Soil erosion prediction using the Revised universal soil loss equation (RUSLE) in Google Earth Engine (GEE) cloud-based platform
Автор: Papaiordanidis S., Gitas I.Z., Katagis T.
Журнал: Бюллетень Почвенного института им. В.В. Докучаева @byulleten-esoil
Рубрика: Статьи
Статья в выпуске: 100, 2019 года.
Бесплатный доступ
High-quality soils are an important resource affecting the quality of life of human societies, as well as terrestrial ecosystems in general. Thus, soil erosion and soil loss are a serious issue that should be managed, in order to conserve both artificial and natural ecosystems. Predicting soil erosion has been a challenge for many years. Traditional field measurements are accurate, but they cannot be applied to large areas easily because of their high cost in time and resources. The last decade, satellite remote sensing and predictive models have been widely used by scientists to predict soil erosion in large areas with cost-efficient methods and techniques. One of those techniques is the Revised Universal Soil Loss Equation (RUSLE). RUSLE uses satellite imagery, as well as precipitation and soil data from other sources to predict the soil erosion per hectare in tons, in a given instant of time. Data acquisition for these data-demanding methods has always been a problem, especially for scientists working with large and diverse datasets...
Soil erosion prediction, rusle, google earth engine, pindos mountain range
Короткий адрес: https://sciup.org/143168540
IDR: 143168540 | DOI: 10.19047/0136-1694-2019-100-36-52
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