Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165627
J. Duarte, V. H. S. Orengo, G. C. Segedi, R. E. Cicerelli
The rainwater is a crucial element in the hydrologic cycle, however, due to human impacts in the environment, this cycle does not work as it should. Roads, highways, buildings, and other kinds of constructions are necessary in a community. These paved areas rely on build drainage systems to function properly, otherwise, the areas impact the waterway and consequently cause serious flooding, such as the ones near to Banco do Brasil, that is a recurring case of flooding’ over the last few years. In large cities, the lack of sustainable and green stormwater infrastructure is marked; That is why this study applies to the area of Plano Piloto- Brasília DF. The identification of these possible flooding areas was performed by analyzing the density and drainage order of the region, along with the calculation of the topographic factor (LS), belonging to the Universal Soil Loss Equation (USLE). Thus, this research aimed to locate the ideal areas for the implantation of rain gardens; A shallow depression to capture, temporarily pond, and absorb run-off water from impervious surfaces, for instance roofs and pavement, as Cityofames has shown (2016).
{"title":"Analysis Of Possible Areas For Rain Gardens Implementation In Plano Piloto, Brasília- Df","authors":"J. Duarte, V. H. S. Orengo, G. C. Segedi, R. E. Cicerelli","doi":"10.1109/LAGIRS48042.2020.9165627","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165627","url":null,"abstract":"The rainwater is a crucial element in the hydrologic cycle, however, due to human impacts in the environment, this cycle does not work as it should. Roads, highways, buildings, and other kinds of constructions are necessary in a community. These paved areas rely on build drainage systems to function properly, otherwise, the areas impact the waterway and consequently cause serious flooding, such as the ones near to Banco do Brasil, that is a recurring case of flooding’ over the last few years. In large cities, the lack of sustainable and green stormwater infrastructure is marked; That is why this study applies to the area of Plano Piloto- Brasília DF. The identification of these possible flooding areas was performed by analyzing the density and drainage order of the region, along with the calculation of the topographic factor (LS), belonging to the Universal Soil Loss Equation (USLE). Thus, this research aimed to locate the ideal areas for the implantation of rain gardens; A shallow depression to capture, temporarily pond, and absorb run-off water from impervious surfaces, for instance roofs and pavement, as Cityofames has shown (2016).","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"75 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":"123066009","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/LAGIRS48042.2020.9165581
H. Aghababaei, G. Ferraioli, V. Pascazio
Dealing with multi-look polarimetric synthetic aperture radar (PolSAR) images requires averaging several independent looks to generate a sample covariance matrix of similar target scattering vectors. Along this, estimation of optimal similarity between target scattering vectors is still an open issue. In the literature, this intrinsic task has been mainly addressed in the information-based, geometric-based and detection-based frameworks. However, the derived measures mainly rely on the model assumption such as fully developed speckle and circular complex Gaussian distribution of the scattering vectors, which may not be held in high-resolution images of urban environments. To cope with this possible issue a discriminative model-free measure is proposed, where the similarity of target scattering is computed in the framework of non-local or patch based algorithm. In particular, the discriminative measure is constructed using the ratio between two pre-estimated covariance matrices of the scattering vectors. Experimental validation of the proposed measure is provided using ALOS-PALSAR image and compared with existing criterions in the literature.
{"title":"Ratio-Based Similarity Criteria For Polarimetric SAR Image","authors":"H. Aghababaei, G. Ferraioli, V. Pascazio","doi":"10.1109/LAGIRS48042.2020.9165581","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165581","url":null,"abstract":"Dealing with multi-look polarimetric synthetic aperture radar (PolSAR) images requires averaging several independent looks to generate a sample covariance matrix of similar target scattering vectors. Along this, estimation of optimal similarity between target scattering vectors is still an open issue. In the literature, this intrinsic task has been mainly addressed in the information-based, geometric-based and detection-based frameworks. However, the derived measures mainly rely on the model assumption such as fully developed speckle and circular complex Gaussian distribution of the scattering vectors, which may not be held in high-resolution images of urban environments. To cope with this possible issue a discriminative model-free measure is proposed, where the similarity of target scattering is computed in the framework of non-local or patch based algorithm. In particular, the discriminative measure is constructed using the ratio between two pre-estimated covariance matrices of the scattering vectors. Experimental validation of the proposed measure is provided using ALOS-PALSAR image and compared with existing criterions in the literature.","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":"123170968","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/LAGIRS48042.2020.9165613
M. Borja, R. Camargo, N. Moreno, E. Turpo, S. Villacís
The data developed by the MapBiomas Amazon initiative http://amazonia.mapbiomas.org/) led by the Amazon Geo-referenced Socio-environmental Information Network’s (RAISG) is of unprecedented spatial and temporal resolution for the Andes region. It’s comprised by a series of annual maps for the years 2000 to 2017 that allow to monitor the extent of transformation in this region using a single regional methodological approach. Several variables were included to solve Andes-specific methodological challenges and they represent adaptations of RAISG’s Amazonian methodology to the Andean region. Among such, is the use of the novel NDFIb index (Turpo, 2018), an adaptation of the NDFI index that aims at mapping Andean Wetlands. Glaciers identification was aided by the fractional abundance of snow (Turpo, 2018), as well as small water bodies identification with McFeeters (1996) NDWI water index. This experience unfolds promising accessibility to novel land cover and land use regional reconstructions and comparisons possible only by the use of large-scale cloud-computing data processing tools, open source technology, spatially and temporally comprehensive remote sensing data, along with RAISG’s standardized protocols and frameworks.
