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Preprint ARTICLE | doi:10.20944/preprints201709.0058.v1

GIS and Remote Sensing Applications in Wetland Mapping and Monitoring

Qiusheng Wu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: GIS; image classification; LiDAR; remote sensing; wetland indicator; global wetland inventory; wetland mapping

Online: 14 September 2017 (17:25:27 CEST)

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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: Environmental And Earth Sciences, Remote Sensing 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/preprints201803.0108.v1

Forest Fire Danger Index Based on Static and Dynamic Parameters Using Satellite Datasets

Suresh Babu KV, Arijit Roy, Ramachandra Prasad P

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Forest fire danger index; MODIS; MOD11; MOD09; MOD14

Online: 14 March 2018 (15:38:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints201808.0352.v2

AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software Toolbox for Cloud/Cloud-Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery Eligible for Systematic ESA EO Level 2 Product Generation

Andrea Baraldi, Dirk Tiede

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: artificial intelligence; color naming; color constancy; cognitive science; computer vision; object-based image analysis (OBIA); physical and statistical data models; radiometric calibration; semantic content-based image retrieval; spatial topological and spatial non-topological information components

Online: 28 August 2018 (07:57:02 CEST)

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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: Environmental And Earth Sciences, Remote Sensing 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/preprints201901.0302.v1

Google Earth, Google SketchUp and GIS software; An Interoperable Work Flow for Generating Elevation Data

José Gomes dos Santos, Kevin Bento, Joaquim Lourenço Txifunga

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: interoperability; digital elevation model; Google Sketchup; geographical information systems-science; free and open source software

Online: 30 January 2019 (05:28:53 CET)

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Preprint ARTICLE | doi:10.20944/preprints201611.0036.v1

High Resolution Satellite Image Classification Using Multi-Task Joint Sparse and Low-Rank Representation

Kunlun Qi, Wenxuan Liu, Chao Yang, Qingfeng Guan, Huayi Wu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: multi-task learning; feature fusion; sparse representation; low-rank representation; scene classification

Online: 7 November 2016 (05:25:11 CET)

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Preprint ARTICLE | doi:10.20944/preprints201608.0048.v1

UAV Low Altitude Photogrammetry for Power Line Inspection

Yong Zhang, Xiuxiao Yuan, Yi Fang, Shiyu Chen

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: UAV remote sensing; power line inspection; dense matching; virtual photography; automatic detection of obstacles in power line corridor

Online: 5 August 2016 (08:07:23 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201701.0023.v1

Urban Sprawl Detection Using Remotely Sensed Data: A Case of Chennai, Tamilnadu

Rajchandar Padmanaban, Pedro Cabral, Avit K. Bhowmik, Alexander Zamyatin, Oraib Almegdadi

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Random forest classification; urban sprawl; spatial metrics; Renyi’s entropy; sustainability; land change modelling; remote sensing; urban growth model; Chennai

Online: 5 January 2017 (09:20:29 CET)

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Preprint ARTICLE | doi:10.20944/preprints201807.0244.v1

Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

Gordana Kaplan, Ugur Avdan

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Image Fusion, Sentinel-1, Sentinel-2, Wetlands, Object-Based Classification, Unmanned Aerial Vehicle

Online: 13 July 2018 (17:11:07 CEST)

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Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

Preprint REVIEW | doi:10.20944/preprints201610.0011.v1

Progress in the Infrared Remote Sensing of Volcanic Activity

Matthew Blackett

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: infrared remote sensing; volcanoes; earth observation, satellites

Online: 5 October 2016 (11:54:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201707.0030.v1

Strengths and Limitations of UAV and Ground-based Structure from Motion Photogrammetry in a Gullied Savanna Catchment

Jack Koci, Javier X. Leon, Ben Jarihani, Roy C. Sidle, Scott N. Wilkinson, Rebecca Bartley

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: digital elevation model; DEM; digital surface model; DSM; great barrier reef; gully erosion; multi-view stereo; point cloud; unmanned aerial vehicle

Online: 13 July 2017 (02:55:02 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201703.0134.v1

Automatic Object-Oriented, Spectral-Spatial Feature Extraction Driven by Tobler’s First Law of Geography for Very High Resolution Aerial Imagery Classification

Zhiyong Lv, Penglin Zhang, Jón Atli Benediktsson

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: spatial-spectral feature; very high spatial resolution image; classification; Tobler’s First Law of Geography

Online: 17 March 2017 (05:06:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints201706.0117.v1

Large-scale, Multi-temporal Remote Sensing of Palaeo-river Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation

Hector A. Orengo, Cameron A. Petrie

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: multi-temporal; seasonal; vegetation; palaeo-river; Indus civilisation; archaeology

Online: 27 June 2017 (04:41:04 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201608.0214.v1

Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling

Lin-Hsuan Hsiao, Ke-sheng Cheng

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: land-use/land-cover (LULC); uncertainty; bootstrap resampling; chi-square threshold; class probability vector (CPV); entropy

Online: 26 August 2016 (11:56:26 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201608.0055.v1

Seismic Damage Recognition Based on Watershed Segmentation of SAR Image Texture Features

Qiang Li, Lixia Gong, Jingfa Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: seismic damage building; watershed segmentation; SAR; texture feature; change detection

Online: 5 August 2016 (12:19:24 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201608.0098.v2

Remote Sensing Based Estimation of Evapo-Transpiration Using Selected Algorithms: The Case of Wonji Shoa Sugar Cane Estate, Ethiopia

Mulugeta Genanu, Tena Alamirew, Gabriel Senay, Mekonnen Gebremichael

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: ET; CWR; Landsat ETM+; Remote Sensing; SEBAL; SSEB; SSEBop

Online: 16 March 2017 (09:21:29 CET)

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Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map on a pixel-by-pixel basis, the actual evapotranspiration (ET_a) of the Wonji Shoa Sugarcane Estate using the Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0 - 6.85, 0.0 – 9.36, 0.0 – 3.61, 0.0 – 6.83 mm/day; using SSEB 0.0 - 6.78, 0.0 – 7.81, 0.0 – 3.65, 0.0 – 6.46 mm/day, and SSEBop were 0.05 - 8.25, 0.0 – 8.82, 0.2 – 4.0, 0.0 – 7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ET_a over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop had higher ET_a values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season, ET_a is limited to water surplus areas only and in wet season, ET_a was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ET_a over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ET_a estimated for the whole estate were 107 Mm³, 140 Mm³, and 178 Mm³ using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.

Preprint ARTICLE | doi:10.20944/preprints201703.0178.v1

A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation

You Li, Lin Li, Dalin Li, Fan Yang, Yu Liu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: mobile laser scanning; voxel; clustering; segmentation

Online: 23 March 2017 (08:48:22 CET)

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Preprint ARTICLE | doi:10.20944/preprints201906.0036.v1

A Global Seamless DEM Based on Multi-Source Data Fusion (GSDEM-30): Product Generation and Evaluation

Linwei Yue, Huanfeng Shen, Lu Liu, Qiangqiang Yuan, Liangpei Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: digital elevation models; multi-source fusion; multi-scale fusion; global evaluation; accuracy validation.

