Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview

. There are an increasing number of digital elevation models (DEMs) available worldwide for deriving elevation differences over time, including vertical changes on glaciers. Most of these DEMs are heavily post-processed or merged, so that physical error modelling becomes difficult and statistical error modelling is required instead. We propose a three-step methodological framework for assessing and correcting DEMs to quantify glacier elevation changes: (i) remove DEM shifts, (ii) check for elevation-dependent biases, and (iii) check for higher-order, sensor-specific biases. A simple, analytic and robust method to co-register elevation data is presented in regions where stable terrain is either plentiful (case study New Zealand) or limited (case study Svalbard). The method is demonstrated using the three global elevation data sets available to date, SRTM, ICESat and the ASTER GDEM, and with automatically generated DEMs from satellite stereo instruments of ASTER and SPOT5-HRS. After 3-D co-registration, significant biases related to elevation were found in some of the stereoscopic DEMs. Biases related to the satellite acquisition geometry (along/cross track) were detected at two frequencies in the automatically generated ASTER DEMs. The higher frequency bias seems to be related to satellite jitter, most apparent in the back-looking pass of the satellite. The origins of the more significant lower frequency bias is uncertain. ICESat-derived elevations are found to be the most consistent globally available elevation data set available so far. Before performing regional-scale glacier elevation change studies or mosaicking DEMs from multiple individual tiles (e.g. ASTER GDEM), we recommend to co-register all elevation data to ICESat as a global vertical reference system.

https://tc.copernicus.org/articles/5/271/2011/tc-5-271-2011.pdf

Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change

The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate-change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges.

/pdf/modeling-soil-processes-review-key-challenges-and-new-4a1lz21a4f.pdf

Modeling Soil Processes: Review, Key Challenges, and New Perspectives

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

/pdf/high-resolution-mapping-of-soil-properties-using-remote-2tbn5zrniy.pdf

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

Over the last 50 years, retreating glaciers and ice caps (GIC) contributed 0.5 mm/yr to sea level rises (SLR), and one third is believed to originate from ice masses bordering the Gulf of Alaska. However, these estimates of ice wastage in Alaska are based on methods that measure a limited number of glaciers and extrapolate the results to estimate ice loss for the many thousands of others. How these methods capture the complex pattern of decadal elevation changes at the scale of individual glacier and mountain range is unclear. Here, combining a comprehensive glacier inventory with elevation changes derived from sequential digital elevation models (DEMs), we found that, between 1962 and 2006, Alaskan glaciers lost 41.9 ± 8.6 km**3/yr water equivalent (w.e.) and contributed 0.12±0.02 mm/yr to SLR. Our ice loss is 34% lower than previous estimates. Reasons for our lower values include the higher spatial resolution of our glacier inventory and the reduction of ice thinning under debris and at the glacier margins which were not resolved in earlier work. Estimates of mass loss from GIC in other mountain regions could be subject to similar revisions.

/pdf/contribution-of-alaskan-glaciers-to-sea-level-rise-derived-pvatvml8gr.pdf

Contribution of Alaskan glaciers to sea-level rise derived from satellite imagery

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument onboard the National Aeronautics and Space Administration's Terra spacecraft has an along-track stereoscopic capability using its a near-infrared spectral band to acquire the stereo data. ASTER has two telescopes, one for nadir-viewing and another for backward-viewing, with a base-to-height ratio of 0.6. The spatial resolution is 15 m in the horizontal plane. Parameters such as the line-of-sight vectors and the pointing axis were adjusted during the initial operation period to generate Level-1 data products with a high-quality stereo system performance. The evaluation of the digital elevation model (DEM) data was carried out both by Japanese and U.S. science teams separately using different DEM generation software and reference databases. The vertical accuracy of the DEM data generated from the Level-1A data is 20 m with 95% confidence without ground control point (GCP) correction for individual scenes. Geolocation accuracy that is important for the DEM datasets is better than 50 m. This appears to be limited by the spacecraft position accuracy. In addition, a slight increase in accuracy is observed by using GCPs to generate the stereo data.

ASTER DEM performance

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ASTER DEM performance