{"title":"A Long-Term Land Cover And Land Use Mapping Methodology For The Andean Amazon","authors":"M. Borja, R. Camargo, N. Moreno, E. Turpo, S. Villacís","doi":"10.1109/LAGIRS48042.2020.9165613","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165613","url":null,"abstract":"The data developed by the MapBiomas Amazon initiative http://amazonia.mapbiomas.org/) led by the Amazon Geo-referenced Socio-environmental Information Network’s (RAISG) is of unprecedented spatial and temporal resolution for the Andes region. It’s comprised by a series of annual maps for the years 2000 to 2017 that allow to monitor the extent of transformation in this region using a single regional methodological approach. Several variables were included to solve Andes-specific methodological challenges and they represent adaptations of RAISG’s Amazonian methodology to the Andean region. Among such, is the use of the novel NDFIb index (Turpo, 2018), an adaptation of the NDFI index that aims at mapping Andean Wetlands. Glaciers identification was aided by the fractional abundance of snow (Turpo, 2018), as well as small water bodies identification with McFeeters (1996) NDWI water index. This experience unfolds promising accessibility to novel land cover and land use regional reconstructions and comparisons possible only by the use of large-scale cloud-computing data processing tools, open source technology, spatially and temporally comprehensive remote sensing data, along with RAISG’s standardized protocols and frameworks.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"26 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":"131400826","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/LAGIRS48042.2020.9165589
Miguel Pincheira, Elena Donini, R. Giaffreda, M. Vecchio
Remote sensing considerably benefits from the fusion of open data from different sources, including far-range sensors mounted on satellites and short-range sensors on drones or Internet of Things devices. Open data is an emerging philosophy attracting an increasing number of data owners willing to share. However, most of the data owners are unknown and thus, untrustable, which makes shared data likely unreliable and possibly compromising associated outcomes. Currently, there exist tools that distribute open data, acting as intermediaries connecting data owners and users. However, these tools are managed by central authorities that set rules for data ownership, access, and integrity, limiting data owners and users. Therefore, a need emerges for a decentralized system to share and retrieve data without intermediaries limiting participants. Here, we propose a blockchain-based system to share and retrieve data without the need for a central authority. The proposed architecture (i) allows sharing data, (ii) maintains the data history (origin and updates), and (iii) allows retrieving and evaluating the data adding trustworthiness. To this end, the blockchain network enables the direct connection of data owners and users. Furthermore, blockchain automatically interacts with participants and keeps a transparent record of their actions. Hence, blockchain provides a decentralized database that enables trust among the participants without a central authority. We analyzed the potentials and critical issues of the architecture in a remote sensing use case of precision farming. The analysis shows that participants benefit from the properties of the blockchain in providing trusted data for remote sensing applications.
