Auswahl der wissenschaftlichen Literatur zum Thema „Rural land use (N.S.W.)“

This study aim to attempt mapping out the Land Use or Land Cover (LULC) status of Regional Project Coordination Committee (RPCC) between 2009-2019 with a view of detecting the land consumption rate and the changes that has taken place using RS and GIS techniques; serving as a precursor to the further study on urban induced variations or change in weather pattern of the cityn Rangpur City Corporation(RCC) is the main administrative functional area for both of Rangpur City and Rangpur division and experiencing a rapid changes in the field of urban sprawl, cultural and physical landscape,city growth. These agents of Land use or Land cover (LULC) varieties are responsible for multi-dimensional problems such as traffic congestion, waterlogging, and solid waste disposal, loss of agricultural land. In this regard, this study fulfills LULC changes by using Geographical Information Systems (GIS) and Remote Sensing (RS) as well as field survey was conducted for the measurement of change detection. The sources of data were Landsat 7 ETM and landsat 8 OLI/TIRS of both C1 level 1. Then after correcting the data, geometrically and radiometrically change detection and combined classification (supervised & unsupervised) were used. The study finds LULC changes built-up area, water source, agricultural land, bare soil in a change of percentage is 17.23, 2.58, -9.94, -10.19 respectively between 2009 and 2019. Among these changes, bare soil is changed to a great extent, which indicates the expansion of urban areas is utilizing the land to a proper extent. Keywords: Urban expansion; land use; land cover; remote sensing; geographic information system (GIS); Rangpur City Corporation(RCC). References Al Rifat, S. A., & Liu, W. (2019). Quantifying spatiotemporal patterns and major explanatory factors of urban expansion in miami metropolitan area during 1992-2016. Remote Sensing, 11(21) doi:10.3390/rs11212493 Arimoro AO, Fagbeja MA, Eedy W. (2002). The Need and Use of Geographic Information Systems for Environmental Impact Assessment in Africa: With Example from Ten Years Experience in Nigeria. AJEAM/RAGEE, 4(2), 16-27. Belal, A.A. and Moghanm, F.S. (2011).Detecting Urban Growth Using Remote Sensing and GIS Techniques in Al Gharbiya Governorate, Egypt.The Egyptian Journal of Remote Sensing and Space Science, 14, 73-79. http://dx.doi.org/10.1016/j.ejrs.2011.09.001 Dewan, A.M. and Yamaguchi, Y. (2009). Using Remote Sensing and GIS to Detect and Monitor and Use and Land Cover Change in Dhaka Metropolitan of Bangladesh during 1960-2005. Environmental Monitor Assessment, 150, 237- 249. Retrieved from http://dx.doi.org/10.1007/s10661-008-0226-5 Djimadoumngar, K.-N., & Adegoke, J. (2018). Satellite-Based Assessment of Land Use and Land Cover (LULC) Changes around Lake Fitri, Republic of Chad. Journal of Sustainable Development, 11(5), 71. doi:10.5539/jsd.v11n5p71 Edwards, B., Frasch, T., & Jeyacheya, J. (2019). Evaluating the effectiveness of land-use zoning for the protection of built heritage in the bagan archaeological zone, Myanmar—A satellite remote-sensing approach. Land use Policy, 88 doi:10.1016/j.landusepol.2019.104174 Fallati, L., Savini, A., Sterlacchini, S., & Galli, P. (2017). Land use and land cover (LULC) of the Republic of the Maldives: first national map and LULC change analysis using remote-sensing data. Environmental Monitoring and Assessment, 189(8). doi:10.1007/s10661-017-6120-2 Fučík, P., Novák, P., & Žížala, D. (2014). A combined statistical approach for evaluation of the effects of land use, agricultural and urban activities on stream water chemistry in small tile-drained catchments of south bohemia, czech republic. Environmental Earth Sciences, 72(6), 2195-2216. doi:10.1007/s12665-014-3131-y Elbeih, S. F., & El-Zeiny, A. M. (2018). Qualitative assessment of groundwater quality based on land use spectral retrieved indices: Case study sohag governorate, egypt. Remote Sensing Applications: Society and Environment, 10, 82-92. doi:10.1016/j.rsase.2018.03.001 Fasal, S. (2000). Urban expansion and loss of agricultural land – A GIS based study of Saharanpur City, India. Environment and Urbanization, 12(2), 133 – 149 He, S., Wang, X., Dong, J., Wei, B., Duan, H., Jiao, J., & Xie, Y. (2019). Three-dimensional urban expansion analysis of valley-type cities: A case study of chengguan district, lanzhou, china. Sustainability (Switzerland), 11(20) doi:10.3390/su11205663 Heimlich, R.E and W.D. Anderson. (2001). Development at the Urban Fringe and Beyond: Impacts on Agriculture and Rural Land. 803, Economic Research Service, U.S. Department of Agriculture, Washington D.C., pg 80 Im, N., Kawamura, K., Suwandana, E., & Sakuno, Y. (2014). Monitoring land use and land cover effects on water quality in cheung ek lake using ASTER images. American Journal of Environmental Sciences, 11(1), 1-12. doi:10.3844/ajessp.2015.1.12 Kalnay, E., & Cai, M. (2003). Impact of urbanization and land-use change on climate. Nature, 423(6939), 528-531. doi:10.1038/nature01675 Matlhodi, B., Kenabatho, P. K., Parida, B. P., & Maphanyane, J. G. (2019). Evaluating land use and land cover change in the gaborone dam catchment, botswana, from 1984-2015 using GIS and remote sensing. Sustainability (Switzerland), 11(19) doi:10.3390/su11195174 Uddin, M. M. M. (2015). Causal relationship between agriculture, industry and services sector for GDP growth in Bangladesh: An econometric investigation. Journal of Poverty, Investment and Development, 8. Mondal, I., Srivastava, V. K., Roy, P. S., & Talukdar, G. (2014). Using logit model to identify the drivers of landuse landcover change in the lower gangetic basin, india. Paper presented at the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, , XL-8(1) 853-859. doi:10.5194/isprsarchives-XL-8-853-2014 Navale, V. B., & Mhaske, S. Y. (2019). Land use/land cover changes in sangamner city by using remote sensing and GIS. International Journal of Recent Technology and Engineering, 8(2), 4614-4621. doi:10.35940/ijrte.B3386.078219 Nicolson, L.D. (1987). The Greening of the cities; Routledge and Kegan Paul, London Nong, D., Fox, J., Miura, T., & Saksena, S. (2015). Built-up Area Change Analysis in Hanoi Using Support Vector Machine Classification of Landsat Multi-Temporal Image Stacks and Population Data. Land, 4(4), 1213–1231. doi:10.3390/land4041213 Park, H., Fan, P., John, R., Ouyang, Z., & Chen, J. (2019). Spatiotemporal changes of informal settlements: Ger districts in ulaanbaatar, mongolia. Landscape and Urban Planning, 191 doi:10.1016/j.landurbplan.2019.103630 Rajeshwari D. (2006). Management of the Urban Environment Using Remote Sensing and Geographic Information Systems.J. Hum. Ecol., 20(4), 269-277. Retrieved from http://www.krepublishers.com/02_journals/JHE/ Rasul, A., Balzter, H., Ibrahim, G., Hameed, H., Wheeler, J., Adamu, B., … Najmaddin, P. (2018). Applying Built-Up and Bare-Soil Indices from Landsat 8 to Cities in Dry Climates. Land, 7(3), 81. doi:10.3390/land7030081 Risma, Zubair, H., & Paharuddin. (2019). Prediction of land use and land cover (LULC) changes using CA-Markov model in Mamuju Subdistrict. Journal of Physics: Conference Series, 1341, 082033. doi:10.1088/1742-6596/1341/8/082033 Schilling, K. E., Jha, M. K., Zhang, Y.-K., Gassman, P. W., & Wolter, C. F. (2008). Impact of land use and land cover change on the water balance of a large agricultural watershed: Historical effects and future directions. Water Resources Research, 44(7). doi:10.1029/2007wr006644 Copyright (c) 2019 Geosfera Indonesia Journal and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

