Análise diagnóstica e prospectiva da cadeia produtiva de energia de biomassa de origem florestal.

bitstream/CNPF-2009-09/42554/1/Doc151.pdf1 CD-ROM

Preserved decision making ability in early multiple sclerosis

Background : The purpose of this study was to assess decision making in patients with multiple sclerosis (MS) at the earliest clinically detectable time point of the disease. Methods : Patients with definite MS (n = 109) or with clinically isolated syndrome (CIS, n = 56), a disease duration of 3 months to 5 years, and no or only minor neurological impairment (Expanded Disability Status Scale [EDSS] score 0-2.5) were compared to 50 healthy controls using the Iowa Gambling Task (IGT). Results : The performance of definite MS, CIS patients, and controls was comparable for the two main outcomes of the IGT (learning index: p = 0.7; total score: p = 0.6). The IGT learning index was influenced by the educational level and the co-occurrence of minor depression. CIS and MS patients developing a relapse during an observation period of 15 months dated from IGT testing demonstrated a lower learning index in the IGT than patients who had no exacerbation (p = 0.02). When controlling for age, gender and education, the difference between relapsing and non-relapsing patients was at the limit of significance (p = 0.06). Conclusion : Decision making in a task mimicking real life decisions is generally preserved in early MS patients as compared to controls. A possible consequence of MS relapsing activity in the impairment of decision making ability is also suspected in the early phase of M

Perinatal or neonatal mortality among women who intend at the onset of labour to give birth at home compared to women of low obstetrical risk who intend to give birth in hospital: A systematic review and meta-analyses

Background: More women are choosing to birth at home in well-resourced countries. Concerns persist that out-of-hospital birth contributes to higher perinatal and neonatal mortality. This systematic review and meta-analyses determines if risk of fetal or neonatal loss differs among low-risk women who begin labour intending to give birth at home compared to low-risk women intending to give birth in hospital. Methods: In April 2018 we searched five databases from 1990 onward and used R to obtain pooled estimates of effect. We stratified by study design, study settings and parity. The primary outcome is any perinatal or neonatal death after the onset of labour. The study protocol is peer-reviewed, published and registered (PROSPERO No.CRD42013004046). Findings: We identified 14 studies eligible for meta-analysis including ~ 500,000 intended home births. Among nulliparous women intending a home birth in settings where midwives attending home birth are well-integrated in health services, the odds ratio (OR) of perinatal or neonatal mortality compared to those intending hospital birth was 1.07 (95% Confidence Interval [CI], 0.70 to 1.65); and in less integrated settings 3.17 (95% CI, 0.73 to 13.76). Among multiparous women intending a home birth in well-integrated settings, the estimated OR compared to those intending a hospital birth was 1.08 (95% CI, 0.84 to 1.38); and in less integrated settings was 1.58 (95% CI, 0.50 to 5.03). Interpretation: The risk of perinatal or neonatal mortality was not different when birth was intended at home or in hospital. Funding: Partial funding: Association of Ontario Midwives open peer reviewed grant. Research in Context: Evidence before this study Although there is increasing acceptance for intended home birth as a choice for birthing women, controversy about its safety persists. The varying responses of obstetrical societies to intended home birth provide evidence of contrasting views. A Cochrane review of randomised controlled trials addressing this topic included one small trial and noted that in the absence of adequately sized randomised controlled trials on the topic of intended home compared to intended hospital birth, a peer reviewed protocol be published to guide a systematic review and meta-analysis including observational studies. Reviews to date have been limited by design or methodological issues and none has used a protocol published a priori.Added value of this study Individual studies are underpowered to detect small but potentially important differences in rare outcomes. This study uses a published peer-reviewed protocol and is the largest and most comprehensive meta-analysis comparing outcomes of intended home and hospital birth. We take study design, parity and jurisdictional support for home birth into account. Our study provides much needed information to policy makers, care providers and women and families when planning for birth.Implications of all the available evidence Women who are low risk and who intend to give birth at home do not appear to have a different risk of fetal or neonatal loss compared to a population of similarly low risk women intending to give birth in hospital

The Hubble Space Telescope UV Legacy Survey of Galactic Globular Clusters. XIII. ACS/WFC Parallel-Field Catalogues