{"title":"A Blockchain-Based Approach To Enable Remote Sensing Trusted Data","authors":"Miguel Pincheira, Elena Donini, R. Giaffreda, M. Vecchio","doi":"10.1109/LAGIRS48042.2020.9165589","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165589","url":null,"abstract":"Remote sensing considerably benefits from the fusion of open data from different sources, including far-range sensors mounted on satellites and short-range sensors on drones or Internet of Things devices. Open data is an emerging philosophy attracting an increasing number of data owners willing to share. However, most of the data owners are unknown and thus, untrustable, which makes shared data likely unreliable and possibly compromising associated outcomes. Currently, there exist tools that distribute open data, acting as intermediaries connecting data owners and users. However, these tools are managed by central authorities that set rules for data ownership, access, and integrity, limiting data owners and users. Therefore, a need emerges for a decentralized system to share and retrieve data without intermediaries limiting participants. Here, we propose a blockchain-based system to share and retrieve data without the need for a central authority. The proposed architecture (i) allows sharing data, (ii) maintains the data history (origin and updates), and (iii) allows retrieving and evaluating the data adding trustworthiness. To this end, the blockchain network enables the direct connection of data owners and users. Furthermore, blockchain automatically interacts with participants and keeps a transparent record of their actions. Hence, blockchain provides a decentralized database that enables trust among the participants without a central authority. We analyzed the potentials and critical issues of the architecture in a remote sensing use case of precision farming. The analysis shows that participants benefit from the properties of the blockchain in providing trusted data for remote sensing applications.","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":"131454128","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/LAGIRS48042.2020.9165610
D. de Abelleyra, S. Verón, S. Banchero, M. J. Mosciaro, T. Propato, A. Ferraina, M. Taffarel, L. Dacunto, A. Franzoni, J. Volante
The availability of spatially explicit information about agricultural crops for large regions in Argentina is scarce. In particular, due to temporal dynamics of agricultural production (i.e., changes in planted crops from year to year) and spectral similarities among herbaceous crops it is difficult to generate crop type maps from remote sensing. Large regions with marked climatic variations, like the main agricultural areas of Argentina, represent an additional challenge. Here we generated a map based on supervised classifications using field samples along 14 agricultural zones. Best classification accuracies were obtained by combining seasonal indices (year, summer and winter), with indices that describe the temporal dynamics of vegetation. Accuracy was increased at regions with high and balanced number of samples and with longer growing seasons. The map allows to identify areas with clusters of one, two or three crops and to characterize areas with different spatial distribution between cropland and no cropland areas.
{"title":"First Large Extent and High Resolution Cropland and Crop Type Map of Argentina","authors":"D. de Abelleyra, S. Verón, S. Banchero, M. J. Mosciaro, T. Propato, A. Ferraina, M. Taffarel, L. Dacunto, A. Franzoni, J. Volante","doi":"10.1109/LAGIRS48042.2020.9165610","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165610","url":null,"abstract":"The availability of spatially explicit information about agricultural crops for large regions in Argentina is scarce. In particular, due to temporal dynamics of agricultural production (i.e., changes in planted crops from year to year) and spectral similarities among herbaceous crops it is difficult to generate crop type maps from remote sensing. Large regions with marked climatic variations, like the main agricultural areas of Argentina, represent an additional challenge. Here we generated a map based on supervised classifications using field samples along 14 agricultural zones. Best classification accuracies were obtained by combining seasonal indices (year, summer and winter), with indices that describe the temporal dynamics of vegetation. Accuracy was increased at regions with high and balanced number of samples and with longer growing seasons. The map allows to identify areas with clusters of one, two or three crops and to characterize areas with different spatial distribution between cropland and no cropland areas.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"2 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":"125528472","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/LAGIRS48042.2020.9165623
A. R. Soares, T. Körting, Leila Maria Garcia Fonseca, A. K. Neves
Segmentation is a fundamental problem in image processing and a common operation in Remote Sensing, which has been widely used especially in Geographic Object-Based Image Analysis (GEOBIA). In this paper, we propose a new unsupervised segmentation algorithm based on the Conditional Random Fields (CRF) theory. The method relies on two levels of information: (1) that comes from an unsupervised classification with Fuzzy C-Means algorithm; (2) the 8-connected neighbourhood of a pixel. The algorithm was tested on a WorldView-2 multispectral image, with 2m of spatial resolution. Results were evaluated using 6 quality measures, and their performance was compared with other image segmentation algorithms that are usually applied by the Remote Sensing community. Results indicate that the proposed algorithm achieved superior overall performance when compared others, despite some over-segmentation.