Online: 5 June 2019 (10:26:30 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201710.0170.v1

A Method to Count Olive Trees in Heterogenous Plantations from Aerial Photographs

Yann H. Chemin, Pieter S.A. Beck

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: tree counting, olive trees, Xylella fastidiosa, Puglia, Italy, algorithms, image processing

Online: 27 October 2017 (12:30:05 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201803.0048.v1

Using GIS and Artificial Neural Network for Deforestation Prediction

Vahid Ahmadi

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: land-cover change; deforestation; neural network; GIS

Online: 7 March 2018 (04:58:04 CET)

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Preprint ARTICLE | doi:10.20944/preprints201612.0075.v1

An Original Application of Image Recognition Based Location in Complex Indoor Environment

Filiberto Chiabrando, Youngsu Cho, Vincenzo Di Pietra, Andrea Lingua, Juil Jeon

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: image recognition bases location; indoor positioning; RGB-D images; LiDAR; DataBase; mobile computing; image retrieval

Online: 15 December 2016 (07:17:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints201902.0046.v1

Localized Learning of Downscaled Soil Moisture

Michael Lewis, Andmorgan Fisher, Clint Smith, John Qu, Paul Houser

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Soil Moisture; Remote Sensing; Landsat; SMAP; Random Forest; Machine Learning; Downscaling; Microwave

Online: 5 February 2019 (08:01:58 CET)

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If given the correct remotely sensed information, machine learning can accurately describe soil moisture conditions in a heterogeneous region at the large scale based on soil moisture readings at the small scale through rule transference across scale. This paper reviews an approach to increase soil moisture resolution over a sample region over Australia using the Soil Moisture Active Passive (SMAP) sensor and Landsat 8 only and a validation experiment using Sentinal-2 and the Advanced Microwave Scanning Radiometer (AMSR-E) over Nevada. This approach uses an inductive localized approach, replacing the need to obtain a deterministic model in favor of a learning model. This model is adaptable to heterogeneous conditions within a single scene unlike traditional polynomial fitting models and has fixed variables unlike most learning models. For the purposes of this analysis, the SMAP 36 km soil moisture product is considered fully valid and accurate. Landsat bands coinciding in collection date with a SMAP capture are down sampled to match the resolution of the SMAP product. A series of indices describing the Soil-Vegetation-Atmosphere Triangle (SVAT) relationship are then produced, including two novel variables, using the down sampled Landsat bands. These indices are then related to the local coincident SMAP values to identify a series of rules or trees to identify the local rules defining the relationship between soil moisture and the indices. The defined rules are then applied to the Landsat image in the native Landsat resolution to determine local soil moisture. Ground truth comparison is done via a series of grids using point soil moisture samples and air-borne L-band Multibeam Radiometer (PLMR) observations done under the SMAPEx-5 campaign. This paper uses a random forest due to its highly accurate learning against local ground truth data yet easily understandable rules. The predictive power of the inferred learning soil moisture algorithm did well with a mean absolute error of 0.054 over an airborne L-band retrieved surface over the same region.

Preprint ARTICLE | doi:10.20944/preprints201711.0172.v1

Augmented Smart Cities Integrating Sensor Web and Spatial Data Infrastructure (SmaCiSENS)

Devanjan Bhattacharya, Marco Painho

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: spatial data infrastructure; sensor web; geographical information system; smart cities; knowledge based system; expert system; spatial technologies

Online: 27 November 2017 (07:49:48 CET)

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Preprint ARTICLE | doi:10.20944/preprints201705.0214.v1

Generalized Aggregation of Sparse Coded Multi-spectral for Satellite Scene Classification

Xian-Hua Han, Yen-Wei Chen

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: multi-spectral analysis; remote sensing images; sparse coding; generalized aggregation; scene recognition

Online: 30 May 2017 (08:54:08 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201810.0695.v1

Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa

Yann Forget, Michal Shimoni, Marius Gilbert, Catherine Linard

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Urban Remote Sensing; Sentinel-1; Landsat 8; Built-Up; Data Fusion; Texture; Africa

Online: 29 October 2018 (16:02:53 CET)

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The rapid urbanization that takes place in developing regions such as Sub-Saharan Africa is associated with a large range of environmental and social issues. In this context, remote sensing is essential to provide accurate and up-to-date spatial information to support risk assessment and decision making. However, mapping urban areas remains a challenge because of their heterogeneity, especially in developing regions where the highest rates of misclassification are observed. Nevertheless, urban areas located in arid climates --- which are among the most vulnerables to anthropogenic impacts, suffer from the spectral confusion occurring between built-up and bare soil areas when using optical imagery. Today, the increasing availability of satellite imagery from multiple sensors allow to tackle the aforementioned issues by combining optical data with Synthetic Aperture Radar (SAR). In this paper, we assess the complementarity of the Landsat 8 and Sentinel-1 sensors to map built-up areas in twelve Sub-Saharan African urban areas, using a pixel-level supervised classification based on the Random Forest classifier. We make use of textural information extracted from SAR backscattering data in order to reduce the speckle noise and to introduce contextual information at the pixel level. Results suggest that combining both optical and SAR features consistently improves classification performances, mainly by enhancing the differentiation between built-up and bare lands. However, the fusion was less beneficial in mountainous case studies, suggesting that including features derived from a Digital Elevation Model (DEM) could improve the reliability of the proposed approach. As suggested by previous studies, combining features computed from both VV and VH polarizations consistently led to better classification performances. On the contrary, introducing textures computed from different spatial scales did not improve the classification performances.

Preprint ARTICLE | doi:10.20944/preprints201906.0270.v1

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: Environmental And Earth Sciences, Remote Sensing Keywords: Land cover mapping; Convolutional neural networks; UNET; Sentinel-2

Online: 26 June 2019 (14:04:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201708.0081.v1

On the Vertical Accuracy of the ALOS World 3D-30m Digital Elevation Model

Çaglar Bayık, Kazimierz Becek, Çetin Mekik, Mustafa Özendi

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: AW3D30; DEM; Runway Method; RWYM

Online: 24 August 2017 (15:51:17 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201810.0625.v1

Introduction of an Experimental Terrestrial Forecasting/Monitoring System at Regional to Continental Scales Based on the Terrestrial System Modeling Platform (v1.1.0)

Stefan Kollet, Fabian Gasper, Slavko Brdar, Klaus Goergen, Harrie-Jan Hendricks-Franssen, Jessica Keune, Wolfgang Kurtz, Volker Küll, Florian Pappenberger, Stefan Poll, Silke Trömel, Prabhakar Shrestha, Clemens Simmer, Mauro Sulis

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: terrestrial modeling; real-time forecasting/monitoring; workflows