GEOPROCESSAMENTO E SENSORIAMENTO REMOTO APLICADO NA DETERMINAÇÃO DA APTIDÃO AGRÍCOLA DE UMA MICROBACIA Cláudia Webber Corseuil1; Sérgio Campos1; Fernanda Leite Ribeiro2; Teresa Cristina Tarlé Pissarra3; Flavia Mazzer Rodrigues3 1Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista, Botucatu, SP, seca@fca.unesp.br 2Departamento de Geociências, Universidade Estadual de Londrina, Londrina, PR3Departamento de Engenharia Rural, Faculdade de Ciências Agrárias e Veterinária, Universidade Estadual Paulista, Jaboticabal, SP 1 RESUMO Atividades como agricultura e pecuária causam impactos significativos no meio ambiente, principalmente quando estas são praticadas de forma intensiva, desconsiderando a fragilidade e o potencial de uso dos recursos naturais. A análise de vários critérios ambientais como, aptidão agrícola do solo, área com necessidade de proteção, zoneamento ambiental, entre outros, permite realizar uma caracterização do meio físico, biótico e sócio-econôminco, voltada para a utilização racional dos recursos naturais. Desta forma, é essencial que se faça o planejamento das atividades a serem desenvolvidas numa área, considerando a aptidão dos recursos nela disponíveis. Para uma utilização racional desses recursos, é necessário considerar o seu potencial de uso. No caso específico dos solos, a interpretação dos levantamentos de solos é de grande importância, pois as características de cada unidade é que determina o seu potencial de uso. Assim, este estudo objetivou analisar as classes de aptidão agrícola das terras de uma bacia hidrográfica por meio de sistema de informação geográfica (SIG). A microbacia do Arroio Ajuricaba localiza-se no Município de Marechal Cândido Rondon-PR, entre as coordenadas UTM 787309m E e 793892m E; 7275026m N e 7281310m N, do Fuso 21, apresentando uma área de 1681ha. A base cartográfica digital utilizada foi o mapa de solos, em escala de semidetalhe. Os resultados permitiram concluir que 42,41% da bacia apresentam uma boa aptidão para lavouras no nível de manejo de alta tecnologia (nível de manejo C), regular para o B e restrita para o A [classe 1(a)bC] e que 12% da área compreendem terras com aptidão regular para lavoura nos três níveis de manejo (classe 1abc). As terras da microbacia (14,24%) apresentam uma aptidão regular para o uso com lavouras no nível de manejo C, restrita para o B e inapta para o A [classe de aptidão 2(b)c]; 15,85% apresentam aptidão boa para pastagem plantada (classe 4P) e 12,21% são consideradas sem aptidão para uso agrícola (classe 6). Podemos dizer que 71,94% das terras possuem aptidão para lavouras, embora apresentem diferentes graus de limitações, que requerem tratamentos distintos para a sua conservação. UNITERMOS: unidades de solo, aptidão agrícola, geoprocessamento. CORSEUIL, C. W.; CAMPOS, S.; RIBEIRO, F. L.; PISSARRA, T. C. T.; RODRIGUES, F. M.. GEOPROCESSING AND REMOTE SENSING APPLIED TO DETERMINATION OF A WATERSHED AGRICULTURAL APTITUDE 2 ABSTRACT Soil use for the development of activities as agriculture and livestock has been causing great alterations in the environment, mainly when these are practiced intensively, disrespecting the fragility and aptitude of the natural resources. Therefore, it is essential that the planning of the agricultural activities is done, taking into consideration the several environmental criteria involved in the decision-making process. Thus, this study aimed to analyze the agricultural aptitude classes of lands from a watershed through geographical information system (GIS). The Arroio Ajuricaba watershed is located in theMunicipalityofMarechal Cândido Rondon- PR among the coordinates UTM 787309m E and 793892m E; 7275026m N and 7281310m N, in the Spindle 21, presenting an area of 1681ha. Soil maps, in semi detail scale, was the digital cartographic base used. The results allowed to conclude that 42.41% of the basin presented a good aptitude for farming in handling level of high technology (handling level C), regular aptitude for B, and restricted aptitude for A [class 1(a)bC] and that 12% of the area had regular aptitude for farming in the three handling levels (class 1abc). The watershed lands (14.24%) presented regular aptitude for farming in handling level C, restricted aptitude for B, and inapt for A [class of aptitude 2(b)c]; 15.85% presented good aptitude for planted pasture (class 4P) and 12.21% were considered without aptitude for agricultural use (class 6). We can say that 71.94% of the lands has aptitude for farming, although they present different degrees of limitations that request different treatments for its conservation. KEY WORDS: soil units, agricultural aptitude, geoprocessing

Climate change induced sea-level rise (SLR) is on its increase globally. Regionally the lowlands of China, Vietnam, Bangladesh, and islands of the Malaysian, Indonesian and Philippine archipelagos are among the world’s most threatened regions. Sea-level rise has major impacts on the ecosystems and society. It threatens coastal populations, economic activities, and fragile ecosystems as mangroves, coastal salt-marches and wetlands. This paper provides a summary of the current state of knowledge of sea level-rise and its effects on both human and natural ecosystems. The focus is on coastal urban areas and low lying deltas in South-East Asia and Vietnam, as one of the most threatened areas in the world. About 3 mm per year reflects the growing consensus on the average SLR worldwide. The trend speeds up during recent decades. The figures are subject to local, temporal and methodological variation. In Vietnam the average values of 3.3 mm per year during the 1993-2014 period are above the worldwide average. Although a basic conceptual understanding exists that the increasing global frequency of the strongest tropical cyclones is related with the increasing temperature and SLR, this relationship is insufficiently understood. Moreover the precise, complex environmental, economic, social, and health impacts are currently unclear. SLR, storms and changing precipitation patterns increase flood risks, in particular in urban areas. Part of the current scientific debate is on how urban agglomeration can be made more resilient to flood risks. Where originally mainly technical interventions dominated this discussion, it becomes increasingly clear that proactive special planning, flood defense, flood risk mitigation, flood preparation, and flood recovery are important, but costly instruments. Next to the main focus on SLR and its effects on resilience, the paper reviews main SLR associated impacts: Floods and inundation, salinization, shoreline change, and effects on mangroves and wetlands. The hazards of SLR related floods increase fastest in urban areas. This is related with both the increasing surface major cities are expected to occupy during the decades to come and the increasing coastal population. In particular Asia and its megacities in the southern part of the continent are increasingly at risk. The discussion points to complexity, inter-disciplinarity, and the related uncertainty, as core characteristics. An integrated combination of mitigation, adaptation and resilience measures is currently considered as the most indicated way to resist SLR today and in the near future.References Aerts J.C.J.H., Hassan A., Savenije H.H.G., Khan M.F., 2000. Using GIS tools and rapid assessment techniques for determining salt intrusion: Stream a river basin management instrument. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 25, 265-273. Doi: 10.1016/S1464-1909(00)00014-9. Alongi D.M., 2002. Present state and future of the world’s mangrove forests. Environmental Conservation, 29, 331-349. Doi: 10.1017/S0376892902000231 Alongi D.M., 2015. The impact of climate change on mangrove forests. Curr. Clim. Change Rep., 1, 30-39. Doi: 10.1007/s404641-015-0002-x. Anderson F., Al-Thani N., 2016. Effect of sea level rise and groundwater withdrawal on seawater intrusion in the Gulf Coast aquifer: Implications for agriculture. Journal of Geoscience and Environment Protection, 4, 116-124. Doi: 10.4236/gep.2016.44015. Anguelovski I., Chu E., Carmin J., 2014. Variations in approaches to urban climate adaptation: Experiences and experimentation from the global South. Global Environmental Change, 27, 156-167. Doi: 10.1016/j.gloenvcha.2014.05.010. Arustienè J., Kriukaitè J., Satkunas J., Gregorauskas M., 2013. Climate change and groundwater - From modelling to some adaptation means in example of Klaipèda region, Lithuania. In: Climate change adaptation in practice. P. Schmidt-Thomé, J. Klein Eds. John Wiley and Sons Ltd., Chichester, UK., 157-169. Bamber J.L., Aspinall W.P., Cooke R.M., 2016. A commentary on “how to interpret expert judgement assessments of twenty-first century sea-level rise” by Hylke de Vries and Roderik S.W. Van de Wal. Climatic