bluuebunny/crossref_metadata_2025 huggingface dataset:
PENGARUH PEMBELAJARAN PARTICIPATIVE TEACHING AND LEARNING GURU TERHADAP PENINGKATAN PRESTASI BELAJAR AFEKTIF SISWA DI MASA PANDEMI
Authors: Butarbutar Injen P. , Munthe Bangun , Sianipar Herlina H. , Nainggolan Juliper , Murniarti Erni
Published: May 2022

The purpose of this study was to see the extent of the influence of teachers' Participative Teaching Learning on the Improvement of Students' Affective Learning Achievement during the Pandemic Period. The number of samples is 45 people. This study uses a descriptive method, while the data analysis carried out in the hypothesis research is the Pearson product-moment correlation with the data collection tool is a questionnaire for Variable X Effect of Teacher Participative Teaching Learning and Variable Y Student Affective Learning Achievement. To find out how far the influence of teacher's participative teaching-learning on improving students' affective learning achievement during the pandemic, the Pearson Product Moment Correlation Statistical Test was used. From the test results obtained correlation test (r) 0.65 with a determination test of 42.25% and to determine whether or not the correlation coefficient is significant at the real level (α) = 0.05, a "t" test is held with the test criteria if the account is obtained from the calculation greater (>) than a table at a significant level of 1-0.05 with DK = n-2 then the hypothesis is accepted and in other cases rejected. From the test results obtained count > table (5.61 > 1.67), then the hypothesis is accepted. Thus, it can be stated that there is a significant influence between the Influence of Teacher Participative Teaching Learning on Increasing Students' Affective Learning Achievement during the Pandemic Period.

 

 

Abstrak

Tujuan penelitian ini adalah untuk melihat sejauhmana Pengaruh Pembelajaran Participative Teaching Learning Guru Terhadap Peningkatan Prestasi Belajar Afektif Siswa di Masa Pandemi. Jumlah sampel sebanyak 45 orang. Penelitian ini menggunakan metode deskriptif, sedangkan analisis data yang dilakukan dalam penelitian hipotesis adalah korelasi product moment pearson dengan alat pengumpulan data adalah angket untuk Variabel X Pengaruh Pembelajaran Participative Teaching Learning Guru dan Variabel Y Prestasi Belajar Afektif Siswa. Untuk mengetahui sejauhmana pengaruh pembelajaran participative teaching learning guru terhadap peningkatan prestasi belajar afektif siswa di masa pandemi, maka digunakan Uji Statistik korelasi product moment pearson. Dari hasil pengujian diperoleh pengujian korelasi (r) 0,65 dengan uji determinasi sebesar 42,25% dan untuk mengetahui signifikan tidaknya koefisien korelasi pada taraf nyata (α) = 0.05 maka diadakan uji “t” dengan kriteria pengujian jika thitung yang didapat dari perhitungan lebih besar (>) dari ttabel pada taraf signifikan 1-0,05 dengan dk= n-2 maka hipotesis diterima dan dalam hal lain ditolak. Dari hasil pegujian diperoleh thitung > ttabel (5,61 > 1,67), maka hipotesis diterima. Dengan demikian dapat dikemukakan Berpengaruh yang berarti antara Pengaruh Pembelajaran Participative Teaching Learning Guru Terhadap Peningkatan Prestasi Belajar Afektif Siswa di Masa Pandemi.
Os limites do poder de Polícia do Estado versus o abuso de autoridade na aplicação de meios não letais pela Polícia Militar
Authors: Weinfortner Alexandre José , Meira Aline Fátima de
Published: May 2022

O presente artigo teve por objetivo analisar os limites de atuação da Polícia Militar com o uso de meios não letais para a configuração ou não do crime de abuso de autoridade. Essa é uma questão de extrema importância para os dias atuais, visto que a atuação do Estado está cada vez mais sendo observada e cobrada por muitos órgãos de proteção aos direitos humanos, bem como uma nova ideologia referente à progressão de meios de contenção. Para tanto, utilizou-se a pesquisa descritivo-explicativa do tipo documental-bibliográfica, com viés dedutivo, dedicando-se aos problemas analítico-conceituais tratados em alguns casos julgados, bem como a explicação dada pela letra da lei. Inicialmente, foram analisados os aspectos do poder de polícia do Estado, sua estrutura e importância.
Circo na educação infantil: o papel da arte circense como método lúdico para o desenvolvimento e aprendizagem
Authors: Schlichting Letícia , Martins Materson Christofer
Published: May 2022

Este trabalho tem como finalidade analisar a possibilidade da utilização da arte circense na Educação Infantil, pois percebemos que ela pode representar uma possibilidade de desenvolver atividades lúdicas e significativas para as crianças, transformando uma prática simples de aquisição de conhecimento em momentos prazerosos para as crianças. A intenção de usar tais métodosem sala de aula não se limita apenas a aprender uma grade de conteúdos, e sim aprender sobre manifestações culturais, novas formas de desenvolver a mente e o corpo. Teremos como ponto de partida o seguinte questionamento: qual o papel da arte circense como método lúdico para o desenvolvimento e a aprendizagem? O objetivo geral do trabalho seria estudar a arte circense na Educação Infantil. Os nossos objetivos específicos são: descrever o papel da arte circense como método lúdico para o desenvolvimento e aprendizagem; identificar elementos ou atividades circenses no contexto da Educação Infantil; especificar atividades circenses para desenvolvimento psicomotor da criança. A metodologia utilizada foi a pesquisa bibliográfica e análise qualitativa de obras que abordam experiências circenses em sala de aula.
O direito à educação para pessoas em privação de liberdade
Authors: Batista Jackson Bruno Alves , Miranda Eduardo Soncini
Published: May 2022

