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Remote mapping of soil erosion risk in Iceland

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Remote mapping of soil erosion risk in Iceland
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Soil erosion is a major global land degradation threat. Improving knowledge of the probable future rates of soil erosion, accelerated by human activity and climate change, is one of the most decisive factors when it comes to making decisions about conservation policies and for earth-system modelers seeking to reduce uncertainty on global predictions [1]. In this context, the use of remote-sensing based methods for soil erosion assessment has been increasing in recent years thanks to the availability of free access satellite data, and it has repeatedly proven to be successful [2, 3]. Accurate information about it is, however, usually known only at the local scale and based on limited field campaigns. Its application to the Arctic presents a number of challenges, due to peculiar soils with short growing periods, winter storms, wind, and frequent cloud and snow cover. However, the benefits of applying these techniques would be especially valuable in arctic areas, where ground local information can be hard to obtain due to hardly accessible roads and lands. Here we propose a hybrid solution, which uses ground truth samples to calibrate the processed remote images over a specific area, to then automate the analysis for larger, less accessible areas. This solution is being developed for soil erosion studies of Iceland specifically, using Sentinel 2 satellite data combined with local assessment data from Iceland’s Soil Conservation Services department, Landgræðslan. Their historical data is more extensive than usual, since they are the oldest soil erosion department in the world. Available data includes parameters of bare ground cover, which can be calculated from satellite images alone, after using information from observationally correct areas without vegetation for calibration; Icelandic soil profiles, to be analyzed to find how the profile relates to soil erosion intensity; as well as the parameters of agriculture use and arable land data including plant species in cultivated lands. For the training phase we employ a dataset composed of 550 cropped georeferenced and atmospherically corrected Sentinel 2A images [4], combined with a Digital Elevation Model (DEM) of Iceland that allows us to detect slopes which can produce landslides or help erosion to occur. The dataset is labelled by six degrees of erosion severity, using measurement points furnished by Landgræðslan. We split it into 2/3 for model training and 1/3 for model testing. These images are in tiles of 10980x10980 pixels (about 600 MB) and cover an area of approximately 100x100 km2. We can crop the images down to preferred size. They contain multispectral data, divided up into 12 bands of varying wavelengths, and a resolution from 10 to 20m. We could add as well some of the 60m bands if necessary. Different band data are combined to create indices which represent or highlight certain features, such as vegetation, soil crusting, bare soil, and red edge indices. Elevation data from the Arctic (north of 60°N, including Iceland) started to be openly available since 2015 through the ArcticDEM project. The DEMs are derived from satellite sub-meter stereo imagery, particularly from WorldView 1-3 and GeoEye-1. This information can be used to detect to what extent plant growth is reduced at higher heights because of longer snow cover, shorter growing period and stronger winds on one side. By using the variation of DEM and building a slope map, we can see that soil erodes more on steep slopes which leads to a higher likelihood of erosion the steeper they are. The tools for geometric and topographic correction include SNAP (Sentinel application platform), Sen2Core, FLAASH (Fast line-of-sight atmospheric analysis of hypercubes), DOS (Dark Object Subtraction) and ATCOR software. This correction reduces effects due to shadows and surface irregularities and corrects the single-date Sentinel-2 Level-1C Top Of Atmosphere (TOA) products from atmospheric effects in order to deliver a Level-2A Bottom-Of-Atmosphere (BOA) reflectance product. After a preprocessing technique based on dimensionality reduction in order to avoid adding too much noise to the algorithm, this labelled data is then used to train a Support Vector Machine (SVM) model for classifying each coordinate. We choose the SVM algorithm as a starting point because it is a fast and reliable algorithm that performs well for classification problems with high-dimensional feature spaces such as ours, and does not require large training sets to achieve high accuracy as other algorithms do (e.g. deep neural networks). The output of the model is a set of coordinates, each with a numeric classification representing soil erosion severity, and used for creating a map of soil erosion severity in a selected area. This methodology has been proven to provide good results, achieving an overall land cover classification accuracy of 94% [5], a performance that can be attributed to the spectral complexity of Sentinel-2 data, particularly the red-edge bands which give room for separability of erosion classes. Low separability is a common limitation to the applicability of classification methods. We address this by using ISODATA and minimum distance methods. Two factors that could affect the accuracy of the delineation of eroded soils using spectral images are the intensity of the soil erosion processes and changes in the spectral characteristics of disturbed soils. The research described here aims at producing a reliable, widely applicable and cost-effective method to classify Icelandic soils into different categories of erosion risk, a proof of concept which, once engineered, could be straightforwardly expanded and applied to other Arctic areas, such as Greenland and Canada.
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Slide ruleIntegrated development environmentConservation lawDecision theoryInsertion lossOrganic computingPoint cloudDirected setPressure volume diagramDegree (graph theory)Computer-generated imageryGeometryFunction (mathematics)Kernel (computing)Musical ensembleBasis <Mathematik>Image resolutionPrice indexPixelSoftwareoutputRepresentation (politics)Latent heatMatter waveSet (mathematics)PreprocessorDigital signalEndliche ModelltheorieStatistical dispersionStandard deviationPredictionAreaProcess (computing)Observational studyCategory of beingDialectPoint cloudWave packetMatter waveMusical ensembleOrder (biology)Support vector machineLevel (video gaming)PredictabilityAreaSet theoryMedical imagingInsertion lossLimit (category theory)Covering spacePrice indexValidity (statistics)ResultantAuditory maskingFinite-state machineFocus (optics)Roundness (object)Set (mathematics)Flow separationComputer animation
Transcript: English(auto-generated)
This is Daniel from Fletan. We are a startup company based on Iceland dedicated to studying soil erosion risk through remote sensing methods. These are our five team members. Soil erosion has become a global degradation threat in recent years. It leads to desertification
and loss of biodiversity and in particular for the Arctic regions it can also lead to the release of organic carbon that has been stored under the permafrost for millions of years and that is one of the foreseen main contributors to climate change in the future. Also the Arctic comes
with its own set of challenges because it's a happily accessible place. It's frequently covered in snow and clouds so remote sensing becomes difficult and the methodologies that
have proven to be successful in other regions of the world cannot be straightforwardly generalized to the Arctic because of its peculiar soils and the fact that the vegetation covers are not not a good indicator. The methodology that we are following so far uses a support vector
machine SVM classifier. We split the data that we have from 80-20 into training and validation sets. The ground truth data has been provided by Land Grazland, the soil conservation agency
of Iceland. So we have about a 10,000 km2 of land for which we have both the classification that has been done by this agency and on the other hand we have Sentinel-2 images that can be compared and we can train on that. These images they need to be pre-processed
first, they need to be geo-referenced and atmospherically corrected. They can in 12 available wavelength bands that we can combine in order to create indices and we also need to develop new masks adapted for the Arctic region
such as for things like legend and permafrost. We also use digital elevation models in order to take into account the fact that soil erosion happens more quickly in steep slopes and that really applies for the Arctic very importantly as well. Now with this the results that we
are obtaining, they show quite a large overall accuracy so we are very confident on our results. Also certain features that can be seen or you can recognize rivers and hilltops and
we can already extract a few conclusions such as the fact that loss of our ability it's a common limitation of these methods and it is being a limitation in our case so that needs to be addressed and it needs to be improved.
We may be able to expand what we are doing in order to cover not only the region for which we have the ground truth data but we can extend it and provide a soil erosion map for all of Iceland and also taking into account that these methods can be applied, that we have
yearly data, we may be able to provide prediction for the future and so far in that way indicate which areas need a more specific focus in the future. And with that I thank you for your attention.