As part of the Hubble Space Telescope UV Legacy Survey of Galactic Globular Clusters, 110 parallel fields were observed with the Wide Field Channel of the Advanced Camera for Surveys, in the outskirts of 48 globular clusters, plus the open cluster NGC 6791. Totalling about $0.3$ square degrees of observed sky, this is the largest homogeneous Hubble Space Telescope photometric survey of Galalctic globular clusters outskirts to date. In particular, two distinct pointings have been obtained for each target on average, all centred at about $6.5$ arcmin from the cluster centre, thus covering a mean area of about $23\,{\rm arcmin^{2}}$ for each globular cluster. For each field, at least one exposure in both F475W and F814W filters was collected. In this work, we publicly release the astrometric and photometric catalogues and the astrometrised atlases for each of these fields.Comment: 30 pages, 23 figures. Accepted by MNRA

Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern Brazil

Revista oficial de la Asociación Española de Teledetección[EN] Vast small inner marsh (SIM) areas have been lost in the past few decades through the conversion to agricultural, urban and industrial lands. The remaining marshes face several threats such as drainage for agriculture, construction of roads and port facilities, waste disposal, among others. This study integrates 17 remote sensing spectral indexes and decision tree (DT) method to map SIM areas using Sentinel 2A images from Summer and Winter seasons. Our results showed that remote sensing indexes, although not developed specifically for wetland delimitation, presented satisfactory results in order to classify these ecosystems. The indexes that showed to be more useful for marshes classification by DT techniques in the study area were NDTI, BI, NDPI and BI_2, with 25.9%, 17.7%, 11.1% and 0.8%, respectively. In general, the Proportion Correct (PC) found was 95.9% and 77.9% for the Summer and Winter images respectively. We hypothetize that this significant PC variation is related to the rice-planting period in the Summer and/or to the water level oscillation period in the Winter. For future studies, we recommend the use of active remote sensors (e.g., radar) and soil maps in addition to the remote sensing spectral indexes in order to obtain better results in the delimitation of small inner marsh areas.[ES] En las últimas décadas se han perdido grandes áreas de pequeñas marismas interiores (SIM) a través de la conversión a tierras agrícolas, urbanas e industriales. Las marismas restantes enfrentan varias amenazas, como el drenaje para la agricultura, la construcción de carreteras e instalaciones portuarias, la eliminación de residuos, entre otras. Este estudio integra 17 índices espectrales de teledetección y un método basado en árboles de decisión (DT) para cartografiar áreas de pequeñas marismas interiores utilizando imágenes del satélite Sentinel 2A de verano e invierno. Los resultados muestran que los índices de teledetección, aunque no han sido desarrollados específicamente para la delimitación de marismas, presentan resultados satisfactorios para clasificar estos ecosistemas. Los índices que demostraron ser más útiles para la clasificación de marismas mediante técnicas de DT en el área de estudio fueron el NDTI, BI, NDPI y BI_2, con 25.9%, 17.7%, 11.1% y 0.8%, respectivamente. En general, la proporción correcta encontrada fue de 95.9% y 77.9% para las imágenes de verano e invierno, respectivamente. Nuestra hipótesis es que esta variación significativa de la proporción correcta está relacionada con el período de siembra del arroz en verano y/o con el período de oscilación del nivel del agua en invierno. Para futuras investigaciones, recomendamos el uso de sensores remotos activos (por ejemplo, radar) y mapas de suelo además de los índices espectrales de teledetección para obtener mejores resultados en la delimitación de pequeñas áreas de marismas interiores.João Paulo Delapasse Simioni thanks the CAPES agency for providing a doctoral fellowship. The au-thors acknowledge the Center for Remote Sensing and Meteorology (CEPSRM) at the Federal University of Rio Grande do Sul (UFRGS) for the support provided for this research.Simioni, JPD.; Guasselli, LA.; Ruiz, LFC.; Nascimento, VF.; De Oliveira, G. (2018). Delimitación de pequeñas marismas interiores mediante índices espectrales y árboles de decisión en el sur de Brasil. Revista de Teledetección. (52):55-66. doi:10.4995/raet.2018.10366SWORD556652Artigas, F. J., Yang, J. 2006. Spectral discrimination of marsh vegetation types in the New Jersey Meadowlands, USA. Wetlands, 26(1), 271. https:// doi.org/10.1672/0277-5212(2006)26[271:sdomvt]2. 