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

{"title":"Multitemporal Spectral Analysis for Algae Detection in an Eutrophic Lake using Sentinel 2 Images","authors":"G. Alba, Ferral Anabella, S. Marcelo, Shimoni Michal","doi":"10.1109/LAGIRS48042.2020.9165633","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165633","url":null,"abstract":"Eutrophication is characterized by excessive plant and algal growth due to the increased of organic matter, carbon dioxide and nutrients in water body. Although eutrophication naturally occurs over centuries as lakes age, human activities have accelerated it processes and caused dramatic changes to the aquatic ecosystems including elevated algae blooms and risk for hypoxia as well as degradation in the quality of drinking water and fisheries. Monitoring eutrophic processes is therefore highly important to human health and to the aquatic environment. However, the spatial and seasonal distribution of the phenomena and its dynamic are difficult to be resolved using conventional methods as water sampling or sparse acquisition of remote sensing data. This research work proposes a methodology that takes advantage of the high temporal resolution of Sentinel-2 (S2) for monitoring eutrophic reservoir. Specifically, it uses large temporal series of S2 images and advanced temporal unmixing model to estimate the abundance of [Chl-a] and algae species in San Roque reservoir, Argentina, in the period August 2016 to August 2019. The spatial patterns and the temporal tendencies of these aquatic indicators, that have a direct link to Eutrophication, were analysed and evaluated using in situ data in order to assess their contribution to the local water management.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"8 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":"127745714","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}

{"title":"Hyperspectral Image Classification Using Random Forest and Deep Learning Algorithms","authors":"J. V. Rissati, P. C. Molina, C. S. Anjos","doi":"10.1109/LAGIRS48042.2020.9165588","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165588","url":null,"abstract":"One of the purposes of hyperspectral remote sensing is to differentiate and identify the materials present on the Earth’s surface by the spectral behavior of each object in the different regions of the electromagnetic spectrum. Such differentiation and identification can be accomplished through different image classification algorithms. However, there is no perfect classifier, since every algorithm has labeling errors. With the advent of orbital and aerial images of very high spatial and spectral resolution, the recognition of the materials present in urban environments is increasingly accurate. Thus, we thoroughly study different methodologies to identify the algorithm that presents the best results in the characterization of urban objects. The hyperspectral image used in the present study represents an area over Houston University - Texas and its surroundings, containing 48 spectral bands, with a spatial resolution of 1 meter and spectral range of 380 nm to 1050 nm. For the identification of 21 classes present in the study area, this paper analyzes two different classification methods: Deep Learning and Random Forest. To improve classification accuracy, performed the feature extraction. To obtain such preliminary results we used tools available in specific software as Normalized Difference Vegetation Index (NDVI), Minimum Noise Fraction (MNF), Principal Component Analysis (PCA) and Soil Adjusted Vegetation Index (SAVI). The image segmentation was performed using two different methods known as Multiresolution Segmentation and Spectral difference. Multiresolution segmentation needs parameters related to form and compactness. The best results were obtained with the values of form = 0.7 and compactness = 0.5, besides the scale of 10. From this, samples of all classes contained in the study area were selected for the training of the algorithms. This step is of paramount importance, as sample collection directly impacts the result of the classifications. After performing these steps, the information obtained from sample collection is entered into the data mining software (WEKA 3.8) to train the classification algorithms. The analysis of the results was performed by cross-validation, thus obtained the confusion matrix, calculated the Overall Accuracy (OA) and Kappa Index. The classification by the Random Forest method had an overall accuracy of 84.72% and a Kappa Index of 0.83. In turn, the Deep Learning algorithm had an overall accuracy of 81.32% and a Kappa index of 0.80. In this case, the classification by the Random Forest method presented better results for the hyperspectral image classification than the Deep Learning method. The accuracy difference obtained between the methods is not considered significant, so it is suggested for future work to analyze other complementary issues such as processing time.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"28 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":"127759435","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}

{"title":"Sugarcane Productivity Estimation Through Processing Hyperspectral Signatures Using Artificial Neural Networks","authors":"C. Espinosa, S. Velásquez, F. L. Hernández","doi":"10.1109/LAGIRS48042.2020.9165683","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165683","url":null,"abstract":"This project uses an artificial neural network to calculate the net primary productivity of an organic sugarcane crop in Hatico’s farm, in Cerrito, Valle del Cauca. The pilot scheme used in this project is composed by 6 treatments of nitrogen fertilization based on green manures (poultry manure and cowpea). During the last two crops’ phenological phases, the artificial neural network was provided with hyperspectral data collected in the field. In addition, an exploratory data study was implemented in order to identify anomalous signs related to the light saturation and the curvature geometry. The first network applied was Autoencoder, in order to reduce the dimensionality of the radiometric resolution of the data. The second network applied was Multilayer Perceptron (MLP), to calculate the productivity values of the patches. After having compared the actual productivity values provided by Cenicaña, this project obtained an accuracy of 91.23% in the productivity predictions.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"10 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":"127966122","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}

