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Preprint ARTICLE | doi:10.20944/preprints202208.0216.v1

Modelling of Soil Spatial Variability in an Olive Grove by Fusing Remote Drone-based Multispectral Data, Ground-based Hyperspectral and Sample Data with Multivariate Geostatistics Taking into Account Change of Support

Antonella Belmonte, Carmela Riefolo, Lovergine Francesco, Annamaria Castrignanò

Subject: Earth Sciences, Geoinformatics Keywords: block cokriging; clay composition; granulometry; multi-collocated cokriging; multi-collocated fac-torial cokriging; regularization; SIDSAM; VIS-NIR-SWIR spectroscopy

Online: 11 August 2022 (11:30:23 CEST)

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Preprint DATA DESCRIPTOR | doi:10.20944/preprints202208.0112.v1

Collecting and Disseminating Ground Truth Data for Generation of Agriculture Statistics

Tran-Thanh Dan, Yrle Greco Frank, Karunanayake Chamaka, Gunasekara Kavinda, Dubey Sangita, Burgard Dean Anthony, Hazarika Kumar Manzul

Subject: Earth Sciences, Geoinformatics Keywords: ground truth data; drone; mobile application; windshield survey; sample design; crop mapping; agriculture statistics; data dissemination; earth observation data; spatial database.

Online: 4 August 2022 (16:18:26 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202207.0438.v1

Monitoring Seasonal Morphobathymetric Change of Nearshore Seafloor Using Drone-Based Multispectral Imagery

Evangelos Alevizos, Dimitrios Alexakis

Subject: Earth Sciences, Geoinformatics Keywords: drones; UAV; bathymetry; shallow water; multispectral; multi-temporal; geomorphology

Online: 28 July 2022 (09:28:41 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202207.0325.v1

Spatial Frequency Analysis by Adopting Regional Analysis with Radar Rainfall in Taiwan

Che-Hao Chang, Riki Rahmad, Shiang-Jen Wu, Chih-Tsung Hsu

Subject: Earth Sciences, Geoinformatics Keywords: Rainfall; QPESUMS; Regional Frequency Analysis; L-moments; Taiwan

Online: 21 July 2022 (10:53:34 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202207.0265.v1

Early Detection of Wildfires with GOES-R Time Series and Deep GRU-Network

Yu Zhao, Yifang Ban

Subject: Earth Sciences, Geoinformatics Keywords: GOES-R; GRU; Deep Learning; Wildfires; Active Fires; Early Detection; Monitoring

Online: 18 July 2022 (10:28:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202207.0071.v1

Development of Global Impervious Surface Area Index for Automatic Spatiotemporal Urban Mapping

Akib Javed, Zhengfeng Shao, Iffat Ara, Md. Enamul Huq, Md. Yeamin Ali, Nayyer Saleem, Muhammad Nasar Ahmad

Subject: Earth Sciences, Geoinformatics Keywords: Urban Mapping; Impervious Surface Area; Google Earth Engine; GISAI; Spectral Index; Landsat

Online: 5 July 2022 (10:07:01 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202207.0012.v1

Recognition of Intersection Traffic Regulations From Crowdsourced Data

Stefania Zourlidou, Monika Sester, Shaohan Hu

Subject: Earth Sciences, Geoinformatics Keywords: traffic-rules; traffic-regulations; crowdsourcing; GPS-trace; trajectories; classification; movement patterns; clustering; collective-behaviour; smart city

Online: 1 July 2022 (10:00:55 CEST)

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In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules of regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which in turn affects vehicle idling time at intersections, fuel consumption, CO_2 emissions and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable and inexpensive way to identify the type of intersection control (e.g. traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicles crossing intersections is proposed. A modification of a well-known clustering algorithm for detecting stopping and decelerating events is presented. These detected events are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per junction arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the junction arms according to their traffic control type dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single junction arm are used (one-arm model) and another where features also from neighbouring junction arms of the same junction are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 94\% to 97\%.

Preprint ARTICLE | doi:10.20944/preprints202205.0351.v2

Analysis of the Infiltration Complex System Using a Novel GSA-Hybrid for Automatic Calibration Focused at Civil Construction

Domingo_Stalin Aguero_Martinez, Thiago Murari, Marceelo Moret

Subject: Earth Sciences, Geoinformatics Keywords: Infiltration; GSA-HYBRID Algorithm; GSA Algorithm; Genetic Algorithms; Calibration; Characteristic Curve

Online: 27 June 2022 (11:18:05 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202206.0013.v1

A Neural Network based Fusion Approach for Improvement of SAR Interferometry based Digital Elevation Models in Plain and hilly terrain of India

Priti Girohi, Ashutosh Bhardwaj

Subject: Earth Sciences, Geoinformatics Keywords: SAR Interferometry (InSAR); Digital Elevation Models (DEM); Neural Networks; DEM Fusion; ICESat-2 spaceborne altimetry

Online: 1 June 2022 (10:11:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202205.0417.v1

Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method

Sabelo Nick Dlamini, Wisdom Mdumiseni Dlamini, Ibrahima Socé Fall

Subject: Earth Sciences, Geoinformatics Keywords: COVID-19; Eswatini; risk mapping; Poisson regression

Online: 31 May 2022 (11:04:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202205.0351.v1

Analysis of the Infiltration Complex System Using a Novel GSA-Hybrid for Automatic Calibration Focused at Civil Construction

Domingo_Stalin Aguero_Martinez, Thiago Murari, Marceelo Moret

Subject: Earth Sciences, Geoinformatics Keywords: Infiltration; GSA-HYBRID Algorithm; GSA Algorithm; Genetic Algorithm; Calibration; Characteristic Curve

Online: 25 May 2022 (10:16:59 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202202.0054.v2

Generative Adversarial Approach to Urban Areas NDVI Estimation Using Information Exclusively from Structural and Textural Analysis of Panchromatic Orthoimagery: A Case Study of Łódź, Poland

Maciej Adamiak, Krzysztof Będkowski, Adam Bielecki

Subject: Earth Sciences, Geoinformatics Keywords: generative adversarial networks; NDVI; green areas; orthophoto; artificial datasets.

Online: 19 April 2022 (10:11:20 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202204.0007.v2

An Approach to Detecting and Mapping Individual Fruit Trees Integrated YOLOv5 with UAV Remote Sensing

Yongzhu Xiong, Xiaofeng Zeng, Yankui Chen, Jiawen Liao, Weiqian Lai, Mingyong Zhu

Subject: Earth Sciences, Geoinformatics Keywords: individual fruit tree (IFT); individual pomelo tree (IPT) detection; deep learning; transfer learning; YOLOv5; remote sensing; unmanned aerial vehicle (UAV); spatial distribution

Online: 4 April 2022 (13:35:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202201.0123.v2

Genus-Physiognomy-Ecosystem Map with Solar Panels Produced at 10m-resolution First-time in a Country Scale through Machine Learning of Multi-temporal Satellite Images

Ram C. Sharma

Subject: Earth Sciences, Geoinformatics Keywords: Sentinel-2; Land cover; Vegetation; Mapping; Plant communities; Machine learning; Genus-Physiognomy-Ecosystem; Gradient Boosting Decision Trees; Solar panel; Vegetation disturbance

Online: 4 April 2022 (10:40:26 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202204.0007.v1

An Approach to Detecting and Mapping Individual Fruit Trees Integrated YOLOv5 with UAV Remote Sensing

Yongzhu Xiong, Xiaofeng Zeng, Yankui Chen, Jiawen Liao, Weiqian Lai, Mingyong Zhu

Subject: Earth Sciences, Geoinformatics Keywords: individual fruit tree (IFT); individual pomelo tree (IPT) detection; deep learning; transfer learning; YOLOv5; remote sensing; unmanned aerial vehicle (UAV); spatial distribution

Online: 1 April 2022 (12:34:02 CEST)

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The location and number data of individual fruit trees is critical for planting area investigation, fruit yield prediction, and smart orchard management and planning. These data are conventionally obtained through manual investigation and statistics with time-consuming and laborious effort. Object detection models in deep learning used widely in computer vision could provide an opportunity for accurate detection of individual fruit trees, which is essential for obtaining the data rapidly and reducing errors of human operations. This study aims to propose an approach to detecting individual fruit trees and mapping their spatial distribution by integrating deep learning with unmanned aerial vehicle (UAV) remote sensing. UAV remote sensing was used to collect high-resolution true-color images of fruit trees in the experimental pomelo tree orchards in Meizhou city, South China. An image dataset of deep learning samples of individual pomelo trees (IPTs) was constructed through visual interpretation and field investigation based on the fruit tree images captured by UAV remote sensing. Four different scales of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) for object detection were selected to train, validate, and test on the image dataset of pomelo trees. The results show that the average precision (AP@0.5) of the four YOLOv5 models for validation reaches 87.8%, 88.5%, 89.1%, and 90.7%, respectively. The larger the model scale, the higher the average accuracy of the detection result of validation. It suggests that YOLOv5x is a preferred high-accuracy model among the YOLOv5 family and is suitable to realize the detection of IPTs. The number of the IPTs in the study area was predicted and counted using YOLOv5x and their spatial distribution map was made using the non-maximum suppression method and ArcGIS software. This study is desired to provide primary data and technical support for smart orchard management in Meizhou city and other fruit-producing areas.

