scholarly journals OBJECT BASED “DAYAS” CLASSIFICATION USING SENTINEL A-2 SATELLITE IMAGERY CASE STUDY CITY OF BENSLIMANE

2019 ◽  
Vol XLII-4/W12 ◽  
pp. 33-39
Author(s):  
M. Benchelha ◽  
F. Benzha ◽  
H. Rhinane
Keyword(s):  
Remote Sensing ◽  
Object Oriented ◽  
Optical Remote Sensing ◽  
Kappa Index ◽  
Object Based ◽  
The Subject ◽  
Object Oriented Approach ◽  
Radiometric Characteristics ◽  
Sentinel 2

<p><strong>Abstract.</strong> The management of “DAYAS” is a major issue in the preservation and maintain of biodiversity and environmental balance, especially in a context where this fragile ecosystems face many degradation factors. The extraction of Dayas is a key component in the management process of this type of wetlands, and has been the subject of many researches related to remote sensing. The methods and instrumentation for optical remote sensing are used to improve the mapping of Dayas, based on the radiometric characteristics of local hydrosystems. The present paper studies the inputs of different methods for the delimitation and extraction of Dayas in the realm of Benslimane city, using Sentinel A-2 imagery for the mapping. The methodology for the application of the pixel-based and the object-oriented approaches requires many steps, starting from an image pre-processing with Sentinel-2 calibration, the calculation of NDWI index, to proceed to the extraction of Dayas from the very high resolution image segmentation, then the application of the object-oriented classification to validate the results. The cartographic results demonstrate the input of the applied methodology in the Dayas extraction in different situations and timing (winter/summer), and allow to measure the cartographic accuracy for each approach, finding 65% of accuracy for the pixel-based approach with Kappa index&amp;thinsp;=&amp;thinsp;0.40 versus 75% of accuracy for the object-oriented approach with Kappa index&amp;thinsp;=&amp;thinsp;0.72. The results achieved inform and orient about optimisation measures and regulations of the Kappa index to improve the Dayas extraction and mapping.</p>

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

scholarly journals Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3982
Author(s):  
Giacomo Lazzeri ◽  
William Frodella ◽  
Guglielmo Rossi ◽  
Sandro Moretti
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Fire Severity ◽  
Experimental Methodology ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

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

2018 ◽  
Author(s):  
Gordana Kaplan ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
Geographical Information ◽  
Wetland Classification ◽  
Significant Information ◽  
Data Set ◽  
Object Based ◽  
Sentinel 2

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.

scholarly journals SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP

Irriga ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 585-598
Author(s):  
Pedro Henrique Jandreice Magnoni ◽  
Cesar De Oliveira Ferreira Silva ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Remote Sensing ◽  
Water Budget ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
In Situ Data ◽  
Joint Application ◽  
E Mail ◽  
Sentinel 2

SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP*     PEDRO HENRIQUE JANDREICE MAGNONI1; CÉSAR DE OLIVEIRA FERREIRA SILVA1 E RODRIGO LILLA MANZIONE2   1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista", Avenida Universitária, n° 3780, Altos do Paraíso, 18610-034, Botucatu, São Paulo, Brasil,  [email protected]; [email protected]. 2 Departamento de Engenharia de Biossistemas, Faculdade de Ciências e Engenharia, Universidade Estadual Paulista “Júlio de Mesquita Filho”, Rua Domingos da Costa Lopes, 780, CEP 17602496, Tupã – SP, Brasil. E-mail: [email protected]. *Este artigo é proveniente das dissertações de mestrado dos dois primeiros autores.     1 RESUMO   Ferramentas baseadas em sensoriamento remoto possibilitam o monitoramento do balanço hídrico da água em diferentes resoluções espaciais e temporais. Ainda assim, modelos que exigem dados in-situ impossibilitam sua aplicação em áreas com escassez de dados. No sentido de lidar com esse desafio, o presente trabalho apresenta uma abordagem de escolha do momento de irrigar, pelo balanço hídrico da água no solo, baseada em estimativa da evapotranspiração real (ETA) obtida com o uso conjunto de imagens multiespectrais do sensor MSI/SENTINEL-2 e dados de uma estação meteorológica pública. A área de estudo foi um pivô central localizado no munícipio de Itatinga-SP. Para a tomada de decisão do momento de irrigar, com base em um manejo por lâmina de irrigação fixa, foi feita a interpolação da fração evapotranspirativa entre os dias com imagens disponíveis para obter a ETA nos dias sem imagens por meio do seu produto com a evapotranspiração de referência. Essa abordagem captou variações climáticas essenciais para a estimativa do balanço hídrico em dias sem imagem. Destaca-se nessa aplicação conjunta sua capacidade de ser realizada sem necessitar de parâmetros específicos da cultura, do microclima ou do relevo, tornando-se interessante para regiões com escassez de dados.   Palavras-chave:  evapotranspiração, momento de irrigar, agriwater.     MAGNONI, P. H. J.; SILVA, C. O. F.; MANZIONE, R. L. REMOTE SENSING APPLIED TO IRRIGATION MANAGEMENT IN AREAS WITH LACK OF DATA: A CASE STUDY IN A CENTRAL PIVOT IN ITATINGA-SP     2 ABSTRACT   Remote sensing-based tools allow the monitoring of water budgets over different spatial and temporal resolutions. Nevertheless, some models require in situ data, preventing their application in areas with a lack of data. To address this challenge, this work presents an approach for irrigation scheduling, based on soil water budget estimation using actual evapotranspiration (ETA) obtained using MSI/SENTINEL-2 multispectral images and data from a public meteorological station. The study area consisted of a central pivot located in the municipality of Itatinga-SP, Brazil. For decision-making of irrigation scheduling, considering a fixed irrigation rate, the evapotranspiration fraction was interpolated between the days with available images to obtain the ETA on the days without images using its product with the reference evapotranspiration. This approach captured essential climate variations for estimating the water budget on non-image days. Noteworthy in this joint application is its suitability to be performed not requiring crop-, microclimate- or relief-specific parameters, making it useful for regions with a lack of data.   Keywords: evapotranspiration, irrigation scheduling, agriwater.