{"title":"Evaluation Of Sentinel-2/Msi Imagery Products Level-2a Obtained By Three Different Atmospheric Corrections For Monitoring Suspended Sediments Concentration In Madeira River, Brazil.","authors":"D. R. D. Santos, J. M. Martínez, T. Harmel, H. Borges, H. Roig","doi":"10.1109/LAGIRS48042.2020.9165652","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165652","url":null,"abstract":"Data provided by spatial sensors combined with remote sensing techniques and analysis of the optical properties of waters allow the mapping of the suspended sediment concentration (SSC) in aquatic bodies. For this, estimation models require data with the lowest possible amount of atmospheric artifacts. In this study we compared the water remote sensing reflectance (Rrs) of the Santo Antônio Hydroelectric Power Plant reservoir in Porto Velho-RO, Brazil, after applying three different atmospheric corrections algorithms in Sentinel-2/MSI imagery products. The atmospheric corrected reflectances of the MODIS sensor were also used for reference. SSC was calculated with models based on the red and near-infrared (NIR) bands over three distinct regions of the reservoir. Reflectance data showed significant variations for Sentinel-2, bands 4 and 8a, and MODIS, bands RED and IR, when different atmospheric correction algorithms were used. SSC maps and estimates were produced to show sediment load variation as a function of hydrological regime. The analyzes showed that the SSC estimates done with Sentinel-2 / MSI satellite images using GRS (Glint Remove Sentinel) atmospheric correction presented an average difference of 27.3% and were the closest to the in situ measurements. SSC estimates from MODIS products were around 34.6% different from estimates made using the GRS atmospheric correction applied to Sentinel-2 / MSI products.","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":"129761142","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}

{"title":"Assessing the damage of forests burnt in central Chile by relating index-derived differences to field data","authors":"M. Peña, A. BravoL, E. Fernández","doi":"10.1109/LAGIRS48042.2020.9165622","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165622","url":null,"abstract":"To assess the damage produced by wildfires on forest ecosystems is a critical task for their subsequent management and ecological restoration. Satellite-based optical images provide reliable ex-ante and ex-post data about vegetation state, making them suitable for the aforementioned purpose. In this study we assessed the damage produced on two forested lands by the series of wildfires occurred in central Chile during summer 2017. Arithmetic differences from pre- and post-fire NDVI (normalized difference vegetation index), NDWI (normalized difference water index) and NBR (normalized burnt ratio) were retrieved from a Sentine1–2 image set embracing four near-anniversary summer dates: 2016 (ex-ante), 2017, 2018 and 2019 (ex-post). The nine index-derived differences resulting were correlated to CBI (composite burn index) data collected in the field during summer 2019, and a model constructed by a stepwise regression was formulated. Results show that planted forests exhibited a somewhat smaller biomass recovery than native ones, in pait due to their post-fire clearing and preparation, deriving in a smaller tree cover. CBI poorly performed because its calculation includes low vegetation strata largely recovered at the time of the field data collection. However, when overstory field data were used alone correlations noticeably increased (${r}$=0,66–0,74). This was because during the field campaign this stratum was still appreciably damaged, thus better matching with the data provided by the indices-derived differences, intrinsically more representative of uppermost vegetation layers. The bum damage was mapped on both study areas employing the best performing regression model, based on $mathrm {N}mathrm {D}mathrm {W}mathrm {I}_{2016-2019}, mathrm {N}mathrm {D}mathrm {W}mathrm {I}_{2016-2017}, mathrm {N}mathrm {B}mathrm {R}_{2016-201mathrm {S}}$ and $mathrm {N}mathrm {B}mathrm {R}_{2016-2017}$ differences (adjusted $mathrm {R}^{2}=0.72, p< 0.005,$ root mean square error =0.38). The use of approaches like this one in other areas of central Chile, where wildfires are increasing their frequency and intensity, might contribute to better lead post-fire management and restoration actions on their damaged forest ecosystems.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"58 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":"117033771","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}

{"title":"Spatial-Temporal Distribution of Drought in The Western Region Of Paraguay (2005-2017)","authors":"M. Paniagua, J. Villalba, M. Pasten","doi":"10.1109/lagirs48042.2020.9165664","DOIUrl":"https://doi.org/10.1109/lagirs48042.2020.9165664","url":null,"abstract":"The Western Region of Paraguay is naturally dry which makes it vulnerable to almost permanent drought events. Hence, low cost drought monitoring is necessary. Therefore, an index derived from satellite image information was used for this purpose. The Normalized Difference Drought Index (NDDI) was used with the objective of studying the characteristics (temporal and spatial distribution) of drought in the Western Region of Paraguay from years 2005 to 2017 and relate drought point values of NDDI to the land use cover of the study area. Terra satellite’s Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor images were used for the calculation of NDDI. The driest month of all years (July) and the wettest month of all years (December) were averaged to analyze spatial tendencies. The NDDI was contrasted spatially with the Land Use Cover of the Western Region to pinpoint the location of the highest values. The year 2015 had the highest value of 0.9847 in agricultural land use in the Department of Boqueron. The NDDI was a good indicator of drought throughout the region and could be a complement for in-situ measurements.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"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":"122695974","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}

