Resultado da Pesquisa
A expressão de busca foi <{dissemination, WEBSCI or dissemination, SCOPUS} and {firstgroup, *SRE-DIPGR-INPE-MCTI-GOV-BR} and year, 2023>.
28 referências encontradas buscando em 17 dentre 17 Arquivos.
Data e hora local de busca: 09/09/2023 23:02.
ADORNO, B. V.; KÖRTING, T. S.; AMARAL, S. Contribution of time-series data cubes to classify urban vegetation types by remote sensing. Urban Forestry and Urban Greening, v. 79, p. e127817, Jan. 2023. DOI: <10.1016/j.ufug.2022.127817>. Disponível em: <http://doi.org/10.1016/j.ufug.2022.127817>.

AL-THUWAYNEE, O. F.; MELILLO, M.; GARIANO, S. L.; PARK, H. J.; KIM, S.-W.; LOMBARDO, L.; HADER, P.; MOHAJANE, M.; QUEVEDO, R. P.; CATANI, F.; AYDDA, A. DEWS: a QGIS tool pack for the automatic selection of reference rain gauges for landslide-triggering rainfall thresholds. Environmental Modelling & Software, v. 162, p. e105657, Apr. 2023. DOI: <10.1016/j.envsoft.2023.105657>. Disponível em: <http://doi.org/10.1016/j.envsoft.2023.105657>.

ARAÚJO, J. A.; GALVÃO, L. S.; DALAGNOL, R. Sensitivity of hyperspectral vegetation indices to rainfall seasonality in the Brazilian savannahs: an analysis using PRISMA data. Remote Sensing Letters, v. 14, n. 3, p. 277-287, mar. 2023. DOI: <10.1080/2150704X.2023.2189031>. Disponível em: <http://doi.org/10.1080/2150704X.2023.2189031>.

BEZERRA, D. X.; LORENZZETTI, J. A.; PAES, R. L. Marine Environmental Impact on CFAR Ship Detection as Measured by Wave Age in SAR Images. Remote Sensing, v. 15, n. 3, p. e3441, July 2023. DOI: <10.3390/rs15133441>. Disponível em: <http://doi.org/10.3390/rs15133441>.

BHAGYA, S. B.; SUMI, A. S.; BALAJI, S.; DANUMAH, J. H.; COSTACHE, R.; RAJANEESH, A.; GOKUL, A.; CHANDRASENAN, C. P.; QUEVEDO, R. P.; JOHNY, A.; SAJINKUMAR, K. S.; SAHA, S.; AJIN, R. S.; MAMMEN, P. C.; ABDELRAHMAN, K.; FNAIS, M. S.; ABIOUI, M. Landslide susceptibility assessment of a part of the western Ghats (India) employing the AHP and F-AHP models and comparison with existing susceptibility maps. Land, v. 12, n. 2, p. e468, Feb. 2023. DOI: <10.3390/land12020468>. Disponível em: <http://doi.org/10.3390/land12020468>.

BRAGION, G. R.; DAL’ASTA, A. P.; AMARAL, S. Bringing to Light the Potential of Angular Nighttime Composites for Monitoring Human Activities in the Brazilian Legal Amazon. Remote Sensing, v. 15, n. 14, p. e3515, July 2023. DOI: <10.3390/rs15143515>. Disponível em: <http://doi.org/10.3390/rs15143515>.

CARVALHO, R. M.; ALMEIDA, C. M.; ESCOBAR SILVA, E. V.; ALVES, R. B. O.; LACERDA, C. S. A. Simulation and Prediction of Urban Land Use Change Considering Multiple Classes and Transitions by Means of Random Change Allocation Algorithms. Remote Sensing, v. 15, n. 1, p. e90, Jan. 2023. DOI: <10.3390/rs15010090>. Disponível em: <http://doi.org/10.3390/rs15010090>.

CESAR, G. M.; OLIVEIRA, N. R.; OLIVEIRA, A. L.; VALÉRIO, A. M.; CHUQUI, M. G.; POMPEU, M.; GAETA, S. A.; FROUIN, R.; KAMPEL, M. Bio-optical properties of South Brazil Bight coastal waters and implications for satellite chlorophyll-a concentration retrieval. International Journal of Remote Sensing, v. 44, n. 7, p. 2428-2457, 2023. DOI: <10.1080/01431161.2023.2201385>. Disponível em: <http://doi.org/10.1080/01431161.2023.2201385>.

DIAS, K. G. C.; VALERIANO, M. M. Delineation of terrain features in Demini?s Watershed - Setentrional Amazonia using regional geomorphometry. Revista Brasileira de Geomorfologia, v. 24, n. 2, p. e2265-21, 2023. DOI: <10.20502/rbg.v24i2.2265>. Disponível em: <http://doi.org/10.20502/rbg.v24i2.2265>.

