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163 results for “LAGOS”
LTER-Italy site Lago Cedrino figure
<p>Geographical representation of the LTER-Italy site Lago Cedrino (LTER_EU_IT_049) - DEIMS-ID <a href="https://deims.org/9010f9db-3d6b-4253-9604-4e10f6714000">https://deims.org/9010f9db-3d6b-4253-9604-4e10f6714000</a></p>
LTER-Italy site Lago Bidighinzu figure
<p>Geographical representation of the LTER-Italy site Lago Bidighinzu (LTER_EU_IT_048) - DEIMS-ID <a href="https://deims.org/3707cf71-7e04-41e3-8afc-518b293f6c07">https://deims.org/3707cf71-7e04-41e3-8afc-518b293f6c07</a></p>
LTER-Italy site Lago di Garda figure
<p>Geographical representation of the LTER-Italy site Lago di Garda (LTER_EU_IT_044) - DEIMS-ID <a href="https://deims.org/c713db56-373c-46cc-8828-ce8cadc4f3bb">https://deims.org/c713db56-373c-46cc-8828-ce8cadc4f3bb</a></p>
LTER-Italy site Lago di Orta figure
<p>Geographical representation of the LTER-Italy site Lago di Orta (LTER_EU_IT_042) - DEIMS-ID <a href="https://deims.org/8bd7d2f8-421a-48bd-b212-04bc1e9f31d5">https://deims.org/8bd7d2f8-421a-48bd-b212-04bc1e9f31d5</a></p>
LAGOS-NE-LIMNO v1.087.3: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 1925-2013
This data package, LAGOS-NE-LIMNO v1.087.3, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. With this release, only this data package is being updated and users are expected to use prior releases of the other types of data. Please see the attached additional documentation for a full description of the changes that have been made for this new release.The data packages that make up LAGOS-NE include the following information on lakes and reservoirs in 17 lake-rich states in the Northeastern and upper Midwestern U.S. (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes greater than one hectare. (2) LAGOS-NE-GEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes and for all spatial resolutions, also called ‘zones’ (i.e., ecoregions, states, counties). These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. (3) LAGOS-NE-LIMNO v1.087.3: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This module includes variables that are most commonly measured by state agencies and researchers for studying eutrophication. For each water quality data value, we also include metadata related to the sampling program, methods, qualifiers with data flags from the original program (qual, not standardized for LAGOS-NE), censor codes from our quality control procedures (censorcode, standardized for LAGOS-NE), and the date of each sample. (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-N
LAGOS-US DEPTH v1.0: Data module of observed maximum and mean lake depths for a subset of lakes in the conterminous U.S.
The LAGOS-US LAKE DEPTH v1.0 module (hereafter, called DEPTH) contains in situ measurements of lake depth for a subset of all lakes (n = 17,675) in the conterminous U.S. > 1 ha (3.7% of 479,950) that are in the LAGOS-US LOCUS v1.0 data module (Smith et al. 2021). All 17,675 lakes in DEPTH have a maximum depth value and 6,137 lakes have a mean depth. DEPTH includes approximately 65 data sources obtained from community, government, and university monitoring programs, as well as academic reports and commercial websites. DEPTH includes lake identifiers, lake location, lake area, lake depth (both maximum and mean depth when available), source information, and data flags. The unique lake identifier (lagoslakeid) for all lakes is the same one used in LAGOS-US LOCUS v1.0.
LAGOS-NE Shallow Lakes: a dataset of lake variables and multi-scaled ecological context variables used to predict and compare trophic status and TP:CHLa relationships between shallow and non-shallow lakes in the Upper Midwest and Northeastern United States.
We conducted a macroscale study of 2,210 shallow lakes (mean depth ≤ 3m or a maximum depth ≤ 5m) in the Upper Midwestern and Northeastern U.S. We asked: What are the patterns and drivers of shallow lake total phosphorus (TP), chlorophyll a (CHLa), and TP–CHLa relationships at the macroscale, how do these differ from those for 4,360 non-shallow lakes, and do results differ by hydrologic connectivity class? To answer this question, we assembled the LAGOS-NE Shallow Lakes dataset described herein, a dataset derived from existing LAGOS-NE, LAGOS-DEPTH, and LAGOS-CLIMATE datasets. Response data variables were the median of available summer (e.g., 15 June to 15 September) values of total phosphorus (TP) and chlorophyll a (CHLa). Predictor variables were assembled at two spatial scales for incorporation into hierarchical models. At the local or lake-specific scale (including the individual lake, its inter-lake watershed [iws] or corresponding HU12 watershed), variables included those representing land use/cover, hydrology, climate, morphometry, and acid deposition. At the regional scale (e.g., HU4 watershed), variables included a smaller set of predictor variables for hydrology and land use/cover. The dataset also includes the unique identifier assigned by LAGOS-NE(lagoslakeid); the latitude and longitude of the study lakes; their maximum and mean depths along with a depth classification of Shallow or non-Shallow; connectivity class (i.e., whether a lake was classified as connected (with inlets and outlets) or unconnected (lacking inlets); and the zone id for the HU4 to which each lake belongs. Along with the database, we provide the R scripts for the hierarchical models predicting TP or CHLa (TPorCHL_predictive_model.R), and the TP—CHLa relationship (TP_CHL_CSI_Model.R) for depth and connectivity subsets of the study lakes.