O presente artigo tem por finalidade conhecer e descrever os direitos dos indivíduos privados de liberdade, partindo da concepção de que a garantia do direito à educação impacta no processo de reintegração social desses indivíduos.Para tanto, utilizou-se como metodologia a pesquisa bibliográfica de cunho qualitativo e quantitativo. Nos últimos 10 anos, observou-se um avanço expressivo no número da população carcerária no estado do Paraná. O perfil desse grupo vem frequentemente se repetindo, a saber: jovens com pouca ou baixa escolaridade, com idades entre 18 e 30 anos. Assim sendo, este presente trabalho busca conhecer os direitos da pessoa privada de liberdade na esfera legal, descrever a modalidade EJA dentro do Sistema Penitenciário do Paraná e explicar as possíveis relações entre a ressocialização do detento e o acesso e a permanência na educação durante o aprisionamento.
A needs analysis to determine the viability of a short course on homeopathy for general medical doctors in eThekweni
Authors:   , Tshabalala Balungile Precious
Published: May 2022

INTRODUCTION: The medical profession has an obligation to prioritize patient’s health and to ensure that patients have enough information to make proper health decisions. This can onlybe possible if there is adequate knowledge of treatment options amongst the general medical practitioners. Homoeopathy is becoming more accepted by the general public which means that the health workers need to have enough knowledge to handle cases successfully. Therefore, short training on homoeopathy may be required for them to have an insight of homoeopathy as a treatment option. It was noted that before implementing the short course on homoeopathy for general medical practitioners, a needs analysis should be conducted. This study investigated the need for a short course in homoeopathy for general medical doctors in the eThekwini/Durban area of KwaZulu-Natal province in South Africa. The study focused on gathering data from general medical doctors in terms of their requirements to formulate a course specifically designed to address their identified needs in respect of knowledge deficits in homoeopathy as well as establishing their interest if such a short course was availed to them. AIM OF THE STUDY: To establish the perceptions and attitudes of general medical practitioners on a short course in homoeopathy for general medical practitioners. METHODOLOGY: A qualitative, exploratory and descriptive research was done. By means of a semi structured interview guide, a measurement of views on the understanding of homoeopathy and perceptions on the need to obtain knowledge in homoeopathy through the short course for general medical practitioners was conducted. Tesch’s Eight Step method was used to analyze the collected data. CONCLUSION: This study discovered that there is a deficiency of homoeopathic knowledge amongst the general medical practitioners in the eThekwini/Durban area of KwaZulu-Natal province in South Africa. However, the study was able to establish an earnest interest amongst general medical practitioners to be equipped with the knowledge and understanding on homoeopathy to manage patients successfully.
Retreatment of a maxillary lateral incisor with type II dens invaginatus: One-year follow-up
Authors: Kolcu Ozge Irem Can , Guven Esra Pamukcu , Senguler Berk , Topaloglu-Ak Asli
Published: May 2022

Dens invaginatus is a developmental dental anomaly that occurs when the enamel organ is folded into the dental papilla prior to calcification of dental tissues. Endodontic treatment of the teeth with dens invaginatus can be challenging and due to atypical and complex anatomy. This case report presents non-surgical endodontic treatment of maxillary lateral incisor with Oehlers Type II Dens Invaginatus. The anamnesis revealed that the maxillary left lateral incisor had root canal treatment in the past; however the symptoms remained. Radiographic and clinical examination revealed an inadequately filled accessory canal and a necrotic unfilled main canal surrounded by a large periapical lesion. Subsequently, endodontic treatment of the main and retreatment of the accessory canal was completed. One year of clinical and radiographical follow-up demonstrated an asymptomatic tooth with a healing periapical lesion. Keywords: Dens in Dente; Incisor; Invaginatus, Retreatment
Copyright © 2022 Contemporary Pediatric Dentistry. This work is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license. (CC BY-NC-ND 4.0) Review The COVID-19 Pandemic: Role of dental professionals in a tertiary care hospital setting
Authors: Khatri Amit , Goyal Tavisha , Kalra Namita , Tyagi Rishi , Kaushal Drishti , Sabherwal Puja
Published: May 2022

The coronavirus disease which originated in Wuhan, China led to a worldwide pandemic. In India, which harbors the second largest population in the world, it created challenges on multiple fronts. In view of the increasing demand for medical healthcare, dental professionals represent an untapped resource to combat the pandemic. In many countries, dental professionals have risen to the challenge in the fight against COVID-19. This review aims to highlight the experience of dental professionals in a tertiary care hospital designated as a dedicated COVID–19 facility as frontline workers while also providing a perspective on the much larger role a dental professional can play in future pandemics. Keywords: Coronavirus; Dentistry; Pandemic
PENGGUNAAN LEMBAR KERJA PESERTA DIDIK (LKPD) BERBASIS PENEMUAN TERBIMBING UNTUK MENINGKATKAN HASIL BELAJAR TRANSFORMASI
Authors: AULIA IIN
Published: May 2022

