to survey them would increase efficiency of monitoring. The purpose of our study was to assess the capability of UAV multispectral imagery to map landcovers in a salt marsh restoration site in the Musquash Estuary, New Brunswick, Canada. We used the Random Forests (RF) supervised classifier and validated our maps using field data. We also evaluated the importance of textural features by running two classifications, with and without textural features. The classification omitting textural features had lower classification and validation accuracies (96.29 % and 91.23 %, respectively) than the classification and validation accuracies obtained by including textural features (99.56 % and 96.84 %, respectively). Additional work is required to test our method in different locations and seasons.
IMPACT OF CLIMATE ON VEGETATION INDICES OVER RAINFED DISTRICTS OF UTTARAKHAND, INDIA
Authors: Sharma V. , Ghosh S. K.
Published: May 2022

Abstract. Due to a change in the landscape, the climate of Uttarakhand state is changing rapidly, impacting the weather, further affecting human beings and vegetation. Nowadays, remote sensing is a favorable tool for monitoring the vegetation condition using NDVI and EVI. Studying the relationship between vegetation and climate more extensively, it is necessary to better understand the anomaly of ecosystems with climate change. This study is carried out to evaluate the vegetation cover dynamics by establishing the association between climate parameters and vegetation indices over the rain-fed districts (Nainital, Bageshwar, Champawat, Dehradun) of Uttarakhand for the period of 20 years. In this study, Google Earth Engine (GEE) is used to extract the MODIS NDVI and EVI at 250 m spatial resolution & 16-day temporal resolution data. The climate parameters for the rain-fed district (study area) are extracted from Indian Meteorological Department (IMD) Pun website for the period from 2000 to 2020. According to the annual vegetation dynamics, the peak attained by both indices is during the monsoon season, and hence they both show identical patterns to each other. Linear Regression Analysis results show a strong impact of climate on vegetation. Both indices shows a positive correlation with climate parameters where minimum temperature and rainfall are strongly correlated with EVI. Thus, the study reveals that EVI is proven to be more appropriate indices for monitoring vegetation cover as compared to NDVI for the study area.
Progress and challenges in glacial lake outburst flood research (2017–2021): a research community perspective
Authors: Emmer Adam , Allen Simon K. , Carey Mark , Frey Holger , Huggel Christian , Korup Oliver , Mergili Martin , Sattar Ashim , Veh Georg , Chen Thomas Y. , Cook Simon J. , Correas-Gonzalez Mariana , Das Soumik , Diaz Moreno Alejandro , Drenkhan Fabian , Fischer Melanie , Immerzeel Walter W. , Izagirre Eñaut , Joshi Ramesh Chandra , Kougkoulos Ioannis , Kuyakanon Knapp Riamsara , Li Dongfeng , Majeed Ulfat , Matti Stephanie , Moulton Holly , Nick Faezeh , Piroton Valentine , Rashid Irfan , Reza Masoom , Ribeiro de Figueiredo Anderson , Riveros Christian , Shrestha Finu , Shrestha Milan , Steiner Jakob , Walker-Crawford Noah , Wood Joanne L. , Yde Jacob C.
Published: May 2022

Abstract. Glacial lake outburst floods (GLOFs) are among the most concerning consequences of retreating glaciers in mountain ranges worldwide. GLOFs have attracted significant attention amongst scientists and practitioners in the past few decades, with particular interests in the physical drivers and mechanisms of GLOF hazard, and socioeconomic and other human-related developments that affect vulnerability to GLOF events. This increased research focus on GLOFs is reflected in the gradually increasing number of papers published annually. This study offers an overview of recent GLOF research by analysing 570 peer-reviewed GLOF studies published between 2017 and 2021 (Web of Science and Scopus databases), reviewing the content, geographical focus as well as other characteristics of GLOF studies. This review is complemented with perspectives from the first GLOF conference (7–9 July 2021, online) where a global GLOF research community from major mountain regions gathered to discuss the current state of the art of integrated GLOF research. Therefore, representatives from 17 countries identified and elaborated trends and challenges and proposed possible ways forward to navigate future GLOF research, in four thematic areas: (i) understanding GLOFs – timing and processes; (ii) modelling GLOFs and GLOF process chains; (iii) GLOF risk management, prevention and warning; (iv) human dimensions of GLOFs and GLOF attribution to climate change.
Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 2: Analytical modeling
Authors: Jézéquel Erwan , Blondel Frédéric , Masson Valéry
Published: May 2022

