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El número de estudios relacionados a la cuantificación de erosión hídrica en el país es limitado. En consecuencia, el presente artículo busca cubrir esta limitación a partir de información satelital. Para ello, se cuantificó la erosión de suelos en los años 1990, 2000 y 2010 aplicando la ecuación de RUSLE, lo cual dio como resultado mapas a 5km de resolución a escala nacional. A partir de lo anterior, los resultados muestran que los rangos altos de erosión se localizan en la región andina y están probablemente relacionados con actividades extractivas. Finalmente, los mapas producidos tienen el potencial de proveer información para diseñar una regulación respecto al control de erosión en el país.

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Submitted by Leticia Schettini (leticia@ufrrj.br) on 2016-10-10T13:01:19Z No. of bitstreams: 1 2011 - Valdemir Lucio Durigon.pdf: 4431691 bytes, checksum: bc255769946a67387959db9cc02dbb28 (MD5) / Made available in DSpace on 2016-10-10T13:01:19Z (GMT). No. of bitstreams: 1 2011 - Valdemir Lucio Durigon.pdf: 4431691 bytes, checksum: bc255769946a67387959db9cc02dbb28 (MD5) Previous issue date: 2016-02-25 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior-CAPES / Soil erosion is a growing problem in today's world, virtually in every continent, mainly due to the rising demand for food, fibers and biofuels. On the other hand, the correct planning of land use, avoiding farming lands with high erosion risk, could minimize the erosion process without compromising food security. The aim of this study was to evaluate erosion risk and soil losses in a watershed using remote sensing and GIS, coupled with temporal evolution of vegetation cover and occupation of land, in areas of the municipalities of Paty do Alferes and Miguel Pereira, Rio de Janeiro State. For this, the RUSLE (Revised Universal Soil Loss Equation) was used as model to predict soil loss, combined with remote sensing data and the derived NDVI (Normalized Difference Vegetation Index), to determine the soil coverage in a GIS (Geographic Information System). Satellite images used in this research included: TM sensor of Landsat-5, ETM+ of Landsat-7, CCD of CBERS-2, and CCD and HRC from CBERS 2B, obtained for the study period. The processed remotely sensed data and the geographic information system, integrated with models for predicting soil loss, showed good performance for environmental analysis. The use of NDVI was a suitable tool for determining the vegetation coverage, to be applied in soil loss prediction models. The soil losses and erosion risks were strongly associated with characteristics of relief, and they can be used for planning land usage and occupation, in urban and rural areas. More studies should be conducted linking GIS, erosion prediction models, and remote sensing data in order to better predict the occurrence of environmental disasters, also to create databases about soil?s response in relation to different causative erosional agents. / A eros?o do solo ? um problema crescente no mundo atual, em praticamente todos os continentes, que se deve ao crescimento da demanda por alimentos, ao mau uso dos solos e a necessidade de produ??o de fibras e combust?veis. Por outro lado, o planejamento correto do uso e cobertura dos solos evitando utilizar ?reas para a agricultura que tenham alto risco de eros?o e alto potencial natural de eros?o pode minimizar o processo, sem comprometer a seguran?a alimentar. O objetivo deste trabalho foi avaliar o risco de eros?o e a perda de solo em uma bacia hidrogr?fica utilizando t?cnicas de sensoriamento remoto e SIG, associados ? evolu??o temporal dos ?ndices de cobertura e ocupa??o do solo, em ?reas nos munic?pios de Paty do Alferes e Miguel Pereira, Estado do Rio de Janeiro. Para isso, utilizou-se o modelo de predi??o de perdas de solos, no caso a RUSLE (?Revised Universal Soil Loss Equation?), associado a t?cnica de sensoriamento remoto integrada ao ?ndice de vegeta??o NDVI (?Normalized Difference Vegetation Index?) para determinar a cobertura dos solos em um SIG (Sistema de Informa??o Geogr?fica). Para tal, foram utilizadas imagens de sat?lite dos sensores TM Landsat-5, ETM+ Landsat-7 CCD CBERS-2 e CCD e HRC do CBERS 2B coletadas no per?odo considerado. Os dados de sensoriamento remoto e do sistema de informa??es geogr?ficas integrados a modelos de predi??o de perdas de solos caracterizaramse como importantes instrumentos na an?lise do meio ambiente. A utiliza??o de NDVI constitui-se em ferramenta adequada para a determina??o da cobertura, para aplica??o em modelos de predi??o de perda de solo. As perdas de solo, o potencial natural de eros?o e o risco mostraram forte rela??o com as caracter?sticas do relevo, podendo ser utilizados no planejamento do uso e ocupa??o do solo em ?reas urbana e rural. Mais estudos devem ser realizados associando SIG, modelos de predi??o de eros?o e t?cnicas de sensoriamento remoto no sentido de melhor prever a ocorr?ncia de desastres ambientais, tamb?m para criar bancos de informa??es sobre o comportamento dos solos em rela??o aos diversos agentes causadores do processo erosivo.