LAGOS-NE-LOCUS v1.01: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 1925-2013
This data package, LAGOS-NE-LOCUS v1.01, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. Three of the data packages each contain different types of data for 51,101 lakes and reservoirs larger than 4 ha in 17 lake-rich U.S. states to support research on thousands of lakes. These three package are: (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes. (2) LAGOS-NEGEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes. These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. And, (3) LAGOS-NE-LIMNO v1.087.1: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. The other two data packages contain supporting data for the LAGOS-NE database: (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NE-GEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of LAGOS-NE-LIMNO. The LAGOS-NE-LOCUS v1.01 module includes information on the physical location and features of all lakes > 4 ha. The information provided for this population of lakes includes: lake unique identifiers, lake area, perimeter, latitude and longitude, and the zone IDs that the lake is located within (e.g., state, county, the hydrologic unit at each level (4, 8, and 12). Citation for
LAGOS-US LANDSAT: Data module of remotely-sensed water quality estimates for U.S. lakes over 4 ha from 1984 to 2020
This data package, LAGOS-US LANDSAT, is one of the extension data modules of the LAGOS-US platform that provides six water quality estimates (chlorophyll, Secchi depth, dissolved organic carbon, total suspended solids, turbidity, and true water color) from remote sensing for lakes ≥ 4 ha in the conterminous U.S. (48 states plus the District of Columbia) for the years 1984-2020. These estimates are generated through machine learning models on in-lake water quality matchups from LAGOS-US LIMNO with Landsat 5, 7, and 8 whole lake median reflectance values and pixel-wise band ratios that are subsequently used to make predictions across the U.S. The LANDSAT module contains remotely sensed reflectance values for 136,977 of the 137,465 lakes ≥ 4 ha from the LAGOS-US research platform. Within the module are a total of 45,867,023 sets of reflectance values, a matchup dataset with a window of up to 7 calendar days with in situ data, and associated water quality parameter predictions for each reflectance set. Additional quality control flags are provided for predictions indicating whether reflectance extractions included negative values, the percent of the maximum pixels ever retrieved for that lake that the predictions are based on, and whether there are shared calendar day predictions due to scene overlap.
LAGOS-US NETWORKS v1.0: Data module of surface water networks characterizing connections among lakes, streams, and rivers in the conterminous U.S
Knowing the degree of surface water connectivity among aquatic ecosystems can help scientists better understand and predict the movement of materials and biota across ecosystems. Methods to quantify surface water networks that include lake and stream connections at broad spatial scales are rare because it is difficult to balance accurate estimates of surface water connectivity and computational challenges. The LAGOS-US NETWORKS (NETS) module contains surface connectivity metrics for lake networks across the conterminous United States. We applied a graph theory approach to identify lake networks (i.e. a set of lakes connected by streams either upstream, downstream, or both) created from the medium resolution NHD lakes, streams, and rivers and subsequently derive surface water connectivity metrics for lakes and networks. Using this approach, we created a total of 898 networks that include 86,511 lakes. The NETS module includes a table with metrics for connections between lakes (both upstream and downstream), dams, network position, and whole networks. NETS also includes a flow table and bidirectional and unidirectional distance tables that provide the distances between every pair of connected lakes.
LAGOS-NE-GEO v1.05: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 1925-2013