Online: 26 October 2018 (10:44:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201808.0066.v1

Synergistic use of radar Sentinel-1 and optical Sentinel-2 imagery for crop mapping: a case study for Belgium

Kristof Van Tricht, Anne Gobin, Sven Gilliams, Isabelle Piccard

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Crop classification; SAR; Optical; time series; Sentinel-1; Sentinel-2; random forest; machine learning

Online: 3 August 2018 (12:01:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201708.0102.v1

A Content-Based Remote Sensing Image Change Information Retrieval Model

Caihong Ma, Wei Xia, Fu Chen, Jianbo Liu, Qin Dai, Liyuan Jiang, Jianbo Duan, Wei Liu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Content-Based Remote Sensing Image Retrieval; Change Information Detection; Information Management; Remote Sensing Data Service

Online: 29 August 2017 (16:18:20 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201705.0142.v1

Gravitation-Based Edge Detection for Hyperspectral Images

Genyun Sun, Aizhu Zhang, Jinchang Ren, Jingsheng Ma, Peng Wang, Yuanzhi Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: edge detection; hyperspectral image; gravitation; remote sensing; feature space

Online: 19 May 2017 (06:00:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201705.0060.v1

Adaptive Grouping Distributed Compressive Sensing Reconstruction of Plant Hyperspectral Data

Ping Xu, Junfeng Liu, Lingyun Xue, Jingcheng Zhang, Bo Qiu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: hyperspectral image; spectral characteristics of plants; spectral adaptive grouping; compressive sensing

Online: 8 May 2017 (11:45:34 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201901.0236.v1

Hybrid 3D Models: When Geomatics Innovations Meet Extensive Built Heritage Complexes

Filiberto Chiabrando, Giulia Sammartano, Antonia Spanò, Alessandra Spreafico

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: 3D models; multi-sensor; multi-scale; SLAM; MMS; LiDAR; UAV; data integration; data fusion; cultural heritage

Online: 23 January 2019 (10:08:42 CET)

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Preprint ARTICLE | doi:10.20944/preprints201811.0515.v1

Using Landsat 8 Images Along with Google Earth Engine for Investigation the Location of the Crashed Aircrafts

Azad Rasul

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: crashed aircraft; NDVI; albedo; MH370; remote sensing; Landsat 8; disaster; Boeing 777; panchromatic band; thermal band

Online: 21 November 2018 (05:09:14 CET)

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Preprint ARTICLE | doi:10.20944/preprints201810.0625.v2

Introduction of an Experimental Terrestrial Forecasting/Monitoring System at Regional to Continental Scales Based on the Terrestrial System Modeling Platform (v1.1.0)

Stefan Kollet, Fabian Gasper, Slavko Brdar, Klaus Goergen, Harrie-Jan Hendricks-Franssen, Jessica Keune, Wolfgang Kurtz, Volker Küll, Florian Pappenberger, Stefan Poll, Silke Trömel, Prabhakar Shrestha, Clemens Simmer, Mauro Sulis

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: terrestrial modeling; real-time forecasting/monitoring; workflows

Online: 16 November 2018 (08:06:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints201801.0129.v1

A New Dynamic Programming Method for UAV Image Stitching Using Optical Flow

Weilong Zhang, Bingxuan Guo, Ming Li, Xuan Liao, Yuan Yao, Xiongwu Xiao, Teng Wu, Wenzhuo Li

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: UAV image; dual-channel; dynamic programming; seam-line; optical flow

Online: 15 January 2018 (18:04:46 CET)

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Preprint ARTICLE | doi:10.20944/preprints201706.0009.v1

10m Sentinel-2A Cloud-Free Composite – Southern Africa 2016

Fabrizio Ramoino, Florin Tutunaru, Fabrizio Pera, Olivier Arino

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Sentinel-2; remote sensing; European Space Agency; Copernicus; continental; cloud-free; composite; darkest pixel; maximum NDVI

Online: 2 June 2017 (05:03:53 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201804.0377.v1

Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

Zhiyong Lv, Tongfei Liu, Penglin Zhang, Jón Atli Benediktsson, Yixiang Chen

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: land cover change detection; adaptive contextual information; bi-temporal remote sensing images

Online: 29 April 2018 (10:52:26 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201708.0089.v1

WebGIS and Geospatial Technologies for Landscape Education on Personalized Learning Contexts

María Luisa de Lázaro Torres, Rafael de Miguel González, Francisco José Morales Yago

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: WebGIS; Landscape; heritage; personalized learning; the cloud; distance learning

Online: 25 August 2017 (18:39:41 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201709.0009.v1

Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000–2015 in a Mediterranean Semiarid Area

Francisco Gomariz-Castillo, Francisco Alonso-Sarría, Fulgencio Cánovas-García

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: machine learning; sequential maximum a posteriori; random forest; support vector machines; land use classification; textural information; contextual information

Online: 4 September 2017 (10:43:26 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201809.0573.v1

Crowd-sourced street-level imagery as a potential source of in-situ data for crop monitoring

Raphaël d'Andrimont, Momchil Iordanov, Guido Lemoine, Janine Yoong, Kamil Nikel, Marijn van der Velde

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: crowdsourcing; citizen science; agriculture; street-view; in-situ; LUCAS; Copernicus

Online: 28 September 2018 (16:30:41 CEST)

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New approaches to collect in-situ data are needed to complement the high spatial (10~m) and temporal (5-day) resolution of Copernicus Sentinel satellite observations. Making sense of Sentinel observations requires high quality and timely in-situ data for training and validation. Classical ground truth collection is expensive, lacks scale, fails to exploit opportunities for automation, and is prone to sampling error. Here we evaluate the potential contribution of opportunistically exploiting crowd-sourced street-level imagery to collect massive high-quality in-situ data in the context of crop monitoring. This study assesses this potential by answering two questions: 1) what is the spatial availability of these images across the European Union (EU)? and 2) can these images be transformed to useful data? To answer the first question, we evaluated the EU availability of street-level images on Mapillary - the largest open-access platform for such images - against the Land Use and land Cover Area frame Survey (LUCAS) 2018, a systematic surveyed sampling of 337031 points. For 37.78% of the LUCAS points a crowd-sourced image is available within a 2-km buffer, with a mean distance of 816.11 m. We estimate that 9.44% of the EU territory has a crowd-sourced image within 300-m from a LUCAS point, illustrating the huge potential of crowd-sourcing as a complementary sampling tool. After artificial and built up (63.14%), and inland water (43.67%) land cover classes, arable land has the highest availability at 40.78%. To answer the second question, we focus on identifying crops at parcel level using all 13.6 million Mapillary images collected in the Netherlands. Only 1.9% of the contributors generated 75.15% of the images. A procedure was developed to select and harvest the pictures potentially best suited to identify crops using the geometries of 785710 Dutch parcels and the pictures' meta-data such as camera orientation and focal length. Availability of crowd-sourced imagery looking at parcels was assessed for 8 different crop groups with the 2017 parcel level declarations. Parcel revisits during the growing season allowed to track crop growth. Examples illustrate the capacity to recognize crops and their phenological development on crowd-sourced street-level imagery. Consecutive images taken during the same capture track allow selecting the image with the best unobstructed view. In the future, dedicated crop capture tasks can improve image quality and expand coverage in rural areas.

