2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)最新文献_第9页

Estimating Crop Yields With Remote Sensing And Deep Learning 利用遥感和深度学习估算作物产量

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/lagirs48042.2020.9165608

R. L. F. Cunha, B. Silva

引用次数: 9

Coverage Changes Detection At Ciénaga Grande, Santa Martacolombia Using Automatic Classification 利用自动分类检测哥伦比亚圣马塔塔省ci日新月异的覆盖变化

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165575

J. S. Vinasco, D. Rodríguez, S. Velásquez, D. F. Quintero, L. Livni, F. L. Hernández

{"title":"Coverage Changes Detection At Ciénaga Grande, Santa Martacolombia Using Automatic Classification","authors":"J. S. Vinasco, D. Rodríguez, S. Velásquez, D. F. Quintero, L. Livni, F. L. Hernández","doi":"10.1109/LAGIRS48042.2020.9165575","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165575","url":null,"abstract":"The Ciénaga Grande, Santa Marta is the largest and most diverse ecosystem of its kind in Colombia. Its primary function is acting as a filter for the organic carbon cycle. Recently, this place has been suffering disruptions due to the anthropic activities taking place in its surroundings. The present study, the changes in the surface of Ciénaga Grande, Santa Marta, Magdalena, Colombia between 2013 and 2018 were determined using semiautomatic detection methods with high resolution data from remote sensors (Landsat 8). The zone of studies was classified in six kinds of surfaces: 1) artificial territories, 2) agricultural temtories, 3) forests and semi-natural areas, 4) wet areas, 5) deep water surfaces& 6) wich is related to clouds as a masking method. Random Forest classifiers were utilized and the Feed For Ward multilayer perceptron neuronal network (ANN) was simultaneously assessed. The training stage for both methods was performed with 300 samples, distributed in equal quantities, over each coverage class. The semi-automatic classification was camed out with an annual frequency, but the monitoring was camed out throughout the analysis period through the performance of three indicators Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI). It was found from the confusion matrix that the Random Forest method more accurately classified four classes while Neural Networks Analysis (NNA) just three. Finally, taking the Random Forest results into account, it was found that the agricultural expansion increased from 7% to 9% and the urban zone increased from 20% to 30% of the total area. As well as a decrease of damp areas from 27% to 12% and forests from 4% to 3% of the total area of study.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129209825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Acquiring And Extraction Of Information Collected By Unmanned Aerial Vehicles And Omnidirectional Cameras And Their Applications Through Management Software 无人机和全向摄像头采集信息的获取与提取及其管理软件应用

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/lagirs48042.2020.9165674

M. D. Abreu

{"title":"Acquiring And Extraction Of Information Collected By Unmanned Aerial Vehicles And Omnidirectional Cameras And Their Applications Through Management Software","authors":"M. D. Abreu","doi":"10.1109/lagirs48042.2020.9165674","DOIUrl":"https://doi.org/10.1109/lagirs48042.2020.9165674","url":null,"abstract":"The growth of technology for aerial and land mapping, as well as information management, has made great progress over the past decade. When we talk about public administration, we envision a sector lacking the use of these new features, as it has been using old models of data acquisition and information management, however slowly opening their eyes to this inevitable advance.Unmanned Aerial Vehicles (UAVs), 360° mapping and Management Software, integrated with a Geographic Information System (GIS), are the latest trend in city management. These features offer quality, agility and reliability, generating an increase in the municipality’s total revenue, along with reducing costs throughout the registration and control process.The objective of this paper is to demonstrate the methodologies applied in the phases of air and ground data acquisition, their processing and generated products, the collection of information from city halls and the import of existing data into Tecsystem’s management software, as well as the different applications of the information in various secretariats of the public municipal administration.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124923619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deformation Monitoring Using Satellite Radar Interferometry 利用卫星雷达干涉测量法监测变形

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165659

M. Crosetto, L. Solari

引用次数: 0

Net Primary Productivity and Dry Matter in Soybean Cultivation Utilizing Datas of Ndvi Multi-Sensors 利用Ndvi多传感器数据分析大豆净初级生产力和干物质

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165573

G. Rodigheri, D. Fontana, L. P. Schaparini, G. A. Dalmago, J. Schirmbeck

{"title":"Net Primary Productivity and Dry Matter in Soybean Cultivation Utilizing Datas of Ndvi Multi-Sensors","authors":"G. Rodigheri, D. Fontana, L. P. Schaparini, G. A. Dalmago, J. Schirmbeck","doi":"10.1109/LAGIRS48042.2020.9165573","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165573","url":null,"abstract":"Net Primary Productivity (NPP) is an important indicator of vegetation growth status and ecosystems health. NPP can be estimated through remote sensing data, using vegetation indices such as NDVI. However, this index may show systematic differences when using several orbital sensors. Therefore, the objective of this paper was to compare the NDVI data obtained from different sensors and evaluate the impact over the soybean biomass and NPP estimates. NDVI data were recorded from 4 sensors, one on the field and others 3 orbitals sensors (Landsat 8/OLI, Sentine12/MSI and TerryMODIS). Measured data on the field, Photosynthetically Active Radiation (PAR) and Dry Matter (DM), were used to modeling the total DM and also NPP. The NDVI data from different sensors showed differences throughout the cycle, but compared to the reference data there was a correlation greater than 0.84. The DM presented a correlation of 0.91 with the field measured MS data while the NPP presented differences of up to $240~mathrm {g}mathrm {C}/mathrm {m}^{2}/$month from in relation to the reference data. Therefore, NDVI obtained from multiple sensors can be used to estimate NPP for surface analysis. However, for more consistent evaluations, a function of adjustment between the NDVI sensor data and NDVI reference data is required, so that the NPP estimation be better correlated to the actual data.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126270734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