This data package, LAGOS-NE-GEO v1.05, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. Three of the data packages each contain different types of data for 51,101 lakes and reservoirs larger than 4 ha in 17 lake-rich U.S. states to support research on thousands of lakes. These three package are: (1) LAGOS-NE-LOCUS: lake location and physical characteristics for all lakes. (2) LAGOS-NE-GEO: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes. These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. And, (3) LAGOS-NE-LIMNO: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. The other two data packages contain supporting data for the LAGOS-NE database: (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NEGEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of LAGOS-NE-LIMNO. The LAGOS-NE-GEO v1.05 module includes information on the ecological context of the census lakes, all lakes > 4 ha in the study extent, their watersheds, and their regions. The information provided in the data tables for this module is organized into three main themes: CHAG - climate, hydrology, atmospheric deposition of nitrogen and sulfur, and surficial geology; LULC - land use/cover, impervious co
LTER-Italy site Lago Maggiore figure
<p>Geographical representation of the LTER-Italy site Lago Maggiore - DEIMS-ID <a href="https://deims.org/f30007c4-8a6e-4f11-ab87-569db54638fe">https://deims.org/f30007c4-8a6e-4f11-ab87-569db54638fe</a></p>
Banco de dados das cheias na Região Hidrográfica do Lago Guaíba em Maio de 2024
<h2><strong>ATENÇÃO: LEIA ATÉ O FINAL</strong></h2> <p><strong>Este é o banco de dados das cheias na Região Hidrográfica do Lago Guaíba em Maio de 2024, ainda em curso no Rio Grande do Sul. </strong>A Região Hidrográfica do Lago Guaíba <strong>não</strong> inclui toda a extensão do Rio Grande do Sul. Existem outras áreas afetadas.</p> <p>Este é um esforço <strong>voluntário</strong> entre diversos pesquisadores da Universidade Federal do Rio Grande do Sul e colaborações externas. Confira outros esforços voluntários na plataforma: <a href="https://storymaps.arcgis.com/stories/a81d69f4bccf42989609e3fe64d8ef48">Rio Grande do Sul | 2024 (arcgis.com).</a></p> <p>Este é um esforço ainda em andamento.</p> <p><em>continue lendo</em></p> <h2><strong>Observações importantes</strong></h2> <ol> <li>Confira as <strong>versões</strong> do banco de dados na barra lateral para obter os dados mais recentes ou não corrompidos;</li> <li>Os dados primários obtidos estão no arquivo geopackage<strong> cheias_rhguaiba_2024_db_{versao}.gpkg</strong>;</li> <li>As <strong>camadas </strong>do geopackage estão catalogadas no arquivo <strong>CSV</strong> de glossário <strong>cheias_rhguaiba_2024_db_{versao}_glossario.csv</strong>;</li> <li><strong>ADA </strong>significa <strong>área diretamente afetada</strong>.</li> <li>As metodologias ainda estão sendo documentadas e detalhadas. Confira aqui as <strong>Notas Técnicas.</strong></li> </ol> <p><em>continue lendo</em></p> <h2><strong>Nomenclatura</strong></h2> <ul> <li>ada = Área Diretamente Afetada</li> <li>poa = Porto Alegre</li> <li>rmpa = Região Metropolitana de Porto Alegre</li> <li>rhguaiba = Região Hidrográfica do Lago Guaíba</li> <li>rs = Rio Grande do Sul</li> </ul> <p><em>continue lendo</em></p> <h2><strong>Como citar</strong></h2> <p>Citação simplificada para reportagens e visualizações (infográficos):</p> <p>Possantti et al. (2024).<strong> Banco de dados das cheias na Região Hidrográfica do Lago Guaíba em Maio de 2024</strong> [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11164049 </p> <p>Citação completa para referência bibliográfica:</p> <p>Possantti, I.; Aguirre, A.; Alberti, C.; Andrades Filho, C.; Azeredo, L.; Balbon, J.; Barbedo, R.; Barcelos, M.; Becker, F.; Bedin, M.; Bregalda, N.; Cacciatore, J.; Camana, M.; Camargo, P.; Cantor, G.; Cardozo, T.; Cargnin, B.; Carrard, G.; Castilhos, M.; Cazanova, R.; Chiarelli, F.; Collishonn, W.; Cornely, A.; Cremon, É.; Cunha, L.; Cunha, R.; Cárdenas, S.; Dorneles, J.; Dornelles, F.; Eckhardt, R.; Fan, F.; Froner, M.; Giaccom, B.; Giasson, S.; Goldenfum, J.; González-Ávila, I.; Gonçalves, C.; Gonçalves, G.; Guasselli, L.; Guimarães, E.; Guimarães, E.; Hellmann, A.; Herrmann, P.; Horstmann, G.; Iablonovski, G.; Iescheck, A.; Kipper, P.; Kobyama, M.; Krasner, M.; Krob, L.; Kuele, P.; Laipelt, L.; Lutz, V.; Maciel, J.; Magalhães, F.; Mallet, J.; Marques, B.; Marques, G.; Meirelles, F.; Mexias, L.; Michel, G.; Michel, R.; Mincarone, M.; Moura, E.; Müller, J.; Neves, É.; Nicolini, I.; Nonnemacher, L.; Novakoski, K.; Oliveira, G.; Oliveira, M.; Ott, P.; Paiva, R.; Peres, L.; Petry, L.; Quevedo, R.; Quintela, R.; Ramos, M.; Rauber, A.; Reis, M.; Ribeiro, M.; Righi, M.; Risso, A.; Rodrigues, R.; Roitman, A.; Rorato, G.; Royer, S.; Ruhoff, A.; Ruoso, E.; Sampaio, M.; Schabbach, L.; Schiaffino, M.; Schmitt, H.; Schumacher, R.; Schwarzer, G.; Serrano, N.; Sigallis, A.; Silva, M.; Silva, S.; Sluter, C.; Soares, L.; Soares, V.; Sousa, L.; Souza, A.; Tschiedel, A.; Ucha, L.; Umbelino, G.; Utzig, E.; Zambrano, F. (2024). <strong>Banco de dados das cheias na Região Hidrográfica do Lago Guaíba em Maio de 2024</strong> (versão 1.2) [Data set]. Zenodo. https://zenodo.org/doi/10.5281/zenodo.11164049</p> <p><em>continue lendo</em></p> <h2><strong>Principais camadas no geopackage</strong></h2> <ul> <li>Mapa da <strong>Área Diretamente Afetada (ADA)</strong> pelas cheias de Maio de 2024.