DINIZ, J. M. F. S.; GAMA, F. F.; REIS, A. A.; OLIVEIRA, C. G.; MARQUES, E. R. G. Estimating stem volume of Eucalyptus sp. and Pinus sp. plantations in Brazil, using Sentinel-1B and ALOS-2/PALSAR-2 data. Journal of Applied Remote Sensing, v. 17, n. 1, p. e014513, Jan. 2023. DOI: <10.1117/1.JRS.17.014513>. Disponível em: <http://doi.org/10.1117/1.JRS.17.014513>.

ESCOBAR-SILVA, E. V.; ALMEIDA, C. M.; SILVA, G. B. L.; BURSTEINAS, I.; ROCHA FILHO, K. L.; OLIVEIRA, C. G.; FAGUNDES, M. R.; PAIVA, R. C. D. Assessing the Extent of Flood-Prone Areas in a South-American Megacity Using Different High Resolution DTMs. Water (Switzerland), v. 15, n. 6, p. e1127, Mar. 2023. DOI: <10.3390/w15061127>. Disponível em: <http://doi.org/10.3390/w15061127>.

FERREIRA, I. J. M.; CAMPANHARO, W. A.; BARBOSA, M. L. F.; SILVA, S. S.; SELAYA, G.; ARAGÃO, L. E. O. C.; ANDERSON, L. O. Assessment of fire hazard in Southwestern Amazon. Frontiers in Forests and Global Change, v. 6, p. e1107417, Mar. 2023. DOI: <10.3389/ffgc.2023.1107417>. Disponível em: <http://doi.org/10.3389/ffgc.2023.1107417>.

FERREIRA, I. J. M.; CAMPANHARO, W. A.; FONSECA, M. G.; ESCADA, M. I. S.; NASCIMENTO, M. T.; VILLELA, D. M.; BRANCALION, P.; MAGNAGO, L. F. S.; ANDERSON, L. O.; NAGY, L.; ARAGÃO, L. E. O. C. Potential aboveground biomass increase in Brazilian Atlantic Forest fragments with climate change. Global Change Biology, v. 29, n. 11, p. 3098-3113, 2023. DOI: <10.1111/gcb.16670>. Disponível em: <http://doi.org/10.1111/gcb.16670>.

GONÇALVES, G. M. S.; BARTELS, G. K.; LIMA, L. S.; BOEIRA, L. S.; COLLARES, G. L. Continuous discharge monitoring of the Mirim-Sao Goncalo system by the index velocity rating curve method. Journal of Hydroinformatics, v. 25, n. 1, p. 20-35, Jan. 2023. DOI: <10.2166/hydro.2023.045>. Disponível em: <http://doi.org/10.2166/hydro.2023.045>.

GONÇALVES, N. B.; SILVA, R. D.; WU, J.; PONTES LOPES, A.; STARK, S. C.; NELSON, B. W. Amazon forest spectral seasonality is consistent across sensor resolutions and driven by leaf demography. ISPRS Journal of Photogrammetry and Remote Sensing, v. 196, p. 93-104, Feb. 2023. DOI: <10.1016/j.isprsjprs.2022.12.001>. Disponível em: <http://doi.org/10.1016/j.isprsjprs.2022.12.001>.

GUERRERO, J. V. R.; ESCOBAR-SILVA, E. V.; CHAVES, M. E. D.; MATAVELI, G. A. V.; MOSCHINI, L. E. Detecting multitemporal land use changes and environmental fragility in a heterogeneous Brazilian landscape. Papers in Applied Geography, v. 9, n. 1, p. 89-103, 2023. DOI: <10.1080/23754931.2022.2117565>. Disponível em: <http://doi.org/10.1080/23754931.2022.2117565>.

LIMA, T. M. A.; GIARDINO, C.; BRESCIANI, M.; BARBOSA, C. C. F.; FABBRETTO, A.; PELLEGRINO, A.; BEGLIOMINI, F. N. Assessment of estimated phycocyanin and chlorophyll-a concentration from PRISMA and OLCI in Brazilian inland waters: a comparison between semi-analytical and machine learning algorithms. Remote Sensing, v. 15, n. 5, p. e1299, Mar. 2023. DOI: <10.3390/rs15051299>. Disponível em: <http://doi.org/10.3390/rs15051299>.

LOUZADA, R. O.; REIS, L. K.; DINIZ, J. M. F. S.; ROQUE, F. O.; GAMA, F. F.; BERGIER, I. Combining optical and microwave remote sensing for assessing gullies in human-disturbed vegetated landscapes. Catena, v. 228, p. e107127, July 2023. DOI: <10.1016/j.catena.2023.107127>. Disponível em: <http://doi.org/10.1016/j.catena.2023.107127>.

LOUZADA, R. O.; ROQUE, F. O.; DINIZ, J. M. F. S.; BERGIER, I. River channel avulsion in the Taquari River megafan of the Brazilian Pantanal: Remote sensing and modeling reveal recent and future changes. Applied Geography, v. 155, p. e102955, June 2023. DOI: <10.1016/j.apgeog.2023.102955>. Disponível em: <http://doi.org/10.1016/j.apgeog.2023.102955>.

