2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)最新文献_第10页

Exploring the Potential of High-Resolution Planetscope Imagery for Pasture Biomass Estimation in an Integrated Crop–Livestock System 探索高分辨率行星望远镜图像在作物-牲畜综合系统中估算牧草生物量的潜力

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165596

A. A. Dos Reis, B. C. Silva, J. P. Werner, Y. F. Silva, J. Rocha, G. Figueiredo, J. Antunes, J. Esquerdo, A. Coutinho, R. Lamparelli, P. G. Magalhães

{"title":"Exploring the Potential of High-Resolution Planetscope Imagery for Pasture Biomass Estimation in an Integrated Crop–Livestock System","authors":"A. A. Dos Reis, B. C. Silva, J. P. Werner, Y. F. Silva, J. Rocha, G. Figueiredo, J. Antunes, J. Esquerdo, A. Coutinho, R. Lamparelli, P. G. Magalhães","doi":"10.1109/LAGIRS48042.2020.9165596","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165596","url":null,"abstract":"Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May – August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g$.mathrm{m}^{-2}$, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g$.mathrm{m}^{-2}(32.75$%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"762 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":"133251636","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

Mapping Pasture Areas In Western Region Of SÃO Paulo State, Brazil 绘制巴西SÃO保罗州西部地区牧场分布图

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165592

A. F. Bonamigo, J. D. Oliveira, R. Lamparelli, G. Figueiredo, E. Campbell, J. Soares, L. Monteiro, M. Vianna, D. Jaiswal, J. Sheehan, L. Lynd

{"title":"Mapping Pasture Areas In Western Region Of SÃO Paulo State, Brazil","authors":"A. F. Bonamigo, J. D. Oliveira, R. Lamparelli, G. Figueiredo, E. Campbell, J. Soares, L. Monteiro, M. Vianna, D. Jaiswal, J. Sheehan, L. Lynd","doi":"10.1109/LAGIRS48042.2020.9165592","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165592","url":null,"abstract":"Brazil is one of the largest exporters of cattle meat production. Most of this production is under pasture areas, with different levels of livestock and field management. Remotely sensed images could be interesting tools to detect distinct temporal and spatial patterns of these systems. In this context, classification algorithms have been proposed to use information from satellite images to map different land covers. The Time-Weighted Dynamic Time Warping (TWDTW) is an algorithm that has the advantage of working well with datasets with enough amounts of temporal information and seasonality patterns. In the present work, the TWDTW was performed to classify pasture managements in farms located in Western region of São Paulo State in Brazil for the years 2017 and 2018, as a primary study. It was used Normalized Difference Vegetation Index (NDVI) time series images from Moderate Resolution Imaging Spectroradiometer – MODIS sensor (products MOD13Q1 and MYD13Q) with 250 meters of spatial resolution. In classifications for the years 2017 and 2018, it was observed a predominance of traditional pasture. Total areas of degraded and traditional pasture were very similar between 2017 and 2018. The year of 2017 showed higher spatial distribution of intensified pastures than year 2018. The classification achieved satisfying results with complete accuracy in validation. The information collected from field visits were important to analyse general aspects of the results. Therefore, in this pilot study TWDTW algorithm demonstrated to have potential in differentiating classes of pasture management. Next steps will be to explor e the possibilities to classify pasture systems in large areas.","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":"130989307","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

Analysis of VHR Image Classification by Single and Ensemble of Classifiers 基于单一分类器和集成分类器的VHR图像分类分析

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165621

M. G. Lacerda, E. H. Shiguemori, A. Damiao, C. S. Anjos, M. Habermann

引用次数: 0

Quality Control Relevance on Acquisition of Large Scale Geospatial Data to Urban Territorial Management 大尺度地理空间数据获取与城市国土管理的质量控制关系