</li> <li>Mapas da <strong>mancha de inundação</strong> (água e lama) observada em 6 e 8 de Maio de 2024 obtidas por métodos de sensoriamento remoto.</li> <li>Mapa das <strong>cicatrizes de deslizamentos</strong> (movimentos de massa) observada em 6 de Maio de 2024 obtidas por métodos de sensoriamento remoto.</li> <li>Mapa de <strong>abrigos</strong> para o enfrentamento da crise na Região Metropolitana de Porto Alegre e arredores.</li> <li>Mapas da <strong>infraestrutura de saneamento</strong> na Região Metropolitana de Porto Alegre.</li> <li>Mapa do <strong>patrimônio cultural</strong> afetado.</li> </ul> <p><em>continue lendo</em></p> <h2><strong>Dados de insumos</strong></h2> <p>Dados de insumos (inputs) para análises diversas estão disponíveis em geopackages para <strong>conveniência </strong>(economia de tempo e recursos computacionais em meio ao enfrentamento da crise). <strong>Não somos autores dessas fontes e recomendamos validações a partir da fonte original</strong>.</p> <p><strong>ANA - Modelo digital de elevação ANADEM (30m de resolução)</strong>. Arquivo: <strong>insumos_ada_anadem_30m_rhguaiba.zip.</strong> Pacote de camadas raster com derivados diversos de análises topográficas, incluindo mapa de HAND, na RH do Lago Guaíba. Fonte: <a href="https://metadados.snirh.gov.br/geonetwork/srv/por/catalog.search#/metadata/a5b8f184-d4e3-45e6-a62b-e76d1f255f55">Catálogo de Metadados da ANA (snirh.gov.br)</a>. Nota: as camadas estão projetadas coordenadas planas.</p> <p><strong>IBGE/Censo - Malhas de setores censitários (2010 e 2022) e municípios IBGE no RS. </strong>Arquivo: <strong>insumos_ibge_rs.gpkg</strong>. Fonte: <a href="https://www.ibge.gov.br/geociencias/organizacao-do-territorio/malhas-territoriais.html">Malhas territoriais | IBGE</a>. Comentário: malha dos censos de 2010 e 2022 com os <strong>microdados básicos</strong> agregados.</p> <p><strong>IBGE/CNEFE - Cadastro Nacional de Endereços para Fins Estatísticos no RS. </strong>Arquivo: <strong>insumos_ibge_cnefe_microdados_2022_rs.gpkg</strong>. Fonte: <a href="https://www.ibge.gov.br/estatisticas/sociais/populacao/38734-cadastro-nacional-de-enderecos-para-fins-estatisticos.html?=&t=resultados">Cadastro Nacional de Endereços para Fins Estatísticos | IBGE</a>. Comentário: pontos dos endereços com a codificação do tipo de estabelecimento.</p> <p><strong>INEP - Catálogo de Escolas do Rio Grande do Sul</strong>. Arquivo: <strong>insumos_inep_escolas_2023_rs.gpkg</strong>. Fonte: <a href="https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/inep-data/catalogo-de-escolas">Catálogo de Escolas — Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira | Inep (www.gov.br)</a></p> <p><strong>NASA - Pontos de deslizamentos de encosta em uma área da serra, na RH do Lago Guaíba</strong>. Arquivo: <strong>insumos_nasa_deslizamentos_2024_rhguaiba.gpkg</strong>. Fonte: <a href="https://maps.disasters.nasa.gov/arcgis/home/item.html?id=b68a02ab6c2544dfa4c9cd7457049423">Landslides Mapped with Planet Imagery for the May 2024 Brasil Floods - Overview (nasa.gov)</a></p> <p><strong>Open Buildings V3 Polygons - Edificações na região hidrográfica. </strong>Arquivo: <strong>insumos_googlebuildings_edificacoes_rhguaiba.gpkg.</strong> Citação: W. Sirko, S. Kashubin, M. Ritter, A. Annkah, Y.S.E. Bouchareb, Y. Dauphin, D. Keysers, M. Neumann, M. Cisse, J.A. Quinn. Continental-scale building detection from high resolution satellite imagery. arXiv:2107.12283, 2021. Fonte: <a href="https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_Research_open-buildings_v3_polygons">Open Buildings V3 Polygons</a>. Comentário: polígonos de edificações extraídos para a Região Hidrográfica do Lago Guaíba.