Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in Google Earth Engine (GEE) cloud-based platform

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. Newly emerged online technologies like Google Earth Engine (GEE) have given access to petabytes of data on demand, alongside high processing power to process them. In this paper we investigated seasonal spatiotemporal changes of soil erosion with the use of RUSLE implemented within GEE, for Pindos mountain range in Greece. In addition, we estimated the correlation between the seasonal components of RUSLE (precipitation and vegetation) and mean RUSLE values

Investigation of aggregation effects in vegetation condition monitoring at a national scale

Abstract Monitoring vegetation condition is an important issue in the Mediterranean region, in terms of both securing food and preventing fires. Vegetation indices (VIs), mathematical transformations of reflectance bands, have played an important role in vegetation monitoring, as they depict the abundance and health of vegetation. Instead of storing raster VI maps, aggregated statistics can be derived and used in long-term monitoring. The aggregation schemes (zonations) used in Greece are the forest service units, the fire service units and the administrative units. The purpose of this work was to explore the effect of the Modifiable Areal Unit Problem (MAUP) in vegetation condition monitoring at the above mentioned aggregation schemes using 16day Normalized Difference Vegetation Index (NDVI) composites acquired by the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite sensor. The effects of aggregation in the context of MAUP were examined by analyzing variance, from which the among polygon variation (objects' heterogeneity) and the within polygon variation (pixels' homogeneity) was derived. Significant differences in objects' heterogeneity were observed when aggregating at the three aggregation schemes, therefore there is a MAUP effect in monitoring vegetation condition on a nationwide scale in Greece with NDVI. Monitoring using the fire service units has significantly higher pixels' homogeneity, therefore there is indication that it is the most appropriate for monitoring vegetation condition on a nationwide scale in Greece with NDVI. Results were consistent between the two major types of vegetation, natural and agricultural. According to the statistical validation, conclusions based on the examined years (2003 and 2004) are justified