Change, 137, 321-328. Doi: 10.1007/s10584-016-1672-7. Barnes C., 2014. Coastal population vulnerability to sea level rise and tropical cyclone intensification under global warming. BSc-thesis. Department of Geography, University of Lethbridge, Alberta Canada. Be T.T., Sinh B.T., Miller F., 2007. Challenges to sustainable development in the Mekong Delta: Regional and national policy issues and research needs. The Sustainable Mekong Research Network, Bangkok, Thailand, 1-210. Bellard C., Leclerc C., Courchamp F., 2014. Impact of sea level rise on 10 insular biodiversity hotspots. Global Ecology and Biogeography, 23, 203-212. Doi: 10.1111/geb.12093. Berg H., Söderholm A.E., Sönderström A.S., Nguyen Thanh Tam, 2017. Recognizing wetland ecosystem services for sustainable rice farming in the Mekong delta, Vietnam. Sustainability Science, 12, 137-154. Doi: 10.1007/s11625-016-0409-x. Bilskie M.V., Hagen S.C., Medeiros S.C., Passeri D.L., 2014. Dynamics of sea level rise and coastal flooding on a changing landscape. Geophysical Research Letters, 41, 927-934. Doi: 10.1002/2013GL058759. Binh T.N.K.D., Vromant N., Hung N.T., Hens L., Boon E.K., 2005. Land cover changes between 1968 and 2003 in Cai Nuoc, Ca Mau penisula, Vietnam. Environment, Development and Sustainability, 7, 519-536. Doi: 10.1007/s10668-004-6001-z. Blankespoor B., Dasgupta S., Laplante B., 2014. Sea-level rise and coastal wetlands. Ambio, 43, 996- 005.Doi: 10.1007/s13280-014-0500-4. Brockway R., Bowers D., Hoguane A., Dove V., Vassele V., 2006. A note on salt intrusion in funnel shaped estuaries: Application to the Incomati estuary, Mozambique.Estuarine, Coastal and Shelf Science, 66, 1-5. Doi: 10.1016/j.ecss.2005.07.014. Cannaby H., Palmer M.D., Howard T., Bricheno L., Calvert D., Krijnen J., Wood R., Tinker J., Bunney C., Harle J., Saulter A., O’Neill C., Bellingham C., Lowe J., 2015. Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore. Ocean Sci. Discuss, 12, 2955-3001. Doi: 10.5194/osd-12-2955-2015. Carraro C., Favero A., Massetti E., 2012. Investment in public finance in a green, low carbon economy. Energy Economics, 34, S15-S18. Castan-Broto V., Bulkeley H., 2013. A survey ofurban climate change experiments in 100 cities. Global Environmental Change, 23, 92-102. Doi: 10.1016/j.gloenvcha.2012.07.005. Cazenave A., Le Cozannet G., 2014. Sea level rise and its coastal impacts. GeoHealth, 2, 15-34. Doi: 10.1002/2013EF000188. Chu M.L., Guzman J.A., Munoz-Carpena R., Kiker G.A., Linkov I., 2014. A simplified approach for simulating changes in beach habitat due to the combined effects of long-term sea level rise, storm erosion and nourishment. Environmental modelling and software, 52, 111-120. Doi.org/10.1016/j.envcsoft.2013.10.020. Church J.A. et al., 2013. Sea level change. In: Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of Intergovernmental Panel on Climate Change. Eds: Stocker T.F., Qin D., Plattner G.-K., Tignor M., Allen S.K., Boschung J., Nauels A., Xia Y., Bex V., Midgley P.M., Cambridge University Press, Cambridge, UK. Connell J., 2016. Last days of the Carteret Islands? Climate change, livelihoods and migration on coral atolls. Asia Pacific Viewpoint, 57, 3-15. Doi: 10.1111/apv.12118. Dasgupta S., Laplante B., Meisner C., Wheeler, Yan J., 2009. The impact of sea level rise on developing countries: A comparative analysis. Climatic Change, 93, 379-388. Doi: 10.1007/s 10584-008-9499-5. Delbeke J., Vis P., 2015. EU climate policy explained, 136p. Routledge, Oxon, UK. DiGeorgio M., 2015. Bargaining with disaster: Flooding, climate change, and urban growth ambitions in QuyNhon, Vietnam. Public Affairs, 88, 577-597. Doi: 10.5509/2015883577. Do Minh Duc, Yasuhara K., Nguyen Manh Hieu, 2015. Enhancement of coastal protection under the context of climate change: A case study of Hai Hau coast, Vietnam. Proceedings of the 10th Asian Regional Conference of IAEG, 1-8. Do Minh Duc, Yasuhara K., Nguyen Manh Hieu, Lan Nguyen Chau, 2017. Climate change impacts on a large-scale erosion coast of Hai Hau district, Vietnam and the adaptation. Journal of Coastal Conservation, 21, 47-62. Donner S.D., Webber S., 2014. Obstacles to climate change adaptation decisions: A case study of sea level rise; and coastal protection measures in Kiribati. Sustainability Science, 9, 331-345. Doi: 10.1007/s11625-014-0242-z. Driessen P.P.J., Hegger D.L.T., Bakker M.H.N., Van Renswick H.F.M.W., Kundzewicz Z.W., 2016. Toward more resilient flood risk governance. Ecology and Society, 21, 53-61. Doi: 10.5751/ES-08921-210453. Duangyiwa C., Yu D., Wilby R., Aobpaet A., 2015. Coastal flood risks in the Bangkok Metropolitan region, Thailand: Combined impacts on land subsidence, sea level rise and storm surge. American Geophysical Union, Fall meeting 2015, abstract#NH33C-1927. Duarte C.M., Losada I.J., Hendriks I.E., Mazarrasa I., Marba N., 2013. The role of coastal plant communities for climate change mitigation and adaptation. Nature Climate Change, 3, 961-968. Doi: 10.1038/nclimate1970. Erban L.E., Gorelick S.M., Zebker H.A., 2014. Groundwater extraction, land subsidence, and sea-level rise in the Mekong Delta, Vietnam. Environmental Research Letters, 9, 1-20. Doi: 10.1088/1748-9326/9/8/084010. FAO - Food and Agriculture Organisation, 2007.The world’s mangroves 1980-2005. FAO Forestry Paper, 153, Rome, Italy. Farbotko C., 2010. Wishful sinking: Disappearing islands, climate refugees and cosmopolitan experimentation. Asia Pacific Viewpoint, 51, 47-60. Doi: 10.1111/j.1467-8373.2010.001413.x. Goltermann D., Ujeyl G., Pasche E., 2008. Making coastal cities flood resilient in the era of climate change. Proceedings of the 4th International Symposium on flood defense: Managing flood risk, reliability and vulnerability, 148-1-148-11. Toronto, Canada. Gong W., Shen J., 2011. The response of salt intrusion to changes in river discharge and tidal mixing during the dry season in the Modaomen Estuary, China.Continental Shelf Research, 31, 769-788. Doi: 10.1016/j.csr.2011.01.011. Gosian L., 2014. Protect the world’s deltas. Nature, 516, 31-34. Graham S., Barnett J., Fincher R., Mortreux C., Hurlimann A., 2015. Towards fair outcomes in adaptation to sea-level rise. Climatic Change, 130, 411-424. Doi: 10.1007/s10584-014-1171-7. COASTRES-D-12-00175.1. Güneralp B., Güneralp I., Liu Y., 2015. Changing global patterns of urban expoàsure to flood and drought hazards. Global Environmental Change, 31, 217-225. Doi: 10.1016/j.gloenvcha.2015.01.002. Hallegatte S., Green C., Nicholls R.J., Corfee-Morlot J., 2013. Future flood losses in major coastal cities. Nature Climate Change, 3, 802-806. Doi: 10.1038/nclimate1979. Hamlington B.D., Strassburg M.W., Leben R.R., Han W., Nerem R.S., Kim K.-Y., 2014. Uncovering an anthropogenic sea-level rise signal in the Pacific Ocean. Nature Climate Change, 4, 782-785. Doi: 10.1038/nclimate2307. Hashimoto T.R., 2001. Environmental issues and recent infrastructure development in the Mekong Delta: Review, analysis and recommendations with particular reference to large-scale water control projects and the development of coastal areas. Working paper series (Working paper No. 4). Australian Mekong Resource Centre, University of Sydney, Australia, 1-70. Hibbert F.D., Rohling E.J., Dutton A., Williams F.H., Chutcharavan P.M., Zhao C., Tamisiea M.E., 2016. Coral indicators of past sea-level change: A global repository of U-series dated benchmarks. Quaternary Science Reviews, 145, 1-56. Doi: 10.1016/j.quascirev.2016.04.019. Hinkel J., Lincke D., Vafeidis A., Perrette M., Nicholls R.J., Tol R.S.J., Mazeion B., Fettweis X., Ionescu C., Levermann A., 2014. Coastal flood damage and adaptation costs under 21st century sea-level rise. Proceedings of the National Academy of Sciences, 111, 3292-3297. Doi: 10.1073/pnas.1222469111. Hinkel J., Nicholls R.J., Tol R.S.J., Wang Z.B., Hamilton J.M., Boot G., Vafeidis A.T., McFadden L., Ganapolski A., Klei R.J.Y., 2013. A global analysis of erosion of sandy beaches and sea level rise: An application of DIVA. Global and Planetary Change, 111, 150-158. Doi: 10.1016/j.gloplacha.2013.09.002. Huong H.T.L., Pathirana A., 2013. Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam. Hydrol. Earth Syst. Sci., 17, 379-394. Doi: 10.5194/hess-17-379-2013. Hurlimann A., Barnett J., Fincher R., Osbaldiston N., Montreux C., Graham S., 2014. Urban planning and sustainable adaptation to sea-level rise. Landscape and Urban Planning, 126, 84-93. Doi: 10.1016/j.landurbplan.2013.12.013. IMHEN-Vietnam Institute of Meteorology, Hydrology and Environment, 2011. Climate change vulnerability and risk assessment study for Ca Mau and KienGiang provinces, Vietnam. Hanoi, Vietnam Institute of Meteorology, Hydrology and Environment (IMHEN), 250p. IMHEN-Vietnam Institute of Meteorology, Hydrology and Environment, Ca Mau PPC, 2011. Climate change impact and adaptation study in The Mekong Delta - Part A: Ca Mau Atlas. Hanoi, Vietnam: Institute of Meteorology, Hydrology and Environment (IMHEN), 48p. IPCC-Intergovernmental Panel on Climate Change, 2014. Fifth assessment report. Cambridge University Press, Cambridge, UK. Jevrejeva S., Jackson L.P., Riva R.E.M., Grinsted A., Moore J.C., 2016. Coastal sea level rise with warming above 2°C. Proceedings of the National Academy of Sciences, 113, 13342-13347. Doi: 10.1073/pnas.1605312113. Junk W.J., AN S., Finlayson C.M., Gopal B., Kvet J., Mitchell S.A., Mitsch W.J., Robarts R.D., 2013. Current state of knowledge regarding the world’s wetlands and their future under global climate change: A synthesis. Aquatic Science, 75, 151-167. Doi: 10.1007/s00027-012-0278-z. Jordan A., Rayner T., Schroeder H., Adger N., Anderson K., Bows A., Le Quéré C., Joshi M., Mander S., Vaughan N., Whitmarsh L., 2013. Going beyond two degrees? The risks and opportunities of alternative options. Climate Policy, 13, 751-769. Doi: 10.1080/14693062.2013.835705. Kelly P.M., Adger W.N., 2000. Theory and practice in assessing vulnerability to climate change and facilitating adaptation. Climatic Change, 47, 325-352. Doi: 10.1023/A:1005627828199. Kirwan M.L., Megonigal J.P., 2013. Tidal wetland stability in the face of human impacts and sea-level rice. Nature, 504, 53-60. Doi: 10.1038/nature12856. Koerth J., Vafeidis A.T., Hinkel J., Sterr H., 2013. What motivates coastal households to adapt pro actively to sea-level rise and increased flood risk? Regional Environmental Change, 13, 879-909. Doi: 10.1007/s10113-12-399-x. Kontgis K., Schneider A., Fox J;,Saksena S., Spencer J.H., Castrence M., 2014. Monitoring peri urbanization in the greater Ho Chi Minh City metropolitan area. Applied Geography, 53, 377-388. Doi: 10.1016/j.apgeogr.2014.06.029. Kopp R.E., Horton R.M., Little C.M., Mitrovica J.X., Oppenheimer M., Rasmussen D.J., Strauss B.H., Tebaldi C., 2014. Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earth’s Future, 2, 383-406. Doi: 10.1002/2014EF000239. Kuenzer C., Bluemel A., Gebhardt S., Quoc T., Dech S., 2011. Remote sensing of mangrove ecosystems: A review.Remote Sensing, 3, 878-928. Doi: 10.3390/rs3050878. Lacerda G.B.M., Silva C., Pimenteira C.A.P., Kopp Jr. R.V., Grumback R., Rosa L.P., de Freitas M.A.V., 2013. Guidelines for the strategic management of flood risks in industrial plant oil in the Brazilian coast: Adaptive measures to the impacts of sea level rise. Mitigation and Adaptation Strategies for Global Change, 19, 104-1062. Doi: 10.1007/s11027-013-09459-x. Lam Dao Nguyen, Pham Van Bach, Nguyen Thanh Minh, Pham Thi Mai Thy, Hoang Phi Hung, 2011. Change detection of land use and river bank in Mekong Delta, Vietnam using time series remotely sensed data. Journal of Resources and Ecology, 2, 370-374. Doi: 10.3969/j.issn.1674-764x.2011.04.011. Lang N.T., Ky B.X., Kobayashi H., Buu B.C., 2004. Development of salt tolerant varieties in the Mekong delta. JIRCAS Project, Can Tho University, Can Tho, Vietnam, 152. Le Cozannet G., Rohmer J., Cazenave A., Idier D., Van de Wal R., de Winter R., Pedreros R., Balouin Y., Vinchon C., Oliveros C., 2015. Evaluating uncertainties of future marine flooding occurrence as sea-level rises. Environmental Modelling and Software, 73, 44-56. Doi: 10.1016/j.envsoft.2015.07.021. Le Cozannet G., Manceau J.-C., Rohmer J., 2017. Bounding probabilistic sea-level projections with the framework of the possible theory. Environmental Letters Research, 12, 12-14. Doi.org/10.1088/1748-9326/aa5528.Chikamoto Y., 2014. Recent Walker circulation strengthening and Pacific cooling amplified by Atlantic warming. Nature Climate Change, 4, 888-892. Doi: 10.1038/nclimate2330. Lovelock C.E., Cahoon D.R., Friess D.A., Gutenspergen G.R., Krauss K.W., Reef R., Rogers K., Saunders M.L., Sidik F., Swales A., Saintilan N., Le Xuan Tuyen, Tran Triet, 2015. The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature, 526, 559-563. Doi: 10.1038/nature15538. MA Millennium Ecosystem Assessment, 2005. Ecosystems and human well-being: Current state and trends. Island Press, Washington DC, 266p. Masterson J.P., Fienen M.N., Thieler E.R., Gesch D.B., Gutierrez B.T., Plant N.G., 2014. Effects of sea level rise on barrier island groundwater system dynamics - ecohydrological implications. Ecohydrology, 7, 1064-1071. Doi: 10.1002/eco.1442. McGanahan G., Balk D., Anderson B., 2007. The rising tide: Assessing the risks of climate changes and human settlements in low elevation coastal zones.Environment and urbanization, 19, 17-37. Doi: 10.1177/095624780707960. McIvor A., Möller I., Spencer T., Spalding M., 2012. Reduction of wind and swell waves by mangroves. The Nature Conservancy and Wetlands International, 1-27. Merryn T., Pidgeon N., Whitmarsh L., Ballenger R., 2016. Expert judgements of sea-level rise at the local scale. Journal of Risk Research, 19, 664-685. Doi.org/10.1080/13669877.2015.1043568. Monioudi I.N., Velegrakis A.F., Chatzipavlis A.E., Rigos A., Karambas T., Vousdoukas M.I., Hasiotis T., Koukourouvli N., Peduzzi P., Manoutsoglou E., Poulos S.E., Collins M.B., 2017. Assessment of island beach erosion due to sea level rise: The case of the Aegean archipelago (Eastern Mediterranean). Nat. Hazards Earth Syst. Sci., 17, 449-466. Doi: 10.5194/nhess-17-449-2017. MONRE - Ministry of Natural Resources and Environment, 2016. Scenarios of climate change and sea level rise for Vietnam. Publishing House of Environmental Resources and Maps Vietnam, Hanoi, 188p. Montz B.E., Tobin G.A., Hagelman III R.R., 2017. Natural hazards. Explanation and integration. The Guilford Press, NY, 445p. Morgan L.K., Werner A.D., 2014. Water intrusion vulnerability for freshwater lenses near islands. Journal of Hydrology, 508, 322-327. Doi: 10.1016/j.jhydrol.2013.11.002. Muis S., Güneralp B., Jongman B., Aerts J.C.H.J., Ward P.J., 2015. Science of the Total Environment, 538, 445-457. Doi: 10.1016/j.scitotenv.2015.08.068. Murray N.J., Clemens R.S., Phinn S.R., Possingham H.P., Fuller R.A., 2014. Tracking the rapid loss of tidal wetlands in the Yellow Sea. Frontiers in Ecology and Environment, 12, 267-272. Doi: 10.1890/130260. Neumann B., Vafeidis A.T., Zimmermann J., Nicholls R.J., 2015a. Future coastal population growth and exposure to sea-level rise and coastal flooding. A global assessment. Plos One, 10, 1-22. Doi: 10.1371/journal.pone.0118571. Nguyen A. Duoc, Savenije H. H., 2006. Salt intrusion in multi-channel estuaries: a case study in the Mekong Delta, Vietnam. Hydrology and Earth System Sciences Discussions, European Geosciences Union, 10, 743-754. Doi: 10.5194/hess-10-743-2006. Nguyen An Thinh, Nguyen Ngoc Thanh, Luong Thi Tuyen, Luc Hens, 2017. Tourism and beach erosion: Valuing the damage of beach erosion for tourism in the Hoi An, World Heritage site. Journal of Environment, Development and Sustainability. Nguyen An Thinh, Luc Hens (Eds.), 2018. Human ecology of climate change associated disasters in Vietnam: Risks for nature and humans in lowland and upland areas. Springer Verlag, Berlin.Nguyen An Thinh, Vu Anh Dung, Vu Van Phai, Nguyen Ngoc Thanh, Pham Minh Tam, Nguyen Thi Thuy Hang, Le Trinh Hai, Nguyen Viet Thanh, Hoang Khac Lich, Vu Duc Thanh, Nguyen Song Tung, Luong Thi Tuyen, Trinh Phuong Ngoc, Luc Hens, 2017. Human ecological effects of tropical storms in the coastal area of Ky Anh (Ha Tinh, Vietnam). Environ Dev Sustain, 19, 745-767. Doi: 10.1007/s/10668-016-9761-3. Nguyen Van Hoang, 2017. Potential for desalinization of brackish groundwater aquifer under a background of rising sea level via salt-intrusion prevention river gates in the coastal area of the Red River