This study aims to improve transformation learning outcomes through the use of guided discovery-based Student Worksheets (LKPD). The subjects of this study were 35 students of class IX at MTsN 19 South Jakarta in the academic year 2021/2022. The Classroom Action Research (CAR) model used is the Kemmis McTaggart model. In this model, one cycle consists of 4 activities, namely planning, acting, observing and reflecting. This research will be carried out in 2 cycles. The research data was obtained through observation, questionnaires, and learning outcomes tests. Data were analyzed quantitatively and qualitatively. Based on the analysis of research data, the results of cycle 1 showed that there was an increase in the learning achievement of students with an average score of 79, the highest score was 95 and the lowest score was 70, and learning completeness was 97.14%. And the results of cycle 2 there is an increase in the average value of students to 80, the highest score is 90 and the lowest value is 70 and learning completeness is 97.14%. From these results, it can be seen that the learning outcomes of students' transformation can be improved by using Guided Discovery-based LKPD. So from this PTK it can be concluded that the use of guided discovery-based worksheets can improve the learning outcomes of transformation of class IX students at MTsN 19 South Jakarta.
ABSTRAKPenelitian ini bertujuan untuk meningkatkan hasil belajar transformasi melalui penggunaan Lembar Kerja Peserta Didik (LKPD) berbasis penemuan terbimbing. Subjek penelitian ini adalah peseta didik kelas IX di MTsN 19 Jakarta Selatan pada tahun pelajaran 2021/2022 sebanyak 35 orang. Model penelitian tindakan Kelas (PTK) yang digunakan adalah model Kemmis McTaggart. Pada model tersebut satu siklus terdiri dari 4 kegiatan yaitu perencanaan (planning), pelaksanaan (acting), pengamatan (observing) dan refleksi (reflecting). Penelitian ini akan dilaksanakan dalam 2 siklus. Data penelitian diperoleh melalui observasi, angket, dan tes hasil belajar. Data dianalisis secara kuantitatif dan kualitatif. Berdasarkan analisis data penelitian menunjukkan hasil siklus 1 terdapat peningkatan pencapaian belajar peserta didik dengan perolehan nilai rata-rata 79, nilai tertinggi 95 dan nilai terendah 70 serta ketuntasan belajar sebesar 97,14 %. Dan hasil siklus 2 terdapat kenaikan nilai rata-rata peserta didik menjadi 80, nilai tertinggi 90 dan nilai terendah 70 serta ketuntasan belajar sebesar 97,14 %. Dari hasil tersebut terlihat bahwa hasil belajar transformasi peserta didik dapat ditingkatkan dengan menggunakan LKPD berbasis Penemuan Terbimbing. Sehingga dari PTK ini dapat disimpulkan bahwa penggunaan LKPD berbasis penemuan terbimbing dapat meningkatkan hasil belajar transformasi peserta didik kelas IX di MTsN 19 Jakarta Selatan.
MENULIS CERPEN DENGAN METODE TATAP HOPBOT PADA MASA COVID - 19
Authors: LISTIANA RINA
Published: May 2022

The COVID -19 pandemic could lead many problems on learning process for teacher and student’s parents, that why we need proper strategy during the leasson to solve thus problems. The learning outcomes during the COVID -19 pandemic on the short story writing skills of Class IXA students can satisfy teachers and make parents proud. Lesson problems during this pandemic can be solved with the innovative methods provided by the teacher. The method which is used by the teacher is Tatap Hopbot in short story writing lessons. This research is a Best Practice research from learning how to write short stories for students of class IXA. Thei results of this study indicate that besides learning, students become more motivated and enthusastic to participate in write short stories lesson in virtual class. Students are able to develop the material, not only based on teacher material but also parents have involved in the development of learning, especially in short story subjects. The results of this Best Practice research have proven that there is an increase in achievement in learning to write short stories which can be seen in the difference in the average results from pre-writing or pre-test 76 to post-test 83. Thus, it is proven that there is an increase in results of 7 or 0.9%.
ABSTRAKPermasalahan pembelajaran di rumah atau online di masa pandemi covid-19 memang menjadikan risau bagi guru, peserta didik, dan khususnya orang tua. Salah satu penyelesaian permasalahan adalah guru perlu menemukan metode yang cocok dalam pembelajaran di masa ini. Dari hasil pembelajaran masa pandemi covid-19 pada keterampilan menulis cerpen peserta didik Kelas IXA, guru dapat merasa puas dan membanggakan orang tua. Hal ini dikarenakan dapat terselesaikan dengan adanya metode inovatif yang diberikan guru. Metode yang digunakan guru adalah Tatap Hopbot pada pelajaran menulis cerpen.Dengan demikian, penelitian ini merupakan sebuah penelitian Best Practice dari pembelajaran menulis cerpen peserta didik kelas IXA. Hasil penelitian ini menunjukan selain pembelajaran sangat memotivasi peserta didik dalam belajar dan menjadikan lebih semangat sehingga kelas virtual menjadi hidup. Peserta mampu mengembangkan materi, dan tidak tepaku pada guru karena pada pembelajaran ini, peran orang tua terlibat dalam perkembangan belajar. Hasil penelitian Best Practice ini terbukti ada peningkatan prestasi dalam pembelajaran menulis cerpen yaitu terdapat pada perbedaan rata-rata hasil dari pra menulis atau pretes 76 dengan postes 83. Dengan demikian, terbukti adanya peningkatan hasil 7 atau 0,9%.
Improved representation of phosphorus exchange on soil mineral surfaces reduces estimates of P limitation in temperate forest ecosystems
Authors: Yu Lin , Caldararu Silvia , Ahrens Bernhard , Wutzler Thomas , Schrumpf Marion , Helfenstein Julian , Pistocchi Chiara , Zaehle Sönke
Published: May 2022