FAPERJ / The importance of studying soils in order to preserve its functions includes conservation efforts whose goal is to ensure preservation of natural resources for future generations. This study works with the scientific hypothesis that changes in vegetation cover in the watershed of Palmares-Ribeir?o do Saco, Rio de Janeiro State, in the period of 2009-2015, favored the erosion reduction in this area. To verify this trend an estimate of soil losses was made with use of RUSLE (Revised Universal Soil Loss Equation) which allows generating information to build up management plans to secure agricultural production, and to preserve natural resources of the region. Thus, the objective was to develop a methodology for assessment of vegetation affected by seasonality and able to reduce erosion, based on the NDVI (Normalized Difference Vegetation Index) in order to reduce part of values overestimation associated to errors in the RUSLE model. As secondary objective, to compare estimates of erosion and natural erosion potential (NEP) in the period of 2009-2015 with those obtained in the period of 1986-2009, in order to verify environmental impacts in the watershed. The area studied was the watershed of Palmares-Ribeir?o do Saco, located between the municipalities of Paty do Alferes and Miguel Pereira in the state of Rio de Janeiro. Even without the adoption of conservation practices by some of the region farmers, the general aspect of data provided by RUSLE and the NEP in recent years has shown that soil losses are tending to decrease, mainly due to the low recorded rainfall indexes, implying at low erosivity values. / A import?ncia do estudo dos solos a fim de preservar suas fun??es compreende os esfor?os conservacionistas que tem por meta garantir a persist?ncia dos recursos naturais para as gera??es futuras. O presente estudo trabalha com a hip?tese cient?fica de que a evolu??o da cobertura vegetal na bacia hidrogr?fica de Palmares-Ribeir?o do Saco, no estado do Rio de Janeiro, ocorrida no per?odo 2009-2015, favoreceu a redu??o do processo erosivo na bacia. Para averiguar tal tend?ncia foi feita a estimativa da perda de solo com uso da RUSLE (Revised Universal Soil Loss Equation) que possibilita gerar informa??es para montar planos de manejo que visem garantir a produ??o agr?cola preservando os recursos naturais da regi?o. Sendo assim, o objetivo principal foi desenvolver uma metodologia para avalia??o da vegeta??o afetada pela sazonalidade capaz de reduzir processos erosivos, com base no NDVI (Normalized Difference Vegetation Index) a fim de reduzir parte das superestimativas associadas ao erro no modelo da RUSLE. E como objetivo secund?rio comparar as estimativas do processo erosivo e o potencial natural de eros?o (PNE) no per?odo 2009-2015 com aquelas obtidas no per?odo 1986-2009, com intuito de verificar os impactos ambientais ocorridos na bacia hidrogr?fica. A ?rea utilizada como objeto de estudo foi ? bacia hidrogr?fica de Palmares-Ribeir?o do Saco, situada entre os munic?pios de Paty do Alferes e Miguel Pereira no estado do Rio de Janeiro. Mesmo sem haver a ado??o de pr?ticas conservacionistas por partes dos produtores na regi?o, o aspecto geral dos dados fornecidos pela RUSLE e o PNE dos ?ltimos anos revelou que as perdas de solo est?o tendendo ? redu??o, sobretudo por conta dos baixos ?ndices pluviom?tricos registrados, implicando em valores baixos de erosividade.