</p> <p><strong>Open Street Maps (OSM) - Vias urbanas e estradas na região hidrográfica. </strong>Arquivo: insumos_openstreetmaps_vias_rhguaiba.gpkg. Fonte: <a href="https://download.geofabrik.de/south-america/brazil/">Index of /south-america/brazil (geofabrik.de)</a></p> <p><strong>SEMA/RS - Base cartográfica 1:25000 do RS. </strong>Arquivo: <strong>insumos_semars_basecarto25k_rs.gpkg</strong>. Fonte: SEMA/RS. Comentário: mapas diversos da base cartográfica do RS, hidrografia, pontes, estradas principais, bacias hidrograficas. </p> <p><strong>SNISB - Cadastro de Barragens na RH do Lago Guaíba. </strong>Arquivo:<strong> insumos_snisb_barragens_2022_rhguaiba.gpkg. Fonte: </strong>Relatório de Segurança de Barragens Edição 2022. Cadastro da Planilha de Dados. Disponível em:<strong> <a href="https://www.snisb.gov.br/portal-snisb/inicio">Início | SNISB - Sistema de Segurança de Barragem</a></strong></p> <p><strong>Sulgás - Redes de distribuição de gás da Sulgás na região hidrográfica.</strong> Arquivo: <strong>insumos_sulgas_tubulacoes_rhguaiba.gpkg</strong>. Fonte: SULGÁS.</p> <p><em>continue lendo</em></p> <h2><strong>Notas</strong></h2> <ul> <li>O geopackage nas versões v.0.1 e v.0.2 está corrompido. Usar versões mais recentes.</li> <li>Para ajustar qualquer problemas entrar em contato com possantti@gmail.com</li> </ul> <p><em>Você pode parar de ler aqui</em></p> <h2><strong>Notas técnicas</strong></h2> <p><strong>Cicatrizes de movimentos de massa por sensoriamento remoto (em andamento)</strong></p> <p><em>Autores: Clódis de Oliveira Andrades Filho, Lorenzo Fossa Sampaio Mexias, Andrea Lopes Iescheck, Bárbara Giaccom, Me. Beatriz da Rosa Cargnin, Claudia Robbi Sluter, Dafne Cavalheiro, Édipo Cremon, Gabriel Schwarzer, Guilherme Garcia de Oliveira, João Igor Dorneles, José Antônio Cacciatore, Henrique Schmitt, Kleverson Ribeiro Novakoski, Laurindo Guasselli, Leandro Petry, Luana Daniela da Silva Peres, Mateus da Silva Reis, Maurício Righi, Michelle Cardoso da Silva, Milton Ribeiro Junior, Pâmela Boelter Herrmann, Raul Gick Schumacher, Renata Pacheco Quevedo, Sergio Mauricio Molano Cárdenas, Victor Matheus Soares.</em></p> <p>Cicatrizes de movimentos de massa são marcas da movimentação de solo e/ou rochas visíveis no terreno, geralmente ao longo de encostas. Estas marcas são oriundas de deslizamentos, fluxos de detritos e lama, queda de blocos e rastejamento de solo. As cicatrizes foram delimitadas a partir de imagens de satélite de alta resolução espacial), por interpretação visual, na composição colorida RGB cor-verdadeira. As seguintes bases foram utilizadas: a) imagens dos satélites World View concedidas para uso emergencial pela National Geospatial-Intelligence Agency - NGA / Diretoria de Serviço Geográfico – DSG – Fonte das Imagens Maxar Technologies 2024 (resolução espacial: 0,3 m a 0,4 m); b) imagens concedidas pela Força Aérea do Chile e Força Aérea Brasileira para uso emergencial advindos dos sistemas EROS C e BlackSky (resolução espacial: 0,9 m); c) imagens do satélite sino-brasileiro CBERS 4A, sensor WPM, com processamento pansharpening (resolução espacial: 2 m) oriundas do Instituto Nacional de Pesquisas Espaciais (INPE), Brasil. <strong>ATENÇÃO</strong>: As cicatrizes de Movimentos de Massa mapeadas até o momento representam uma parcela da área total atingida por movimentos de massa na escarpa sul do Planalto Meridional-RS em Maio de 2024. O trabalho segue em andamento e passando por revisões frente as áreas possivelmente impactadas por movimentos de massa. Realização: Laboratório Latitude - Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia (CEPSRM) / Programa de Pós-graduação em Sensoriamento Remoto (PPGSR) / Departamento de Geodésia | Instituto de Geociências (IGeo) | Universidade Federal do Rio Grande do Sul (UFRGS).</p> <p>Camada:</p> <ul> <li>rhguaiba_deslizamentos_20240506_v{data mais recente}</li> </ul> <p><strong>Manchas de inundação Planet e Skysat (finalizado)</strong></p> <p><em>Autores: Guilherme Garcia de Oliveira , Rafael Rodrigo Eckhardt.</em></p> <p>A mancha de inundação para as bacias hidrográficas foi gerada a partir da integração de imagens ópticas de sensoriamento remoto (Planet, Skysat e WorldView-2, obtidas em 06/05/2024), modelo digital do terreno da Região Funcional 1 (MDT-RF1) (SPGG-RS & CGEO), modelo digital de elevação FABDEM e pontos coletados em campo, durante ou próximo do pico da inundação. As imagens foram utilizadas para extração de pontos limites da inundação, por interpretação visual, de modo a identificar não somente os limites com água em superfície, mas também os limites identificáveis com lama/detritos, indicando a provável passagem da onda de cheia pelo local. Esses pontos foram utilizados para extração de cota de inundação ao longo dos vales, usando o MDT-RF1 quando disponível. A cota de inundação foi interpolada pelo método de krigagem ordinária. Após isso, foi realizada a subtração da cota de inundação interpolada pela elevação obtida por meio do MDE, resultando em profundidades estimadas de água e limites da mancha de inundação. Os pontos de campo foram usados para validação do produto. Apenas quando necessário, em função da resolução dos modelos digitais usados, foi editada a mancha de inundação manualmente, procedimento adotado em algumas áreas dos vales fluviais quando constatada discrepância entre a mancha produzida e a mancha observada nas imagens de satélite. Observação: MDT-RF1 foi disponibilizado na resolução espacial de 2,5 m, gerado a partir de imagens aéreas, na resolução espacial de 0,35 m, obtidas em levantamento realizado em 2018. Projeto coordenado pela SPGG-RS em parceria com o 1º CGEO, Exército Brasileiro.