Abstract. This work aims at developing an analytical model for the velocity and turbulence in the wake of a wind turbine that takes meandering into account and where the expansion and the meandering of the wake can be independently calibrated. The velocity and turbulence breakdown presented in the companion paper allows a better interpretation of the physical phenomena at stake and facilitates the modelling, in particular when it comes to wakes in non-neutral atmosphere. A model for the dominating terms of these breakdowns is here proposed, using only five input parameters: the widths (in vertical and horizontal directions) of the non-meandering wake, the standard deviation of wake meandering (in both directions) and a mixing length. This model can be used either in the FFOR for a static approach or in the MFOR combined with the dynamic wake meandering model for an unsteady approach. The resulting shapes are tested on a neutral and an unstable LES dataset that was computed with Meso-NH. The model shows good results for the axial velocity in both directions. For the axial turbulence, the horizontal profiles are satisfying but further research is needed on the treatment of shear and the parametrisation of the missing terms to better reproduce the vertical asymmetry.
Addiction to Online Video Games among First Year Medical Students of a College located in Western India
Authors: Zala Darshana , Misra Shobha
Published: May 2022

Introduction: The elderly population is growing much faster. Ageing is a global phenomenon and it is expected that by 2050 every country in the world will have substantial increase in population aged 60 years and above. Malnutrition is common among older people over 60 years of age. They are likely to experience morbidity, premature mortality, poor quality of life and reduced functional ability than normally nourished persons. Malnutrition increases health care costs, reduces productivity, and slows economic growth, which can perpetuate a cycle of poverty and ill-health. Hence combating malnutrition in all its forms is one of the greatest global health challenges. Objective: 1) To assess the nutritional status among elderly population in Hyderabad. 2) To assess the risk factors which are associated with malnutrition among elderly population. Method: A Cross sectional study was conducted in July - September 2021 among elderly persons aged 60 years and above residing in urban slums of Hyderabad. Considering the estimated prevalence of malnutrition among elderly population to be 14.5%, the sample size was calculated as 198 and was rounded off to 200. Data was entered into Microsoft Excel and analysed using Epi Info version 7.2.2.6. A predesigned, pre-tested questionnaire was used to collect data. Malnutrition was assessed using Mini Nutritional Assessment (MNA). Results: Among 200 participants, 52.5% were females. The mean age of the study population was 68.3 years. Among them 18% of the study participants were malnourished, 27% were at risk of malnutrition and 55% had normal nutritional status. Conclusion: Early diagnosis and prompt treatment of elderly people at high risk for malnutrition may improve their nutritional status and prognosis.
Knowledge, Attitude and Practice of Family Planning Methods among Women of Reproductive Age Group attending Primary Health Centres in North-East India
Authors: Chelladurai Chelsia , Keisam Avinash , Touthang Jangkholun
Published: May 2022

Introduction: Warranting access to modern family planning methods constitutes a basic human right and globally around 270 million women of reproductive age group have an unmet need for contraception. The study objective was to estimate the knowledge, misconceptions and practices related to family planning methods among women of reproductive age group attending primary health centre. Method: A cross-sectional hospital-based study was conducted in four health centers from January to February 2019 among women of reproductive age group (15-49 years). Data regarding socio-demographic characteristics, knowledge, attitude and practices pertaining to family planning methods were collected using a pre-tested interview schedule. Descriptive and analytical statistical analyses like mean, proportions, Chi-square and Multivariable logistic regression were conducted. A p value of less than 0.05 was considered statistically significant. Results: In this study 348 women were enrolled, of which 39.4% had adequate knowledge regarding family planning methods. Most participants had a healthy attitude towards the use of family planning methods and considered them beneficial whereas 59% stated their religious faith and cultural beliefs restricted the use of contraceptives. Unmet need of contraceptives was 60.2% while 39.8% used any one of the modern contraceptive methods. Lower age (AOR-1.26), Primi-parous (AOR-2.76) and women having a lower monthly income (AOR-2.51) were more likely to have an unmet need of contraception. Conclusion: Despite easy accessibility and low cost of the contraceptives, their use was limited. The high proportion of unmet needs mandates the scaling up of activities to increase the awareness regarding modern contraceptives. Additionally, behavioral change communication strategies are in great need for implementation to counter the negative attitudes pertaining to family planning practices.
Perceived Stress and Suicidal Ideation among the Farmers: A Cross Sectional Study from the Rural Field Practice Area of a Medical College in Karnataka
Authors: Joshi Anjana R , Bant Dattatraya D
Published: May 2022