Preprint ARTICLE | doi:10.20944/preprints201709.0098.v2

A Conversion Method to Determine Regional Vegetation Cover Factor from Standard Plots based on Large Sample Theory and TM Images: A Case Study in the Eastern Farming-pasture Ecotone of Northern China

Degen Lin, Yuan Gao, Yaoyao Wu, Peijun Shi, Huiming Yang, Jing’ai Wang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: farming-pasture ecotone; TM image; remote sensing; vegetation cover factor; scale conversion; land use; high resolution image

Online: 21 September 2017 (16:33:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201807.0076.v1

Flood Prevention and Emergency Response System Powered by Google Earth Engine

Cheng-Chien Liu, Ming-Chang Shieh, Ming-Hsun Ko, Kung-Hwa Wang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: flood; disaster prevention; emergency response; decision making, Google earth engine

Online: 4 July 2018 (15:33:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201706.0030.v1

4SM: A Novel Self-calibrated Algebraic Ratio Method for Satellite Derived Bathymetry and Water Column Correction

Yann George MOREL

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: remote sensing; attenuation coefficient; ratio method; soil line; bottom reflectance; shallow substrate; satellite derived bathymetry; water column correction; bottom typing

Online: 6 June 2017 (06:33:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201805.0383.v1

Land, Income and Land-Use Diversification in Khyber Pakhtunkhwa Province of Northern Pakistan

Siddique Ullah Baig, Syed Muhammad Jibran Shah, Bahadar Nawab Khattak

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: land-use; satellite imagery; agriculture

Online: 28 May 2018 (05:20:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201709.0155.v1

When to Detect Changes in Object-Based Image Analysis: Before or after Classification?

Kaisheng Luo, Fu-lu Tao, Juana P. Moiwo

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: object-oriented technique; change detection; eCognition® software; landuse

Online: 29 September 2017 (12:51:40 CEST)

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Preprint DATA DESCRIPTOR | doi:10.20944/preprints201805.0047.v1

Interactive 3D Models and Animations for Understanding Earth’s Coordinate Systems

Sanjeev Kumar Srivastava

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: map projections; coordinate systems; earth's 3D models; earth's 2D representation

Online: 2 May 2018 (13:29:41 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201712.0005.v1

Hydrodynamic Simulation of a Cloudburst Event in Asi Ganga Valley of Indian Himalayan Region Using MIKE11 and Geomatics Techniques

Vasanta Govind Kumar Villuri, Kamal Jain, Ajay Gairola, Srinivas Pasupuleti

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Cloudburst; GIS; MIKE11; DEM; Hydrodynamic model

Online: 1 December 2017 (10:33:45 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: Environmental And Earth Sciences, Remote Sensing 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/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: Environmental And Earth Sciences, Remote Sensing 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/preprints202107.0100.v1

Investigation of June 2020 Giant Saharan Dust Storm Using Remote Sensing Observations and Model Reanalysis

A. Asutosh, V. Vinoj, M. Nuncio

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Dust storm; Aerosols; Satellite remote sensing; Radiative forcing; Thermodynamics

Online: 5 July 2021 (13:16:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201801.0139.v1

Cave Pearl Data Logger – A flexible Arduino-based Logging Platform for Long-Term Monitoring in Harsh Environments

Patricia A. Beddows, Edward K. Mallon

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: data logger; environmental monitoring network; open source; submersible; under-water; critical zone observatory; cave; Yucatan Peninsula, vadose hydrology; subterranean karst estuary

Online: 16 January 2018 (10:40:15 CET)

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Preprint ARTICLE | doi:10.20944/preprints201901.0050.v1

Delineation of Cocoa Agroforests Using Multi-Season Sentinel-1 SAR Images: Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping

Frederick N. Numbisi, Frieke M. B. Van Coillie, Robert De Wulf

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: mapping cocoa agroforests; Congo Basin rainforest; sentinel-1; SAR; GLCM textures; grey level quantization; random forest algorithm; machine learning; classification uncertainty

Online: 7 January 2019 (09:56:10 CET)

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Preprint ARTICLE | doi:10.20944/preprints201711.0142.v1

Soil and Crop/Tree Segmentation from Remotely Sensed Data by Using Digital Surface Models

Adriano Mancini, Jack Dyson, Emanuele Frontoni, Primo Zingaretti

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: segmentation; multi-spectral camera; soil: tree; raster scanning; UAV application

Online: 22 November 2017 (05:37:02 CET)

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The increased availability of high resolution remote sensor data for precision agriculture 1 applications permits users to aquire deeper and more relevant knowledge about crops states that lead 2 inevitably to better decisions. The algorithm libraries being developed and evolved around these 3 applications rely on multi-spectral or hyper-spectral data acquired by using manned or unmanned 4 platforms. The current state of the art makes thorough use of vegetational indicies to guide the 5 operational management of agricultural land plots. One of the most challenging sub-problems is 6 to correctly identify and separate crop from soil. Thresholding techniques based on Normalized 7 Difference Vegetation Index (NDVI) or other such similar metrics have the advantage of being simple, 8 easy to read transformations of the data packed with useful information. Obvious difficulties arise 9 when crop/tree and soil have similar spectral responses as in case of grass filled areas in vineyards. 10 In this case grass and canopy are close in terms of NDVI values and thresholding techniques will 11 generally fail. Radiometric approaches could be integrated or replaced by a geometric approach that 12 is based on terrain data like Digital Surface Models (DSMs). These models are one of the ouputs 13 of orthorectification engines usually present in data acquired by using unmanned platforms. In 14 this paper we present two approaches based on DSM that are able to segment crop/tree from soil 15 while over gradient terrain. The DSM data are processed through a two dimensional data slicing or 16 reduction technique. Each slice is separately processed as a one dimensional time series to derive the 17 terrain and tree structures separately, here interpreted as object probability densities. In particular 18 the first approach is a Cartesian grid rasterization (CARSCAN) of the terrain and the second is its 19 immediate generalisation or radial grid rasterization of the DSM model (FANSCAN). The FANSCAN 20 recovers information from the original image at greater frequencies on the Fourier plane. These 21 approaches enable the identification of crop/tree from soil in case of slopes or hilly terrain without 22 any constraint on the displacement / direction of plant/tree row. The proposed algorithm uses pure 23 DSM information even if it is possible to fuse its output with other classifiers.