</p> <p>Camada:</p> <ul> <li>rhguaiba_planet-skysat_inundacao_obs_20240506</li> </ul> <p><strong>Manchas de água e lama Sentinel 2</strong></p> <p><em>Autores: Laipelt, L.; Iablonovski, G.; Alberti, C.; Mincarone, M.; Possantti, I.; Rorato, G.;</em></p> <p>A partir de imagem de satélite do sensor Sentinel-2 do dia 06/05/2024 e 08/05/2024, foram classificadas as áreas inundadas (por índice normalizado de água - NDWI) e cobertas por lama (por índice normalizado de lama - BSI) da região da Bacia Hidrográfica do Lago Guaiba para obter a Área Diretamente Afetada. O resultado de água foi validado manualmente a partir da interpretação visual da imagem em cor natural.</p> <p>Camadas:</p> <ul> <li>rhguaiba_sentinel2_agua_obs_20240506</li> <li>rhguaiba_sentinel2_agua_obs_20240506_validada</li> <li>rhguaiba_sentinel2_lama_obs_20240506</li> <li>rhguaiba-sinosgravatai_sentinel2_agua_obs_20240508</li> <li>rhguaiba-sinosgravatai_sentinel2_agua_obs_20240508_validada</li> </ul> <p><strong>Simulação Hidrodinâmica HEC-RAS</strong></p> <p><em>Autores: Laipelt, L.</em></p> <p>Mancha de inundacao simulada (modelo HEC RAS) para ajusatar o nivel em 550cm no Cais Maua. Inclui também mapa de severidade física do escoamento simulado. Classes de severidade física: 1 – Baixa severidade 2 - Média 3 – Alta. Este é um mapa de perigo baseado na equação IP= Veloc x Profundidade de. Não temos tanta confiança na profundidade dos modelos hidrodinâmicos em escala regional visto que não possuimos as batimetria dos rios. Além da resolução espacial. Classificação foi feita baseada nos artigos:</p> <p>https://www.scielo.br/j/mercator/a/bYfg3jbM7cgqNzyZq5dyDKt/?format=html&lang=pt# https://www.sciencedirect.com/science/article/pii/S1462075802000328 </p> <p>Camadas:</p> <ul> <li>rhguaiba_hecras_inundacao_simulada_550cm</li> <li>rhguaiba_hecras_severidade_simulada_20240506</li> </ul> <h2><strong>Registro histórico</strong></h2> <p><strong>[08/01/2025 14h00] - Versão v.1.4. </strong>Atualização com a camada da área protegida pelo Sistema de Proteção Contra Cheias (Pôlderes).</p> <p><strong>[27/10/2024 16h00] - Versão v.1.3. </strong>Atualização da camada de deslizamentos - cicatrizes de movimentos de massa (total mapeadas: 15 mil).</p> <p><strong>[05/08/2024 18h00] - Versão v.1.2. </strong>Atualização da camada de cicatrizes de movimentos de massa (total mapeadas: 11 mil). Próximos melhoramentos: incluir pontos de amostragem da coleta de agua e sedimentos. Camadas de insumo diversas, incluindo Mapbiomas e CAR.</p> <p><strong>[29/06/2024 9h00] - Versão v.1.1. </strong>Atualização da camada de cicatrizes de movimentos de massa (total mapeadas: 5 mil). Próximos melhoramentos: incluir pontos de amostragem da coleta de agua e sedimentos. Camadas de insumo diversas, incluindo Mapbiomas e CAR.</p> <p><strong>[12/06/2024 9h00] - Versão v.1.0. </strong>Nova camada com mapeamento de cicatrizes de deslizamentos de encostas, obtida por métodos de sensoriamento remoto (dado ainda em produção). Nova camada de insumo com pontos mapeados pela NASA onde ocorreram movimentos de massa. Ajuste na convenção de datas no nomes para o formato YYYYMMDD.<strong> </strong>Próximos melhoramentos: incluir pontos de amostragem da coleta de agua e sedimentos. Camadas de insumo diversas, incluindo Mapbiomas e CAR.</p> <p><strong>[27/05/2024 20h00] - Versão v.0.9. </strong>Camada definitiva da inundação por cenas Planet, sendo que as camadas intermediárias das sub-bacias foram removidas. Nova versão ampliada da ADA (Área Diretamente Afetada), totalizando 6 mil km². Relação ampliada de abrigos da crise. Nova camada com o histórico da situação dos municípios (decretos de calamidade pública). Próximos melhoramentos: incluir pontos de validadao de campo da ADA nos vales, incluir pontos de amostragem da coleta de agua e sedimentos. Mapa de cicatrizes de movimentos de massa. Camadas de insumo diversas, incluindo Mapbiomas e CAR.