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165682

A. Filho, P. Borba, V. Silva, A. Cerdeira, A. Poz

{"title":"Quality Control Relevance on Acquisition of Large Scale Geospatial Data to Urban Territorial Management","authors":"A. Filho, P. Borba, V. Silva, A. Cerdeira, A. Poz","doi":"10.1109/LAGIRS48042.2020.9165682","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165682","url":null,"abstract":"Quality control (QC) of geospatial data is relevant to urban territorial management to ensure accurate data for government to make strategic decisions when planning cities. The acquisition and control of geospatial data in the Brazilian government must follow INDE – National Data Spatial Infrastructure – through the Technical Specifications. The cadastral cartography from urban areas in Brasilia was updated and divided into 10 areas. Acquired data includes classes, features, attributes and metadata on 1: 1,000 scale. High resolution images and LIDAR data were used to assist the QC process. The first step of the QC was to check positional accuracy. Samples were applied for each class in the mapping block with 4% rate on the feature random selection and all features class had the same level of confidence. Then, three stages were automatically verified: logical consistency, commision and attribute thematic accuracy evaluations. The process also includes the visual interpretation for omission and classification, which involves a certain subjectivity. Everything was executed with QGIS, FME, Erdas Imagine, Postgresql, PostGIS and a plugin specifically developed for that, the DSGTools. The results show that in general, the quantity of errors were low. However, many errors were detected in the elements completeness and thematic accuracy, specially in áreas 1, 2, 3, 6 and 9. In the opposite, the logical consistency and positional accuracy presented the lowest quantity of errors, which does not diminish the relevance of these errors, since it compromises the usability of the data.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"9 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":"126752867","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: 2

Brazildam: A Benchmark Dataset For Tailings Dam Detection 巴西坝:尾矿坝检测的基准数据集

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165620

E. Ferreira, M. Brito, R. Balaniuk, M. Alvim, J. A. D. Santos

{"title":"Brazildam: A Benchmark Dataset For Tailings Dam Detection","authors":"E. Ferreira, M. Brito, R. Balaniuk, M. Alvim, J. A. D. Santos","doi":"10.1109/LAGIRS48042.2020.9165620","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165620","url":null,"abstract":"In this work we present BrazilDAM, a novel public dataset based on Sentinel-2 and Landsat-8 satellite images covering all tailings dams cataloged by the Brazilian National Mining Agency (ANM). The dataset was built using georeferenced images from 769 dams, recorded between 2016 and 2019. The time series were processed in order to produce cloud free images. The dams contain mining waste from different ore categories and have highly varying shapes, areas and volumes, making BrazilDAM particularly interesting and challenging to be used in machine learning benchmarks. The original catalog contains, besides the dam coordinates, information about: the main ore, constructive method, risk category, and associated potential damage. To evaluate BrazilDAM’s predictive potential we performed classification essays using state-of-the-art deep Convolutional Neural Network (CNNs). In the experiments, we achieved an average classification accuracy of 94.11% in tailing dam binary classification task. In addition, others four setups of experiments were made using the complementary information from the original catalog, exhaustively exploiting the capacity of the proposed dataset.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"4 3 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":"116765590","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: 6

Preliminary Analysis For Automatic Tidal Inlets Mapping Using Google Earth Engine 利用Google Earth Engine自动测绘潮汐入口的初步分析

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165650

J. Sartori, J. B. Sbruzzi, E. L. Fonseca

{"title":"Preliminary Analysis For Automatic Tidal Inlets Mapping Using Google Earth Engine","authors":"J. Sartori, J. B. Sbruzzi, E. L. Fonseca","doi":"10.1109/LAGIRS48042.2020.9165650","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165650","url":null,"abstract":"This work aims to define the basic parameters for the automatic mapping of the channel between the Lagoa do Peixe and the Atlantic Ocean, which is located in the municipalities of Tavares and Mostardas, Rio Grande do Sul state, Brazil. The automatic mapping is based on an unsupervised classification of Landsat 8 satellite images at the Google Earth Engine cloud computing platform. The images used were selected to present both channel situations (opened and closed). Three images were selected with acquisition dates that presented the open channel and three that presented the closed channel. Each image was classified using the K-means clustering method, using separately band 6, band 7 (both located at shortwave infrared - SWIR) and the Normalized Difference Water Index (NDWI). Once the number of clusters must be defined a priori by the analyst, as well as the training sample area, these parameters were tested over the dataset and clustering results were compared. All of the generated clusters maps were analyzed over 10 random points, identifying the clustering hits and errors. Due to the absence of reference maps, all the final clustering maps for each date were compared with the composite true color image from the same acquisition date. The NDWI cluster maps showed the best results in separating water and non-water pixels.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"45 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":"114982621","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

Pairs (Re)Loaded: System Design & Benchmarking For Scalable Geospatial Applications 成对(重)加载:可扩展地理空间应用的系统设计和基准测试

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165675

C. Albrecht, N. Bobroff, B. Elmegreen, Marcus Freitag, H. Hamann, Ildar Khabibrakhmanov, Klein Levente, Siyuan Lu, F. Marianno, J. Schmude, X. Shao, Carlo Siebenschuh, Rui Zhang