0.co;2Belloli, T. F. 2016. Environmental Impacts Due to Rice, Large Banhado Environmental Protection Area - RS. Federal University of Rio Grande do Sul. Retrieved from https://www.lume.ufrgs.br/bitstream/ handle/10183/158968/001023034.pdf?sequence=1Belluco, E., Camuffo, M., Ferrari, S., Modenese, L., Silvestri, S., Marani, A., Marani, M. 2006. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sensing of Environment, 105(1), 54-67. https://doi.org/10.1016/j.rse.2006.06.006Canadian Wetland Inventory Technical Group. 2016. Canada Wetland Inventory (Data Model). Stonewall. Retrieved from http://www.ducks.ca/assets/2017/01/ CWIDMv7_01_E.pdfClevers, J. G. P. W., Leeuwen, H. J. C. Van, Sensing, R., Verhoef, W. 1989. Estimanting apar by means of vegetation indeces: a sensitivity analysis. XXIX ISPRS Congress Technical Commission VII: Interpretation of Photographic and Remote Sensing Data, 691-698.Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46. https:// doi.org/10.1016/0034-4257(91)90048-BDeering, D. W. 1975. Measuring forage production of grazing units from Landsat MSS data. Proceedings of 10th International Symposium on Remote Sensing of Environment, 1975, 1169-1178.Delegido, J., Verrelst, J., Alonso, L., Moreno, J. 2011. Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7), 7063-7081. https://doi.org/10.3390/s110707063Di Vittorio, C. A., Georgakakos, A. P. 2018. Land cover classification and wetland inundation mapping using MODIS. Remote Sensing of Environment, 204, 1-17. https://doi.org/10.1016/j.rse.2017.11.001Dong, Z., Wang, Z., Liu, D., Song, K., Li, L., Jia, M., Ding, Z. 2014. 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R., Bertness, M. D. 2017. Biogeography of salt marsh plant zonation on the Pacific coast of South America. Journal of Biogeography, 12, 238-247. https://doi.org/10.1111/ jbi.13109Fluet-Chouinard, E., Lehner, B., Rebelo, L. M., Papa, F., Hamilton, S. K. 2015. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sensing of Environment, 158, 348-361. https://doi.org/10.1016/j.rse.2014.10.015Friedl, M.A. M. A., Brodley, C. E. C. E. 1997. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61(3), 399- 409. https://doi.org/10.1016/S0034-4257(97)00049-7Gao, B. C. 1996. NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257- 266. https://doi.org/10.1016/S0034-4257(96)00067-3Gedan, K. B., Crain, C. M., Bertness, M. D. 2009. 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Definição e Classificação das Áreas Úmidas (AUs) Brasileiras : Base Científica para uma Nova Política de Proteção e Manejo Sustentável Prefácio : Lista dos autores e suas instituições : Centro de Pesquisa Do Pantanal, BrazilJunk, W. J., Bayley, P. B., Sparks, R. E. 1989. The Flood Pulse Concept in River-Floodplain Systems. International Large River Symposium.Junk, W. J., Piedade, M. F. 2015. Áreas Úmidas (AUs) Brasileiras: Avanços e Conquistas Recentes. Boletim Ablimno, 41(2), 20-24.Junk, W. J., Piedade, M. T. F., Lourival, R., Wittmann, F., Kandus, P., Lacerda, L. D., Agostinho, A. A. 2014. Brazilian wetlands: Their definition, delineation, and classification for research, sustainable management, and protection. Aquatic Conservation: Marine and Freshwater Ecosystems, 24(1), 5-22. https://doi. org/10.1002/aqc.2386Kandus, P., Minotti, P., Malvárez, A. I. 2008. Distribution of wetlands in Argentina estimated from soil charts. Acta Scientiarum - Biological Sciences, 30(4), 403-409. https://doi.org/10.4025/actascibiolsci.v30i4.5870Kaplan, G., Avdan, U. 2017. Mapping and Monitoring Wetlands Using SENTINEL 2 Satellite Imagery. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV, 271-277. https:// doi.org/10.5194/isprs-annals-IV-4-W4-271-2017Kaplan, G., Avdan, U. 2017. Wetland Mapping Using Sentinel 1 SAR Data. In Suha Ozden, R. Cengiz Akbulak, Cuneyt Erenoglu, Oznur Karaca, Faize Saris, & Mustafa Avcioglu (Eds.), International Symposium on GIS Applications in Geography & Geosciences.Kaufman, Y., Tanre, D. 1992. 1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30(2). https://doi.org/10.1109/36.134076Kulawardhana, R. W., Thenkabail, P. S., Vithanage, J., Biradar, C., Islam, M. A. a, Gunasinghe, S., Alankara, R. 2007. Evaluation of the wetland mapping methods using Landsat ETM+ and SRTM data. Journal of Spatial Hydrology, 7(2), 62-96. https://doi. org/10.1017/CBO9780511806049Lacaux, J. P., Tourre, Y. M., Vignolles, C., Ndione, J. A., Lafaye, M. 2007. Classification of ponds from highspatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sensing of Environment, 106(1), 66-74. https://doi.org/10.1016/j. rse.2006.07.012Leite, M. G., Guasselli, L. A. 2013. Spatio-temporal dynamics of aquatic macrophytes in Banhado Grande, Gravataí River basin,. Para Onde!?, 7(1), 17-24.Liu, L., Liu, Y. H., Liu, C. X., Wang, Z., Dong, J., Zhu, G. F., Huang, X. 2013. Potential effect and accumulation of veterinary antibiotics in Phragmites australis under hydroponic conditions. Ecological Engineering, 53, 138-143. https://doi.org/10.1016/j. ecoleng.2012.12.033Mahdavi, S., Salehi, B., Amani, M., Granger, J. E., Brisco, B., Huang, W., Hanson, A. 2017. ObjectBased Classification of Wetlands in Newfoundland and Labrador Using Multi-Temporal PolSAR Data. Canadian Journal of Remote Sensing, 43(5), 432-450. https://doi.org/10.1080/07038992.2017.1342206Maltchik, L., Rolon, A. S., Guadagnin, D. L., Stenert, C. 2004. Wetlands of Rio Grande do Sul, Brazil: a classification with emphasis on plant communities. Acta Limnol. Bras, 16(2), 137-151.Mao, R., Ye, S.