Evaluating digital terrain indices for soil wetness mapping – a Swedish case study

Abstract

Abstract. Trafficking wet soils within and near stream and lake buffers can cause soil disturbances, i.e. rutting and compaction. This -in turn -can lead to increased surface flow, thereby facilitating the leaking of unwanted substances into downstream environments. Wet soils in mires, near streams and lakes have particularly low bearing capacity and are therefore more susceptible to rutting. It is therefore important to model and map the extent of these areas and associated wetness variations. This can now be done with adequate reliability using a high-resolution digital elevation model (DEM). In this article, we report on several digital terrain indices to predict soil wetness by wet-area locations. We varied the resolution of these indices to test what scale produces the best possible wet-areas mapping conformance. We found that topographic wetness index (T WI ) and the newly developed cartographic depth-to-water index (D TW ) were the best soil wetness predictors. While the T WI derivations were sensitive to scale, the D TW derivations were not and were therefore numerically robust. Since the D TW derivations vary by the area threshold for setting stream flow initiation, we found that the optimal threshold values for permanently wet areas varied by landform within the Krycklan watershed, e.g. 1-2 ha for till-derived landforms versus 8-16 ha for a coarsetextured alluvial floodplain.

Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping

“…Comparison with other models, as well as our reference data set, indicate that overall patterns of wetland distribution are well-captured within our study area. The TWI input variable was of greatest importance to our T model (Figure 3), reflecting the importance of local topographic and hydrographic conditions in wetland occurrence: a relationship also demonstrated by other topographically-based wet areas mapping efforts such as those described by [82,83]. Indeed, the TWI showed the greatest influence of all input variables in each of the four probability models, regardless of what additional input variables were included (Figure 3).…”

High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes

“…Divergence in the optimal CA for modeling different soil and vegetation characteristics using DTW is indicated by the range of values reported in the literature [19][20][21]24,55]. In this the foothills landscape is dominated by the fine textured glacial till parent material, and the strength of the relationship between SI and DTW differs with different CA for all three species (Figure 2).…”

“…In addition, detailed physical and chemical soil properties, as well as soil, vegetation, and drainage type are found to be subject to topographic controls, and DTW with a 4 ha CA effectively estimated these in the Foothills Natural Region of Alberta [19]. In the Swedish boreal landscape DTW has been shown to be a good predictor of soil wetness, and while it was insensitive to the DEM scale, the optimal CA threshold varied by landform (from 1-2 ha on slowly permeable till deposits to 8-16 ha on coarse-textured deposits where water would drain quickly) [20]. The soil moisture regime, drainage class, and depth-to-mottles were strongly related with DTW at a 2 ha catchment area in young aspen-dominated boreal plain forests [21].…”

Remote sensing proxies of productivity and moisture predict forest stand type and recovery rate following experimental harvest

“…The approach focuses on defining wet soils and areas close to lakes, streams, and mires as they are particularity susceptible to soil disturbances (e.g., rutting and compaction) (Ågren et al, 2014;Murphy et al, 2008a). The depth to water estimates were produced following the wet areas mapping approach described by White et al (2012) and includes: (1) creating a continuous routing flow (Hornberger and Boyer, 1995;Jenson and Domingue, 1988) of water over the ALS-derived terrain model, using a combination of depression filling and breaching; (2), predicting the locations of streams on the surface based on a flow accumulation threshold; (3) interpolating the water table between surface features and calculating the cartographic depth to water.…”