{"title":"An Unsupervised Segmentation Method For Remote Sensing Imagery Based On Conditional Random Fields","authors":"A. R. Soares, T. Körting, Leila Maria Garcia Fonseca, A. K. Neves","doi":"10.1109/LAGIRS48042.2020.9165623","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165623","url":null,"abstract":"Segmentation is a fundamental problem in image processing and a common operation in Remote Sensing, which has been widely used especially in Geographic Object-Based Image Analysis (GEOBIA). In this paper, we propose a new unsupervised segmentation algorithm based on the Conditional Random Fields (CRF) theory. The method relies on two levels of information: (1) that comes from an unsupervised classification with Fuzzy C-Means algorithm; (2) the 8-connected neighbourhood of a pixel. The algorithm was tested on a WorldView-2 multispectral image, with 2m of spatial resolution. Results were evaluated using 6 quality measures, and their performance was compared with other image segmentation algorithms that are usually applied by the Remote Sensing community. Results indicate that the proposed algorithm achieved superior overall performance when compared others, despite some over-segmentation.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"69 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":"124606364","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/LAGIRS48042.2020.9165665
P. Silva, J. Rodrigues, F. L. Santos, A. A. Pereira, J. Nogueira, C. DaCamara, R. Libonati
The Brazilian savanna (Cerrado) is one of the most important biodiversity hotspots in the world. Being a fire-dependent biome, its structure and vegetation dynamics are shaped by and rely on the natural occurring fire regime. Over the last decades, Cerrado has been increasingly threatened by accelerated land cover changes, namely the uncontrolled and intense use of fire for land expansion. This is particularly seen in Brazil’s new agricultural frontier in northeastern Cerrado: the MATOPIBA region. Changes in MATOPIBA’s fire regime resulting from this rapid expansion are still poorly understood. Here we use satellite-derived datasets to analyze burned area patterns in MATOPIBA over the last 18 years, at the microregions level. We further evaluate the role of climate and land use in spatial and temporal burned area variability and assess their trends in the last two decades. Results show an increased contribution of MATOPIBA to Cerrado’s total burned area over the last few years: Maranhão and Tocantins present the highest values of total burned area with some microregions burning more than twice its area over the study period. Climate is shown to play a relevant role in MATOPIBA’s fire activity, explaining 52% of the interannual variance, whereas land use and burned area were found to have more complex interactions that are highly dependent on the regional context. Lastly, climate and land use drivers are found to have an overall increasing trend over the last two decades, whereas burned area trends show much heterogeneity within MATOPIBA.
{"title":"Drivers Of Burned Area Patterns In Cerrado: The Case Of Matopiba Region","authors":"P. Silva, J. Rodrigues, F. L. Santos, A. A. Pereira, J. Nogueira, C. DaCamara, R. Libonati","doi":"10.1109/LAGIRS48042.2020.9165665","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165665","url":null,"abstract":"The Brazilian savanna (Cerrado) is one of the most important biodiversity hotspots in the world. Being a fire-dependent biome, its structure and vegetation dynamics are shaped by and rely on the natural occurring fire regime. Over the last decades, Cerrado has been increasingly threatened by accelerated land cover changes, namely the uncontrolled and intense use of fire for land expansion. This is particularly seen in Brazil’s new agricultural frontier in northeastern Cerrado: the MATOPIBA region. Changes in MATOPIBA’s fire regime resulting from this rapid expansion are still poorly understood. Here we use satellite-derived datasets to analyze burned area patterns in MATOPIBA over the last 18 years, at the microregions level. We further evaluate the role of climate and land use in spatial and temporal burned area variability and assess their trends in the last two decades. Results show an increased contribution of MATOPIBA to Cerrado’s total burned area over the last few years: Maranhão and Tocantins present the highest values of total burned area with some microregions burning more than twice its area over the study period. Climate is shown to play a relevant role in MATOPIBA’s fire activity, explaining 52% of the interannual variance, whereas land use and burned area were found to have more complex interactions that are highly dependent on the regional context. Lastly, climate and land use drivers are found to have an overall increasing trend over the last two decades, whereas burned area trends show much heterogeneity within MATOPIBA.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"74 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":"128402230","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/LAGIRS48042.2020.9165617
S. Puliafito, T. Bolaño Ortiz, R. Pascual, A. López-Noreña, L. Berná
Changes in snow albedo (SA) on several basins of the central Andes of Argentina are associated with the possible deposition of light-absorbing particles (LAP) in the austral spring. To demonstrate this possibility, we correlate SA with daily data of snow cover (SC), aerosol optical depth (AOD) and land surface temperature (LST) available from the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board NASA Terra satellite during 2000-2016, and other derived parameters such as days after albedo (DAS) and snow precipitation (SP) from the Tropical Rainfall Measuring Mission (TRMM). We used satellite pixels with 100% snow cover to obtain monthly average value of SA, LST, AOD, DAS and SP performing multiple regression analysis. Further, we analysed biomass burning emissions in northem Argentina using MODIS products MCD64 collection C6 as possible source for snow pollution. Aerosol deposition and trajectories were analysed using WRF-Chem atmospheric numerical prediction model, with inventories of regional anthropogenic emissions of own elaboration (lat. 0.025°x long. 0.025°) and the estimation of open burning emissions from the FINN global inventory (Fire INventory from NCAR).