-Y., Zhang, X.-H. 2018. SoilAggregate-Associated Organic Carbon Along Vegetation Zones in Tidal Salt Marshes in the Liaohe Delta. CLEAN - Soil, Air, Water, 1-7. https://doi.org/10.1002/clen.201800049McFeeters, S. K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432. https://doi.org/10.1080/01431169608948714Mcowen, C. J., Weatherdon, L. V, Bochove, J.-W. Van, Sullivan, E., Blyth, S., Zockler, C., Fletcher, S. 2017. A global map of saltmarshes. Biodiversity Data Journal, 5(5), e11764. https://doi.org/10.3897/BDJ.5.e11764Miranda, C. de S., Paranho Filho, A. C., Pott, A. 2018. Changes in vegetation cover of the Pantanal wetland detected by vegetation index: a strategy for conservation. Biota Neotropica, 18(1), 1-6. https://doi.org/10.1590/1676-0611-bn-2016-0297Mondal, I., Bandyopadhyay, J. 2014. Coastal Wetland Modeling Using Geoinformatics Technology of Namkhana Island, South 24 Parganas, WB, India. Open Access Library Journal, 975, 1-17. https://doi.org/10.4236/oalib.1100975Nielsen, S. 1994. Geomorfologia da bacia do rio GravataíRS. In Bacia do rio Gravataí-RS: informações básicas para a gestão territorial (pp. 1-18). Porto Alegre: Proteger.Nunes da Cunha, C., Piedade, M. T. F., Junk, W. J. 2015. Classificação e Delineamento das Áreas Úmidas Brasileiras e de seus Macrohabitats. EdUFMT (Vol. 1). Cuiaba. https://doi.org/10.1017/CBO9781107415324.004Pearson, R. L., Miller, L. D. 1972. Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of the Shortgrass Prairie. Remote Sensing of Environment, 8, 1355-1365.Pontius, R. G., Millones, M. 2011. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407-4429. https://doi.org/10.1080/01431161.2011.552923Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119-126. https://doi.org/10.1016/0034-4257(94)90134-1Ramos, R. A., Pasqualetto, A. I., Balbueno, R. A., Quadros, E. L. L. de, Neves, D. D. das. 2014. Mapeamento e diagnóstico de áreas úmidas no Rio Grande do Sul, com o uso de ferramentas de geoprocessamento. In Anais do Simposio de Áreas Protegidas (pp. 17-21). Viçosa.Ramsar. 2002. A Framework for Wetland Inventory. 8th Meeting of the Conference of the Contracting Parties to the Convention on Wetlands. Valencia. Retrieved from http://archive.ramsar.org/pdf/inventoryframework-2002.pdfRichardson, A. J., Wiegand, C. L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12), 1541-1552.Rossato, M. S. 2011. Os climas do Rio Grande do Sul: variabilidade, tendências e tipologia. Universidade Federal do Rio Grande do Sul.Rouse, J. W., Hass, R. H., Schell, J. A., Deering, D. W. 1973. Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satellite (ERTS) Symposium, 1, 309-317. https://doi.org/citeulike-article-id:12009708Ruiz, L. F. C., Caten, A. ten, Dalmolin, R. S. D. 2014. Árvore de decisão e a densidade mínima de amostras no mapeamento da cobertura da terra. Ciência Rural, 44(6), 1001-1007. https://doi.org/10.1590/S0103-84782014000600008Sakané, N., Alvarez, M., Becker, M., Böhme, B., Handa, C., Kamiri, H. W., Langensiepen, M., Menz, G., Misana, S., Mogha, N. G., Möseler, B. M., Mwita, E. J., Oyieke, H. A., Van Wijk, M. T. 2011. Classification, characterisation, and use of small wetlands in East Africa. Wetlands, 31, 1103. https://doi.org/10.1007/s13157-011-0221-4Sharma, A., Panigrahy, S., Singh, T. S., Patel, J. G., Tanwar, H. 2014. Wetland Information System Using Remote Sensing and GIS in Himachal Pradesh , India. Asian Journal of Geoinformatics, 14(4), 13-22.Sharpe, P. J., Kneipp, G., Forget, A. 2016. Comparison of Alternative Approaches for Wetlands Mapping: A Case Study from three U.S. National Parks. Wetlands, 36(3), 547-556. https://doi.org/10.1007/s13157-016-0764-5Silva, R. C. da. 2016. Estudo da dinâmica da fragilidade ambiental na Bacia Hidrográfica do Rio Gravataí, RS. Universidade Federal da Bahia.Simioni, J. P. D., Guasselli, L. A., Etchelar, C. B. 2017. Connectivity among Wetlands of EPA of Banhado Grande, RS Conetividade entre Áreas Úmidas, APA do Banhado Grande, RS. Brazilian Journal of Water Resources, 22(15). https://doi.org/10.1590/2318-0331.011716096Stefano, L. de. 2003. WWF ' s Water and Wetland Index Summary of Water Framework Directive results. 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Análise de viabilidade econômica de um sistema de produção modal de eucalipto para lenha na região de Itapeva, SP.