MARQUES CARVALHO, R.; ALMEIDA, C. M.; ESCOBAR SILVA, E. V.; ALVES, R. B. O.; LACERDA, C. S. A. Simulation and prediction of urban land use change considering Multiple classes and transitions by means of random change Aalocation algorithms. Remote Sensing, v. 15, n. 1, p. e90, Jan. 2023. DOI: <10.3390/rs15010090>. Disponível em: <http://doi.org/10.3390/rs15010090>.

MATAVELI, G. A. V.; CHAVES, M. E. D.; GUERRERO, J.; ESCOBAR SILVA, E. V.; CONCEIÇÃO, K.; OLIVEIRA, G. Correction to: Mining Is a Growing Threat within Indigenous Lands of the Brazilian Amazon (Remote Sensing, (2022), 14, 16, (4092), 10.3390/rs14164092). Remote Sensing, v. 15, n. 11, p. e2809, June 2023. DOI: <10.3390/rs15112809>. Disponível em: <http://doi.org/10.3390/rs15112809>.

PELLEGRINO, A.; FABBRETTO, A.; BRESCIANI, M.; LIMA, T. M. A.; BRAGA, F.; PAHLEVAN, N.; BRANDO, V. E.; KRATZER, S.; GIANINETTO, M.; GIARDINO, C. Assessing the Accuracy of PRISMA Standard Reflectance Products in Globally Distributed Aquatic Sites. Remote Sensing, v. 15, n. 8, p. e2163, Apr. 2023. DOI: <10.3390/rs15082163>. Disponível em: <http://doi.org/10.3390/rs15082163>.

PRIETO, J. D.; LIMA, L.; MERMOZ, S.; BOUVET, A.; REICHE, J.; WATANABE, M.; SANT'ANNA, S. J. S.; SHIMABUKURO, Y. E. Inter-comparison of optical and SAR-based forest disturbance warning systems in the Amazon shows the potential of combined SAR-optical monitoring. International Journal of Remote Sensing, v. 44, n. 1, p. 59-77, Jan. 2023. DOI: <10.1080/01431161.2022.2157684>. Disponível em: <http://doi.org/10.1080/01431161.2022.2157684>.

QUEVEDO, R. P.; VELASTEGUI-MONTOYA, A.; MONTALVÁN-BURBANO, N.; MORANTE-CARBALLO, F.; KORUP, O.; DALELES RENNÓ, C. Land use and land cover as a conditioning factor in landslide susceptibility: a literature review. Landslides, v. 20, n. 6, p. 967-982, May 2023. DOI: <10.1007/s10346-022-02020-4>. Disponível em: <http://doi.org/10.1007/s10346-022-02020-4>.

RUIZ, P. R. S.; ALMEIDA, C. M.; SCHIMALSKI, M. B.; LIESENBERG, V.; MITISHITA, E. A. Multi-approach Integration of ALS and TLS Point Clouds for a 3-D Building Modeling at LoD3. International Journal of Architectural Computing, v. 21, n. 2, p. 1-27, 2023. DOI: <10.1177/14780771231176029>. Disponível em: <http://doi.org/10.1177/14780771231176029>.

SANTOS, B. D.; PINHO, C. M. D.; PÁEZ, A.; AMARAL, S. Identifying Urban and Socio-Environmental Patterns of Brazilian Amazonian Cities by Remote Sensing and Machine Learning. Remote Sensing, v. 15, n. 12, p. e3102, June 2023. DOI: <10.3390/rs15123102>. Disponível em: <http://doi.org/10.3390/rs15123102>.

SILVA, G. M.; ADAMI, M.; GALBRAITH, D.; NASCIMENTO, R. G. M.; WANG, Y.; SHIMABUKURO, Y. E.; EMMERT, F. Spatial Distribution of Secondary Forests by Age Group and Biomass Accumulation in the Brazilian Amazon. Forests, v. 14, n. 5, p. e924, May 2023. DOI: <10.3390/f14050924>. Disponível em: <http://doi.org/10.3390/f14050924>.

ZHANG, T. Y.; FU, Q.; LI, C.; LIU, F.; WANG, H.; HAN, L.; QUEVEDO, R. P.; CHEN, T.; LEI, N. Correction to: Modeling landslide susceptibility using data mining techniques of kernel logistic regression, fuzzy unordered rule induction algorithm, SysFor and random forest (Natural Hazards, (2022), 114, 3, (3327-3358), 10.1007/s11069-022-05520-7). Natural Hazards, v. 115, n. 2, p. 1873, Jan. 2023. DOI: <10.1007/s11069-022-05756-3>. Disponível em: <http://doi.org/10.1007/s11069-022-05756-3>.