Preprint ARTICLE | doi:10.20944/preprints202203.0337.v1

Stacking Ensemble of Machine Learning Methods for Landslide Susceptibility Mapping in Zhangjiajie City, Hunan Province, China

Yuke Huan, Lei Song, Umair Khan, Baoyi Zhang

Subject: Earth Sciences, Geoinformatics Keywords: landslide susceptibility; stacking ensemble; machine learning; random forest; gradient boosting decision tree; extreme gradient boosting

Online: 25 March 2022 (03:43:32 CET)

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The current study aims to apply and compare the performance of six machine learning algorithms, including three basic classifiers: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGB), as well as their hybrid classifiers, using the logistic regression (LR) method (RF+LR, GBDT+LR, and XGB+LR), in order to map the landslide susceptibility of Zhangjiajie City, Hunan Province, China. First, a landslide inventory map was created with 206 historical landslide points and 412 non-landslide points, which was randomly divided into two datasets for model training (80%) and model testing (20%). Second, 15 landslide conditioning factors (i.e., altitude, slope, aspect, plane curvature, profile curvature, relief, roughness, rainfall, topographic wetness index (TWI), normalized difference vegetative index (NDVI), distance to roads, distance to rivers, land use/land cover (LULC), soil texture, and lithology) were initially selected to establish a landslide factor database. Thereafter, the multicollinearity test and information gain ratio (IGR) technique were applied to rank the importance of the factors. Subsequently, we used a series of metrics (e.g., accuracy, precision, recall, f-measure, area under the ROC (receiver operating characteristic) curve (AUC), kappa index, mean absolute error (MAE), and root mean square error (RMSE)) to evaluate the accuracy and performance of the six models. Based on the AUC values derived from the models, the GBDT+LR model with the highest AUC value (0.8168) was identified as the most efficient model for mapping landslide susceptibility, followed by the XGB+LR, XGB, RF+LR, GBDT, and RF models, which achieved AUC values of 0.8124, 0.8118, 0.8060, 0.7927, and 0.7883, respectively. The results from this study suggest that the stacking ensemble machine learning method is promising for use in landslide susceptibility mapping in the Zhangjiajie area and is capable of targeting the areas prone to landslides.

Preprint ARTICLE | doi:10.20944/preprints202203.0253.v1

Scale-specific Prediction of Topsoil Organic Carbon Contents using Terrain Attributes and SCMaP Soil Reflectance Composites

Markus Möller, Simone Zepp, Martin Wiesmeier, Heike Gerighausen, Uta Heiden

Subject: Earth Sciences, Geoinformatics Keywords: soil reflectance composites; digital soil modeling; soil organic carbon; GEOBIA, Landsat; terrain analysis

Online: 17 March 2022 (11:42:28 CET)

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Preprint ARTICLE | doi:10.20944/preprints202202.0250.v1

Natural Disaster Identification and Mapping of Tsunami and Earthquake in Indonesia Using Satellite Imagery Analysis (Case Study: Aceh, Palu, and Yogyakarta)

M. Rifqi Jundullah, Arie Wahyu Wijayanto

Subject: Earth Sciences, Geoinformatics Keywords: satellite images; disaster mapping; GIS

Online: 21 February 2022 (10:09:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints202202.0054.v1

Generative Adversarial Approach to Urban Areas NDVI Estimation Using Information Exclusively from Structural and Textural Analysis of Panchromatic Orthoimagery: A Case Study of Łódź, Poland

Maciej Adamiak, Krzysztof Będkowski, Adam Bielecki

Subject: Earth Sciences, Geoinformatics Keywords: generative adversarial networks; remote sensing; NDVI; green areas; orthophoto; artificial datasets.

Online: 3 February 2022 (15:11:58 CET)

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Preprint ARTICLE | doi:10.20944/preprints202201.0431.v1

Mobile Phone Indoor Scene Recognition Location Method Based on Semantic Constraint of Building Map

Liu Jianhua, Feng Guoqiang, Luo Jingyan, Wen Danqi, Chen Zheng, Wang Nan, Zeng Baoshan, Wang Xiaoyi, Li Xinyue, Gu Botong

Subject: Earth Sciences, Geoinformatics Keywords: cell phone indoor positioning; scene recognition; building map; map location anchor; YOLOv5; geocoding matching

Online: 28 January 2022 (08:55:08 CET)

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At present, indoor localization is one of the core technologies of location-based services (LBS), and there exist numerous scenario-oriented application solutions. Visual features, as the main semantic information to help people understand the environment and thus occupy the dominant part, many techniques about indoor scene recognition are widely adopted. However, the engineering application problem of cell phone indoor scene recognition and localization has not been well solved due to insufficient semantic constraint information of building map and the immaturity of building map location anchors (MLA) matching positioning technology. To address the above problems, this paper proposes a cell phone indoor scene recognition and localization method with building map semantic constraints. Firstly, we build a library of geocoded entities for building map location anchors (MLA), which can provide users with "immersive" real-world building maps on the one hand and semantic anchor point constraints for cell phone positioning on the other. Secondly, using the improved YOLOv5s deep learning model carried on the mobile terminal, we recognize the universal map location anchors (MLA) elements in building scenes by cell phone camera video in real-time. Lastly, the spatial location of the scene elements obtained from the cell phone video recognition is matched with the building MLA to achieve real-time positioning and navigation. The experimental results show that the model recognition accuracy of this method is above 97.2%, and the maximum localization error is within the range of 0.775 m, and minimized to 0.5 m after applying the BIMPN road network walking node constraint, which can effectively achieve high positioning accuracy in the building scenes with rich MLA element information. In addition, the building map location anchors (MLA) has universal characteristics, and the positioning algorithm based on scene element recognition is compatible with the extension of indoor map data types, so this method has good prospects for engineering applications.

Preprint ARTICLE | doi:10.20944/preprints202201.0417.v1

Space-based Deformation Monitoring of Coastal Urban Areas: The Case of Limassol’s Coastal Front

Kyriaki Fotiou, Dimitris Kakoullis, Marina Pekri, George Melillos, Ramon Brcic, Michael Eineder, Diofantos G. Hadjimitsis, Chris Danezis

Subject: Earth Sciences, Geoinformatics Keywords: Deformation Monitoring; Land Subsidence; Coastal Areas; PSI; SAR; Cyprus

Online: 27 January 2022 (11:30:23 CET)

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Preprint ARTICLE | doi:10.20944/preprints202201.0352.v1

Pixel Level Classification Confidence for Remote Sensing Imagery: An Evaluation of Three Interpolation Based Methods

Shiguo Jiang

Subject: Earth Sciences, Geoinformatics Keywords: Per-pixel classification confidence; spatial pattern; image classification; accuracy assessment; interpolation method

Online: 24 January 2022 (11:53:46 CET)

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Preprint ARTICLE | doi:10.20944/preprints202201.0331.v1

Geochemical Association Rules of Elements Mined using Clustered Events of Spatial Auto-correlation: A Case Study in the Chahanwusu River Area, Qinghai Province, China

Baoyi Zhang, Zhengwen Jiang, Yiru Chen, Nanwei Cheng, Umair Khan, Jiqiu Deng

Subject: Earth Sciences, Geoinformatics Keywords: Concentration field; Spatial auto-correlation; Association rules; Apriori algorithm; Element co-occurrence

Online: 21 January 2022 (13:42:44 CET)

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Preprint ARTICLE | doi:10.20944/preprints202201.0288.v1

GIS Planning, Web and 3D Web GIS Applications for the Management and Analysis of MV/LV Electrical Networks in the Region of Medjez el Bab (North-West of Tunisia)

Mohamed Hafedh Hamza, Mohamed Chmit

Subject: Earth Sciences, Geoinformatics Keywords: MV/LV network; GIS planning; Spatial network analysis; 3D virtual city; Web and 3D Web GIS applications

Online: 20 January 2022 (08:32:37 CET)

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Preprint ARTICLE | doi:10.20944/preprints202201.0202.v1

Crop Detection Using Time Series of Sentinel-2 and Sentinel-1 and Existing Land Parcel Information Systems

Herman Snevajs, Karel Charvat, Vincent Onckelet, Jiri Kvapil, Frantisek Zadrazil, Hana Kubickova, Jana Seidlova, Iva Bartlova

Subject: Earth Sciences, Geoinformatics Keywords: crop detection; Sentinel 1; Sentinel 2; supervised classification; unsupervised classification; time series; agriculture; food security

Online: 14 January 2022 (11:18:59 CET)

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Preprint ARTICLE | doi:10.20944/preprints202201.0123.v1

Genus-Physiognomy-Ecosystem Map with 88 Legends Produced at 10m-resolution First-time in a Country Scale through Machine Learning of Multi-temporal Satellite Images

