با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Steady infiltration rate spatial modeling from remote sensing data and terrain attributes in southeast Brazil
سرعت نفوذ ثابت مدل سازی فضایی از داده های سنجش از دور و زمین ورزشی در جنوب شرقی برزیل-2020 This paper aims to describe the development of steady infiltration rate (SIR) spatial prediction models using accessible
input data. The models were created from SIR data collected through simulated rainfall at 71 points
in part of the Cachimbal stream watershed (a Paraíba do Sul River tributary watershed) in Rio de Janeiro state
– Brazil, using as covariates: terrain attributes derived from digital elevation model (DEM), remote sensing
data and soil class, physical and chemical attributes maps. Itwas discussed how different land uses and soil degradation
levels affect SIR and how NDVI can be used to represent themon SIR modeling. Among the soil physical
properties, bulk density (BD) and total sand (TS)were selected as covariates. SIR was higherwhen lower the bulk
density and higher the sand content. Soil types play a big role in SIR, highlighting the Gleissolos Háplicos
(Gleysols) as the soil class that presented the lower average SIR values and the Latossolos Vermelho Amarelos
and Nitossolos Háplicos (Ferralsols and Nitisols) that presented the highest. Topographic position Index (TPI),
curvature, and TopographicWetness Index (TWI)were the terrain covariates used in the models. Their usage indicate
lower SIR in concave, lower and wetter parts of the landscape. The results demonstrated that is possible to
achieve satisfactory results for SIR spatialmodeling using easily accessible data (remote sensing and terrain attributes),
but soil information is also necessary to develop better prediction models. Keywords: Rainfall simulator | Vegetation indexes | Acrisols | Cambisols |
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