Abstract. Phosphorus (P) availability affects the response of terrestrial ecosystems to environmental and climate change (e.g. elevated CO2), yet the magnitude of this effect remains uncertain. This uncertainty arises mainly from a lack of quantitative understanding of the soil biological and geochemical P cycling processes, particularly the P exchange with soil mineral surfaces, which is often described by a Langmuir sorption isotherm. We first conducted a literature review on P sorption experiments and terrestrial biosphere models (TBMs) using Langmuir isotherm. We then developed a new algorithm to describe the inorganic P exchange between soil solution and soil matrix based on the double-surface Langmuir isotherm and extracted empirical equations to calculate the sorption capacity and Langmuir coefficient. We finally tested the conventional and new models of P sorption at five beech forest sites in Germany along a soil P stock gradient using the QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) TBM. We found that the conventional (single-surface) Langmuir isotherm approach in most TBMs largely differed from P sorption experiments regarding the sorption capacities and Langmuir coefficients, and simulated a too low soil P buffering capacity. Conversely, the double-surface Langmuir isotherm approach adequately reproduced the observed patterns of soil inorganic P pools. The better representation of inorganic P cycling using the double Langmuir approach also improved simulated foliar N and P concentrations, and the patterns of gross primary production and vegetation carbon across the soil P gradient. The novel model generally reduces the estimates of P limitation compared to the conventional model, particularly at the low-P site, as the model constraint of slow inorganic P exchange on plant productivity is reduced.
Can soil spectroscopy contribute to soil organic carbon monitoring on agricultural soils?
Authors: Reyes Javier , Ließ Mareike
Published: May 2022

Abstract. Carbon sequestration in soils under agricultural use can contribute to climate change mitigation. However, the spatial-temporal monitoring of soil organic carbon (SOC) requires more efficient data acquisition. The use of soil Vis-NIR spectroscopy is a promising research field in this context. However, the interpretation of the recorded spectral signal with regards to SOC is not trivial due to the complexity of the soil matrix, and factors affecting the measurements under field conditions. A model-building process is required to relate the spectral signal to the SOC content. For this study, spectral on-the-go proximal measurements and soil sampling were conducted on a long-term field experiment (LTE) located in the state of Saxony-Anhalt, Germany. SOC values ranged between 14–25 g kg−1 due to different fertilization treatments. Partial least squares regression (PLSR) models were built on behalf of spectral laboratory and field measurements conducted with two spectrometers and preprocessed by various methods. A data correction of the field data was done with three different approaches: linear transformation, piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). The models were then thoroughly interpreted with regards to spectral wavelength importance using regression coefficients (RC) and variable importance in projection scores (VIP). The detailed wavelength importance analysis disclosed the challenge of using soil spectroscopy for SOC monitoring. The use of spectrometers with a differing spectral resolution for soil Vis-NIR measurements under varying soil conditions revealed shifts in wavelength importance. Still, some wavelengths related to SOC were extracted (560 nm, 1330 nm, 1400 nm, 1720 nm, and 1900 nm) by various preprocessing methods and were highly important in models trained on both, laboratory, and field measurements. Furthermore, we showed, that the correction of spectral field data with spectral laboratory measurements improved the predictive performance of the models built on behalf of the proximal on-the-go sensing measurements.
A newly integrated ground temperature dataset of permafrost along the China-Russia crude oil pipeline route in Northeast China
Authors: Li Guoyu , Ma Wei , Wang Fei , Jin Huijun , Alexander Fedorov , Chen Dun , Wu Gang , Cao Yapeng , Zhou Yu , Mu Yanhu , Mao Yuncheng , Zhang Jun , Gao Kai , Jin Xiaoying , He Ruixia , Li Xinyu , Li Yan
Published: May 2022

Abstract. The thermal state of permafrost in the present and future is fundamental to the ecosystem evolution, hydrological process, carbon release, and infrastructure integrity in cold regions. From 2011, we began to establish a permafrost monitoring network along the China-Russia crude oil pipelines (CRCOPs) route at the eastern flank of the northern Da Xing'anling Mountains in Northeast China. Based on meteorological data near the southern limit of latitudinal permafrost (SLLP), ground temperature data in 20 boreholes with the depths of 10–60.6 m, soil volumetric liquid water contents and 2-D electrical resistivity tomography (ERT) data, we compiled an integrated dataset of the ground thermal state along the CRCOPs route. Study results demonstrate that permafrost in the vicinity of SLLP has experienced marked warming (2011–2020) to climate change, manifested as the rising permafrost temperatures at depth. Local thermal disturbances triggered by the construction and operation of CRCOPs have resulted in significant permafrost warming and subsequent thawing on the right-of-way (ROW) of the pipeline. This permafrost thaw will persist, but it can be alleviated by adopting mitigative measures, such as insulation layer and thermosyphons. The in-situ observational dataset is of great value for assessing the variability of permafrost under the linear disturbances of the CRCOPs and related environmental effects, for understanding hydro-thermal-mechanical interactions between the buried pipelines and permafrost foundation soils, and for evaluating the operational and structural integrity of the pipeline systems in the future. The dataset is available at the Third Pole Environment Data Center (http://doi.org/10.11888/Cryos.tpdc.272357 (Li, 2022)).
EPIPOLAR LINE-BASED LATERAL VIBRATION MEASUREMENT BY USING TWO CAMERAS
Authors: Javed A. , Park J. , Lee H. , Kim B. , Han Y.
Published: May 2022