Preprint ARTICLE | doi:10.20944/preprints201806.0188.v1

Spectral-Spatial Dimensionality Reduction of APEX Hyperspectral Imagery for Tree Species Classification; a Case Study of Salzach Riparian Mixed Forest

Zahra Dabiri, Stefan Lang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: minimum noise fraction (MNF) transformation; object-based image analysis (OBIA); APEX hyperspectral imagery; Random forest (RF) classifier; multiresolution segmentation (MRS); tree species classification

Online: 12 June 2018 (10:55:07 CEST)

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Tree species composition is an important key element for biodiversity and sustainable forest management, and hyperspectral data provide detailed spectral information, which can be used for tree species classification. There are two main challenges for using hyperspectral imagery: a) Hughes phenomena, meaning by increasing the number of bands in hyperspectral imagery, the number of required classification samples would increase exponentially, and b) in a more complex environment, such as riparian mixed forest, focusing on spectral variability per pixel may not be adequate for definability of tree species. Therefore, the focus of this study is to assess spectral-spatial dimensionality reduction of airborne hyperspectral imagery by using minim noise fraction (MNF) transformation, and object-based image analysis (OBIA). An airborne prism experiment (APEX) hyperspectral imagery was used. A study area was a riparian mixed forest located along the Salzach river, and six tree species including Picea abies, Populus (canadensis and balsamifera), Fraxinus excelsior, Alnus incana, and Salix alba were selected. A machine learning algorithm random forest (RF) was used to train and apply a prediction model for classification. Using a spectral dimensionality reduced APEX, a pixel-level classification was also done. According to a confusion matrix, the object-level classification of MNF-derived components achieved the overall accuracy of 85 %, and kappa coefficient of 0.805. The performance of classes according to producer’s accuracy varied between 80% for Fraxinus excelsior, Alnus incana, and Populus canadensis to 90% for Salix alba and Picea abies. Comparison the results to a pixel-level classification, showed a better performance of object-level classification (an overall accuracy of 63% and Kappa coefficient of 0.559 were achieved for pixel-level classification). The performance of classes using pixel-based classification varied 45 % for Alnus incana to 80% for Picea abies. In general, Spectral-spatial complexity reduction using MNF transformation and object-level classification yielded a statistically satisfactory results.

Preprint ARTICLE | doi:10.20944/preprints201801.0088.v1

The Effect of Temporal Gradient of Vegetation Indices on Early-Season Wheat Area Estimation Using Random Forest Classification

Mousa Saei Jamal Abad, Ali A. Abkar, Barat Mojaradi

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: wheat classification; random forest; spectral gradient difference; vegetation indices

Online: 10 January 2018 (09:13:02 CET)

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The early-season area estimation of the winter wheat crop as a strategic product is important for decision makers. Classification of multi-temporal images is an approach which is affected by many factors like appropriate training sample size, proper frequency and acquisition times, vegetation indices (VIs) type, temporal gradient of spectral bands and VIs, appropriate classifier and missed values because of cloudy conditions. This paper addresses the impact of appropriate frequency and acquisition times and VIs type along with the spectral and VI gradient on random forest (RF) classifier when missed values exist in multi-temporal images. To investigate the appropriate temporal resolution for image acquisition, the study area was selected on an overlapping area between two LDCM paths. In our developed method, the miss values of cloudy bands for each pixel are retrieved by the mean of k-nearest ordinary pixels. Then the multi-temporal image analysis is performed by considering different scenarios provided by decision makers in terms of desired crop types that should be extracted at early-season in the study areas. The classification results obtained by the RF decrease by 1.6% when temporally missed values retrieved by the proposed method, which is an acceptable result. Moreover, the experimental results demonstrated that if temporal resolution of Landsat 8 increased to one week the classification task can be conducted earlier with almost better results in terms of OA and kappa. The impact of incorporating VIs along with the temporal gradients of spectral bands and VIs as new features in RF demonstrated that the OA and Kappa are improved 3.1% and 6.6%, respectively. Furthermore, the obtained result showed that if only one image from seasonal changes of crops is available, the temporal gradient of VIs and spectral bands play the main role to discriminate remarkably wheat from barley. The experiments also demonstrated that if both wheat and barley merge to a single class the crop area can be estimated two months earlier with 97.1 and 93.5 in terms of OA and kappa, respectively.

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: Environmental And Earth Sciences, Remote Sensing 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/preprints201807.0209.v1

UAV Remote Sensing for Biodiversity Monitoring: Are Forest Canopy Gaps Good Covariates?

Martin B. Bagaram, Diego Giuliarelli, Gherardo Chirici, Francesca Giannetti, Anna Barbati

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Unmanned Aerial systems (UAS); , RGB high resolution imagery; forest canopy gaps; understorey; vertical species diversity; microhabitat-bearing trees; contrast split segmentation; drone

Online: 12 July 2018 (05:36:22 CEST)

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Forest canopy gaps are important for the ecosystem dynamics. Depending on tree species, small canopy openings might be also associated to intra-crown porosity and to space between crowns. Yet, little is known on the relationships between the fine-scaled pattern of canopy openings and biodiversity features. This research explored the possibility of i)- mapping forest canopy gaps from a very high resolution orthomosaic (10 cm), processed from a versatile imaging platform such as unmanned aerial vehicles (UAV), ii)- to derive patch metrics that can be tested as covariates of variables of interest for forest biodiversity monitoring. This is attempted in a test area of 240 ha covered by temperate deciduous forest types in Central Italy and containing 50 forest inventory plots of about 530 m². Correlation and linear regression techniques were used to explore relationships between patch metrics and understorey (density, development and species diversity) or forest habitat biodiversity variables (density of micro-habitat bearing trees, vertical species profile, tree species diversity). The results revealed that small openings in the canopy cover (75% smaller than 7 m²) can be faithfully extracted from UAV RGB imagery, using the red band and contrast split segmentation. Highest correlations were observed in the mixed forest (beech and turkey oak), while beech forest had the poorest ones and turkey oak forest displayed intermediate results. Moderate to strong linear relationships were found between gap metrics and understorey variables in mixed forest type, with adjusted R² from linear regression ranging from 0.52 to 0.87. Equally good results, in the same forest types, were observed for forest habitat biodiversity variables (0.52<adjusted R²<0.79) with highest values found for density of trees with microhabitats and vertical species profile. In conclusion, this research highlights that UAV remote sensing can potentially provide covariate surfaces of variables of interest for forest biodiversity monitoring, conventionally collected in forest inventory plots. By integrating the two sources of data, these variables can be mapped over small forest areas with satisfactory levels of accuracy, at a much higher spatial resolution than would be possible by field-based forest inventory solely.