Introduction: Psychological Stress and other mental health problems are highly neglected in Indian farming Community. Changing climatic, weather and environmental conditions affected the growth of crops and yield. This has resulted in various stressful conditions faced by the farmers. The psychological stress leads to tensions in the family relationship, variations in their daily life, and poor operational decisions while farming and even may lead to farm accidents, suicides among them. Objectives: To study the Stress levels perceived by the farmers and the factors determining the stress among them and to study the suicidal ideation among farmers in the previous 6 months. Method: A Community based Cross sectional study was carried out in the villages of Kalghatagi taluk, among 324 marginal & small scale farmers of age group 18-60 years, using pretested semi-structured questionnaire comprising of socio-demographic details, occupational characteristics, perceived stress and the reasons for stress. The 4 item Perceived Stress Scale was used for the assessment of Stress levels. Higher Score indicates high levels of Stress. Results: Among 324 farmers, 69.8% of the farmers were males, majority of them belonged to 50-60 years, and most of them were small scale farmers. The mean perceived score was 6.29+4.38 SD. Majority of the farmers reported crop failure (35%) and debt (37%) as the reasons for stress. The major determinants of stress among farmers were crop failure, debt and family problems. Among 324 farmers 25.3% reported suicidal ideation in the previous 6 months. The Mean stress scores were high among those with suicidal ideation (12.69±1.12 SD). Conclusion: The study shows that stress levels were high among farmers and are related to the failure of crops, inability to pay the debts and other factors. The Mental health problems of the farmers should be addressed at early stages to reduce the burden of farmers’ suicides. Primary mental health care should be made available to the rural population.
Design and Fabrication of Portable Tilting Furnace for Aluminium Melting
Authors: Yusuf Muhammad , Bahri Syamsul , Agustinawati Agustinawati
Published: May 2022

Machining is a metal cutting process using a machine tool. The machining process will produce the products and the chips or the scraps. The metal of machining chips made from machining processes can be recycled into new materials. The metal of machining chips can be recycled through the smelting process. This research aims to design and fabrication a crucible furnace that can support the recycling process of the metal scraps of machining chips into a new material—the stove is designed to melt non-ferrous metals using gas fuel. The tilting-pot crucible furnace type was created in this research. The crucible furnaces are used for nonferrous metals such as bronze, brass, zinc, and aluminum alloys. The crucible furnace capacities are limited to 10 kg. Liquefied Petroleum Gas (LPG) was used as the fuel for the furnace. The construction of the stove is cylindrically designed. The burner was installed upright at the bottom of the stove. The combustion process is done by mixing fuel LPG gas and air from the blower. The stove was tested on the aluminum machining chips using locally available materials in Lhokseumawe Aceh. The thermometer Lutron TM-906A was used to measure the temperature inside the crucible. From the results of the design and testing of the furnace that has been done, it can be concluded that the aluminum machining chips of 6kg are melting at a temperature of 640oC for 52 minutes, and the LPG gas fuel required is 1.30kg. The crucible furnace was designed to attain a maximum temperature of 700°C.
Las primeras emisiones de Gordiano III (238-244 d. C.) y la apertura de la ceca de Viminacium en Moesia superior
Authors: Royo Martínez María del Mar
Published: May 2022

En este trabajo analizamos las emisiones más antiguas de uno de los primeros emperadores de la denominada crisis del siglo III d. C.: Gordiano III (238-244 d. C.). Durante los primeros años en los que estuvo al frente del imperio bajo la tutela de su madre Maecia Faustina y del círculo de consejeros de ésta, se desarrollaron algunas transformaciones en la administración y en los asuntos fiscales y monetarios. Una de ellas fue la creación de la ceca de Viminacium en la provincia balcánica de Baja Moesia. En el presente artículo se estudiarán tanto las posibles causas que determinaron su aparición, como las consecuencias de su fundación.