delta, Vietnam. Environment, Development and Sustainability. Nguyen Tho, Vromant N., Nguyen Thanh Hung, Hens L., 2008. Soil salinity and sodicity in a shrimp farming coastal area of the Mekong Delta, Vietnam. Environmental Geology, 54, 1739-1746. Doi: 10.1007/s00254-007-0951-z. Nguyen Thang T.X., Woodroffe C.D., 2016. Assessing relative vulnerability to sea-level rise in the western part of the Mekong River delta. Sustainability Science, 11, 645-659. Doi: 10.1007/s11625-015-0336-2. Nicholls N.N., Hoozemans F.M.J., Marchand M., Analyzing flood risk and wetland losses due to the global sea-level rise: Regional and global analyses.Global Environmental Change, 9, S69-S87. Doi: 10.1016/s0959-3780(99)00019-9. Phan Minh Thu, 2006. Application of remote sensing and GIS tools for recognizing changes of mangrove forests in Ca Mau province. In Proceedings of the International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences, Ho Chi Minh City, Vietnam, 9-11 November, 1-17. Reise K., 2017. Facing the third dimension in coastal flatlands.Global sea level rise and the need for coastal transformations. Gaia, 26, 89-93. Renaud F.G., Le Thi Thu Huong, Lindener C., Vo Thi Guong, Sebesvari Z., 2015. Resilience and shifts in agro-ecosystems facing increasing sea-level rise and salinity intrusion in Ben Tre province, Mekong Delta. Climatic Change, 133, 69-84. Doi: 10.1007/s10584-014-1113-4. Serra P., Pons X., Sauri D., 2008. Land cover and land use in a Mediterranean landscape. Applied Geography, 28, 189-209. Shearman P., Bryan J., Walsh J.P., 2013.Trends in deltaic change over three decades in the Asia-Pacific Region. Journal of Coastal Research, 29, 1169-1183. Doi: 10.2112/JCOASTRES-D-12-00120.1. SIWRR-Southern Institute of Water Resources Research, 2016. Annual Report. Ministry of Agriculture and Rural Development, Ho Chi Minh City, 1-19. Slangen A.B.A., Katsman C.A., Van de Wal R.S.W., Vermeersen L.L.A., Riva R.E.M., 2012. Towards regional projections of twenty-first century sea-level change based on IPCC RES scenarios. Climate Dynamics, 38, 1191-1209. Doi: 10.1007/s00382-011-1057-6. Spencer T., Schuerch M., Nicholls R.J., Hinkel J., Lincke D., Vafeidis A.T., Reef R., McFadden L., Brown S., 2016. Global coastal wetland change under sea-level rise and related stresses: The DIVA wetland change model. Global and Planetary Change, 139, 15-30. Doi:10.1016/j.gloplacha.2015.12.018. Stammer D., Cazenave A., Ponte R.M., Tamisiea M.E., 2013. Causes of contemporary regional sea level changes. Annual Review of Marine Science, 5, 21-46. Doi: 10.1146/annurev-marine-121211-172406. Tett P., Mee L., 2015. Scenarios explored with Delphi. In: Coastal zones ecosystems services. Eds., Springer, Berlin, Germany, 127-144. Tran Hong Hanh, 2017. Land use dynamics, its drivers and consequences in the Ca Mau province, Mekong delta, Vietnam. PhD dissertation, 191p. VUBPRESS Brussels University Press, ISBN 9789057186226, Brussels, Belgium. Tran Thuc, Nguyen Van Thang, Huynh Thi Lan Huong, Mai Van Khiem, Nguyen Xuan Hien, Doan Ha Phong, 2016. Climate change and sea level rise scenarios for Vietnam. Ministry of Natural resources and Environment. Hanoi, Vietnam. Tran Hong Hanh, Tran Thuc, Kervyn M., 2015. Dynamics of land cover/land use changes in the Mekong Delta, 1973-2011: A remote sensing analysis of the Tran Van Thoi District, Ca Mau province, Vietnam. Remote Sensing, 7, 2899-2925. Doi: 10.1007/s00254-007-0951-z Van Lavieren H., Spalding M., Alongi D., Kainuma M., Clüsener-Godt M., Adeel Z., 2012. Securing the future of Mangroves. The United Nations University, Okinawa, Japan, 53, 1-56. Water Resources Directorate. Ministry of Agriculture and Rural Development, 2016. Available online: http://www.tongcucthuyloi.gov.vn/Tin-tuc-Su-kien/Tin-tuc-su-kien-tong-hop/catid/12/item/2670/xam-nhap-man-vung-dong-bang-song-cuu-long--2015---2016---han-han-o-mien-trung--tay-nguyen-va-giai-phap-khac-phuc. Last accessed on: 30/9/2016. Webster P.J., Holland G.J., Curry J.A., Chang H.-R., 2005. Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309, 1844-1846. Doi: 10.1126/science.1116448. Were K.O., Dick O.B., Singh B.R., 2013. Remotely sensing the spatial and temporal land cover changes in Eastern Mau forest reserve and Lake Nakuru drainage Basin, Kenya. Applied Geography, 41, 75-86. Williams G.A., Helmuth B., Russel B.D., Dong W.-Y., Thiyagarajan V., Seuront L., 2016. Meeting the climate change challenge: Pressing issues in southern China an SE Asian coastal ecosystems. Regional Studies in Marine Science, 8, 373-381. Doi: 10.1016/j.rsma.2016.07.002. Woodroffe C.D., Rogers K., McKee K.L., Lovdelock C.E., Mendelssohn I.A., Saintilan N., 2016. Mangrove sedimentation and response to relative sea-level rise. Annual Review of Marine Science, 8, 243-266. Doi: 10.1146/annurev-marine-122414-034025.

DETERMINAÇÃO DA CAPACIDADE DE USO DAS TERRAS DO MUNICÍPIO DE SÃO MANUEL - SP Osmar Delmanto JuniorSérgio CamposLincoln Gehring CardosoZacarias Xavier de BarrosDepartamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista, Botucatu, SP. CP 237, CEP 18603-970. E-mail: seca@fca.unesp.br 1 RESUMO Esse trabalho objetivou a elaboração da carta de capacidade de uso das terras do Município de São Manuel - SP, visando o planejamento adequado da ocupação do solo, utilizado-se de Sistema de Informação Geográfica (SIG). A bacia situa-se entre as coordenadas geográficas 22º 28’ 20” e 22º 53’ 10” de latitudes S e os meridianos 48º 21’ 52” e 48º 48’ 00” de longitudes W Gr., apresentando uma área de 60.988ha. O mapa de capacidade de uso da terra do município foi elaborado a partir dos mapas de classes de declive e de solo, tomando-se por base as características de cada um e utilizando-se da tabela de julgamento de classes de capacidade de uso. As áreas da classe e subclasses de capacidade de uso das terras determinadas pelo SIG –IDRISI foram: IIe,s (22,64%); IIIe (8,62%); IIIe,s (15,83%); IIIs (29,97%); IVe (10,82%); VIe (3,95%) e VIIe (0,96%). Os resultados permitiram inferir que as subclasses mais significativas foram a IIIs e IIe,s. As classes de declive de 0 a 20% ocorrem em mais de ¾ do Município, sendo as áreas planas, as mais representativas, pois ocorrem em quase 1/3 da área total. O SIG mostrou-se uma excelente ferramenta para determinação da capacidade de uso da terra, demonstrando que a utilização do geoprocessamento facilita e agiliza o cruzamento dos dados digitais, permitindo seu armazenamento, que poderão ser utilizados para outras análises em futuros planejamentos geoambientais. UNITERMOS: Sistema de Informações Geográficas, unidades de solo, classes de declive, capacidade de uso da terra DELMANTO JUNIOR, O.; CAMPOS S.; CARDOSO, L.G.; BARROS, Z.X. LAND USE CAPABILITY DETERMINATION OF SÃO MANUEL MUNICIPALITY-SP 2 ABSTRACT The present work purposed a land use capability chart development from São Manuel Municipality-SP using a Geographical Information System - Idrisi aiming to contribute for a better territorial organization and soil occupation planning. The basin is located in the 22º 28’ 20” geographical co-ordinates at 22º 53’ 10” S latitude, 48º 21’ 52” and 48º 48’ 00” meridian of W Gr. longitude, presenting an area of 9180,12ha. The chart of the basin land use capability was elaborated from the soil and sloping class charts based on each one characteristics and using the class table of use capability. The class and subclass areas determined through the Geographical Information System IDRISI presented the following values: IIe,s (22,64%), IIIe (8,62%), IIIe,s (15,83%), IIIs (29,97%), IVe (10,82%), VIe (3,95%) and VIIe (0,96%). The results allowed to infer that the most significant sub classes were IIIs and IIe,s. The 0 to 20% sloping classes have occurred in more than ¾ of the district. Plane areas were the most significant since they occur in nearly 1/3 of the whole area. The Geographical Information System IDRISI has been an excellent tool to determine land use capability, specially related with geo-processing use. The later also facilitates the input, analysis and display of spatial environmental information as well as data digital storage which could be used for other analyses in further geo-environmental design. KEYWORDS: Geoprocessing, land-use capability, Geographical Information Systems.