Ram C. Sharma, Keitarou Hara

Subject: Earth Sciences, Geoinformatics Keywords: Sentinel-2, Land cover, Vegetation, Mapping, Plant communities, Machine learning, Genus-Physiognomy-Ecosystem, Gradient Boosting Decision Trees

Online: 10 January 2022 (15:02:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202112.0487.v1

Modelling Buildings in CityGML LOD1: Building Parts, Terrain Intersection Curve and Address Features

Evgeny Shirinyan, Dessislava Petrova-Antonova

Subject: Earth Sciences, Geoinformatics Keywords: 3D City Model; CityGML 2.0; Spatial Data Integration

Online: 30 December 2021 (12:51:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202112.0443.v1

Assessing the Potential of Multi-spectral and Multi-temporal Satellite Images for Classification and Mapping of Plant Communities in a Temperate Region

Ram C. Sharma, Hidetake Hirayama, Masatsugu Yasuda, Miki Asai, Keitarou Hara

Subject: Earth Sciences, Geoinformatics Keywords: Sentinel-2; Land cover; Vegetation; Mapping; Plant communities; Machine learning; Genus-Physiognomy-Ecosystem; Gradient Boosting Decision Trees

Online: 28 December 2021 (10:49:28 CET)

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Preprint ARTICLE | doi:10.20944/preprints202112.0417.v1

Towards a ‘City in Nature’: Evaluating the Cultural Ecosystem Services Approach to Support Urban Green Space Management

Yi Fan Koh, Ho Huu Loc, Edward Park

Subject: Earth Sciences, Geoinformatics Keywords: Cultural ecosystem services; urban green space management; Singapore; public participation geographic information system; social media text mining analysis

Online: 27 December 2021 (09:48:44 CET)

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Preprint ARTICLE | doi:10.20944/preprints202112.0404.v1

Timescape: A Simple Spatiotemporal Interpolation Tool

Marco Ciolfi, Francesca Chiocchini, Rocco Pace, Giuseppe Russo, Marco Lauteri

Subject: Earth Sciences, Geoinformatics Keywords: Spatiotemporal Modelling; Ecological Modelling; Sparse Data; Minkowskian Geometry; Time Series Analysis; Spatial Statistics; Isoscapes

Online: 24 December 2021 (11:16:17 CET)

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Preprint ARTICLE | doi:10.20944/preprints202112.0309.v1

Representing Geographic Space as a Hierarchy of Recursively Defined Subspaces for Computing the Degree of Order

Bin Jiang, Chris de Rijke

Subject: Earth Sciences, Geoinformatics Keywords: Living structure; pattern language; life; wholeness; coherence; structural beauty

Online: 20 December 2021 (11:51:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints202112.0143.v1

Integration of Sentinel-3 and MODIS Vegetation Indices with ERA-5 Agro-meteorological Indicators for Operational Crop Yield Forecasting

Jędrzej S. Bojanowski, Sylwia Sikora, Jan P. Musiał, Edyta Woźniak, Katarzyna Dąbrowska-Zielińska, Przemysław Slesiński, Tomasz Milewski, Artur Łączyński

Subject: Earth Sciences, Geoinformatics Keywords: satellite data; machine learning; data calibration; thermal time; growing degree days; Extreme Gradient Boosting; crop yield; crop monitoring

Online: 8 December 2021 (15:42:11 CET)

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Preprint ARTICLE | doi:10.20944/preprints202112.0078.v1

Spatiotemporal Analysis of Soil Moisture Variation in the Jiangsu Water Supply Area of the South-to-North Water Diversion Using ESA CCI Data

Yue Wang, Jianjun Cao, Yongjuan Liu, Ying Zhu, xuan Fang, Qing Huang, wolfgang wagner

Subject: Earth Sciences, Geoinformatics Keywords: ESA CCI; soil moisture; EEMD; Mann-Kendall; temporal and spatial variation; Jiangsu water supply area (JWSA)

Online: 6 December 2021 (14:56:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202112.0077.v1

Deep Learning of High-Resolution Aerial Imagery for Coastal Marsh Change Detection: A Comparative Study

Grayson R. Morgan, Cuizhen Wang, Zhenlong Li, Steven R. Schill, Daniel R. Morgan

Subject: Earth Sciences, Geoinformatics Keywords: Deep Learning; Machine Learning; Change Detection; Coastal; Marsh; Remote Sensing; Aerial Imagery

Online: 6 December 2021 (13:31:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints202111.0007.v1

Monitoring of Forty Years of Agricultural Expansion in the Oum Er Rbia Valley (Morocco). The Use of Google Earth Engine Compared to Sentinel Application Platform

María Adell, José Antonio Domínguez-Gómez, Juan Soria

Subject: Earth Sciences, Geoinformatics Keywords: African agriculture; Irrigation; Landsat; Remote Sensing; Reservoir.

Online: 1 November 2021 (11:26:45 CET)

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Preprint ARTICLE | doi:10.20944/preprints202109.0408.v1

Assessing the Vertical Displacement of the Grand Ethiopian Renaissance Dam during its Filling using DInSAR technology and its Potential Acute Consequences on the Downstream Countries

Hesham El-Askary, Amr Fawzy, Rejoice Thomas, Wenzhao Li, Nicholas LaHaye, Erik Linstead, Thomas Piechota, Daniele Struppa, Mohamed Abdelaty Sayed

Subject: Earth Sciences, Geoinformatics Keywords: Grand Ethiopian Renaissance Dam; Main and Saddle Dams; Ground Displacement; Sentinel-1; Dam Filling; Geological Structures

Online: 23 September 2021 (12:32:03 CEST)

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The Grand Ethiopian Renaissance Dam (GERD), formerly known as the Millennium Dam, is currently under construction and has been filling at a fast rate without sufficient known analysis on possible impacts on the body of the structure. The filling of GERD not only has an impact on the Blue Nile Basin hydrology, water storages and flow but also pose massive risks in case of collapse. Rosaries Dam located in Sudan at only 116 km downstream of GERD, along with the 20 million Sudanese benefiting from that dam, would be seriously threatened in case of the collapse of GERD. In this study, through the analysis of Sentinal-1 satellite imagery we show concerning deformation patterns associated with different sections of the GERD’s Main Dam (structure RCC Dam type) and the Saddle Dam (Embankment Dam type). We processed 109 descending mode scenes from Sentinel-1 SAR imagery, from December 2016 to July 2021, using the Differential Synthetic Aperture Radar Interferometry technique to demonstrate the deformation trends of both - the GERD’s Main and Saddle Dams. The time-series generated from the analysis clearly indicates different displacement trends at various sections of the GERD as well as the Saddle Dam. Results of the multi temporal data analysis on and around the project area show inconsistent subsidence at the extremities of the GERD Main Dam, especially the west side of the dam where we recorded varying displacements in the range of 10 mm to 90 mm at the crest of the dam. We conducted the current analysis after masking the images with a coherence value of 0.9 and hence, the subsequent results are extremely reliable and accurate. Further decomposition of the subsiding rate has revealed higher vertical displacement over the west side of the GERD’s Main Dam as compared to the east side. The local geological structures consisting of weak zones under the GERD’s accompanying Saddle Dam adds further instability to its structure. We identified seven critical nodes on the Saddle Dam that match the tectonic faults lying underneath it, and which display a varying degree of vertical displacements. In fact, the nodes located next to each other displayed varying displacement trends: one or more nodes displayed subsidence since 2017 while the other node in the same section displayed uplift. The geological weak zones underneath and the weight of the Saddle Dam itself may somewhat explain this inconsistency and the non-uniform vertical displacements. For the most affected cells, we observed a total displacement value of ~90 mm during the whole study period (~20 mm/year) for the Main Dam while the value of the total displacement for the Saddle dam is ~380 mm during the same period (~85 mm/year). Analysis through CoastSat tool also suggested a non-uniformity in trends of surface water-edge at the two extremities of the Main Dam.

Preprint ARTICLE | doi:10.20944/preprints202107.0046.v1

Combining Remote Sensing Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

Johannes H. Uhl, Stefan Leyk, Zekun Li, Weiwei Duan, Basel Shbita, Yao-Yi Chiang, Craig A. Knoblock

Subject: Earth Sciences, Geoinformatics Keywords: urbanization; long-term settlement patterns; built-up land data; global human settlement layer; historical maps; topographic map processing; data integration.

Online: 2 July 2021 (10:03:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202105.0444.v1

DLR HySU - A Benchmark Dataset for Spectral Unmixing

Daniele Cerra, Miguel Pato, Kevin Alonso, Claas Köhler, Mathias Schneider, Raquel de los Reyes, Emiliano Carmona, Rudolf Richter, Franz Kurz, Peter Reinartz, Rupert Müller

Subject: Earth Sciences, Geoinformatics Keywords: Spectral Unmixing; Imaging Spectrometer; Hyperspectral; Benchmark Dataset; Dimensionality Estimation; Endmember Extraction; Abundance Estimation; HySpex.