引用次数: 3

Evaluation Of Random Forest–Based Analysis For The Gypsum Distribution In The Atacama Desert 基于随机森林分析的阿塔卡马沙漠石膏分布评价

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165655

D. Hoffmeister, M. Herbrecht, Tanja Kramm, P. Schulte

{"title":"Evaluation Of Random Forest–Based Analysis For The Gypsum Distribution In The Atacama Desert","authors":"D. Hoffmeister, M. Herbrecht, Tanja Kramm, P. Schulte","doi":"10.1109/LAGIRS48042.2020.9165655","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165655","url":null,"abstract":"Gypsum-rich material covers the hillslopes above $sim$1000 m of the Atacama and forms the particular landscape. In this contribution, we evaluate random forest-based analysis in order to predict the gypsum distribution in a specific area of-3000 km2, located in the hyperarid core of the Atacama. Therefore, three different sets of input variables were chosen. These variables reflect the different factors forming soil properties, according to digital soil mapping. The variables are derived from indices based on imagery of the ASTER and Landsat-8 satellite, geomorphometric parameters based on the Tandem-X World DE$mathrm{M}^{mathrm{T}mathrm{M}}$, as well as selected climate variables and geologic units. These three different models were used to evaluate the Ca-content derived from soil surface samples, reflecting gypsum content. All three different models derived high values of explained variation ($mathrm{r}^{2}gt$0.886), the RMSE is $sim$4500 mg$cdot kmathrm{g}^{-1}$ and the NRMSE is $sim$6%. Overall, this approach shows promising results in order to derive a gypsum content prediction for the whole Atacama. However, further investigation on the independent variables need to be conducted. In this case, the ferric oxides index (representing magnetite content), slope and a temperature gradient are the most important factors for predicting gypsum content.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"9 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":"130822012","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

Using Remote Sensing Images and Cloud Services on Aws to Improve Land Use and Cover Monitoring 利用Aws上的遥感图像和云服务改善土地利用和覆盖监测

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165649

K. Ferreira, G. R. Queiroz, G. Câmara, R. C. Souza, L. Vinhas, R. F. B. Marujo, R. Simões, C. Noronha, R. W. Costa, J. S. Arcanjo, V. Gomes, M. C. Zaglia

引用次数: 15

The Urban Heat Island of Porto Alegre, Rs, Southern Brazil: An Analysis Between 1985 and 2019 Through the Radiative Transfer in the Infrared Thermal. 巴西南部阿雷格里港城市热岛:1985 - 2019年红外热辐射传输分析

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165591

E. Kaiser, R. Linn, S. Rolim, P. Käfer, N. S. da Rocha, L. R. Diaz, A. Grondona, S. Costa, G. Hallal

{"title":"The Urban Heat Island of Porto Alegre, Rs, Southern Brazil: An Analysis Between 1985 and 2019 Through the Radiative Transfer in the Infrared Thermal.","authors":"E. Kaiser, R. Linn, S. Rolim, P. Käfer, N. S. da Rocha, L. R. Diaz, A. Grondona, S. Costa, G. Hallal","doi":"10.1109/LAGIRS48042.2020.9165591","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165591","url":null,"abstract":"The objective of this study was to verify the evolution of surface temperature associated with land use and land cover from 1985 to 2019 in Porto Alegre, RS, Brazil. The methodological procedures were performed in five steps: 1. Definition of the study area; 2. Land use and land cover classification from images of Landsat 5 satellite Thematic Mapper (TM) and Operational Land Imager (OLI) from Landsat 8 satellite; 3. Calculation of surface temperature from TM sensor band 6 and OLI sensor band 10; 4. Analysis of temperature evolution over the historical series; and 5. Temporal relation between surface temperature and land use and land cover classes. The results demonstrated that higher temperatures were associated to the evolution of two classes of land use and land cover: urban area and exposed soil, with the former occupying 31% in 1989 to 75% in 2018 of the study area. When comparing the first and last decade of the historical series for each season, there was an average increase of 4. 18°C in the surface temperature of the districts. Thus, adopting policies that mitigate the effects caused by densification and urban sprawl are necessary, mainly through the conservation of vegetated areas and water reservoirs, as these are crucial for the maintenance of air humidity and evapotranspiration.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"42 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":"124743161","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