Abstract. Vibration measurement techniques can be categorized into contact-type and non-contact-type techniques. These types of techniques can add mass-loading to a lightweight structure resulting in the negative performance of a structure, because sensors, high contrast speckles or targets should be mounted on a structure. Moreover, non-contact-type vibration measurement techniques have only been tested to detect vibrations using a single camera. As the vibrations occurring at the opposite sides of a rotating structure in a region of interest (ROI) can be different from each other. For 3-dimensional (3D) vibration measurement, the same position in videos acquired from two cameras should be used. Because the videos acquired by two cameras placed perpendicular to the structure can be used to detect the vibrations in the x-direction as well as y-direction. In this study, an epipolar line-based corresponding point selection on a rotating cylindrical structure was performed, to extract the same ROIs from videos recorded by two cameras. A fundamental matrix was constructed by using the targets attached on the structure and in the background. The coordinates of the mid-pixel of the ROI in a video acquired by one camera was used to determine the epipolar line for the same ROI in the video acquired by another camera. Then an edge-based vibration measurement technique was applied to measure the vibration in the extracted ROIs. The results were used to reconstruct a 3D vibration signal. The 3D vibration measurement results can be used to effectively recognize the deformations resulting in the negative performance of a structure.
APPLICATION OF RGB-D SLAM IN 3D TUNNEL RECONSTRUCTION BASED ON SUPERPIXEL AIDED FEATURE TRACKING
Authors: Zhang R. , Shi S. , Yi X. , Jing M.
Published: May 2022

Abstract. In large-scale projects such as hydropower and transportation, the real-time acquisition and generation of the 3D tunnel model can provide an important basis for the analysis and evaluation of the tunnel stability. The Simultaneous Localization And Mapping (SLAM) technology has the advantages of low cost and strong real-time, which can greatly improve the data acquisition efficiency during tunnel excavation. Feature tracking and matching are critical processes of traditional 3D reconstruction technologies such as Structure from Motion (SfM) and SLAM. However, the complicated rock mass structures on the tunnel surface and the limited lighting environment make feature tracking and matching difficult. Manhattan SLAM is a technology integrating superpixels and Manhattan world assumptions, in which both line features and planar features can be better extracted. Rock mass discontinuities including traces and structural planes are distributed on the inner surface of tunnels, which can be extracted for feature tracking and matching. Therefore, this paper proposes a 3D reconstruction pipeline for tunnels, in which, the Manhattan SLAM algorithm is applied for camera pose parameters estimation and the sparse point cloud generation, and the Patch-based Multi-View Stereo (PMVS) is adopted for dense reconstruction. In this paper, the Azure Kinect DK sensor is used for data acquisition. Experiments are proceeded and the results show that the proposed pipeline based on Manhattan SLAM and PMVS performs good robustness and feasibility for tunnels 3D reconstruction.
BUILDING CHANGE DETECTION IN AIRBORNE LASER SCANNING AND DENSE IMAGE MATCHING POINT CLOUDS USING A RESIDUAL NEURAL NETWORK
Authors: Politz F. , Sester M.
Published: May 2022

Abstract. National Mapping Agencies (NMAs) acquire nation-wide point cloud data from Airborne Laser Scanning (ALS) sensors as well as using Dense Image Matching (DIM) on aerial images. As these datasets are often captured years apart, they contain implicit information about changes in the real world. While detecting changes within point clouds is not a new topic per se, detecting changes in point clouds from different sensors, which consequently have different point densities, point distributions and characteristics, is still an on-going problem. As such, we approach this task using a residual neural network, which detects building changes using height and class information on a raster level. In the experiments, we show that this approach is capable of detecting building changes automatically and reliably independent of the given point clouds and for various building sizes achieving mean F1-Scores of 80.5% and 79.8% for ALS-ALS and ALS-DIM point clouds on an object-level and F1-Scores of 91.1% and 86.3% on a raster-level, respectively.
GEOMATICS SUPPORTING KNOWLEDGE OF CULTURAL HERITAGE AIMED AT RECOVERY AND RESTORATION
Authors: Vacca G. , Dessi A.
Published: May 2022

Abstract. The study presented aims a practical contribution to the use of the Terrestrial Laser Scanner and the Close Range Photogrammetry geomatic techniques and to their integration for the knowledge and development of the historical-architectural heritage, both in the step of planning a restorative conservation project, and in the subsequent step of the restoration works. In particular, these techniques and their integration were applied at the process of study, planning and execution of the restoration of the San Giovanni Battista church in Fonni (Sardinia, Italy), founded in the 16th century. The building, due to structural problems and humidity infiltrations, was subject to a serious intervention for consolidation and conservative restoration.In the first phase of the work, preliminary to the design, a TLS survey was made that allowed to obtain an accurate 3D model of the church. In a second phase, during the restoration work, CRP surveys were carried out and the accuracy of this technique was verified through a comparison with TLS surveys. The integration of the two techniques to improve the texture of point clouds detected with TLS was also tested.
NOVEL SINGLE TREE DETECTION BY TRANSFORMERS USING UAV-BASED MULTISPECTRAL IMAGERY
Authors: Dersch S. , Schöttl A. , Krzystek P. , Heurich M.
Published: May 2022