Entikong is a sub-districts located in the borderline, northern end of Sanggau Regency directly adjacent to Sarawak, Malaysia. The growth of Entikong as a center of growth does not provide a downward trickle effect, but it creates an excessive resources exploitation effect to the surrounding area (backwash effect). The land use within an area should be adjusted to its function. For that reason, this research will determine the priority and rank of land use by using the Analytic Hierarchy Process (AHP). The ranking is based on four aspects of criteria; social, economic, institutional, and environmental. The hierarchy model is sorted into alternatives, criteria, and sub-criteria. The criteria and subcriteria are compared, as well as the value of consistency. After data processing and analyzing with Expert Choice software version 11, the researcher found that the main priority of land use in Entikong is for plantation, which is 29,7%. Keywords: AHP, Land Use, Expert Choice References Adimihardja, A. (2006). Strategi mempertahankan multifungsi pertanian di indonesia. Jurnal Litbang Pertanian. Bourgeois, R., Penunia, E., Bisht, S., & Boruk, D. (2017). Foresight for all: Co-elaborative scenario building and empowerment. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2017.04.018 Ernan Rustiadi, Sunsus Saefulhakim, D. R. P. (2011). Perencanaan dan Pengembangan Wilayah. Restpent Press. Fandelli, C. (2014). Bisnis Konservasi Pendekatan Baru Dalam Pengelolaan Sumberdaya Alam dan Lingkungan Hidup (2nd ed.). Yogyakarta: Gadjah Mada University Press. Retrieved from http://ugmpress.ugm.ac.id/id/product/sains-teknologi/bisnis-konservasi-pendekatan-baru-dalam-pengelolaan-sumberdaya-alam-dan-lingkungan-hidup Giyarsih, S. R. (2010). POLA SPASIAL TRANSFORMASI WILAYAH DI KORIDOR YOGYAKARTA-SURAKARTA Spatial Pattern of Regional Transformation In Yogyakarta-Surakarta Corridor. Forum Geografi. Hidayat, W., Rustiadi, E., & Kartodihardjo, H. (2015). Dampak Pertambangan Terhadap Perubahan Penggunaan Lahan dan Kesesuaian Peruntukan Ruang (Studi Kasus Kabupaten Luwu Timur, Provinsi Sulawesi Selatan). Jurnal Perencanaan Wilayah Dan Kota. https://doi.org/10.5614/jpwk.2015.26.2.5 IPCC. (2000). Land Use, Land-Use Change, and Forestry. Forestry. https://doi.org/DOI: 10.2277/0521800838 Ishartono & Raharjo, S. T. (2015). Sustainable Development Goals (SDGs) Dan Pengentasan Kemiskinan. Social Work Jurnal. https://doi.org/ttps://doi.org/10.24198/share.v6i2.13198 Prawira, N. G. A., & Ariastita, P. G. (2014). Rumusan Insentif dan Disinsentif Pengendalian Konversi Lahan Pertanian di Kabupaten Gianyar. Jurnal Teknik Pomits. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences. https://doi.org/10.1504/IJSSCI.2008.017590

A CULTURA DA CANA-DE-AÇÚCAR COMO FATOR DE RISCO PARA OS CÓRREGOS E AS NASCENTES BRUNA SOARES XAVIER DE BARROS1 E ZACARIAS XAVIER DE BARROS2 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista, Botucatu, SP, zacariasxb@fca.unesp.br1Pós-Doutoranda do Programa de Pós-Graduação - Energia na Agricultura, FCA/UNESP - Botucatu, SP. 2Professor Titular do Departamento de Engenharia Rural, FCA/UNESP - Botucatu, SP. 1 RESUMO O levantamento do uso e ocupação do solo tornou-se muito importante para se conhecer e determinar as principais culturas de uma região, pois a ação antrópica pode modificar profundamente as feições de uma paisagem nativa. Neste contexto, este trabalho visou analisar a ocupação do solo na bacia Fazenda Serra Negra, Botucatu-SP, no período de 1962 a 2014, no intuito de verificar a influência das diferentes culturas sobre as redes de drenagem e as nascentes na bacia. A bacia está situada entre as coordenadas de 22º 46’ 42” a 22º 48’ 12” de latitude S e 48º 24’ 04” a 48º 25’ 54” longitude Wgr, perfazendo uma área de 963,97 ha. O estudo possibilitou constatar que os córregos existentes em 1962 cederam espaços para o plantio da cana-de-açúcar restando apenas vestígios da rede de drenagem; pode-se também constatar que a várzea sofreu diminuição devido à construção de uma rede de drenos. Palavras-chave: Imagens aéreas; bacia hidrográfica; ocupação do solo. BARROS, B. S. X.; BARROS, Z. X.SUGAR CANE GROWING AS A RISK FACTOR FOR STREAMS AND SPRINGS 2 ABSTRACT The survey of land use and occupation has become very important to know and determine the main crops of the region, because human activities can profoundly change the features of a native landscape. In this context, this study aimed to analyze land use in the Fazenda Serra Negra drainage basin, Botucatu, São Paulo, from 1962 to 2014, in order to investigate the influence of different cultures on the drainage systems and springs in the basin. This basin is located between coordinates 22º46’42” to 22°48’'12”S latitude and 48°24’04” to 48°25’54”W longitude, covering an area of 963.97 ha. Through this study, it was possible to see that the existing streams in 1962 gave room to sugarcane growing, leaving only traces of the drainage system; it can also be seen that the plain suffered a decrease due to the building of a drain network. Keywords: aerial images; hydrographic basin; land use.

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). Geological Investigation of Tagwai Dams using Remote Sensing Technique, Minna Niger State, Nigeria. Journal of Environment, 1(01), pp. 26-32. Amadi, A., & Olasehinde, P. (2010). Application of remote sensing techniques in hydrogeological mapping of parts of Bosso Area, Minna, North-Central Nigeria. International Journal of Physical Sciences, 5(9), pp. 1465-1474. Aplin, P., & Smith, G. (2008). Advances in object-based image classification. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B7), pp. 725-728. Ayele, G. T., Tebeje, A. K., Demissie, S. S., Belete, M. A., Jemberrie, M. A., Teshome, W. M., . . . Teshale, E. Z. (2018). Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia. Air, Soil and Water Research, 11, p 1178622117751603. Azevedo, J. A., Chapman, L., & Muller, C. L. (2016). Quantifying the daytime and night-time urban heat island in Birmingham, UK: a comparison of satellite derived land surface temperature and high resolution air temperature observations. Remote Sensing, 8(2), p 153. Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., . . . van Coillie, F. (2014). Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, pp. 180-191. Bukata, R. P., Jerome, J. H., Kondratyev, A. S., & Pozdnyakov, D. V. (2018). Optical properties and remote sensing of inland and coastal waters: CRC press. Camps-Valls, G., Tuia, D., Bruzzone, L., & Benediktsson, J. A. (2014). Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE signal processing magazine, 31(1), pp. 45-54. Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., . . . Lu, M. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, pp. 7-27. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), pp. 171-209. Cheng, G., Han, J., Guo, L., Liu, Z., Bu, S., & Ren, J. (2015). Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images. IEEE transactions on geoscience and remote sensing, 53(8), pp. 4238-4249. Cheng, G., Han, J., Zhou, P., & Guo, L. (2014). Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS Journal of Photogrammetry and Remote Sensing, 98, pp. 119-132. Coale, A. J., & Hoover, E. M. (2015). Population growth and economic development: Princeton University Press. Congalton, R. G., & Green, K. (2008). Assessing the accuracy of remotely sensed data: principles and practices: CRC press. Corner, R. J., Dewan, A. M., & Chakma, S. (2014). Monitoring and prediction of land-use and land-cover (LULC) change Dhaka megacity (pp. 75-97): Springer. Coutts, A. M., Harris, R. J., Phan, T., Livesley, S. J., Williams, N. S., & Tapper, N. J. (2016). Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning. Remote Sensing of Environment, 186, pp. 637-651. Debnath, A., Debnath, J., Ahmed, I., & Pan, N. D. (2017). Change detection in Land use/cover of a hilly area by Remote Sensing and GIS technique: A study on Tropical forest hill range, Baramura, Tripura, Northeast India. International journal of geomatics and geosciences, 7(3), pp. 293-309. Desheng, L., & Xia, F. (2010). Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1(4), pp. 187-194. Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3), pp. 390-401. Dronova, I., Gong, P., Wang, L., & Zhong, L. (2015). Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification. Remote Sensing of Environment, 158, pp. 193-206. Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, pp. 259-272. Elmhagen, B., Destouni, G., Angerbjörn, A., Borgström, S., Boyd, E., Cousins, S., . . . Hambäck, P. (2015). Interacting effects of change in climate, human population, land use, and water use on biodiversity and ecosystem services. Ecology and Society, 20(1) Farhani, S., & Ozturk, I. (2015). Causal relationship between CO 2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environmental Science and Pollution Research, 22(20), pp. 15663-15676. Feng, L., Chen, B., Hayat, T., Alsaedi, A., & Ahmad, B. (2017). The driving force of water footprint under the rapid urbanization process: a structural decomposition analysis for Zhangye city in China. Journal of Cleaner Production, 163, pp. S322-S328. Fensham, R., & Fairfax, R. (2002). Aerial photography for assessing vegetation change: a review of applications and the relevance of findings for Australian vegetation history. Australian Journal of Botany, 50(4), pp. 415-429. Ferreira, N., Lage, M., Doraiswamy, H., Vo, H., Wilson, L., Werner, H., . . . Silva, C. (2015). Urbane: A 3d framework to support data driven decision making in urban development. Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on. Garschagen, M., & Romero-Lankao, P. (2015). Exploring the relationships between urbanization trends and climate change vulnerability. Climatic Change, 133(1), pp. 37-52. Gokturk, S. B., Sumengen, B., Vu, D., Dalal, N., Yang, D., Lin, X., . . . Torresani, L. (2015). System and method for search portions of objects in images and features thereof: Google Patents. Government, N. S. (2007). Niger state (The Power State). Retrieved from http://nigerstate.blogspot.com.ng/ Green, K., Kempka, D., & Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric engineering and remote sensing, 60(3), pp. 331-337. Gu, W., Lv, Z., & Hao, M. (2017). Change detection method for remote sensing images based on an improved Markov random field. Multimedia Tools and Applications, 76(17), pp. 17719-17734. Guo, Y., & Shen, Y. (2015). Quantifying water and energy budgets and the impacts of climatic and human factors in the Haihe River Basin, China: 2. Trends and implications to water resources. Journal of Hydrology, 527, pp. 251-261. Hadi, F., Thapa, R. B., Helmi, M., Hazarika, M. K., Madawalagama, S., Deshapriya, L. N., & Center, G. (2016). Urban growth and land use/land cover modeling in Semarang, Central Java, Indonesia: Colombo-Srilanka, ACRS2016. Hagolle, O., Huc, M., Villa Pascual, D., & Dedieu, G. (2015). A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENμS and Sentinel-2 images. Remote Sensing, 7(3), pp. 2668-2691. Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), pp. 117-124. Henderson, J. V., Storeygard, A., & Deichmann, U. (2017). Has climate change driven urbanization in Africa? Journal of development economics, 124, pp. 60-82. Hu, L., & Brunsell, N. A. (2015). A new perspective to assess the urban heat island through remotely sensed atmospheric profiles. Remote Sensing of Environment, 158, pp. 393-406. Hughes, S. J., Cabral, J. A., Bastos, R., Cortes, R., Vicente, J., Eitelberg, D., . . . Santos, M. (2016). A stochastic dynamic model to assess land use change scenarios on the ecological status of fluvial water bodies under the Water Framework Directive. Science of the Total Environment, 565, pp. 427-439. Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, pp. 91-106. Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y.-H. (2000). Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128(1-2), pp. 109-120. Jiang, L., Wu, F., Liu, Y., & Deng, X. (2014). Modeling the impacts of urbanization and industrial transformation on water resources in China: an integrated hydro-economic CGE analysis. Sustainability, 6(11), pp. 7586-7600. Jin, S., Yang, L., Zhu, Z., & Homer, C. (2017). A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sensing of Environment, 195, pp. 44-55. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., . . . Mitchard, E. T. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), p 70. Kaliraj, S., Chandrasekar, N., & Magesh, N. (2015). Evaluation of multiple environmental factors for site-specific groundwater recharge structures in the Vaigai River upper basin, Tamil Nadu, India, using GIS-based weighted overlay analysis. Environmental earth sciences, 74(5), pp. 4355-4380. Koop, S. H., & van Leeuwen, C. J. (2015). Assessment of the sustainability of water resources management: A critical review of the City Blueprint approach. Water Resources Management, 29(15), pp. 5649-5670. Kumar, P., Masago, Y., Mishra, B. K., & Fukushi, K. (2018). Evaluating future stress due to combined effect of climate change and rapid urbanization for Pasig-Marikina River, Manila. Groundwater for Sustainable Development, 6, pp. 227-234. Lang, S. (2008). Object-based image analysis for remote sensing applications: modeling reality–dealing with complexity Object-based image analysis (pp. 3-27): Springer. Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014). A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47(1), pp. 389-411. Liddle, B. (2014). Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Population and Environment, 35(3), pp. 286-304. Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation: John Wiley & Sons. Liu, Y., Wang, Y., Peng, J., Du, Y., Liu, X., Li, S., & Zhang, D. (2015). Correlations between urbanization and vegetation degradation across the world’s metropolises using DMSP/OLS nighttime light data. Remote Sensing, 7(2), pp. 2067-2088. López, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe: a case in Morelia city, Mexico. Landscape and urban planning, 55(4), pp. 271-285. Luo, M., & Lau, N.-C. (2017). Heat waves in southern China: Synoptic behavior, long-term change, and urbanization effects. Journal of Climate, 30(2), pp. 703-720. Mahboob, M. A., Atif, I., & Iqbal, J. (2015). Remote sensing and GIS applications for assessment of urban sprawl in Karachi, Pakistan. Science, Technology and Development, 34(3), pp. 179-188. Mallinis, G., Koutsias, N., Tsakiri-Strati, M., & Karteris, M. (2008). Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS Journal of Photogrammetry and Remote Sensing, 63(2), pp. 237-250. Mas, J.-F., Velázquez, A., Díaz-Gallegos, J. R., Mayorga-Saucedo, R., Alcántara, C., Bocco, G., . . . Pérez-Vega, A. (2004). Assessing land use/cover changes: a nationwide multidate spatial database for Mexico. International Journal of Applied Earth Observation and Geoinformation, 5(4), pp. 249-261. Mathew, A., Chaudhary, R., Gupta, N., Khandelwal, S., & Kaul, N. (2015). Study of Urban Heat Island Effect on Ahmedabad City and Its Relationship with Urbanization and Vegetation Parameters. International Journal of Computer & Mathematical Science, 4, pp. 2347-2357. Megahed, Y., Cabral, P., Silva, J., & Caetano, M. (2015). Land cover mapping analysis and urban growth modelling using remote sensing techniques in greater Cairo region—Egypt. ISPRS International Journal of Geo-Information, 4(3), pp. 1750-1769. Metternicht, G. (2001). Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system. Ecological modelling, 144(2-3), pp. 163-179. Miller, R. B., & Small, C. (2003). Cities from space: potential applications of remote sensing in urban environmental research and policy. Environmental Science & Policy, 6(2), pp. 129-137. Mirzaei, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206. Mohammed, I., Aboh, H., & Emenike, E. (2007). A regional geoelectric investigation for groundwater exploration in Minna area, north west Nigeria. Science World Journal, 2(4) Morenikeji, G., Umaru, E., Liman, S., & Ajagbe, M. (2015). Application of Remote Sensing and Geographic Information System in Monitoring the Dynamics of Landuse in Minna, Nigeria. International Journal of Academic Research in Business and Social Sciences, 5(6), pp. 320-337. Mukherjee, A. B., Krishna, A. P., & Patel, N. (2018). Application of Remote Sensing Technology, GIS and AHP-TOPSIS Model to Quantify Urban Landscape Vulnerability to Land Use Transformation Information and Communication Technology for Sustainable Development (pp. 31-40): Springer. Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), pp. 1145-1161. Nemmour, H., & Chibani, Y. (2006). Multiple support vector machines for land cover change detection: An application for mapping urban extensions. ISPRS Journal of Photogrammetry and Remote Sensing, 61(2), pp. 125-133. Niu, X., & Ban, Y. (2013). Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. International journal of remote sensing, 34(1), pp. 1-26. Nogueira, K., Penatti, O. A., & dos Santos, J. A. (2017). Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, pp. 539-556. Oguz, H., & Zengin, M. (2011). Analyzing land use/land cover change using remote sensing data and landscape structure metrics: a case study of Erzurum, Turkey. Fresenius Environmental Bulletin, 20(12), pp. 3258-3269. Pohl, C., & Van Genderen, J. L. (1998). Review article multisensor image fusion in remote sensing: concepts, methods and applications. International journal of remote sensing, 19(5), pp. 823-854. Price, O., & Bradstock, R. (2014). Countervailing effects of urbanization and vegetation extent on fire frequency on the Wildland Urban Interface: Disentangling fuel and ignition effects. Landscape and urban planning, 130, pp. 81-88. Prosdocimi, I., Kjeldsen, T., & Miller, J. (2015). Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models. Water resources research, 51(6), pp. 4244-4262. Rawat, J., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), pp. 77-84. Rokni, K., Ahmad, A., Solaimani, K., & Hazini, S. (2015). A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. International Journal of Applied Earth Observation and Geoinformation, 34, pp. 226-234. Sakieh, Y., Amiri, B. J., Danekar, A., Feghhi, J., & Dezhkam, S. (2015). Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran. Journal of Housing and the Built Environment, 30(4), pp. 591-611. Santra, A. (2016). Land Surface Temperature Estimation and Urban Heat Island Detection: A Remote Sensing Perspective. Remote Sensing Techniques and GIS Applications in Earth and Environmental Studies, p 16. Shrivastava, L., & Nag, S. (2017). MONITORING OF LAND USE/LAND COVER CHANGE USING GIS AND REMOTE SENSING TECHNIQUES: A CASE STUDY OF SAGAR RIVER WATERSHED, TRIBUTARY OF WAINGANGA RIVER OF MADHYA PRADESH, INDIA. Shuaibu, M., & Sulaiman, I. (2012). Application of remote sensing and GIS in land cover change detection in Mubi, Adamawa State, Nigeria. J Technol Educ Res, 5, pp. 43-55. Song, B., Li, J., Dalla Mura, M., Li, P., Plaza, A., Bioucas-Dias, J. M., . . . Chanussot, J. (2014). Remotely sensed image classification using sparse representations of morphological attribute profiles. IEEE transactions on geoscience and remote sensing, 52(8), pp. 5122-5136. Song, X.-P., Sexton, J. O., Huang, C., Channan, S., & Townshend, J. R. (2016). Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sensing of Environment, 175, pp. 1-13. Tayyebi, A., Shafizadeh-Moghadam, H., & Tayyebi, A. H. (2018). Analyzing long-term spatio-temporal patterns of land surface temperature in response to rapid urbanization in the mega-city of Tehran. Land Use Policy, 71, pp. 459-469. Teodoro, A. C., Gutierres, F., Gomes, P., & Rocha, J. (2018). Remote Sensing Data and Image Classification Algorithms in the Identification of Beach Patterns Beach Management Tools-Concepts, Methodologies and Case Studies (pp. 579-587): Springer. Toth, C., & Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, pp. 22-36. Tuholske, C., Tane, Z., López-Carr, D., Roberts, D., & Cassels, S. (2017). Thirty years of land use/cover change in the Caribbean: Assessing the relationship between urbanization and mangrove loss in Roatán, Honduras. Applied Geography, 88, pp. 84-93. Tuia, D., Flamary, R., & Courty, N. (2015). Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions. ISPRS Journal of Photogrammetry and Remote Sensing, 105, pp. 272-285. Tzotsos, A., & Argialas, D. (2008). Support vector machine classification for object-based image analysis Object-Based Image Analysis (pp. 663-677): Springer. Wang, L., Sousa, W., & Gong, P. (2004). Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International journal of remote sensing, 25(24), pp. 5655-5668. Wang, Q., Zeng, Y.-e., & Wu, B.-w. (2016). Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China. Renewable and Sustainable Energy Reviews, 54, pp. 1563-1579. Wang, S., Ma, H., & Zhao, Y. (2014). Exploring the relationship between urbanization and the eco-environment—A case study of Beijing–Tianjin–Hebei region. Ecological Indicators, 45, pp. 171-183. Weitkamp, C. (2006). Lidar: range-resolved optical remote sensing of the atmosphere: Springer Science & Business. Wellmann, T., Haase, D., Knapp, S., Salbach, C., Selsam, P., & Lausch, A. (2018). Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing. Ecological Indicators, 85, pp. 190-203. Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), pp. 884-893. Willhauck, G., Schneider, T., De Kok, R., & Ammer, U. (2000). Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. Proceedings of XIX ISPRS congress. Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., . . . Young, S. A. (2009). Overview of the CALIPSO mission and CALIOP data processing algorithms. Journal of Atmospheric and Oceanic Technology, 26(11), pp. 2310-2323. Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., & Tucker III, C. J. (2015). Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations: Springer. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., & Schirokauer, D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72(7), pp. 799-811. Zhou, D., Zhao, S., Zhang, L., & Liu, S. (2016). Remotely sensed assessment of urbanization effects on vegetation phenology in China's 32 major cities. Remote Sensing of Environment, 176, pp. 272-281. Zhu, Z., Fu, Y., Woodcock, C. E., Olofsson, P., Vogelmann, J. E., Holden, C., . . . Yu, Y. (2016). Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014). Remote Sensing of Environment, 185, pp. 243-257.