Preprint ARTICLE | doi:10.20944/preprints201903.0122.v1

Comparative Analysis of Land Cover Classification Using ML and SVM Classifier for LISS-iv Data

Rahul Neware

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Classification, SVM Classifier, ML Classifier, Supervised and Unsupervised Classification, Object-based Classification, Multispectral Data

Online: 11 March 2019 (09:01:44 CET)

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Preprint ARTICLE | doi:10.20944/preprints201711.0003.v1

Wetland Mapping Using SAR Data from Sentinel-1A and TanDEM-X Missions: Comparative Study in the Biebrza Floodplain (Poland)

Magdalena Mleczko, Marek Mróz

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Sentinel-1A; TanDEM-X science phase; wetlands mapping

Online: 1 November 2017 (04:37:20 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: Environmental And Earth Sciences, Remote Sensing 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/preprints201801.0268.v1

A Javascript GIS Platform Based on Invocable Geospatial Web Services

Konstantinos Evangelidis, Theofilos Papadopoulos

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: open GIS; geospatial Web services; geospatial Web semantics; Web GIS; Node.js; Javascript

Online: 29 January 2018 (05:29:38 CET)

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Preprint ARTICLE | doi:10.20944/preprints201812.0213.v1

Earthquake Triggered Multi-Hazard and Risk Study Based on Remote Sensing and Geographic Information System

Aditya Saputra, Christopher Gomez, Ioannis Delikostidis, Peyman Zawar-Reza, Danang Sri Hadmoko, Junun Sartohadi

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: earthquake multi-hazard and risk; coseismic landslide; outcrop study; liquefaction

Online: 18 December 2018 (04:37:30 CET)

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Yogyakarta City is one of the big city which is located in Java Island, Indonesia. Yogyakarta City, including study area (Pleret Sub District), are very prone to earthquake hazards, because close to several active earthquake sources. For example, Sunda Megathrust which often generates a big earthquake which can affect the study area. The Sunda Megathrust extends from north to south and west to east along the Sumatra and Java Islands. Furthermore, an active normal fault called as Opak Fault pass through right in the middle of Study area and divides the study area into east and west zone. Recently, after the devastating earthquake in 2006, the population of the study area increases significantly. As a result, the housing demand is also increasing. However, due to the absence of earthquake building code in the study area, locals tend to build improper new houses. Furthermore, in some part of the mountainous area in the study area, there are some building found in unstable slopes area. Due to this condition, the multi-hazard and risk study needs to be done in Pleret. The increasing of population and improper houses in Pleret Sub-District can lead to amplify the impact. Thus, the main objective of this study is to assess the multi-hazards and risk of earthquake and other related secondary hazards such as ground amplification, liquefaction, and coseismic landslide. The method mainly utilised the geographic information system, remote sensing and was fit up by the outcrop study. The results show that the middle part of the study area has a complex geological structure. It was indicated by a lot of unchartered faults was found in the outcrops. Furthermore, the relatively prone areas to earthquake can be determined. In term of the coseismic landslide, the prone area to the coseismic landslide is located in the east part of the study area in the middle slope of Baturagung Escarpment. The highly potential area of liquefaction is dominated in the central part of the study area. In term of building collapsed probability, the result shows that the safest house based on statistical analysis is the residential house with the building attribute of wood structure, roof cast material, distance more than 15 km from the earthquake source, and located above the Nglanggran Formation. Finally, the multi-hazard and risk analysis show that the middle part of the study area is more vulnerable than the other part of Pleret Sub-District.

Preprint ARTICLE | doi:10.20944/preprints201807.0336.v1

Using Multitemporal Landsat ETM+ Imagery to Determine a Sustainable Exploitation Patterns of the Forest Resources in the Moldo-Transylvanian Carpathians - Romania

Ana-Maria Ciobotaru, Ion Andronache, Helmut Ahammer, Herbert Jelinek, Marko Radulovic, Radu-Daniel Pintilii, Daniel Peptenatu, Cristian Constantin Draghici, Razvan-Mihail Papuc, Roxana-Andreea Radu, Alexandra Grecu, Andreea-Karina Gruia, Marian Marin, Adrian-Gabriel Simion, Mircea-Cristian Visan, Ioan-Vlad Loghin, Rasmus Fensholt

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: forest disturbance; deforestation; sustainability; fractal analysis; entropy; forest management.

Online: 18 July 2018 (15:36:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201812.0227.v1

Integration of UAV, Sentinel-1 and Sentinel-2 Data for Mangrove Plantations Aboveground Biomass Monitoring in Senegal

José Antonio Navarro, Nur Algeet, Alfredo Fernández-Landa, Jéssica Esteban, Pablo Rodríguez-Noriega, María Luz Guillén-Climent

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: digital aerial photogrammetry; SAR; model-assisted; biomass estimation; Copernicus; unmanned aerial vehicles

Online: 19 December 2018 (02:56:20 CET)

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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: Environmental And Earth Sciences, Remote Sensing Keywords: drone video; human action recognition; CNN; Support vector machine (SVM)

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

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Preprint TECHNICAL NOTE | doi:10.20944/preprints201811.0529.v1

Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study

Yara Mohajerani, Michael Wood, Isabella Velicogna, Eric Rignot

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Calving Front; Image Segmentation; U-Net; Convolutional Neural Network; Machine Learning; Greenland

Online: 21 November 2018 (14:05:00 CET)

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Preprint ARTICLE | doi:10.20944/preprints201912.0282.v1

Integrated Method for Rice Cultivation Monitoring Using Sentinel-2 Data and Leaf Area Index

Abdelraouf M. Ali, Igor Savin, Anton Poddubsky, Mohamed Aboelghar, Nasser Salem

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Sentinal-2; NDVI; LAI; rice; yield

Online: 21 December 2019 (11:00:09 CET)

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Rice is an essential crop for national food security in Egypt. Increasing the population calls for regular increases in rice production. At the same time, cultivated rice crop areas should be decreased because of the gradual scarcity of irrigation water. This means more rice production should be gained from less rice area. This situation calls for the annual accurate system for rice monitoring and yield estimation. Therefore, it is necessary to apply a remotely sensed based system for rice cultivation assessment using satellite imagery parallel with field measurements of some biophysical parameters. Multi-temporal normalized difference vegetation index (NDVI) extracted from twelve sentinel-2 imagery cover the whole summer season with variance and maximum value assessed by ground control points (GCPs), were used to isolate uncultivated areas, then to isolate rice areas and other vegetation covers. object-based classification methods with kappa co-efficient 0.9261 and overall accuracy 94.92% was generated to discriminate rice crop area and other summer crops on the study area. Leaf area index (LAI) for the experiment the l site was calculated using the surface energy balance algorithm for Land (SEBAL) model and then tested versus measured (LAI). NDVI and LAI were used to generate an empirical ran rice yield prediction model. Then, this model was used to produce rice to yield a map. The study was carried out in an experimental site in Kafr Elsheikh governorate with a total area of 5040 Hectare. Produced cultivated land use map showed 95% overall accuracy. High similarity was observed between measured and calculated (LAI) with high accuracy of R2 = 0.94. of Rice, yield map showed expected to yield more to than a month before harvest. The generated yield map was tested using a correlation coefficient between actual yield and estimated yield with high accuracy R2 = 0.9. This method is applicable to estimate the acreage and productivity of rice in the northern Nile delta in adequate time before harvest.