</p> <p><strong>[24/05/2024 20h00] - Versão v.0.8. </strong>Novas camadas de manchas de inundação com cenas Planet para Rio dos Sinos e Rio Gravataí. Nova versão ampliada da ADA (Área Diretamente Afetada). Relação ampliada de abrigos da crise. Nova camada com o histórico da situação das EBAPs e ETAs na RMPA. Nova camada com mapa completo de diques na RMPA. Nova camada com o histórico da situação de barragens selecionadas na RH do Lago Guaíba. Nova camada de insumo: católogo de escolas do INEP. Nova camada de insumo: cadastro geral de barragens na RH do Lago Guaíba. Nova camada de insumo: pacote de mapas raster com análises topográficas do MDE ANADEM, incluindo HAND da RH do Lago Guaíba. Próximos melhoramentos: incluir pontos de validadao de campo da ADA nos vales, incluir pontos de amostragem da coleta de agua e sedimentos. Camadas de insumo diversas, incluindo Mapbiomas e CAR.</p> <p><strong>[22/05/2024 10h00] - Versão v.0.7. </strong>Relação ampliada de abrigos da crise, com tabela CSV em separado. Nova camada de insumo tubulações de gás da SulGás. Substituição da camada de insumo de endereços CNEFE pela camada divulgada pelo IBGE em 21/5, com microdados (incluindo CEP). Nova camada de insumo: topografia do modelo digital de elevação ANADEM na região hidrográfica (Modelo Copernicus corrigido para vegetação no Brasil). Próximos melhoramentos: incluir status de barragens e saneamento durante a crise, incluir pontos de validadao de campo da ADA nos vales, incluir pontos de amostragem da coleta de agua e sedimentos. Camadas de insumo diversas, incluindo Mapbiomas, CAR, HAND.</p> <p><strong>[20/05/2024 21h00] - Versão v.0.6. </strong>Relação ampliada de abrigos da crise. Atualização dos autores. Atualização da ADA (área diretamente afetada) a partir de manchas obtidas na bacia do Rio dos Sinos e Rio Gravataí (dia 8 de Maio). Nova camada de pontos de nível levantados em campo em Porto Alegre.<strong> </strong>Remoção da análise municipal.<strong> </strong>Proximos melhoramentos: incluir status de barragens e saneamento durante a crise, incluir pontos de validadao de campo da ADA nos vales, incluir pontos de amostragem da coleta de agua e sedimentos.</p> <p><strong>[13/05/2024 22h00] - Versão v.0.5. </strong>Relação ampliada de abrigos da crise. Atualização dos autores. Nova nomenclatura de nomes para português. Título foi modificado para "cheias" em vez de "inundação". Atualização da ADA (área diretamente afetada). Nova camada de severidade física do escoamento simulado por HEC-RAS (primeira cheia). Geopackages adicionais de insumos carregados em separado do banco de dados principal. Proximos melhoramentos: completar camadas de saneamento basico para Regiao Metropolitana, incluir insumos para analises sociais e economicas, incluir pontos de validadao de campo da ADA, incluir pontos de amostragem da coleta de agua e sedimentos.</p> <p><strong>[10/05/2024 22h00]</strong> - <strong>Versão v.0.4.</strong> Nova versão da analise de municipios com endereços CNEFE; Novas camadas de manchas de inundação nos vales Taquari, Caí e Pardo com cenas Planet e Skysat; Relação ampliada de abrigos da crise. Atualização dos autores. Proximos melhoramentos: ajustar camada de abrigos e camada Sentinel de lama para sistema de referência WGS 84.</p> <p> </p> <p> </p>
Lago Argentino digital core scans and stratigraphic logs
<p>This dataset includes full resolution (20 micron per pixel) digital core scans of all lake cores collected during the 2019 GCO project coring of Lago Argentino.</p> <p>A second folder includes a stratigraphic log and description of each core, created in PSICAT.</p> <p>This dataset is uploaded alongside the submission "Physical limnology and sediment dynamics of Lago Argentino, the world’s largest ice-contact lake" to JGR: Earth Surface.</p> <p>All analyses were conducted at the Continental Scientific Drilling Facility at the University of Minnesota.</p> <p>For any questions about this dataset, please contact vanwy048@umn.edu .</p>
Bathymetry and Sediment thickness distribution of Lago dei Seracchi alpine lake, Rutor basin, Aosta Valley, Italy
<p>Maps of water depth and sediment accumulation in an Italian proglacial lake, done by Ground Penetrating Radar (GPR) in July 2021. Supporting Time domain reflectometry surveys and geotechnical analyses on the sediments are also provided. For details, see the readme file in the dataset folder.</p>
LAGOS-NE – Lake nutrient chemistry and geospatial data to measure spatial structure of ecosystem properties in a 17-state region of the U.S.