Online: 19 May 2021 (13:25:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202103.0407.v1

Combination of Geostatistical and Geovisualisation Techniques for Analysing 120 Year Earthquake Events in Indonesia Using Open-Source Software

Fatwa Ramdani, Vivid Chairunnisa

Subject: Earth Sciences, Geoinformatics Keywords: geostatistics; geovisualisation; rshiny; earthquake; Indonesia

Online: 16 March 2021 (09:30:11 CET)

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Working Paper ARTICLE

Photogrammetry Using UAV-Mounted GNSS RTK: Georeferencing Strategies without GCPs

Martin Štroner, Rudolf Urban, Jan Seidl, Tomáš Reindl, Josef Brouček

Subject: Earth Sciences, Geoinformatics Keywords: Drone; GNSS RTK; UAV; photogrammetry; precision; accuracy; elevation

Online: 11 March 2021 (11:49:25 CET)

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Preprint ARTICLE | doi:10.20944/preprints202102.0516.v1

Detecting the Attack of the Fall Armyworm (Spodoptera Frugiperda) in Cotton Plants with Machine Learning and Spectral Measurements

Felipe David Georges Gomes, Mayara Maezano Faita Pinheiro, Danielle Ellis Garcia Furuya, Wesley Nunes Gonçalves, José Marcato Júnior, Mirian Fernandes Furtado Michereff, Maria Carolina Blassioli-Moraes, Miguel Borges, Raúl Alberto Alaumann, Veraldo Liesenberg, Lúcio André de Castro Jorge, Ana Paula Marques Ramos, Lucas Prado Osco

Subject: Earth Sciences, Geoinformatics Keywords: machine learning; insect-damage; spectral data; theoretical model

Online: 23 February 2021 (14:12:28 CET)

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In cotton cultivars, an insect that causes irreversible damage is the Spodoptera frugiperda, known as the fall armyworm. Since the visual detection of plants is a burdensome task for human inspection, the spectral information related to plant damage, registered on a spectral scale, can be useful. These measurements, associated with machine learning techniques, produce useful information for a rapid and non-invasive inspection method development. To contribute to this gap fulfillment, this paper proposes a machine learning framework to model the spectral response of cotton plants under the attack of the fall armyworm. Additionally, a theoretical model is presented, built from the results of the machine learning analysis, to infer this damage with up-to-date orbital sensors. The data was composed of the reflectance measurements collected at a cotton field with control plants and plants submitted to Spodoptera frugiperda damage. Their spectral response was recorded with a hand-held spectroradiometer ranging from 350 to 2,500 nm, for eight consecutive days. Different machine learning models were evaluated and the overall best model was defined by accuracies comparisons on a testing-set. A ranking approach was adopted based on the model accuracy, returning the most contributive wavelengths for the classification. Sequentially, an unsupervised neural network (Self-Organizing Map - SOM) was implemented to reduce data-dimensionality and assist in the definition of important spectral regions. The regions were associated with the spectral bands of the two sensors (OLI and MSI) and a theoretical model using a band simulation process with the overall best machine learning model was proposed. The results indicated that the Random Forest (RF) algorithm is the most suitable to predict cotton-plants damaged by insects and that the last day of analysis (8th day) was better to separate it, with F-measure equals 0.912. The ranking approach combined with the SOM method indicated the spectral regions at the red to near-infrared (650 to 1,350 nm) and shortwave infrared (1,570 to 1,640 nm) as the most important regions to the analysis. The proposed theoretical model simulated with the OLI and MSI sensor-bands returned an F-Measure of 0.865 and 0.886, respectively. In conclusion, this framework can be used to map cotton-plants under insect-attack. The theoretical model presents high accuracy to infer the insect-damaged on cotton plants based on multispectral bands from other sensors, being a useful tool for future research that intends to evaluate it in other areas and at different field scales.

Preprint ARTICLE | doi:10.20944/preprints202102.0338.v1

Ensemble Learning of Multi-Source Satellite Sensors Dataset for Estimating Forest Biomass in New England Region

Ram C. Sharma

Subject: Earth Sciences, Geoinformatics Keywords: Forests; biomass; ALOS-2 PALSAR-2; Sentinel-1 CSAR; Sentinel-2 MSI; Landsat 8 OLI; ensemble learning.

Online: 16 February 2021 (14:15:01 CET)

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Preprint ARTICLE | doi:10.20944/preprints202102.0251.v1

Evaluation Model of Remote Sensing Satellites Cooperative Observation Capability

Zhonggang Zheng, Kun Fu, Qingmei Li

Subject: Earth Sciences, Geoinformatics Keywords: remote sensing; collaborative application; observation capability; evaluation

Online: 10 February 2021 (10:27:14 CET)

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Preprint ARTICLE | doi:10.20944/preprints202012.0721.v1

A Precision Evaluation Method for Remote Sensing Data Sampling Based on Hexagon Discrete Grid

Yue Ma, Guoqing Li, Xiaochuang Yao, Jin Ben, Qianqian Cao, Long Zhao, Lianchong Zhang, Rui Wang

Subject: Earth Sciences, Geoinformatics Keywords: Remote sensing; Global discrete grid; Accuracy evaluation; Hexagon grid

Online: 29 December 2020 (09:19:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints202011.0287.v1

Modeling of Urban Growth Using Cellular Automata and GIS Case of Benslimane in Morocco

BENCHELHA MOHAMED, Benzha Fatiha, Rhinane Hassan, BENCHELHA SAID, BENCHELHA TAOUFIK, BENCHELHA NAJAT, Bezza Mohamed

Subject: Earth Sciences, Geoinformatics Keywords: Urban growth; cellular automata; Benslimane; GIS; Landsat

Online: 9 November 2020 (22:56:32 CET)

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Preprint ARTICLE | doi:10.20944/preprints202009.0512.v1

Worldwide Detection of Informal Settlements via Topological Analysis of Crowdsourced Digital Maps

Satej Soman, Anni Beukes, Cooper Nederhood, Nicholas Marchio, Luís M.A. Bettencourt

Subject: Earth Sciences, Geoinformatics Keywords: OpenStreetMap; cities; slums; network analysis; remote sensing; human development; urban planning; GIS; cloud computing

Online: 22 September 2020 (08:58:35 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202009.0034.v1

Validation of Airborne Sensor Photogrammetric Digital Terrain and Global Digital Elevation Models around Mekelle City, Ethiopia

Hareya Biryhane, Tulu B. Bedada, Berhan Gessesse, Martin Vermeer

Subject: Earth Sciences, Geoinformatics Keywords: vertical accuracy; photogrammetric DTM; ASTER; SRTM; TanDEM-X; orthometric height; geoid height

Online: 2 September 2020 (08:30:48 CEST)

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The quality of photogrammetric-based derived products like orthophotos, digital terrain models (DTMs) and digital line maps as well as the global digital elevation models (DEM) are rigorously dependent on the accuracy of image orientation. This paper evaluates the vertical accuracy of aerial photogrammetric Digital Terrain Model (DTM), Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), and TerraSAR-X's twin satellite of TanDEM-X (TDX) datasets against in-situ orthometric heights computed from ellipsoidal heights and the 2008 Earth Gravitational Model (EGM2008) derived geoid heights in Ethiopia. The quality of the four global digital elevation models was also validated against the aerial photogrammetric DTM measurements. Besides, the accuracies of the photogrammetric DTM and the four DEM products were checked for their compliance to the American Society for Photogrammetry and Remote Sensing (ASPRS) standards as well as the Ethiopian national vertical data evaluation standards. The study showed that the photogrammetric DTM is in a good agreement with the reference orthometric heights compared to SRTM, ASTER and TDX datasets. More precisely, the result has an absolute accuracy of 1.67 m at Linear Error (LE) 95% confidence level, while the absolute accuracy of SRTM3 arc seconds (SRTM3) at LE 90% (11.91 m) is better than its product specification (16 m). The absolute accuracy of SRTM1 arc second (SRTM1) (7.70 m at LE 90%) surpasses that of SRTM3, whereas the absolute accuracy of ASTER DEM is somehow below its product specification. TDX also has the same vertical accuracy (10.29 m at LE 90%) compared to its product specification (10 m). Furthermore, the vertical accuracy of the photogrammetric DTM meets the100 cm vertical accuracy of the 2015 ASPRS standard. However, it does not meet the Ethiopian national vertical data accuracy requirement standard, i.e., RMSEz of ± 0.45 m. In general, the photogrammetric DTM, SRTM1, and TDX have been proven a superior product over the SRTM3 and ASTER DEMs, and better to use these products for high-level precision and accuracy required applications.