ICE Thickness Using Ground Penetrating Radar at Znosko Glacier on King George Island 利用探地雷达探测乔治王岛Znosko冰川冰层厚度

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165584

C. Bello, N. Santillán, A. Cochachin, S. Arias, W. Suarez

引用次数: 8

Fragmented Or Compact: The Case Of Periurban Municipalities in the Northwest of the Metropolitan Area of Buenos Aires 碎片化还是紧凑化:布宜诺斯艾利斯大都市区西北部城市周边的案例

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165639

A. P. Flores, M. Gaudiano

{"title":"Fragmented Or Compact: The Case Of Periurban Municipalities in the Northwest of the Metropolitan Area of Buenos Aires","authors":"A. P. Flores, M. Gaudiano","doi":"10.1109/LAGIRS48042.2020.9165639","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165639","url":null,"abstract":"The accelerated growth of cities since the middle of the last century occupies a prominent place in urban agendas. The development of planning strategies depends on the knowledge and understanding this phenomenon. Therefore, identifying the modification pattern in the spatial configuration is of paramount importance. In this sense, the high level of detail offered by remote sensing technologies makes it possible to estimate the distribution of human settlements and their relationship to other coverages. The information obtained allows to analyze spatial contiguity and general expansion but other indicators are needed to identify spatial singularities. This work aims to present a compaction indicator and ii:agmentation indicator, useful for identifying local configuration patterns and their temporal variation. The study area consists of the Moreno, Pilar, Gral Rodriguez, Luján and Mercedes municipalities of the metropolitan area of Buenos Aires (AMBA) for the period 1986–2019. The results indicate an increase in impervious surfaces higher than 300% in this period and the detection of new urban centres in those municipalities. In the future it is hoped to replicate the techniques presented throughout the AMBA in order to contribute to medium and long-term temtorial planning.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127685893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Google Earth Engine: Application Of Algorithms For Remote Sensing Of Crops In Tuscany (Italy) 谷歌地球引擎:算法在意大利托斯卡纳农作物遥感中的应用

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165561

J. Clemente, G. Fontanelli, G. Ovando, Y. Roa, A. Lapini, E. Santi

{"title":"Google Earth Engine: Application Of Algorithms For Remote Sensing Of Crops In Tuscany (Italy)","authors":"J. Clemente, G. Fontanelli, G. Ovando, Y. Roa, A. Lapini, E. Santi","doi":"10.1109/LAGIRS48042.2020.9165561","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165561","url":null,"abstract":"Remote sensing has become an important mean to assess crop areas, specially for the identification of crop types. Google Earth Engine (GEE) is a free platform that provides a large number of satellite images from different constellations. Moreover, GEE provides pixel-based classifiers, which are used for mapping agricultural areas. The objective of this work is to evaluate the performance of different classification algorithms such as Minimum Distance (MD), Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART) and Naïve Bayes (NB) on an agricultural area in Tuscany (Italy). Four different scenarios were implemented in GEE combining different information such as optical and Synthetic Aperture Radar (SAR) data, indices and time series. Among the five classifiers used the best performers were RF and SVM. Integrating Sentinel-1 (S1) and Sentinel-2 (S2) slightly improves the classification in comparison to the only S2 image classifications. The use of time series substantially improves supervised classifications. The analysis carried out so far lays the foundation for the integration of time series of SAR and optical data.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127746779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Aerial Image Segmentation In Urban Environment For Vegetation Monitoring 城市环境航拍图像分割用于植被监测

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165618

J. Martins, D. Sant’Ana, J. M. Junior, H. Pistori, W. Gonçalves

引用次数: 1

Time Series Of Salt Crusts Imaged By A Dual Polarization Spaceborne Synthetic Aperture Radar (Sar) At C-Band Over An Andean Altiplano Salar Of Northern Chile 智利北部安第斯高原c波段双偏振星载合成孔径雷达(Sar)盐壳时间序列研究

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165684

M. Barber, A. Delsouc, W. Perez, I. Briceño

{"title":"Time Series Of Salt Crusts Imaged By A Dual Polarization Spaceborne Synthetic Aperture Radar (Sar) At C-Band Over An Andean Altiplano Salar Of Northern Chile","authors":"M. Barber, A. Delsouc, W. Perez, I. Briceño","doi":"10.1109/LAGIRS48042.2020.9165684","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165684","url":null,"abstract":"A dense time series of Synthetic Aperture Radar acquisitions at 6-day intervals between July 2017 to January 2019 collected with the C-band constellation Sentinel 1A and 1B is used to study salt crust evolution in an highland salar. Microwave response of halite crystal aggregates is linked to surface roughness of the salt crusts by means of a surface scattering model which includes multiple scattering at second order in media with complex permittivity such as brine-soil mixtures. The time series enabled to estimate co-polarised vertical-vertical backscattering coefficient variations as large as 8.8 dB on a 4-month period which implied a height standard deviation increase from 0.5 to 4.5 mm as modeled by the surface scattering model. Backscattering coefficient variations between 0.8 to 2 dB per month are found for three different crusts, which demonstrated different growth rates of the crystals. Crystal growth rate might be driven by the kind of water input (rainfall or snow in Andean salars), probably due to the negative effect of water droplets on impinging halite crystal surface in comparison to snow. Results showed that cross-polarised backscattering coefficient is sensitive to snow accumulation and appeared to be sensitive to subsurface conditions.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132422974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0