Abstract. Surface water and energy fluxes are essential components of the Earth system. Surface latent heat fluxes provide major energy input to the atmosphere. Despite the importance of these fluxes, state-of-the-art data sets of surface energy and water fluxes largely differ. The present paper introduces a new framework for the estimation of surface energy and water fluxes at the land surface, which allows for temporally and spatially high-resolved flux estimates at the quasi-global scale (50° S, 50° N) (High resOlution Land Atmosphere Parameters from Space – HOLAPS v1.0). The framework makes use of existing long-term satellite and reanalysis data records and ensures internally consistent estimates of the surface radiation and water fluxes. The manuscript introduces the technical details of the developed framework and provides results of a comprehensive sensitivity and evaluation study. Overall the root mean square difference (RMSD) was found to be 51.2 (30.7) W m−2 for hourly (daily) latent heat flux, and 84 (38) W m−2 for sensible heat flux when compared against 48 FLUXNET stations worldwide. The largest uncertainties of latent heat flux and net radiation were found to result from uncertainties in the solar radiation flux obtained from satellite data products.

CAPACIDADE DE USO DAS TERRAS DA BACIA DO RIBEIRÃO ÁGUA FRIA – BOFETE (SP) Sérgio CamposThiago Godinho dos SantosCristiane Lopes da SilvaZacarias Xavier de BarrosLincoln Gehring CardosoDepartamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista, CP 237, CEP 18603-970, Botucatu - SP, E-mail: seca@fca.unesp.br. 1 RESUMO A determinação da capacidade de uso das terras numa bacia é muito importante para o planejamento e uso do solo, pois o uso inadequado e sem planejamento dessas terras provocam a baixa produtividade das culturas. Este trabalho visou definir as classes homogêneas de capacidade de uso da terra da bacia do Ribeirão Água Fria - Bofete (SP) para atender ao planejamento de práticas de conservação do solo desta área. A bacia situa-se entre as coordenadas geográficas 22o 58' 30`` a 23o 04' 30`` de latitude S e 48o 09' 30`` a 48o 18' 30`` de longitude W Gr., apresentando uma área de 9.180,12 hectares. A carta de capacidade de uso da terra da bacia foi elaborada a partir da carta clinográfica obtida por Santos et al. (1999), mapa pedológico do Estado de São Paulo (Oliveira et al., 1999), da tabela de julgamento de classes de capacidade de uso do solo (França, 1963) e das recomendações constantes no manual para levantamento utilitário do meio físico e classificação das terras no sistema de capacidade de uso (Lepsch et al., 1983). A discriminação, o mapeamento e a quantificação das áreas das classes e subclasses de capacidade de uso pelo Sistema de Informação Geográfica - IDRISI apresentaram os seguintes valores: IIIe,s - 517,020 ha (5,63%); IIIs - 863,150 ha (9,40%); IVe - 846,730 ha (9,23%); VIe - 871,110 ha (9,49%) e VIIe - 6082,115 ha (66,25%). Os resultados permitiram concluir que a bacia essencialmente constituída por 2/3 pela subclasse VIIe, ou seja, são terras que podem ser utilizadas por pastagens com uso moderado ou florestas, pois apresentam problemas complexos de erosão por causa de sua declividade. O Sistema de Informação Geográfica IDRISI permitiu através de seus módulos discriminar, mapear e quantificar as áreas das classes e subclasses de capacidade de uso das terras da bacia com rapidez e confiabilidade. UNITERMOS: Capacidade de uso, unidades de solo, classes de declive, bacia hidrográfica. CAMPOS, S., SANTOS, T.G., SILVA, C.L., BARROS, Z.X., CARDOSO, L.G. LAND USE CAPACITY OF AGUA FRIA STREAM BASIN – BOFETE (SP) 2 ABSTRACT The land use capacity determination is considered to be very important on land use planning, since its inadequate utilization can lead to low crop productivity. This work aimed to define the homogeneous classes of land use capacity of Agua Fria stream Basin – Bofete (SP) in order to help the soil conservation procedure planning in this area. The Basin is located from 22°58’30’’ to 23° 04’30’’ southern latitude and 48° 09’30’’ to 48°18’30’’ western longitude, in a 9180.12 ha area. The chart of land use capacity was established based on the clinographic chart by Santos et al. (1999), São Paulo state pedological map (Oliveira et al, 1999), class determination chart of soil use capacity (França, 1963) and the recommendations from the manual for physical environmental utilitary survey in the using capacity system (Lepsch et al, 1983). The determination, mapping and quantification of class and subclass areas of using capacity by the Geographic Information System – IDRISI presented the following values: IIIe,s – 517.020 ha (5.63%); IIIs 863.150 ha (9.40 %); IVe – 846.730 ha (9.23%); VIe – 871.110 ha (9.49 %) e VIIe – 6082.115 ha (66.25 %). The results allowed to conclude that the Basin is 2/3 essentially constituted by VIIe subclass, i.e., land that could be used for moderate grazing or forests, since it has complex erosion problems due to its slope. The Geographic Information System IDRISI by its modules enabled to discriminate, map and quantify the land use capacity class and subclass areas of the basin fast and reliably. KEY-WORDS: Using capacity, soil unit, sloping class, hidrographic basin.

Abstract. Daily, quasi-global (50° N–S and 180° W–E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchment-scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making diagnostic structural improvements to hydrologic models whenever possible.

Numerous socio-economic activities depend on the seasonal rainfall and groundwater recharge cycle across the Central American Isthmus. Population growth and unregulated land use changes resulted in extensive surface water pollution and a large dependency on groundwater resources. This chapter uses stable isotope variations in rainfall, surface water, and groundwater of Costa Rica, Nicaragua, El Salvador, and Honduras to develop a regionalized rainfall isoscape, isotopic lapse rates, spatial-temporal isotopic variations, and air mass back trajectories determining potential mean recharge elevations, moisture circulation patterns, and surface water-groundwater interactions. Intra-seasonal rainfall modes resulted in two isotopically depleted incursions (W-shaped isotopic pattern) during the wet season and two enriched pulses during the Mid-Summer Drought and the months of the strongest trade winds. Notable isotopic sub-cloud fractionation and near-surface secondary evaporation were identified as common denominators within the Central American Dry Corridor. Groundwater and surface water isotope ratios depicted the strong orographic separation into the Caribbean and Pacific domains, mainly induced by the governing moisture transport from the Caribbean Sea, complex rainfall producing systems across the N-S mountain range, and the subsequent mixing with local evapotranspiration, and, to a lesser degree, the eastern Pacific Ocean fluxes. Groundwater recharge was characterized by a) depleted recharge in highland areas (72.3%), b) rapid recharge via preferential flow paths (13.1%), and enriched recharge due to near-surface secondary fractionation (14.6%). Median recharge elevation ranged from 1,104 to 1,979 m a.s.l. These results are intended to enhance forest conservation practices, inform water protection regulations, and facilitate water security and sustainability planning in the Central American Isthmus.