Preprint REVIEW | doi:10.20944/preprints202008.0627.v1

A Discussion on the Evolution of the Pathfinding Algorithms

Tong Pan, Shuk Ching Pun-Cheng

Subject: Earth Sciences, Geoinformatics Keywords: pathfinding; algorithms; multi-criteria; multi-modal; multi-network; transportation

Online: 28 August 2020 (09:09:37 CEST)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202008.0535.v1

ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data

Kathryn Elmer, Raymond Soffer, J. Pablo Arroyo-Mora, Margaret Kalacska

Subject: Earth Sciences, Geoinformatics Keywords: spectral processing; reflectance; spectrometer; spectroradiometer

Online: 25 August 2020 (03:37:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202008.0415.v1

Monitoring Coronavirus COVID-19/SARS-CoV-2 Pandemic using GIS Dashboard: International and Indonesia Context

Edy Irwansyah, Widodo Budiharto, Danu Widhyatmoko, Achmad Istamar, Fepri Putra Panghurian

Subject: Earth Sciences, Geoinformatics Keywords: pandemic; covid-19; monitoring; GIS dashboard; emergency spatial support centre

Online: 19 August 2020 (11:48:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202008.0293.v1

PhotonLabeler: An Inter-disciplinary Platform for Visual Interpretation and Labeling of ICESat-2 Geolocated Photon Data

Lonesome Malambo, Sorin C. Popescu

Subject: Earth Sciences, Geoinformatics Keywords: ICESat-2; photon-counting lidar; photon labeling; visualization; ATL03; ATL08; visual interpretation; solar-induced noise

Online: 13 August 2020 (08:08:40 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202008.0271.v1

Soil Loss and Sediment Export from Land Use of the George Town Conurbation Catchment

Zullyadini A. Rahaman, Mohamad Adam Omar, Narimah Samat, and Mohd Amirul Mahamud

Subject: Earth Sciences, Geoinformatics Keywords: geographic information system; land demand; land use; universal soil loss erosion

Online: 12 August 2020 (05:09:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202008.0107.v1

Environmental Urban Plan for Failaka Island, Kuwait: A Study in Urban Geomorphology

Ahmed Hassan, Muhammad G Almatar, Magdy Torab, Casey D Allen

Subject: Earth Sciences, Geoinformatics Keywords: Urban Geomorphology; geoarchaeology; suitability Model-GIS; Environmental Analytical Hierarchical process (EAHP); Failaka Island

Online: 5 August 2020 (04:24:17 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202008.0043.v1

Displacements Monitoring over Czechia by IT4S1 System for Automatised Interferometric Measurements Using Sentinel-1 Data

Milan Lazecky, Emma Hatton, Pablo J. Gonzalez, Ivana Hlavacova, Eva Jirankova, Frantisek Dvorak, Zdenek Sustr, Jan Martinovic

Subject: Earth Sciences, Geoinformatics Keywords: Displacement measurement; High performance computing; Radar interferometry; Synthetic aperture radar

Online: 2 August 2020 (15:57:56 CEST)

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Sentinel-1 satellite system continuously observes European countries in a relatively high revisit frequency of 6 days per orbital track. Given the Sentinel-1 configuration, most areas in Czechia are observed every 1–2 days by different tracks in a moderate resolution. This is attractive for various types of analyses by various research groups. The starting point for processing is an original data provided by ESA, for interferometry (InSAR) this level is a Single Look Complex (SLC) data. This work represents advantages of storing data augmented to a specifically corrected level of data, SLC-C. The presented database contains Czech nation-wide Sentinel-1 data stored in burst units that have been preprocessed to the state of a consistent well-coregistered dataset of SLC-C. These are resampled SLC data with their phase values reduced by a topographic phase signature, ready for fast interferometric analyses (an interferogram is generated by a complex conjugate between two stored SLC-C files). The data can be used directly into multitemporal interferometry techniques, e.g. Persistent Scatterers (PS) or Small Baseline (SB) techniques applied here. A further development of the nation-wide system utilising SLC-C data would lead into a dynamic state where every new pre-processed burst triggers a processing update to detect unexpected changes from InSAR time series and therefore provide a signal for early warning against a potential dangerous displacement, e.g. a landslide, instability of an engineering structure or a formation of a sinkhole. An update of the processing chain would also allow use of cross-polarised Sentinel-1 data, needed for polarimetric analyses. The current system is running at a national supercomputing centre IT4Innovations in interconnection to the Czech Copernicus Collaborative Ground Segment (CESNET), providing fast on-demand InSAR results over Czech territories. A full nation-wide PS processing using data over Czechia has been performed in 2017, discovering several areas of land deformation. Its downsampled version and basic findings are demonstrated within the article.

Preprint ARTICLE | doi:10.20944/preprints202007.0750.v1

Model-based Estimation of Amazonian Forests Recovery Time After Drought and Fire Events

Bruno De Faria, Gina Marano, Camille Piponiot, Ludmila Rattis, Andre Rech, Alessio Collalti

Subject: Earth Sciences, Geoinformatics Keywords: Amazon; recovery time; aboveground biomass; climate change; 3-PG

Online: 31 July 2020 (13:51:03 CEST)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202007.0556.v1

Solar3D: A 3D Extension of GRASS GIS r.sun for Estimating Solar Radiation in Urban Environments

Jianming Liang, Jianhua Gong, Xiuping Xie, Jun Sun

Subject: Earth Sciences, Geoinformatics Keywords: Solar radiation; 3D city models; Urban environment; GRASS GIS r.sun; 3D extension

Online: 23 July 2020 (12:20:40 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202007.0555.v1

Quantifying melt dynamics on a debris-covered Himalayan glacier using repeated UAS photogrammetry derived DSM and point cloud differencing

Niti Mishra, Gargi Chaudhuri, Kumar Mainali, Suraj Mal, Babulal Tiruwa, Paras Singh

Subject: Earth Sciences, Geoinformatics Keywords: UAS, debris-covered glacier, trans-Himalaya, aerial photogrammetry, structure from motion, DSM differencing, point cloud differencing, glacial mass balance, ice-cliffs

Online: 23 July 2020 (12:00:27 CEST)

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Debris-covered glaciers are a notable feature in the greater Himalaya, and their ongoing mass loss under changing climate will affect the water resources of over a billion people. The current knowledge of the mass balance of Himalayan glaciers is restricted by the paucity of in-situ measurements of glaciers in both space and time, as well as the resolution of satellite remote sensing imageries. Recently, the use of Unmanned Aerial System (UAS) imagery has shown the potential to bridge this gap by enabling very detailed monitoring of inaccessible glacial areas. UAS imagery-based monitoring of Himalayan glaciers has so far been limited to a single glacier in the entire Himalaya, providing a limited understanding of spatial variability in glacier mass balance and driving factors. In the first UAS based glacial mass change estimation in the trans-Himalaya, we conducted two Unmanned Aerial System (UAS) surveys (May and November 2019) over the debris-covered Annapurna III glacier in the Himalaya. We performed Structure-from-Motion (SfM) analysis and utilized differential GPS field observations to derive geometrically accurate point clouds, ortho-mosaics and digital surface models (DSMs). The glacial volumetric loss was estimated from DSM differencing, and the magnitude and spatial variability of glacier surface change was derived from 3-D differencing of point clouds. Results revealed a heterogeneous glacial melt pattern, with an average elevation loss of 0.89 m during the monitored time period. The majority of the glacial tongue exhibited surface lowering except the area above and around the glacial snout that surprisingly exhibited significant elevation gain. Both the highest magnitude of mass loss and the highest spatial variability in mass change was observed in areas with exposed ice-cliffs and supraglacial ponds. Glacial surface velocity derived from manual feature tracking showed velocity ranging from 0-4.1 m. A detailed evaluation of specific areas allowed an improved understanding of the complex interplay of factors leading to observed surface change. Our findings expand the extent of UAS based monitoring of debris-covered glaciers in the Himalaya and conclude that UAS derived 3D topographic products will become increasingly important for monitoring of thinning debris-covered glaciers.

Working Paper ARTICLE

A Rapid Extraction Method for Regional-Scale Agricultural Disasters Based on Google Earth Engine

Zhengrong Liu, Huanjun Liu, Chong Luo, Haoxuan Yang, Xiangtian Meng, Yongchol Ju, Dong Guo

Subject: Earth Sciences, Geoinformatics Keywords: Google Earth Engine; MODIS; disaster monitoring; remote sensing index

Online: 21 July 2020 (03:12:22 CEST)

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Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of spatial and temporal disaster patterns of large-scale and long-duration series. Based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products with 250-m spatial resolution synthesized over 16 days from the period 2005–2019 to develop a rapid and effective method for monitoring disasters across a wide spatiotemporal range. Three types of disaster monitoring and scope extraction models are proposed: the normalized difference vegetation index (NDVI) median time standardization model (R_{NDVI_TM(i)}), the NDVI median phenology standardization model (R_{NDVI_AM(i)(j)}), and the NDVI median spatiotemporal standardization model (R_{NDVI_ZM(i)(j)}). The optimal disaster extraction threshold for each model in different time phases was determined using Otsu’s method, and the extraction results were verified by medium-resolution images and ground-measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang Province from 2010–2019 was extracted, and the spatial and temporal patterns of the disasters were analyzed based on meteorological data. This analysis revealed that the three aforementioned models exhibited high disaster monitoring and range extraction capabilities, with verification accuracies of 97.46%, 96.90%, and 96.67% for R_{NDVI_TM(i)}, R_{NDVI_AM(i)}, and _(j)R_{NDVI_ZM(i)(j)}, respectively. The spatial and temporal disaster distributions were found to be consistent with the disasters of the insured plots and the meteorological data across the entire province. Moreover, different monitoring and extraction methods were used for different disasters, among which wind hazard and insect disasters often required a delay of 16 days prior to observation. Each model also displayed various sensitivities and were applicable to different disasters. Compared with other techniques, the proposed method is fast and easy to implement. This new approach can be applied to numerous types of disaster monitoring as well as large-scale agricultural disaster monitoring and can easily be applied to other research areas. This study presents a novel method for large-scale agricultural disaster monitoring.