{"title":"Snow Albedo Reduction in Central Andes by Atmospheric Aerosols: Case Study on the Tunuyán Basin (Argentina)","authors":"S. Puliafito, T. Bolaño Ortiz, R. Pascual, A. López-Noreña, L. Berná","doi":"10.1109/LAGIRS48042.2020.9165617","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165617","url":null,"abstract":"Changes in snow albedo (SA) on several basins of the central Andes of Argentina are associated with the possible deposition of light-absorbing particles (LAP) in the austral spring. To demonstrate this possibility, we correlate SA with daily data of snow cover (SC), aerosol optical depth (AOD) and land surface temperature (LST) available from the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board NASA Terra satellite during 2000-2016, and other derived parameters such as days after albedo (DAS) and snow precipitation (SP) from the Tropical Rainfall Measuring Mission (TRMM). We used satellite pixels with 100% snow cover to obtain monthly average value of SA, LST, AOD, DAS and SP performing multiple regression analysis. Further, we analysed biomass burning emissions in northem Argentina using MODIS products MCD64 collection C6 as possible source for snow pollution. Aerosol deposition and trajectories were analysed using WRF-Chem atmospheric numerical prediction model, with inventories of regional anthropogenic emissions of own elaboration (lat. 0.025°x long. 0.025°) and the estimation of open burning emissions from the FINN global inventory (Fire INventory from NCAR).","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"17 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":"133911064","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/LAGIRS48042.2020.9165648
M. L. Rodrigues, T. Körting, G. R. de Queiroz, C. P. Sales, L. A. R. D. Silva
In the last decades, the Brazilian Cerrado biome has undergone major transformations due to the expansion of the agricultural frontier. The region called MATOPIBA acronym for states Maranhão, Tocantins, Piauí, and Bahia can be considered very attractive for agricultural expansion. The Cerrado predominates in the MATOPIBA region (91% of the area), also having small areas of the Amazon and Caatinga biomes to the northeast and east, respectively. In this work, we will present a study to identify center pivot irrigation systems in the MATOPIBA region using remote sensing images from Landsat-8 satellite. The methodology is based on the use of robust edge detection techniques such as Canny, Circular Hough Transform (CHT) and time series extraction through the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13Q1 which has two vegetation indices NDVI and EVI. These time series will be used to filter the detected circles, seeking to eliminate the circles that do not correspond to center pivots. Our approach detected 80% of the center pivots mapped by the Brazilian National Water Agency (ANA) used as a knowledge base. The states with better detection were Piauí and Bahia that showed the accuracy of 90% and 85% respectively, Maranhão obtained 57% and Tocantins 41%.
{"title":"Detecting Center Pivots In Matopiba Using Hough Transform And Web Time Series Service","authors":"M. L. Rodrigues, T. Körting, G. R. de Queiroz, C. P. Sales, L. A. R. D. Silva","doi":"10.1109/LAGIRS48042.2020.9165648","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165648","url":null,"abstract":"In the last decades, the Brazilian Cerrado biome has undergone major transformations due to the expansion of the agricultural frontier. The region called MATOPIBA acronym for states Maranhão, Tocantins, Piauí, and Bahia can be considered very attractive for agricultural expansion. The Cerrado predominates in the MATOPIBA region (91% of the area), also having small areas of the Amazon and Caatinga biomes to the northeast and east, respectively. In this work, we will present a study to identify center pivot irrigation systems in the MATOPIBA region using remote sensing images from Landsat-8 satellite. The methodology is based on the use of robust edge detection techniques such as Canny, Circular Hough Transform (CHT) and time series extraction through the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13Q1 which has two vegetation indices NDVI and EVI. These time series will be used to filter the detected circles, seeking to eliminate the circles that do not correspond to center pivots. Our approach detected 80% of the center pivots mapped by the Brazilian National Water Agency (ANA) used as a knowledge base. The states with better detection were Piauí and Bahia that showed the accuracy of 90% and 85% respectively, Maranhão obtained 57% and Tocantins 41%.","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":"133961320","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：