Pub Date : 2020-03-01DOI: 10.1109/lagirs48042.2020.9165608
R. L. F. Cunha, B. Silva
Increasing the accuracy of crop yield estimates may allow improvements in the whole crop production chain, allowing farmers to better plan for harvest, and for insurers to better understand risks of production, to name a few advantages. To perform their predictions, most current machine learning models use NDVI data, which can be hard to use, due to the presence of clouds and their shadows in acquired images, and due to the absence of reliable crop masks for large areas, especially in developing countries. In this paper, we present a deep learning model able to perform pre-season and in-season predictions for five different crops. Our model uses crop calendars, easy-to-obtain remote sensing data and weather forecast information to provide accurate yield estimates.
{"title":"Estimating Crop Yields With Remote Sensing And Deep Learning","authors":"R. L. F. Cunha, B. Silva","doi":"10.1109/lagirs48042.2020.9165608","DOIUrl":"https://doi.org/10.1109/lagirs48042.2020.9165608","url":null,"abstract":"Increasing the accuracy of crop yield estimates may allow improvements in the whole crop production chain, allowing farmers to better plan for harvest, and for insurers to better understand risks of production, to name a few advantages. To perform their predictions, most current machine learning models use NDVI data, which can be hard to use, due to the presence of clouds and their shadows in acquired images, and due to the absence of reliable crop masks for large areas, especially in developing countries. In this paper, we present a deep learning model able to perform pre-season and in-season predictions for five different crops. Our model uses crop calendars, easy-to-obtain remote sensing data and weather forecast information to provide accurate yield estimates.","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":"130975803","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}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165575
J. S. Vinasco, D. Rodríguez, S. Velásquez, D. F. Quintero, L. Livni, F. L. Hernández
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.
圣玛尔塔的cisamnaga Grande是哥伦比亚同类生态系统中最大、最多样化的生态系统。它的主要功能是作为有机碳循环的过滤器。最近，由于周围发生的人为活动，这个地方受到了破坏。本文以2013 - 2018年哥伦比亚马格达莱纳省圣玛尔塔省cisamuaga Grande, Santa Marta, Magdalena为研究对象，利用Landsat 8遥感高分辨率数据，采用半自动探测方法对其地表变化进行了研究。研究区分为6类地表:1)人工领土，2)农田，3)森林和半自然区域，4)潮湿区域，5)深水表面和6)与云作为掩蔽方法相关的地表。采用随机森林分类器，同时对Feed For Ward多层感知器神经网络(ANN)进行评估。这两种方法的训练阶段在每个覆盖类上以等量分布的300个样本进行。半自动化分类是按年频率进行的，但监测是通过归一化植被指数(NDVI)、增强植被指数(EVI)和归一化差水指数(NDWI)三个指标的表现进行的。从混淆矩阵中发现，随机森林方法可以更准确地分类4个类别，而神经网络分析(NNA)只能准确分类3个类别。最后，考虑随机森林的结果，发现农业扩张从7%增加到9%，城区从20%增加到30%。此外，潮湿地区从27%减少到12%，森林从4%减少到3%。
{"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}
Pub Date : 2020-03-01DOI: 10.1109/lagirs48042.2020.9165674
M. D. Abreu
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.
{"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}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165659
M. Crosetto, L. Solari
The paper is focused on the Persistent Scatterer Interferometry (PSI) technique. First, it addresses the substantial evolution of PSI in the last twenty years. Three main factors are identified: the availability of SAR images, the development of advanced data processing techniques, and the increase of the computation capability. The paper then addresses the PSI deformation monitoring initiatives at regional and national scale. Finally, in the last section, it is described a pan European deformation monitoring service: the European Ground Motion Service (EGMS).