Preprint ARTICLE | doi:10.20944/preprints202007.0207.v1

Catchment-Scale Flood Modelling in Data-Sparse Regions Using Open-Access Geospatial Technology

Iguniwari Thomas Ekeu-wei, Alan George Blackburn

Subject: Earth Sciences, Geoinformatics Keywords: Open-access; geospatial; remote sensing; hydrodynamic model; CAESAR-LISFLOOD; data-sparse; flood risk management

Online: 10 July 2020 (08:13:07 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202007.0072.v1

A Rapid Extraction Method of Regional Scale Agricultural Disasters Based on Google Earth Engine

Zhengrong Liu, Huanjun Liu, Chong Luo, Haoxuan Yang, Yongchol Ju, Dong Guo

Subject: Earth Sciences, Geoinformatics Keywords: Google Earth Engine; MODIS; Disaster monitoring

Online: 5 July 2020 (12:32:00 CEST)

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Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of temporal and spatial disasters patterns of large-scale long time series. In order to find out a rapid and effective method to monitor disaster in a wide range, based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products of 250 meter spatial resolution synthesized in 16 days during the year 2005-2019 and three kinds of disaster monitoring and scope extraction models are proposed: normalized vegetation index median time standardization (RNDVI_TM(i)) model, the normalized vegetation index median phenology Standardization(RNDVI_AM(i)(j)) model, normalized vegetation index median spatiotemporal Standardization (RNDVI_ZM(i)(j)) model. The optimal threshold of disaster extraction for each model in different time phases was determined by Otsu method, and the extraction results were verified by Medium resolution image and ground measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang province from 2010 to 2019 was extracted and the temporal and spatial pattern of disasters was analyzed based on the meteorological data. It shows that the three above-mentioned models have high disaster monitoring and range extraction capabilities with the verification accuracy of RNDVI_TM(i) 97.46%, RNDVI_AM(i)(j) 96.90%, and RNDVI_ZM(i)(j) 96.67% respectively. The spatial and temporal distribution of disasters is consistent with the disaster of the insured plots and meteorological data in the whole province. Meanwhile, it turns out that different monitoring and extraction methods are used in different disasters, among which wind hazard and insect disasters often need to be delayed for 16 days to observe. Each model also has various sensitivity and applicability to different disasters. Compared with other methods, this method is fast, and convenient, which allows it to be used for large-scale agricultural disaster monitoring and is easy to be applied into other research areas. The research provides a new idea for large-scale agricultural disaster monitoring.

Preprint ARTICLE | doi:10.20944/preprints202002.0326.v2

Serverless GEO Labels for the Semantic Sensor Web

Anika Graupner, Daniel Nüst

Subject: Earth Sciences, Geoinformatics Keywords: GEO label; serverless; semantic sensor web; discovery; visualisation; sensor web

Online: 30 June 2020 (08:06:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202005.0345.v1

Effective, Low-cost Methods of Applying Computer Vision to Public Earth Observation Data

Michael Evans, Taylor Minich

Subject: Earth Sciences, Geoinformatics Keywords: conservation; deep learning; Earth Engine; remote sensing; renewable energy; Tensorflow; U-Net

Online: 21 May 2020 (11:41:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202005.0155.v1

CORINE Land Cover Data and Actual Arrangement of Buildings in Research on Processes of Concentration and Dispersion of Settlement: Example of Poland

Paweł Sudra

Subject: Earth Sciences, Geoinformatics Keywords: CLC 2018 level of detail; comparison of remote sensing and geodetic data; settlement; land use monitoring; urban sprawl

Online: 9 May 2020 (08:22:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0370.v1

Development of Worldwide Tsunami Hazard Map for Evacuation Planning and Rescue Operations

Jais Jose, E. Yuvaraj, S. Aswin, Arjun Suresh

Subject: Earth Sciences, Geoinformatics Keywords: tsunami hazard map; maps API; HTML; JavaScript; JQuery

Online: 20 April 2020 (16:07:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0188.v1

A Study of Annual and Seasonal Variations in Tropospheric Ozone (O₃) Concentrations over India

Komal Gupta, Arnab Saha

Subject: Earth Sciences, Geoinformatics Keywords: ozone; OMI; seasonal variations; satellite remote sensing

Online: 12 April 2020 (09:14:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0136.v1

Multiple Groundwater Parameters and Environmental Changes using Geospatial Techniques in the Permian Basin, Texas

Robert Nelson, Joonghyeok Heo

Subject: Earth Sciences, Geoinformatics Keywords: groundwater quality; environmental change; Permian Basin; Texas

Online: 9 April 2020 (05:50:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0119.v1

Effects of Hard and Soft Equality Constraints on Reliability Analysis

Vinicius Francisco Rofatto, Marcelo Tomio Matsuoka, Ivandro Klein, Mauricio Roberto Veronez, Luiz Gonzaga Da Silveira, Jr.

Subject: Earth Sciences, Geoinformatics Keywords: constraints; hypothesis testing; outlier detection; Monte Carlo; quality control; geodesy

Online: 8 April 2020 (05:21:58 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0032.v1

Indoor Positioning Using PnP Problem on Mobile Phone Images

Hana Kubíčková, Karel Jedlička, Radek Fiala, Daniel Beran

Subject: Earth Sciences, Geoinformatics Keywords: indoor positioning system; image-based positioning system; computer vision; SIFT; feature detection; feature description; cell phone camera; PnP problem; projection matrix; epipolar geometry; OpenCV

Online: 3 April 2020 (11:59:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202003.0467.v1

Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?

Vinicius Francisco Rofatto, Marcelo Tomio Matsuoka, Ivandro Klein, Mauricio Roberto Veronez, Luiz Gonzaga Da Silveira, Jr.

Subject: Earth Sciences, Geoinformatics Keywords: constraints; hypothesis testing; outlier detection; Monte Carlo; quality control; geodesy

Online: 31 March 2020 (22:59:14 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202003.0339.v1

Compatibility of Aerial and Terrestrial LiDAR for Quantifying Forest Structural Diversity

Elizabeth A. LaRue, Franklin W. Wagner, Songlin Fei, Jeff W. Atkins, Robert T. Fahey, Chris M. Gough, Brady S. Hardiman

Subject: Earth Sciences, Geoinformatics Keywords: ALS; forest ecology; forest structure; NEON; macrosystems biology; TLS

Online: 23 March 2020 (06:42:29 CET)

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Preprint ARTICLE | doi:10.20944/preprints202003.0241.v1

Identification of Drought and Performance Evaluation of MODIS and TRMM through Remote Sensing: A Case Study in North and West Africa during 2002–2018

Malak Henchiri, Qi Liu, Bouajila Essifi, Shahzad Ali, Wilson Kalisa, Sha Zhang, Bai Yun, Jiahua Zhang

Subject: Earth Sciences, Geoinformatics Keywords: North and West Africa; drought; DSI; TVDI; NVSWI; spatial correlation; change trend of drought

Online: 15 March 2020 (12:38:03 CET)

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Preprint ARTICLE | doi:10.20944/preprints202002.0442.v1

UAV-based Survey of Glaciers in Himalayas: Opportunities and Challenges

RAAJ Ramsankaran, P.J. Navinkumar, Ajay Dashora, Anil Kulkarni

Subject: Earth Sciences, Geoinformatics Keywords: Digital Elevation Models; ortho-mosaicked images; glacier; remote sensing; Unmanned Aerial Vehicle

Online: 28 February 2020 (13:34:22 CET)

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Preprint ARTICLE | doi:10.20944/preprints202002.0334.v1

Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

Somayeh Nezami, Ehsan Khoramshahi, Olli Nevalainen, Ilkka Pölönen, Eija Honkavaara

Subject: Earth Sciences, Geoinformatics Keywords: deep learning; drone imagery; hyperspectral image classiﬁcation; tree species classification; 3D convolutional neural networks

Online: 24 February 2020 (01:13:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202002.0326.v1

Serverless GEO Labels for the Semantic Sensor Web

Anika Graupner, Daniel Nüst

Subject: Earth Sciences, Geoinformatics Keywords: GEO label; serverless; semantic sensor web; discovery; visualisation; sensor web

Online: 23 February 2020 (13:51:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202002.0142.v1

Global Positioning System Interferometric Reflectometry for Accurate Tide Gauge Measurement: Insights from South Beach, Oregon, United States

Kutubuddin Ansari, Tae-Suk Bae, Samed Inyurt

Subject: Earth Sciences, Geoinformatics Keywords: SSA; ARMA; GPS; TG; SEPT

Online: 11 February 2020 (09:36:52 CET)

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Preprint ARTICLE | doi:10.20944/preprints202002.0074.v1

