18 October 2016 Monitoring soil aggregates dynamics at a plot scale using multitemporal image texture and colour analysis
Author Affiliations +
Monitoring of soil aggregate breakdown remains, even at the micro-plot scale, a challenge. Remote sensing has shown its potential to assess many different soil properties and is a fast and non-destructive method to investigate soil susceptibility to water erosion. We designed an outdoor experiment to monitor soil aggregates breakdown under natural rainfall at a micro-plot scale using a regular camera. Five soils susceptible to detachment (silty loam with various organic matter content, loam and sandy loam) were photographed once per day. We collected images and rainfall data from November 2014 until February 2015. Considering that the soil surface roughness causes shadow cast, the blue/red band ratio is used to observe the soil aggregates changes. In addition, a Gray Level Co-occurrence Matrix (GLCM) is used to extract the image texture entropy which reflects the process of soil aggregates breakdown. In our research the entropy calculated at 135 degrees along the direction of shadows gives best results. Our results show that both entropy and shadow index follow the wetting and drying cycles with a decrease due to a rain event. This decrease is small due to low rainfall intensity (< 2.5 mmh-1) for the entire period that the experiment ran. However, the biggest rain event of 20 mmday-1 resulted in a decrease in entropy, meaning that sufficient rainfall energy was present to trigger the soil aggregates break down. This research concludes that both entropy and shadow index obtained with a regular camera enable the monitoring of soil aggregate breakdown at a high spatial resolution.
Conference Presentation
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Irena Ymeti, Irena Ymeti, Harald van der Werff, Harald van der Werff, Freek van der Meer, Freek van der Meer, Victor Jetten, Victor Jetten, "Monitoring soil aggregates dynamics at a plot scale using multitemporal image texture and colour analysis", Proc. SPIE 10005, Earth Resources and Environmental Remote Sensing/GIS Applications VII, 100050S (18 October 2016); doi: 10.1117/12.2241163; https://doi.org/10.1117/12.2241163

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