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: Environmental And Earth Sciences, Remote Sensing 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 ARTICLE | doi:10.20944/preprints201802.0081.v1

Relative Velocity Measurement with Spectrometer and Its Integrated Navigation

Zhonghua Wang, Yiming Peng, Jiu Liu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: navigation; velocity measurement; X-ray pulsar; Mars explorer; formation flight

Online: 11 February 2018 (06:46:14 CET)

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Preprint ARTICLE | doi:10.20944/preprints201804.0090.v1

Flood Potential Mapping of Pesanggrahan River Watershed in South and West Jakarta with LIDAR Data Segmentation

Indra Riyanto, Lestari Margatama, S. Samsinar, Babag Purbantoro, Luhur Bayuaji, Dodi Sudiana, Josaphat Tetuko Sri Sumantyo

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: flood mapping; urban flood potential; LIDAR; image segmentation; Digital Surface Model; Digital Elevation Model

Online: 8 April 2018 (10:02:20 CEST)

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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: Environmental And Earth Sciences, Remote Sensing Keywords: Amazon; recovery time; aboveground biomass; climate change; 3-PG

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

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Preprint ARTICLE | doi:10.20944/preprints201804.0134.v1

Point Cloud Data Retrieval from 3D Geospatial Database for Automated Road Median Extraction

Pankaj Kumar, Paul Lewis, Conor P. McElhinney

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: airborne laser scanning; geospatial database; data retrieval; road median; attributes

Online: 11 April 2018 (04:27:42 CEST)

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Laser scanning systems make use of Light Detection and Ranging (LiDAR) technology to acquire accurately georeferenced sets of dense 3D point cloud data. The information acquired using these systems produces better knowledge about the terrain objects which are inherently 3D in nature. The LiDAR data acquired from mobile, airborne or terrestrial platforms provides several benefit over conventional sources of data acquisition in terms of accuracy, resolution and attributes. However, the large volume and scale of LiDAR data have inhibited the development of automated feature extraction algorithms due to the extensive computational cost involved in it. Moreover, the heterogeneously distributed point cloud, which represents objects with varying size, point density, holes and complicated structures pose a great challenge for data processing. Currently, geospatial database systems do not provide a robust solution for efficient storage and accessibility of raw data in a way that data processing could be applied based on optimal spatial extent. In this paper, we present Global LiDAR and Imagery Mobile Processing Spatial Environment (GLIMPSE) system that provides a framework for storage, management and integration of 3D LiDAR data acquired from multiple platforms. The system facilitates an efficient accessibility to the raw dataset, which is hierarchically represented in a geographically meaningful way. We utilise the GLIMPSE system to automatically extract road median from Airborne Laser Scanning (ALS) point cloud. In the first part of this paper, we detail an approach to efficiently retrieve the point cloud data from the GLIMPSE system for a particular geographic area based on user requirements. In the second part, we present an algorithm to automatically extract road median from the retrieved LiDAR data. The developed road median extraction algorithm utilises the LiDAR elevation and intensity attributes to distinguish the median from the road surface. We successfully tested our algorithms on two road sections consisting of distinct road median types based on concrete and grass-hedge barriers. The use of GLIMPSE improved the efficiency of the road median extraction in terms of fast accessibility to ALS point cloud data for the required road sections. The developed system and its associated algorithms provide a comprehensive solution to the user's requirement for an efficient storage, integration, retrieval and processing of large volumes of LiDAR point cloud data. These findings and knowledge contribute to a more rapid, cost-effective and comprehensive approach to surveying road networks.

Working Paper ARTICLE

Potential and Limitations of Satellite Altimetry Constellations for Monitoring Surface Water Storage Changes – A Case Study in the Mississippi Basin

Denise Dettmering, Laura Ellenbeck, Daniel Scherer, Christian Schwatke, Christoph Niemann

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: satellite altimetry; terrestrial water storage; Mississippi basin; SWOT

Online: 27 August 2020 (11:54:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201812.0250.v2

Modelling Built-Settlements between Remotely-Sensed Observations

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: Environmental And Earth Sciences, Remote Sensing Keywords: Built, urban growth, random forest, dasymetric, population

Online: 8 April 2019 (13:24:22 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: Environmental And Earth Sciences, Remote Sensing 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/preprints201809.0068.v1

A Molecular Basis of Olfaction: Odorant-Specific Electric-Field Emission

Frank LaBella, Filipe Fernandes

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: olfaction, olfactory, odorants, pheromones, smell, electric field, electromagnetic radiation, electric field sensor, insect antennae

Online: 4 September 2018 (14:31:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201710.0166.v1

Using Self Organizing Maps to Visualize Structural Change in Geographic Regions of Interest: Las Vegas across the Years

John M. Wandeto, Henry O. Nyongesa, Yves Remond, Birgitta Dresp-Langley

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: satellite images; image analysis; self organizing maps; quantization error; structural change; demographic data

Online: 27 October 2017 (04:11:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201805.0150.v1

Assesment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of Meteorological Extremes Over the Southeast of Iberian Peninsula

Fulgencio Cánovas-García, Sandra García-Galiano, Francisco Alonso-Sarría

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Quantitative Precipitation Estimates; Validation; PERSIANN-CCS; meteorological radar; Satellite Rainfall Estimates

Online: 9 May 2018 (15:37:29 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: Environmental And Earth Sciences, Remote Sensing 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/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: Environmental And Earth Sciences, Remote Sensing 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/preprints202102.0338.v1

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

Ram C. Sharma

Subject: Environmental And Earth Sciences, Remote Sensing 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/preprints201803.0021.v1

Map Archive Mining: Visual-analytical Approaches to Explore Large Historical Map Collections

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

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: map processing; retrospective landscape analysis; visual data mining, image retrieval, low-level image descriptors, color moments, t-distributed stochastic neighborhood embedding, USGS topographic maps, Sanborn fire insurance maps

Online: 2 March 2018 (10:36:23 CET)

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Preprint ARTICLE | doi:10.20944/preprints201810.0558.v1

Accuracy Assessment of Digital Terrain Model Datasets Sources for Hydrogeomorphological Modelling at Mediterranean Catchments

Lukas Graf, Mariano Moreno-de las Heras, Maurici Ruiz, Josep Fortesa, Aleix Calsamiglia, Julián García-Comendador, José A. López-Tarazón, Joan Estrany

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: digital terrain models; DTM vertical accuracy; DTM comparison; hydrologeomorphological Modelling; Mediterranean catchments

Online: 24 October 2018 (08:27:47 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: Environmental And Earth Sciences, Remote Sensing 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/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: Environmental And Earth Sciences, Remote Sensing Keywords: geostatistics; geovisualisation; rshiny; earthquake; Indonesia

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

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Preprint ARTICLE | doi:10.20944/preprints201808.0358.v1