This dataset includes data for the lake water quality and geospatial variables that describe climate, hydrology, land use land cover, and lake characteristics that were used to study spatial structure in lake properties at the sub-continental scales (Lapierre et al. Quantifying spatial structure to improve understanding of the relationships between climate, landscape, and lake ecosystem properties, to be submitted to Ecology). All observations came from LAGOS-NELIMNO v. 1.054.1 and LAGOS-NEGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS-NE contains a complete census of lakes great than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for the LAGOS-NELIMNO v. 1.054.1 dataset and were mostly generated by government agencies (state, federal, tribal) and universities. In this analysis, we compiled lake water quality data from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOS-NELIMNO v. 1.054.1 (2002-2011). We report the median total nitrogen, total phosphorus, secchi depth, and chlorophyll values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics including variables related to lake morphometry, climate, hydrology, atmospheric deposition, land use and land cover.
LAGOS-NE v.1.054.1 Lake water clarity time series (1987-2011), climate, and geophysical data for 601 lakes across a 17-state region of the United States
Time series of median summer water clarity (secchi) values from 601 unique lakes in the Midwest and Northeast United States. Water clarity observations were derived from the Lake Multi-Scaled Geospatial and Temporal Database LAGOS-NELIMNO version 1.054.1. These data were used to assess long-term changes in water clarity from 1987-2011, and the potential drivers of those trends (Lottig et al. in press). Summer open water period was used to approximate the stratified period in the study lakes, which was defined as June 15 to September 15. Over the 25-year time period, each lake had to have at least a single summer water clarity observation for 22 of 25 years. The median number of secchi measurements that were used to derive a single annual median value for each lake was approximately 9. Of the over 14,000 annual estimates of water clarity that we generated, only two percent of those annual values were generated from a single observation and median number of observations for each lake over the 25-year study period was 223. Each unique lake with water clarity data also has supporting geophysical data, including climate, land use, hydrology, and topography derived at multiple spatial scales. Lake-specific characteristics, such as depth and area, are also reported. The geospatial data came from LAGOS-NEGEO version 1.03 except for the annual climate data which was aggregated at the HUC8 spatial scale from monthly PRISM data. For more specific information on how LAGOS-NE was created, see Soranno et al. 2015. Citations: Lottig, N.R., P-N. Tan, T. Wager, K.S. Cheruvelil, P.A. Soranno, E.H. Stanley, C.E Scott, C.A. Stow, and S. Yuan. in press. Macroscale patterns of synchrony identify complex relationships among spatial and temporal ecosystem drivers. Ecosphere Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., G
LAGOS-US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous U.S.
This data package, LAGOS-US LOCUS v1.0, is one of the core data modules of the LAGOS-US platform that provides an extensible research-ready platform to study the 479,950 lakes and reservoirs larger than or equal to 1 ha in the conterminous US (48 states plus the District of Columbia). This data module contains information on the location, identifiers, and physical characteristics of lakes and their watersheds. The characteristics in this module include: variables that can be obtained from GIS data such as location and geometry; variables that can be derived using GIS processing such as lake watersheds and their geometry, lake glaciation history, and lake connectivity; and commonly used identifiers from GIS and other data products useful for linking with LAGOS-US. LOCUS is based on a snapshot of the high-resolution National Hydrography Dataset product available at the initiation of the project that provided the basis for locating, identifying, and characterizing the geometry of all lakes in LAGOS-US. The database design that supports the LAGOS-US research platform was created based on several important design features. Lakes are the fundamental unit of consideration, all lakes in the spatial extent must be represented (above a minimum size) and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other 2 core data modules that are part of the LAGOS-US platform: GEO (which includes geospatial ecological context at multiple spatial and temporal scales for lakes and their watersheds) and LIMNO (in situ lake surface-water physical, chemical, and biological measurements through time) that are each found in their own data packages.
LAGOS-NE-GIS v1.0: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 2013-1925
This data package, LAGOS-NE-GIS v1.0, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. Three of the data packages each contain different types of data for 51,101 lakes and reservoirs larger than 4 ha in 17 lake-rich U.S. states to support research on thousands of lakes. These three package are: (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes. (2) LAGOS-NE-GEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes. These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. And, (3) LAGOS-NE-LIMNO v1.087.1: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. The other two data packages contain supporting data for the LAGOS-NE database: (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NE-GEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of LAGOS-NE-LIMNO. The LAGOS-NE GIS v1.0 module includes GIS datasets for: lake polygons and their hydrologic classification; wetland polygons and their classification; streams as a line coverage and their classification by stream order; the zones used for this study (state and county; hydrologic units [at the 4, 8 and 12 scales]); and, lake watersheds (IWS). We also include boundaries of U.S. stat
Timeseries of lake margin, lake area, water level, mascon solutions of Lago Greve
<p>These are the dataset of timeseries of lake extent, lake area, water level, and mass around the lake, of Lago Greve presented in the study.</p> <p>1. Lake extent<br> Filename: LagoGreve.shp<br> Note: LagoGreve.cpg, LagoGreve.dbf, LagoGreve.prj, LagoGreve.shx are also needed.</p> <p>2. Timeseries of lake area<br> Filename: LakeArea_LagoGreve.csv</p> <p>3. Timeseries of water level<br> Filename: Lakelevel_LagoGreve.csv</p> <p>4. Timeseries of grace mascon solution<br> Filename: MeanMasconLagoGreve_CSRv06.csv</p> <p>5. Fitting model for mascon solution<br> Filename: MeanMasconFitting.csv</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.