Abstract. Single tree detection has been a major research topic concerning automatic forest inventory using remote sensing data. Recently, deep learning-based approaches in remote sensing forestry have gained attention because of the prospect of improved accuracy. In this study, we present a novel tree detection method based on the detection transformer (DETR), which applies a transformer in combination with a pre-trained convolutional neural network to detect individual trees using high-resolution multispectral imagery. The test site (Kranzberg Forest Roof Experiment - KROOF) is located in Bavaria, north of Munich, and is characterised by a mixed forest which consists of large groups of European beeches (Fagus sylvatica) surrounded by Norway spruces (Picea abies). The image data were acquired with a MicaSense RedEdge-MX Dual camera mounted to UAV. Two flight mission were conducted at an altitude of around 85 m with a flight speed of 5 m/sec, resulting in a ground resolution of about 5 cm. 125 trees were surveyed by tacheometric means in the field for testing, and 1390 trees were labelled by visual interpretation of the multispectral imagery for training and validation. The novel tree detection method based on DETR shows promising results and outperforms the standard, well-known object detection method YOLOv4 in mixed and deciduous test plots. More detailed, F1-scores were evaluated for coniferous plot at 83%, for mixed plot at 86% and for deciduous plot at 71%. The corresponding figures for YOLOv4 are 87% coniferous, 65% mixed and 67% deciduous. In terms of accuracy, DETR is inferior by 6% in coniferous plot, however superior by 28% and 5% in mixed and deciduous plot, respectively. Compared to YOLOv4, we found that DETR sometimes failed to detect small coniferous trees. Moreover, both deep learning-based methods tend to over-detect single trees in deciduous test areas. In sum, transformer-based tree detection shows great potential to improve single tree detection.
INTRA-FIELD CROP YIELD VARIABILITY BY ASSIMILATING CUBESAT LAI IN THE APSIM CROP MODEL
Authors: Ziliani M. G. , Altaf M. U. , Aragon B. , Houborg R. , Franz T. E. , Lu Y. , Sheffield J. , Hoteit I. , McCabe M. F.
Published: May 2022

Abstract. Predicting within-field crop yield early in the season can help address crop production challenges to improve farmers’ economic return. While yield prediction with remote sensing has been a research aim for years, it is only recently that observations with the suited spatial and temporal resolutions have become accessible to improve crop yield predictions.Here we developed a yield prediction framework that integrates daily high-resolution (3 m) CubeSat imagery into the APSIM crop model. The approach trains a regression model that correlates simulated yield to simulated leaf area index (LAI) from APSIM. That relationship is then employed to determine the optimum date at which the regression best predicts yield from the LAI. Additionally, our approach can forecast crop yield by utilizing a particle filter to assimilate CubeSat-based LAI in the model APSIM to generate yield maps at 3 m several weeks before the optimum regression date. Our method was evaluated for a rainfed site located in the US Corn belt, using a collection of spatially varying yield data. The proposed approach does not need in situ data to rain the regression, with outcomes reporting that even with a single assimilation step, accurate yield predictions were provided up to 21 days before the optimum regression date. The spatial variability of crop yield was reproduced fairly well, with a good correlation against in situ measurements (R2 = 0.73 and RMSE = 1.69), demonstrating that high-resolution yield predictions early in the season have great potential to meet and improve upon digital agricultural goals.
COMPARISON OF DIGITAL TERRAIN MODELS FROM TWO PHOTOCLINOMETRY METHODS
Authors: Kirk R. L. , Mayer D. P. , Dundas C. M. , Wheeler B. H. , Beyer R. A. , Alexandrov O.
Published: May 2022

Abstract. We evaluate the horizontal resolution and vertical precision for digital topographic models (DTMs) of the Moon derived from image radiance information, a process known as photoclinometry (PC) or shape-from-shading (SfS). We use the implementations in two available planetary image processing software systems, single image PC in the U.S. Geological Survey Integrated Software for Imagers and Spectrometers (ISIS) system, and multi-image SfS in the Ames Stereo Pipeline (ASP), and test results obtained with and without use of a starting solution from stereo, with single and multiple images, and for varying illumination conditions. To obtain the higher quality reference DTMs against which the products can be evaluated, we derived DTMs by stereoanalysis of Lunar Reconnaissance Orbiter Narrow-Angle Camera (LROC NAC) images at their native pixel spacing of ∼0.5 m, then produced a 16-m/post stereo DTM from images downsampled to 4 m/pixel and refined it with images at 16 m/pixel. When used with a single image, both algorithms improved resolution (by a factor of 1.4 for PC and 2.4 for SfS compared to stereo). An albedo map produced in ISIS by ratioing the image to a simulation based on the stereo DTM was well correlated with one output by SfS. The albedo correction was crucial for PC with ∼60° incidence but not at ∼80°. DTMs produced by PC and SfS without a starting stereo DTM had larger errors but good detail, and could be useful for many applications. In SfS, it was necessary to increase smoothing to get a usable DTM when the weighting on an a priori DTM was reduced. Multi-image SfS including modeling of spatially varying albedo reduced vertical errors by factors of 1.5 or more compared to single-image SfS.
BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNING
Authors: May S. , Dupuis A. , Lagrange A. , De Vieilleville F. , Fernandez-Martin C.
Published: May 2022