bitstream/item/141820/1/Comunicado-Tecnico-365-2015.pd

Impacto do rendimento de corte e da densidade de plantio na rentabilidade da silvicultura em pequenas propriedades em Santa Cruz do Sul-RS.

A adoção de uma densidade de plantio mais racional por parte de muitos pequenos produtores ainda representa um desafio aos extensionistas silviculturais na região de Santa Cruz do Sul - RS. Vários estudos foram realizados buscando demonstrar a melhor alternativa de diversos espaçamentos na região, mas questões relacionadas à perda de rendimento nas operações de colheita ainda são raros, assim como o potencial impacto de uma capacitação em colheita florestal para estes pequenos agricultores. Assim, o objetivo deste trabalho foi avaliar o impacto do aumento da densidade de plantio e do rendimento da operação de corte no retorno econômico de um sistema de produção florestal recomendado para pequenos produtores da região de Santa Cruz do Sul/RS. Foram utilizadas técnicas de entrevistas e painel com especialistas para o delineamento do modal recomendado, e utilizados os indicadores de viabilidade econômica Valor Anual Equivalente (VAE), Taxa Interna de Retorno (TIR), Relação Benefício/Custo (B/C) e Custo Médio Ponderado de Produção (CMPP). A produção de eucalipto na pequena propriedade mostrou-se viável economicamente, a densidade de plantio recomendada de 1.667 plantas por hectare apresentou os melhores resultados econômicos. O rendimento de corte teve impacto significativo no aumento do retorno econômico da atividade e a consideração da perda de rendimento nas operações de colheita devido ao aumento do espaçamento foi fundamental para evitar interpretações equivocadas dos resultados