Geomorphometric Methods for Burial Mound Recognition and Extraction from High Resolution LiDAR DEMs

Mihai Niculita

Subject: Earth Sciences, Geoinformatics Keywords: archaeological topography; tumulus; burial mound; geomorphometry; high-resolution; DEM; LiDAR; Random Forest

Online: 6 February 2020 (02:43:29 CET)

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Preprint ARTICLE | doi:10.20944/preprints202002.0042.v1

A Simple Yet Effective Approach of Building Footprint Extraction in Indonesia

Danang Budi Susetyo, Aldino Rizaldy, M. Irwan Hariyono, Nugroho Purwono, Fahrul Hidayat, Rizka Windiastuti, Tia Rizka N. Rachma, Prayudha Hartanto

Subject: Earth Sciences, Geoinformatics Keywords: building footprint; LiDAR; classification; segmentation

Online: 4 February 2020 (10:27:59 CET)

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Preprint ARTICLE | doi:10.20944/preprints202001.0385.v1

A Machine Learning based Approach for Wildfire Susceptibility Mapping. The Case Study of Liguria Region in Italy

Marj Tonini, Mirko D'Andrea, Guido Biondi, Silvia Degli Esposti, Andrea Trucchia, Paolo Fiorucci

Subject: Earth Sciences, Geoinformatics Keywords: wildfires; susceptibility mapping; machine learning; random forest; model validation; Liguria region

Online: 31 January 2020 (11:40:30 CET)

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Working Paper ARTICLE

Indicators for Analyzing Village Territories in the Baltic Sea Coast Areas

Maris Kalinka, Sanda Geipele, Andrejs Lazdins, Una Krutova, Jurijs Holms

Subject: Earth Sciences, Geoinformatics Keywords: spatial village planning; coastal areas; local community; indicators of village planning; GIS application; GIS layers; territorial development

Online: 12 January 2020 (13:38:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints202001.0078.v1

An Intuitive and Realistic Visualization Method for Seabed Terrain

Zixian Xiao, Liheng Tan, Hongli Liu, Bingchuan Jiang, Xiong You

Subject: Earth Sciences, Geoinformatics Keywords: seabed; sediment; terrain; visualization; physically based rendering; realistic

Online: 9 January 2020 (08:56:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints201912.0392.v1

Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events?

Xiaolong Liu, Zhengtao Shi, Guangcai Huang, Yanchen Bo, Guangjie Chen

Subject: Earth Sciences, Geoinformatics Keywords: time series; lake changes; remote sensing; inland lake; lake disturbance

Online: 30 December 2019 (04:45:43 CET)

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Preprint ARTICLE | doi:10.20944/preprints201912.0232.v1

Control Points Selection based on Maximum External Reliability for Designing Geodetic Networks

Marcelo Tomio Matsuoka, Vinicius Francisco Rofatto, Ivandro Klein, Mauricio Roberto Veronez, Luiz Gonzaga Da Silveira, João Batista Silva Neto, Ana Cristina Ramos Alves

Subject: Earth Sciences, Geoinformatics Keywords: geodetic network; outlier; reliability; reference points; surveying; quality control

Online: 18 December 2019 (03:12:56 CET)

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Preprint ARTICLE | doi:10.20944/preprints201911.0393.v1

Sentinel-1 SLC Preprocessing Workflow for Polarimetric Applications: A Generic Practice for Generating Dual-pol Covariance Matrix Elements in SNAP S-1 Toolbox

Dipankar Mandal, Divya Sekhar Vaka, Narayana Rao Bhogapurapu, V. S. K. Vanama, Vineet Kumar, Yalamanchili S. Rao, Avik Bhattacharya

Subject: Earth Sciences, Geoinformatics Keywords: Sentinel-1; PolSAR; synthetic aperture radar; earth observation; SNAP

Online: 30 November 2019 (11:39:51 CET)

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Preprint ARTICLE | doi:10.20944/preprints201911.0361.v1

Assessing OpenStreetMap Completeness for Management of Natural Disaster by Means of Remote Sensing: A Case Study of Three Small Island States

Ran Goldblatt, Nicholas Jones, Jenny Mannix

Subject: Earth Sciences, Geoinformatics Keywords: openstreetmap; disaster management; osm; disaster relief

Online: 28 November 2019 (11:10:33 CET)

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Preprint ARTICLE | doi:10.20944/preprints201911.0184.v1

Geo-Mapping of Areas Vulnerable to Ala-River Basin Flood Disaster Risk in Akure, Ondo State, Nigeria

Gani Olalekan Bakare, Samuel Bamikole Omosulu, John Taiwo Famutimi

Subject: Earth Sciences, Geoinformatics Keywords: flood disaster risk; vulnerability to flood disaster risk; arc-geographical information system (arcgis); geo-mapping

Online: 15 November 2019 (16:54:51 CET)

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Flood is identified as one of the major disasters in the world; it destroys both human and properties across the world, where lives are lost, properties, public infrastructure, farmlands and agricultural produce with farm crops carted away as a result of flood disaster. Studies revealed that the flood in itself is not the danger, but the level of human vulnerability to flooding disaster risk, which enhances its destructive capabilities. However, based on the challenges poses by flood disaster risk, this research identifies Ala river in Akure as a potential cause of flood, considering its location and other human activities around the river. Therefore, the research used Ala-river a case study to identify and mapped out areas susceptible to flood disaster risk. The research made use of both literature review and conducted goe-data gathering with the application GIS-computer database to retrieve georeferencing relevant data from the fieldwork in the study area of Ala-river basin to mapped out locations vulnerable to achieve the research aim. The research adopted a Geo-mapping of the vulnerable area to Ala-River basin using arc-GIS tool in combination with other software such as IKONAS and OLI (Operation Land Imager) for the production of the study area imagery, ER-ITERIM was used for the collection of rainfall data and FAO was applied for digital soil mapping. These applications produced; the land use/land cover map, digital elevation map, buffer map using 30 meters setback, annual rainfall map, soil types map, vulnerability map and soil textural table for the study area. Analysis of the produced and generated maps shows 316 buildings vulnerability to flood disaster risk; the soil texture and types, and alternative use to which the soil types can be useful. The research recommends that demolition of the identified 316 buildings prone to flood disaster and compliance of building construction to 30 meters setback by developers. Others are the conversion of the future land setback for urban agricultural purposes and preservation of water retention areas for agricultural activities during the dry season among others. The study concludes that relevant government agencies in the State and in particular in Akure South Local Government should ensure prompt compliance and implementation of the recommendations to avoid potential flood disaster risks.

Preprint ARTICLE | doi:10.20944/preprints201910.0275.v1

Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Minh D. Nguyen, Oscar B. Villanueva, Duong D. Bui, Phong T. Nguyen, Lars Ribbe

Subject: Earth Sciences, Geoinformatics Keywords: Landsat; Sentinel 2; harmonization; crop monitoring; Google Earth Engine

Online: 24 October 2019 (06:02:04 CEST)

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Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The synthesizing of ongoing satellite missions by ESA (Sentinel 2) and NASA (Landsat7/8) provides this unprecedented opportunity at a global scale; nonetheless, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a complete stream processing in the Google Earth Engine cloud platform to generate harmonized surface reflectance images of Landsat7,8 and Sentinel 2 missions. The harmonized images were generated for two agriculture schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during the period 2018-2019. We evaluated the performance of several pre-processing steps needed for the harmonization including image co-registration, brdf correction, topographic correction, and band adjustment. This study found that the miss-registration between Landsat 8 and Sentinel 2 images, varied from 10 meters in Ninh Thuan, Vietnam to 32 meters in Bekaa, Lebanon, and if not treated, posed a great impact on the quality of the harmonized dataset. Analysis of a pair overlapped L8-S2 images over the Bekaa region showed that after the harmonization, all band-to-band spatial correlations were greatly improved from (0.57, 0.64, 0.67, 0.75, 0.76, 0.75, 0.79) to (0.87, 0.91, 0.92, 0.94, 0.97, 0.97, 0.96) in bands (blue, green, red, nir,swir1,swir2, ndvi) respectively. We demonstrated that dense observation of the harmonized dataset can be very helpful for characterizing cropland in highly dynamic areas. We detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns (likely cycles of paddy rice) in Ninh Thuan province only during the year 2018. We fitted the temporal signatures of the NDVI time series using harmonic (Fourier) analysis. Derived phase (angle from the starting point to the cycle's peak) and amplitude (the cycle's height) were combined with max-NDVI to generate an R-G-B image. This image highlighted croplands as colored pixels (high phase and amplitude) and other types of land as grey/dark pixels (low phase/amplitude). Generated harmonized datasets that contain surface reflectance images (bands blue, green, red, nir, swir1, swir2, and ndvi at 30 meters) over the two studied sites are provided for public usage and testing.