Abstract. Global warming modifies the climate balance. Warming parameters are observed by many Earth Observation satellite systems, and the huge amount of data modifies the way to process them. This paper presents a few studies relative to damage detection on buildings, occurred during natural disasters. Recent advances in deep learning techniques are used for the building detection such as EfficientNet networks. Additional networks as Siamese models are used to evaluate the damage level with pre- and post-event images. Different techniques to merge detection masks are described and compared to a multiclass segmentation network. Results are presented and performances of the different solutions are compared.
PROCESSING AND PUBLISHING THE ACTIVE DEFORMATION AREAS OF ALL EUROPE – CONCEPT AND FIRST IMPLEMENTATION STEPS FOR A LOW-COST SOLUTION
Authors: Navarro J. A. , García D. , Crosetto M. , Monserrat O.
Published: May 2022

Abstract. The European Ground Motion Service provides a plethora of information about land subsidence in the form of Persistent Scatterers (PS). Data for all over Europe will be soon available, facilitating the analysis of ground deformation at a continental level. However, Active Deformation Areas (ADA) may be used instead of PS, since those are more understandable for humans. The CTTC already developed a tool, namely ADAfinder, to produce the said ADAs out of input PS. The authors are involved in an ongoing project, at its very early stages, targeted at computing the Active Deformation Areas for all Europe using the PS data provided by the EGMS, making the results available to the community by means of a WebGIS. A few concepts are already very clear, such as the high volume of information to process, the need of a specific software chain to do the actual processing and the economic cost that storing and making the results accessible might imply. This paper describes how the data volume and economic factors led to the selection of an in-house hosted & developed solution rather than resorting to well-established and expensive providers of cloud-based solutions. Furthermore, the set of self-developed tools required to implement the production chain are also explained. Also, the open-source tool used to prepare a server able to store the data and make it available to the public are described here. Very preliminary but promising figures concerning processing and response times have been included too.
MAPPING VEGETATION AND MEASURING THE PERFORMANCE OF MACHINE LEARNING ALGORITHM IN LULC CLASSIFICATION IN THE LARGE AREA USING SENTINEL-2 AND LANDSAT-8 DATASETS OF DEHRADUN AS A TEST CASE
Authors: Srivastava A. , Bharadwaj S. , Dubey R. , Sharma V. B. , Biswas S.
Published: May 2022

Abstract. In recent years, the data science and remote sensing communities have started to align due to user-friendly programming tools, access to high-end consumer computing power, and the availability of free satellite data. In particular, publicly available data from the European Space Agency’s “Sentinel” and American Earth observation satellite” landsat” missions have been used in various remote sensing applications.Google Earth Engine (GEE) is such a tool that publicly allow the use of these available datasets, there is a large amount of available data in GEE, which are being used for computing and analysing purpose. In this article, we compare the classification performance of four supervised machine learning algorithms: Classification and Regression Tree (CART), Random forests (RF), Gradient tree boosting (GTB), Support vector machines (SVM). The study area is located at 30.3165° N, 78.0322° E near the Himalayan foothills, with four land-use land-cover (LULC) classes. The satellite imagery used for the classification were multi-temporal scenes from Sentinel-2 and LANDSAT-8 covering spring, summer, autumn, and winter conditions. Here we collected a total of 2084 sample points in which 536, 506, 505, 540 points belong to urban, water, forest and agriculture points respectively. which were divided into training (70%) and evaluation (30%) subsets. Accuracy was assessed through metrics derived from an error matrix, for accuracy measurement we use confusion and Cohen’s kappa calculation method.We have calculated CART (Accuracy 93.52% and Kappa coefficient 91.36%), Random Forest (Accuracy 95.86% and Kappa coefficient 94.48%),Gradient Tree Boost (Accuracy 95.33% and Kappa coefficient 93.37%),Support Vector Machine (Accuracy 73.54% and Kappa coefficient 76.28%) for Landsat 8 data sets and CART (Accuracy 89.24% and Kappa coefficient 85.64%), Random Forest (Accuracy 91.45% and Kappa coefficient 88.59%),Gradient Tree Boost (Accuracy 87.71% and Kappa coefficient 83.58%),Support Vector Machine (Accuracy 84.96% and Kappa coefficient 79.99%) for Sentinel2 data sets. Further analysis for accuracy and machine learning algorithm are discussed in result section.
RANDOM FOREST FOR CLASSIFYING AND MONITORING 50 YEARS OF VEGETATION DYNAMICS IN THREE DESERT CITIES OF THE UAE
Authors: Dahy B. , Issa S. , Saleous N.
Published: May 2022

