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Soil surveys are necessary sources of information for land use planning, but they are not always available. This study proposes the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas. From a digitalized soil map and terrain parameters derived from the digital elevation model in ArcView environment, several sets of multiple logistic regressions were defined using statistical software Minitab, establishing relationship between explanatory terrain variables and soil types, using either the original legend or a simplified legend, and using or not stratification of the study area by drainage classes. Terrain parameters, such as elevation, distance to stream, flow accumulation, and topographic wetness index, were the variables that best explained soil distribution. Stratification by drainage classes did not have significant effect. Simplification of the original legend increased the accuracy of the method on predicting soil distribution. Key words: GIS, DEM, soil survey, terrain analysis MAPEAMENTO DIGITAL DE SOLOS UTILIZANDO REGRESSÕES LOGÍSTICAS MÚLTIPLAS E PARÂMETROS DO TERRENO NO SUL DO BRASILRESUMO: Os levantamentos de solos são fontes de informação necessárias para o planejamento de uso das terras, entretanto eles nem sempre estão disponíveis. Este estudo propõe o uso de regressões logísticas múltiplas na predição de ocorrência de classes de solos a partir de áreas de referência. Baseado no mapa original de solos em formato digital e parâmetros do terreno derivados do modelo numérico do terreno em ambiente ArcView, vários conjuntos de regressões logísticas múltiplas foram definidas usando o programa estatístico Minitab, estabelecendo relações entre as variáveis do terreno independentes e tipos de solos, usando tanto a legenda original como uma legenda simplificada, e usando ou não estratificação da área de estudo por classes de drenagem. Os parâmetros do terreno como elevação, distância dos rios, acúmulo de fluxo e índice de umidade topográfica foram as variáveis que melhor explicaram a distribuição das classes de solos. A estratificação por classes de drenagem não teve efeito significativo. A simplificação da legenda aumentou a precisão do método na predição da distribuição dos solos. Palavras-chave: levantamento de solos, SIG, MNT, análise do terreno

Robust and accurate regional estimates of C storage in soils are currently an important research topic because of ongoing debate about human‐induced changes in the terrestrial C cycle. Widely available geoprocessing tools were applied to estimate native soil organic C (SOC) stocks of Rio Grande do Sul state in southern Brazil to a depth of 30 cm from previously sampled soil pedons under undisturbed vegetation. The study used a statewide comprehensive soil survey comprising a small‐scale soil map, a climate map, and a soil pedon database. Soil organic C stocks under native vegetation were calculated with two different approaches: the Tier 1 method of the Intergovernmental Panel on Climate Change (IPCC) and a refined method based on actual field measurements derived from soil profile data. Highest SOC stocks occurred in Neossolos Quartzarênico hidromórfico (Aquents), Organossolos Tiomórficos (Hemists), Latossolos Brunos (Udox), and Vertissolos Ebânicos (Uderts) soil classes. Before human use of soils, most C was stored in the Latossolos Vermelhos (Udox) and Neossolos Regolíticos (Orthents), which occupy a large area of Rio Grande do Sul. Generally, IPCC default reference SOC stocks compared well with SOC stocks calculated from soil pedons. The total SOC stock of Rio Grande do Sul was estimated at 1510.3 Tg C (5.8 kg C m−2) by the IPPC method and 1597.5 ± 363.9 Tg C (7.4 ± 1.9 kg C m−2) calculated from soil pedons. The SOC digital map and SOC database developed in this study provide crucial background information for state‐level contemporary assessment of C stocks and soil C sequestration programs and initiatives.