scholarly journals The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency

2020 ◽  
Vol 202 ◽  
pp. 06036
Author(s):  
Nurhadi Bashit ◽  
Novia Sari Ristianti ◽  
Yudi Eko Windarto ◽  
Desyta Ulfiana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Analysis ◽  
Satellite Imagery ◽  
Agricultural Land ◽  
Scale Parameter ◽  
Land Use Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area

2018 ◽  
Vol 65 (3) ◽  
pp. 481-499 ◽  
Author(s):  
Rida Khellouk ◽  
Ahmed Barakat ◽  
Abdelghani Boudhar ◽  
Rachid Hadria ◽  
Hayat Lionboui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Arid Area ◽  
Optical Remote Sensing ◽  
Surface Soil Moisture ◽  
Sensing Data ◽  
Semi Arid

scholarly journals A Framework for Geographic Object-Based Image Analysis (GEOBIA) based on geographic ontology

2015 ◽  
Vol XL-7/W4 ◽  
pp. 27-33 ◽  
Author(s):  
H. Y. Gu ◽  
H. T. Li ◽  
L. Yan ◽  
X. J. Lu
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Systematic Approach ◽  
Semantic Network ◽  
Image Objects ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object ◽  
Objective Model

GEOBIA (Geographic Object-Based Image Analysis) is not only a hot topic of current remote sensing and geographical research. It is believed to be a paradigm in remote sensing and GIScience. The lack of a systematic approach designed to conceptualize and formalize the class definitions makes GEOBIA a highly subjective and difficult method to reproduce. This paper aims to put forward a framework for GEOBIA based on geographic ontology theory, which could implement "Geographic entities - Image objects - Geographic objects" true reappearance. It consists of three steps, first, geographical entities are described by geographic ontology, second, semantic network model is built based on OWL(ontology web language), at last, geographical objects are classified with decision rule or other classifiers. A case study of farmland ontology was conducted for describing the framework. The strength of this framework is that it provides interpretation strategies and global framework for GEOBIA with the property of objective, overall, universal, universality, etc., which avoids inconsistencies caused by different experts’ experience and provides an objective model for mage analysis.

scholarly journals THE METROLOGY AND THE BIM APPROACH: A NEW COGNITIVE PARADIGM ABOUT THE ANCIENT CONSTRUCTION

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 123-127
Author(s):  
P. Fiamma
Keyword(s):  
Laser Scanning ◽  
Object Oriented ◽  
Historical Building ◽  
Charles Eastman ◽  
Object Oriented Approach ◽  
The University ◽  
Cloud Of Points ◽  
Specific Project ◽  
Specific Construction

Abstract. Nowadays, some important issues in the BIM field and research are still open, and are not deeply known and really understood yet by all the BIM users and approaches; unfortunately, this critical situation implies, often, incorrect approaches and ineffective applications, especially using BIM for existing historical building, where the object oriented approach has so many limitations. In fact, the current laser scanning technology can detect just a cloud of points that is considered "non-intelligent" according to the BIM method and meaning. The paper presents an important case study: the Cathedral of Piazza dei Miracoli (Miracles Square) in Pisa. The method we propose consists of an innovative procedure combining in a two-way manner the correspondence levels between metrology and ontology of the constructive components. The paper proves that is not important to obtain the best possible level of modeling performance. We need a specific BIM approach, for “a specific building”, according with “a specific project”. We believe that it is precisely in this capacity of matching between metrological and constructive knowledge (mediated by computer graphics technology) that it is possible to identify the response to an important programmatic indication in the BIM field: "how much BIM?". This sentence was explained by Charles Eastman (recognized worldwide as the father of BIM) during his "lectio magistralis" at the Master BIM of the University of Pisa (www.unibim.it). We guess this is the way to be free from any pre-established intervention standards, and so to be adapting to a specific construction for a specific intervention’s level using specific resources.

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