Working Paper ARTICLE

A Health SDI: The Benefits of and A Call-to-Action for Building a Global Health SDI

Ajay Gupta, Ramani Peruvemba, Paul A. Churchyard

Subject: Earth Sciences, Geoinformatics Keywords: Spatial Data Infrastructure; Social Determinants of Health; Healthcare; Health; Geospatial Data Analytics; Geocoding; GeoHealth; GIS; Open Standards; Population Health; Disaster Response; Emergency Response

Online: 23 October 2019 (10:27:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201910.0184.v1

Oasis Evolution in Response to Human Induced Water Resources Reallocation in South Xinjiang during Past Four Decades

Yuanxu Ma, Jianping Wang, Fang Yan, Aynur Mamat

Subject: Earth Sciences, Geoinformatics Keywords: oasis; climatic change; human activities; water resources; remote sensing; Tarim River basin

Online: 16 October 2019 (09:46:33 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201812.0250.v3

Annually Modelling Built-settlements between Remotely-sensed Observations Using Relative Changes in Subnational Populations and Lights at Night

Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica Steele, Forrest Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna Clarke, Thomas Esch, Andrew J. Tatem

Subject: Earth Sciences, Geoinformatics Keywords: Built-settlements; urban features; spatial growth; , random forest; dasymetric modelling; population

Online: 9 October 2019 (10:48:20 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201909.0082.v1

InSAR and Landsat ETM+ incorporating with CGPS and SVM to Determine Subsidence Rates and Effects on Mexico City

Davod Poreh, Saied Pirasteh

Subject: Earth Sciences, Geoinformatics Keywords: Mexico City; subsidence; InSAR; GPS; PSI; SVM

Online: 7 September 2019 (01:19:04 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201909.0042.v1

Trend Dynamics of GRACE Terrestrial Water Storage in the Nile River Basin

Emad Hasan, Aondover Tarhule

Subject: Earth Sciences, Geoinformatics Keywords: GRACE TWS, GRACE-FO, Nile River Basin, Spatial autocorrelation, OLS, GWR.

Online: 4 September 2019 (13:26:12 CEST)

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GRACE-derived Terrestrial Water Storage Anomalies (TWSA) continue to be used in an expanding array of studies to analyze numerous processes and phenomena related to terrestrial water storage dynamics, including groundwater depletions, lake storage variations, snow, and glacial mass changes, as well as floods, droughts, among others. So far, however, few studies have investigated how the factors that affect total water storage (e.g., precipitation, runoff, soil moisture, evapotranspiration) interact and combine over space and time to produce the mass variations that GRACE detects. This paper is an attempt to fill that gap and stimulate needed research in this area. Using the Nile River Basin as case study, it explicitly analyzes nine hydroclimatic and anthropogenic processes, as well as their relationship to TWS in different climatic zones in the Nile River Basin. The analytic method employed the trends in both the dependent and independent variables applying two geographically multiple regression (GMR) approaches: (i) an unweighted or ordinary least square regression (OLS) model in which the contributions of all variables to TWS variability are deemed equal at all locations; and (ii) a geographically weighted regression (GWR) which assigns a weight to each variable at different locations based on the occurrence of trend clusters, determined by Moran’s cluster index. In both cases, model efficacy was investigated using standard goodness of fit diagnostics. The OLS showed that trends in five variables (i.e., precipitation, runoff, surface water soil moisture, and population density) significantly (p<0.0001) explain the trends in TWSA for the basin at large. However, the models R2 value is only 0.14. In contrast, the GWR produced R2 values ranging between 0.40 and 0.89, with an average of 0.86 and normally distributed standard residuals. The models retained in the GWR differ by climatic zone. The results showed that all nine variables contribute significantly to the trend in TWS in the Tropical region; population density is an important contributor to TWSA variability in all zones; ET and Population density are the only significant variables in the semiarid zone. This type of information is critical for developing robust statistical models for reconstructing time series of proxy GRACE anomalies that predate the launch of the GRACE mission and for gap-filling between GRACE and GRACE-FO.

Preprint CONCEPT PAPER | doi:10.20944/preprints201909.0016.v1

Long Term Monitoring of Ghana’s Forest Reserves Using Google Earth Engine

Jeff Dacosta Osei, S. A. Andam-Akorful, Edward Matthew Osei jnr

Subject: Earth Sciences, Geoinformatics Keywords: land cover; classification Spatial and temporal Analysis; forest cover; Google Earth Engine (GEE); MODIS; Landsat; NOAA AVHRR

Online: 2 September 2019 (04:51:15 CEST)

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Farm activities continued sand winning operations and the allocation of plots of land to prospective developers in Ghana pose a serious threat to the forest covers and lifespan of the Forest and game reserves. With all the positive add ups to the country from forests, Ghana has lost more than 33.7%(equivalent to 2,500,000 hectares) of its forest, since the early 1990s between 2005 and 2010, the rate of deforestation in Ghana was estimated at 2.19% per annum; the sixth highest deforestation rate globally for that period. This shows how important forest monitoring can be to the forestry commission in Ghana. Despite the frameworks which have been developed to help Ghana to protect and restore its forest resources, inadequate monitoring systems remain a barrier to effective implementation. In this study, Google earth engine was used to map and analyze the structural changes of forest cover using JavaScript to query and compute Landsat, MODIS and NOAA AVHRR satellite imageries of the study area (Ghana) with spatial resolutions 30m, 250m and 7km respectively. A supervised classification was performed on three multi-temporal satellite imageries and a total of six major land use and land cover classes were identified and mapped. By using random Forest-classification technique, from 1985 to 2018 recorded by NOAA AVHRR, forest cover has decreased by 66% and 2000 to 2018 recorded by Landsat and MODIS 61% and 47% respectively. A decrease in the forest has been as a result of anthropogenic activities in Ghana. A change detection analysis was performed on these images and it was noted that Ghana is losing forest reserves in every 5years. Overlay of the reserved forest of the 2000 and the classified map of 2018 shows vegetation changed during 2000-2018 remarkably. Therefore, forest-related institutions like the Forestry Commission can employ and use this monitoring system on Google Earth Engine for processing satellite images particularly Landsat, MODIS and NOAA AVHRR for forest cover monitoring and analysis for fast, efficient and reliable results.

Preprint ARTICLE | doi:10.20944/preprints201908.0289.v1

Artificial Intelligence and Convolutional Neural Network for Recognition of Human Interaction by Video from Drone

Ghazal Shamsipour, Saied Pirasteh

Subject: Earth Sciences, Geoinformatics Keywords: drone video; human action recognition; CNN; Support vector machine (SVM)

Online: 28 August 2019 (03:52:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201908.0225.v1

Analysis of Water Bodies under Partial Cloud Conditions Using Satellite Images

Rahul Neware, Mansi Thakare

Subject: Earth Sciences, Geoinformatics Keywords: water bodies; satellite images; vector data; SVM; positive and negative buffering; polygons

Online: 21 August 2019 (10:30:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201908.0159.v2

Japanese Lexical Variation Explained by Spatial Contact Patterns

Péter Jeszenszky, Yoshinobu Hikosaka, Satoshi Imamura, Keiji Yano

Subject: Earth Sciences, Geoinformatics Keywords: GIScience; dialect geography; digital humanities; spatial modelling; historical GIS; geostatistics; linguistic variation; language change; language contact

Online: 16 August 2019 (06:12:58 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201908.0159.v1

Japanese Lexical Variation Explained by Historical Contact Patterns

Péter Jeszenszky, Yoshinobu Hikosaka, Satoshi Imamura, Keiji Yano

Subject: Earth Sciences, Geoinformatics Keywords: GIScience; dialect geography; digital humanities; spatial modelling; historical GIS; geostatistics; linguistic variation; language change; language contact

Online: 14 August 2019 (09:20:33 CEST)

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Working Paper ARTICLE

Towards an Underground Utilities 3D Data Model for Land Administration

Jingya Yan, Siow Wei Jaw, Kean Huat Soon, Andreas Wieser, Geri Schrotter

Subject: Earth Sciences, Geoinformatics Keywords: 3D data model; underground utility networks; underground space planning; underground mapping; utility cadastre; land administration

Online: 14 August 2019 (07:43:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201906.0270.v2

Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems

Andrei Stoian, Vincent Poulain, Jordi Inglada, Victor Poughon, Dawa Derksen

Subject: Earth Sciences, Geoinformatics Keywords: Land cover mapping; Convolutional neural networks; UNET; Sentinel-2

Online: 9 August 2019 (11:54:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201908.0021.v1

How Well Can Spaceborne Digital Elevation Models Represent a Man-Made Structure: A Runway Case Study

Kazimierz Becek, Volkan Akgül, Samed Inyurt, Çetin Mekik , Patrycja Pochwatka

Subject: Earth Sciences, Geoinformatics Keywords: spaceborbe DEM; SRTM; TanDEM; AW3D30m; runway method; Zonguldak; suitability assessment

Online: 2 August 2019 (09:01:43 CEST)

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Working Paper ARTICLE

A Recursive Definition of Goodness of Space for Bridging the Concepts of Space and Place for Sustainability

Bin Jiang

Subject: Earth Sciences, Geoinformatics Keywords: scaling law; head/tail breaks; living structure; beauty; streets; cities

Online: 26 July 2019 (04:19:25 CEST)

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