When the climate change contributes to a greater amount of extreme weather that does also affect the soil erosion and the number of areas that are being affected by the soil erosion. The areas affected by the soil erosion do often suffers from economic loss because of the loss in agricultural land caused by the soil erosion. The reason being that the soil losses nutrients something that leads to less harvest and poorer quality of the water. This bachelor thesis aims to study how soil erosion gets affected by the climate change, and more specific the extreme weather, by using a RUSLE analysis.  By knowing in what extent different areas most likely get affected by soil erosion, there is great possibilities to protect those areas by protection against erosion. In this thesis three analysis have been performed over areas located in Byälvens runoff-areas to extend the knowledge about how extreme weather affect the areas and out of that get a guideline for how similar areas might be affected. As a data source for the analysis Lantmäteriet, SGU and SMHI was used.  The result shows that three analysed areas in Byälvens runoff-areas most likely will lose 93-97 percent more sediment if the precipitation increases with 50 percent compared to 2020 annual value compared to the precipitation between the years of 1961 and 1991. This could be because of the climatological factors that affect the result and the increasement in land loss due to soil erosion. The increasement in soil erosion can depend on human activities that changes the soil structure, climatological factors, but it can also be because of the coal in soil. / När klimatförändringarna bidrar till ett ökat antal extremväder så påverkar det jorderosionen och ökar andelen områden som påverkas av jorderosion. Områden som drabbas av jorderosion påverkas ofta negativt ekonomiskt eftersom resultatet av jorderosion ofta är i form av förluster av odlade områden, minskade näringsämnen vilket påverkar framtida skördar samt ger sämre vattenkvalitet. Detta kandidatarbete syftar till att studera hur jorderosion påverkas av klimatförändringarna, med fokus på extremväder, främst med hjälp av en RUSLE-analys. Genom att veta i vilken omfattning olika områden sannolikt kan bli drabbade av jorderosion så finns det goda möjligheter att förhindra en ökad jorderosion genom erosionsskydd. I det här arbetet gjordes det analyser över tre områden som är lokaliserade i Byälvens avrinningsområde för att förstå hur dessa kan bli påverkade vid en ökad andel extremväder eftersom det kan ge en riktlinje för hur andra liknande områden påverkas. Som datakälla till analysen användes Lantmäteriet, SGU och SMHI. Resultatet visar på att de tre analyserade områdena i Byälvens avrinningsområde sannolikt kommer att förlora mellan 93–97% mer sediment om nederbördsmängderna i området ökar med 50% jämfört med 2020-årsvärde i jämförelse med genomsnittsnederbörden mellan åren 1961 och 1991. Detta kan bero på de ökade klimatologiska faktorerna som påverkar och ökar jorderosionen i de undersökta områdena. Ökade jorderosioner på en plats som tidigare inte haft några omfattande jorderosions händelser kan bero på mänskliga aktiviteter som förändrar jordens struktur, klimatologiska faktorer men, även på det organiskt bundna kolet i jordarten.

The goal of this study is to develop a model using GIS to estimate the source and quantity of accumulated sediment in the Emory & Henry College (EHC) duck pond.  Located in the Highlands of Southwest Virginia, the 1,194 acre duck pond watershed consists primarily of agricultural, forested, and low density urban land uses. The Revised Universal Soil Loss Equation (RUSLE) and the Sediment Distributed Delivery (SEDD) prediction models were used to determine the quantity of eroded sediment and the sediment yield at the duck pond, respectively.  These models require numerous computations, which were performed at the watershed scale with the aid of ArcGIS software.  In ArcGIS the watershed was broken into a raster grid of approximately 5,200 discrete 100 foot by 100 foot grid cells. The resulting watershed erosion model identified two main sources of sediment: a cluster of farms relatively close to and east of the duck pond, and a harvested timber site north of the duck pond.  The model predicted that 1,076 tons of sediment are delivered into the duck pond annually. The estimated sediment yield was then compared to the estimated amount deposited between October 2011 and September 2012, as measured by a topographic survey.  The model prediction was found to be within a factor of 6.3x of the measured value.  The predicted and measured sediment yields as well as identified erosion sources can be used to develop a water quality improvement plan and to help alleviate the need for periodic dredging. / Master of Science

Rivers and streams are adversely affected by an increase in sedimentation in their waters from eroding land. High sediment loads in streams can bury fish eggs and prevent hatching, increasing nutrients in the water causing algae blooms, or even contaminating the water with heavy metals carried in or on the aggregates. The erodibility of soil is valuable knowledge to all land users so that we may predict soil loss and its potential to pollute streams. This is done by using the Revised Universal Soil Loss Equation (RUSLE). By predicting soil loss from a given landscape, land managers can take mitigation measures. The precision of the current scale available for soil erodibility (K-factor) by the US Department of Agriculture is not useful to small landowners or on a site-by-site basis. In California’s Central Coast, a grassland hillslope toposequence was investigated in a Los Osos-Diablo soil series complex. Geographic information systems software was used for spatial analysis of variation in the K-factor as well as interpolating areas that were not sampled. Analysis of soils’ particle size, infiltration rate, organic matter content, and structure across the toposequence allowed calculation of the soils’ K-factor values. K-factor values for the footslope, backslope, and shoulder were found to be statistically different from one another. All slope position’s average K-factor values were statistically different than the published Los Osos and Diablo series’ K-factor with the exception of the backslope which was not significantly different than Diablo’s K-factor value. The average of all K-factors was found not to be statistically different than the Los Osos’ K-factor but it was statistically different from the Diablo’s soil series K-factor. The USDA K-factors overestimated the predicted soil loss for the study site.

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