Efficient delineation of nested depression hierarchy in digital elevation models for hydrological analysis using level-set method

Qiusheng Wu, Charles R. Lane, Lei Wang, Melanie K. Vanderhoof, Jay R. Christensen, Hongxing Liu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: depression filling; digital elevation models; hydrological analysis; level-set method; LiDAR; surface depressions

Online: 20 August 2018 (14:13:34 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201812.0136.v1

Analysis of Spatiotemporal Variation of Land Subsidence in Beijing Plain, China

Lin Guo, Huili Gong, Feng Zhu, Lin Zhu, Chaofan Zhou, Yike Sun

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Land subsidence; InSAR; Small baseline interferometry; Mann-Kendall mutation test; Fishnet

Online: 11 December 2018 (16:49:47 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: Environmental And Earth Sciences, Remote Sensing 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/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: Environmental And Earth Sciences, Remote Sensing Keywords: satellite images; disaster mapping; GIS

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

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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: Environmental And Earth Sciences, Remote Sensing 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 REVIEW | doi:10.20944/preprints201807.0431.v1

Availability of Global and National Scale Land Cover Products and Their Accuracy in Mountainous Areas of Ethiopia: A Review

Binyam Tesfaw Hailu, Mekbib Fekadu, Thomas Nauss

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: LULC Products, systematic review, Bale Mountains National Park, Ethiopia

Online: 23 July 2018 (15:33:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201803.0226.v1

Pushbroom Hyperspectral Data Orientation by Combining Feature-Based and Area-Based Co-Registration Techniques

Kévin Barbieux

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Pushbroom; Georeferencing; Correction; Particle Image Velocimetry; Orientation; Hyperspectral; Co-Registration; Correlation

Online: 27 March 2018 (12:40:40 CEST)

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Direct georeferencing of airborne pushbroom scanner data usually suffers from the limited precision of navigation sensors onboard of the aircraft. The bundle adjustment of images and orientation parameters, used to perform geocorrection of frame images during the post-processing phase, cannot be used for pushbroom cameras without difficulties: it relies on matching corresponding points between scan lines, which is not feasible in the absence of sufficient overlap and texture information. We address this georeferencing problem by equipping our aircraft with both a frame camera and a pushbroom scanner: the frame images and the navigation parameters measured by a couple GPS/Inertial Measurement Unit (IMU) are input to a bundle adjustment algorithm; the output orientation parameters are used to project the scan lines on a Digital Elevation Model (DEM) and on an orthophoto generated during the bundle adjustement step; using the image feature matching algorithm Speeded Up Robust Features (SURF), corresponding points between the image formed by the projected scan lines and the orthophoto are matched, and through a least-squares method, the boresight between the two cameras is estimated and included in the calculation of the projection; finally, using Particle Image Velocimetry (PIV) on the gradient image, the projection is deformed into a final image that fits the geometry of the orthophoto. We apply this algorithm to five test acquisitions over Lake Geneva region (Switzerland) and Lake Baikal region (Russia). The results are quantified in terms of Root Mean Square Error (RMSE) between matching points of the RGB orthophoto and the pushbroom projection. From a first projection where the Interior Orientation Parameters (IOP) are known with limited precision and the RMSE goes up to 41 pixels, our geocorrection estimates IOP, boresight and Exterior Orientation Parameters (EOP) and produces a new projection which RMSE with the reference orthophoto is around two pixels.

Preprint ARTICLE | doi:10.20944/preprints201802.0043.v1

Geoinformation Modeling of Flooded Areas in Settlements in the Example of Lutsk

Anna Shostak, Volodymyr Voloshyn, Oleksandr Melnyk, Pavlo Manko

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: geoinformation modeling, settlement territory, approximation, digital terrain models, TIN-models, water level, flood process.

Online: 5 February 2018 (15:56:04 CET)

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Preprint ARTICLE | doi:10.20944/preprints201807.0017.v1

A New Simplified DSM-to-DTM Algorithm – dsm-to-dtm-step

Thomas Krauß

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Digital Surface Model, Digital Terrain Model, Steep edge detection

Online: 2 July 2018 (16:42:05 CEST)

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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: Environmental And Earth Sciences, Remote Sensing 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/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: Environmental And Earth Sciences, Remote Sensing 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/preprints201808.0388.v1

Satellite Image Fusion to Detect Changing Surface Permeability and Emerging Urban Heat Islands in a Fast-Growing City

Rajchandar Padmanaban, Pedro Cabral, Avit K Bhowmik

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Satellite Image Fusion, Land Surface Temperature, Surface Permeability, Landuse and Landcover, Soil Adjusted Vegetation Index, Urban Heat Island, Fast Growing City, Remote sensing

Online: 21 August 2018 (16:58:09 CEST)

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Rapid and extensive urbanization has adversely impacted humans and ecological entities in the recent decades through a decrease in surface permeability and the emergence of urban heat islands (UHI). While detailed and continuous assessments of surface permeability and UHI are crucial for urban planning and management of landuse zones, they have mostly involved time consuming and expensive field studies, and single sensor derived large scale aerial and satellite imageries. We demonstrated the advantage of fusing imageries from multiple sensors for landuse and landcover (LULC) change assessments as well as for assessing surface permeability and UHI emergence in Tirunelveli, Tamilnadu, India. Cartosat-2 and Landsat-7 ETM+ imageries from 2007 and 2017 were fused and classified using a Rotation Forest (RF), while surface permeability and temperature were quantified using Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST) index, respectively. Fused images exhibited higher classification accuracies than non-fused images, i.e. overall kappa coefficient values 0.83 and 0.75, respectively. We observed an overall increase of 20 km2 (45%) in the coverage of urban (dry, real estate plots and built-up) areas, while a decrease of 27 km2 (37%) for vegetated (cropland and forest) areas in Tirunelveli between 2007 and 2017. The SAVI values indicated an extensive decrease in surface permeability for Tirunelveli overall (0.4) and also for almost all LULC zones. The LST values exhibited an associated overall increase (1.30C) of surface temperature in Tirunelveli with the highest increase (2.40C) for urban built-up areas between 2007 and 2017. The SAVI-LST combined metric depicted the Southeastern built-up areas in Tirunelveli as a potential UHI hotspot, while a caution for the Western riparian zone for UHI emergence in 2017. Our results provide important metrics for surface permeability and UHI monitoring, and inform urban and zonal planning authorities about the advantages of satellite image fusion.

Preprint ARTICLE | doi:10.20944/preprints201801.0244.v1

Analysis of Remotely Sensed Data for Mapping Land Cover Types by Applying a Maximum Likelihood Classifier Algorithm, in Al-Ahsaa Oasis, Eastern Region, Saudi Arabia

Abdelrahim Salih

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Remote sensing, classification, Al-Ahsaa, Saudi Arabia, Land cover

Online: 25 January 2018 (15:56:02 CET)

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Environmental and Earth Sciences