Abstract. The United Arab Emirates (UAE), a dryland country, has since its independence, emphasized on giant greening projects. Monitoring the trend of greening progress in the UAE has gained importance for environmental management and carbon footprint monitoring. Hence, this study created and analysed a time-series (TS) vegetation map to track and analyse vegetation dynamics over an extended period of fifty years. Study area included three selected desert cities of the UAE, Abu Dhabi (AD) capital city, Dubai city and Al Ain city. Random Forest algorithm was applied on Landsat multi-temporal images from 1972 until 2021 for classifying and monitoring the vegetation dynamics and change trajectories. Four vegetation subclasses (coastal/wetland vegetation, urban vegetation, farms/crop fields, and natural/artificial forests), were assessed then grouped and mapped as one vegetation class. With the adopted approach, we achieved overall classification accuracy ranging from 86% to 94%, with kappa coefficients ranging from 0.7200 to 0.8800. Current study showed that the vegetation cover extent in the UAE was at a constant growth for the past five decades from only 1,231.1 ha in 1972 to 23,176.46 ha in 2021, 19 times folds. Furthermore, it showed that desert cities tend to increase their vegetation cover while continuing their steady urban growth. The other drivers found include demographic increase and governmental policies (granting farms to locals and environmental protection laws). Finally, the approach implemented in this research can effectively and reliably be used in other urban centres for future monitoring and management of the vegetation cover status in the country.
GEOSPATIAL APPROACH FOR URBAN ENVIRONMENTAL QUALITY ASSESSMENT
Authors: Singh S. , Jain K.
Published: May 2022

Abstract. Growing population and change in gentrification patterns of urban areas affect the environment quality, especially in the developing nations. Most cities are thereby, facing serious environmental sustainability challenges. A resilient urban planning is required to solve these environmental problems, which in turn need information that is not available in the desired scales, hence, making it difficult to comprehend. In this study, an approach is proposed to comprehend the environmental quality of a city at community level. Landsat 8 images are used to compute the biophysical indicators, namely, land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), modified normalized difference water index (MNDWI) and normalized difference impervious surface index (NDISI) in ArcGIS software. These indicators are then integrated with the indicators from the government census data to compute the community level index for urban environmental quality (UEQI). The results depicted that the highest value of our developed index occurred in the places where there is more of greenery and less built-up area. Based on the obtained UEQI values, different areas of the city are divided into five categories. The index values suggest that urban greening significantly contribute to enhance the urban environmental quality. Also, it highlights the critical zones where intervention should be made by the planners and policymakers for the sustainability of the city.
DEFORESTATION DETECTION WITH WEAK SUPERVISED CONVOLUTIONAL NEURAL NETWORKS IN TROPICAL BIOMES
Authors: Soto P. J. , Costa G. A. O. P. , Ortega M. X. , Bermudez J. D. , Feitosa R. Q.
Published: May 2022

Abstract. Deep learning methods are known to demand large amounts of labeled samples for training. For remote sensing applications such as change detection, coping with that demand is expensive and time-consuming. This work aims at investigating a noisy-label-based weak supervised method in the context of a deforestation mapping application, characterized by a high class imbalance between the classes of interest, i.e., deforestation and no-deforestation. The study sites correspond to different regions in the Amazon and Brazilian Cerrado biomes. To mitigate the lack of ground-truth labeled training samples, we devised an unsupervised pseudo-labeling scheme based on the Change Vector Analysis technique. The experimental results indicate that the proposed approach can improve the accuracy of deforestation detection applications.
ICE FLOW VELOCITY MAPPING IN GREENLAND USING HISTORICAL IMAGES FROM 1960s TO 1980s: SCHEME DESIGN
Authors: Yu Z. , Cao Z. , Yu C. , Qiao G. , Li R.
Published: May 2022

Abstract. Ice flow velocity over long time series in Greenland plays a vital role in estimating and predicting the mass balance of the Greenland Ice Sheet and its contribution to global sea level rise. However, there are few Greenland ice flow velocity products with large spatial coverage available showing the Greenland ice flow velocity pattern before the 1990s. We proposed three methods, including parallax decomposition, grid-based NCC image matching, feature and gird-based image matching with constraints for estimation of surface velocity in East Antarctica based on ARGON KH-5 and LANDSAT imagery, and a systematic compilation method for the ice surface velocity in East Antarctica from the 1960s to 1980s. Based on the above methods and cartographic processes, this study designed a framework for the mapping of the historical ice flow velocity of the Greenland ice sheet. Currently, the early LANDSAT images covering several glaciers in North Greenland and Northwest Greenland have been processed and applied to velocity mapping using the cartographic process proposed in this study, and some preliminary results have been obtained.
MAPPING EUCALYPTUS SPECIES USING WORLDVIEW 3 AND RANDOM FOREST
Authors: Alonso L. , Picos J. , Armesto J.
Published: May 2022

Abstract. Recent advances in remote sensing technologies have allowed the development of new innovative methodologies to obtain geospatial information about Eucalyptus genus distribution. This is an important task for forest stakeholders due to the high presence of this genus in forest plantations worldwide. Therefore, the next step in research should focus on exploring remote sensing possibilities to discern between Eucalytpus species. It would be an important step forward in forest management since different Eucalyptus species present different characteristics and properties that imply different management plans and industrial usages. This study accomplish the classification of E. nitens and E. globulus, the most common Eucalyptus species in the Iberian Peninsula. Worldview-3 images and random forest are used in a forest area placed in Galicia (Northwest of Spain). The differentiation of Eucalyptus species resulted in a producer’s accuracy of 84% and a users’ accuracy of 70% for E. nitens, while for E. globulus accuracy metrics did not reach 70%. The most important bands in the classification were the coastal blue and the blue, followed by the red related ones. The resulting unequal accuracy metrics might be caused by an imbalanced presence of both species in the selected study area. Therefore, further studies might be developed in different locations.