{"title":"Optimization Of A Random Forest Classifier For Burned Area Detection In Chile Using Sentinel-2 Data","authors":"E. Roteta, P. Oliva","doi":"10.1109/LAGIRS48042.2020.9165585","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165585","url":null,"abstract":"Due to the high variability of biomes throughout the country, the classification of burned areas is a challenge. We calibrated a random forest classifier to account for all this variability and ensure an accurate classification of burned areas. The classifier was optimized in three steps, generating a version of the burned area product in each step. According to the visual assessment, the final version of the BA product is more accurate than the perimeters created by the Chilean National Forest Corporation, which overestimate large burned areas because it does not consider the inner unburned areas and, it omits some small burned areas. The total burned surface from January to March 2017 was 5,000 km2 in Chile, 20 % of it belonging to a single burned area in the Maule Region, and with 91 % of the total burned surface distributed in 6 adjacent regions of Central Chile.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"44 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":"124121836","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}

{"title":"Land Use Data In The Middle Maule River Sub-Basin: Classification And Comparison Between 1999 And 2019","authors":"M. Tapia, M. Morais","doi":"10.1109/lagirs48042.2020.9165586","DOIUrl":"https://doi.org/10.1109/lagirs48042.2020.9165586","url":null,"abstract":"The use of satellite images is a modern strategy for the evaluation and prediction of various weather scenarios. In addition, this is a key tool for the development of environmental sciences. Since the end of the last decade, Chile has been suffering from a megadrought associated with climate change. In this context, this study proposes to evaluate the role of land use change in the Middle Maule River sub-basin, located in the Maule Region, Chile. This is an important sector characterized by a significant agricultural and hydroelectric contribution. To do so, this study performs a supervised classification of land cover through the usage of QGIS software and Landsat images for the years 1999 and 2019. The results show the growth of areas without vegetation due to a great drought facing the Central Zone of the country. Additionally, there is a decrease in available bodies of water. This article leaves open future research on the impact of the main economic activities of the region.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"36 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":"123158221","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}

{"title":"Advances and Challenges of UAV SFM MVS Photogrammetry and Remote Sensing: Short Review","authors":"E. F. Berra, M. Peppa","doi":"10.1109/LAGIRS48042.2020.9285975","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9285975","url":null,"abstract":"Interest in Unnamed Aerial Vehicle (UAV)-sourced data and Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) photogrammetry has seen a dramatic expansion over the last decade, revolutionizing the fields of aerial remote sensing and mapping. This literature review provides a summary overview on the recent developments and applications of light-weight UA V s and on the widely-accepted SfM - MVS approach. Firstly, the advantages and limitations of UAV remote sensing systems are discussed, followed by an identification of the different UA V and miniaturised sensor models applied to numerous disciplines, showing the range of systems and sensor types utilised recently. Afterwards, a concise list of advantages and challenges of UAV SfM-MVS is provided and discussed. Overall, the accuracy and quality of the SfM-MVS-derived products (e.g. orthomosaics, digital surface model) depends on the quality of the UAV data set, characteristics of the study area and processing tools used. Continued development and investigation are necessary to better determine the quality, precision and accuracy of UAV SfM-MVS derived outputs.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"396 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":"130758416","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}

{"title":"Deforestation Polygon Assessment Tool: Providing Comprehensive Information On Deforestation In The Brazilian Cerrado Biome","authors":"L. M. Pascoal, L. Parente, H. M. Sérgio Nogueira, L. G. F. Júnior","doi":"10.1109/LAGIRS48042.2020.9165580","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165580","url":null,"abstract":"Considered a conservation hotspot of the world biodiversity and a key region for the agriculture production in Brazil, the Cerrado biome has only 7.5% of its native vegetation as fully protected areas. Given this, in 2016 the Brazilian government started an official project to monitoring deforestation in the biome, through the so-called PRODES-Cerrado, responsible for mapping deforested areas from 2000 on, and DETER-Cerrado, responsible to generate deforestation alerts. Seeking to contribute with both context information and confidence levels for the polygons produced by these two monitoring systems, we developed the Deforestation Polygon Assessment Tool. This web-based platform process and presents several analysis for PRODES-Cerrado and DETER-Cerrado polygons using automatic assessments (e.g. BFastMonitor and Weights of Evidence), field validation and spatial analysis with key datasets (e.g. National Land Registry, Land-Use and Land-Cover maps). The platform implements an interactive map which allows a fast and comprehensive visualization of different layers, as well as a Deforestation Report at the polygon level, which gathers all the information about each polygon, providing greater reliability and understanding of the deforestation dynamics in the Cerrado. Future improvements in the platform will consider additional, spatial relations in order to assist government agencies to either prevent or reduce deforestation ocurrences in each municipality in the Cerrado biome.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"27 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":"114515954","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}