{"title":"Deformation Monitoring Using Satellite Radar Interferometry","authors":"M. Crosetto, L. Solari","doi":"10.1109/LAGIRS48042.2020.9165659","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165659","url":null,"abstract":"The paper is focused on the Persistent Scatterer Interferometry (PSI) technique. First, it addresses the substantial evolution of PSI in the last twenty years. Three main factors are identified: the availability of SAR images, the development of advanced data processing techniques, and the increase of the computation capability. The paper then addresses the PSI deformation monitoring initiatives at regional and national scale. Finally, in the last section, it is described a pan European deformation monitoring service: the European Ground Motion Service (EGMS).","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"12 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":"127542906","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}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165573
G. Rodigheri, D. Fontana, L. P. Schaparini, G. A. Dalmago, J. Schirmbeck
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.
{"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}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165584
C. Bello, N. Santillán, A. Cochachin, S. Arias, W. Suarez
Ground Penetrating Radar (GPR) survey was carried out to estimate the ice thickness and mapping the bedrock topography at Znosko glacier on King George Island, Antarctic Peninsula during 25th Peruvian Antarctic Expedition (2018). GPR surveying did at 5.2 MHz frequency with a 16 m antenna gap (transmitter and receiver). The mean ice thickness profiles vary from 7 to 123 m across the 350 m profile length. This high-resolution survey also identified a different type of ices and glaciological features which will help in modelling the nature of the glaciers in the future.
{"title":"ICE Thickness Using Ground Penetrating Radar at Znosko Glacier on King George Island","authors":"C. Bello, N. Santillán, A. Cochachin, S. Arias, W. Suarez","doi":"10.1109/LAGIRS48042.2020.9165584","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165584","url":null,"abstract":"Ground Penetrating Radar (GPR) survey was carried out to estimate the ice thickness and mapping the bedrock topography at Znosko glacier on King George Island, Antarctic Peninsula during 25th Peruvian Antarctic Expedition (2018). GPR surveying did at 5.2 MHz frequency with a 16 m antenna gap (transmitter and receiver). The mean ice thickness profiles vary from 7 to 123 m across the 350 m profile length. This high-resolution survey also identified a different type of ices and glaciological features which will help in modelling the nature of the glaciers in the future.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"76 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":"128222310","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}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165639
A. P. Flores, M. Gaudiano
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.
{"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}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165561
J. Clemente, G. Fontanelli, G. Ovando, Y. Roa, A. Lapini, E. Santi
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.
{"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}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165618
J. Martins, D. Sant’Ana, J. M. Junior, H. Pistori, W. Gonçalves
Urban forests are crucial for the population well-being and improvement of the quality of life. For example, they contribute to the rain damping and to the improvement of the local climate. Therefore a correct and accurate mapping of this resource is fundamental for its correct management. We investigated a method that combines machine learning and SLIC superpixel techniques using different Superpixels (k) number to map trees in the metropolitan region of the municipality of Campo Grande-MS, Brazil with aerial orthoimages with GSD (Ground Sample Distance) of 10 cm. The combination of superpixels and machine learning algorithms were checked out with a set of weka classifiers and achieved good results i.e. F-1 %98.2, MCC %88.4 and Accuracy of % 96.8, supporting that this method is efficient when used for urban trees mapping.
{"title":"Aerial Image Segmentation In Urban Environment For Vegetation Monitoring","authors":"J. Martins, D. Sant’Ana, J. M. Junior, H. Pistori, W. Gonçalves","doi":"10.1109/LAGIRS48042.2020.9165618","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165618","url":null,"abstract":"Urban forests are crucial for the population well-being and improvement of the quality of life. For example, they contribute to the rain damping and to the improvement of the local climate. Therefore a correct and accurate mapping of this resource is fundamental for its correct management. We investigated a method that combines machine learning and SLIC superpixel techniques using different Superpixels (k) number to map trees in the metropolitan region of the municipality of Campo Grande-MS, Brazil with aerial orthoimages with GSD (Ground Sample Distance) of 10 cm. The combination of superpixels and machine learning algorithms were checked out with a set of weka classifiers and achieved good results i.e. F-1 %98.2, MCC %88.4 and Accuracy of % 96.8, supporting that this method is efficient when used for urban trees mapping.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"110 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":"127979332","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}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165684
M. Barber, A. Delsouc, W. Perez, I. Briceño
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.
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