Automated spatiotemporal landslide mapping over large areas using RapidEye time series data

In the past, different approaches for automated landslide identification based on multispectral satellite remote sensing were developed to focus on the analysis of the spatial distribution of landslide occurrences related to distinct triggering events. However, many regions, including southern Kyrgyzstan, experience ongoing process activity requiring continual multi-temporal analysis. For this purpose, an automated object-oriented landslide mapping approach has been developed based on RapidEye time series data complemented by relief information. The approach builds on analyzing temporal NDVI-trajectories for the separation between landslide-related surface changes and other land cover changes. To accommodate the variety of landslide phenomena occurring in the 7500 km2 study area, a combination of pixel-based multiple thresholds and object-oriented analysis has been implemented including the discrimination of uncertainty-related landslide likelihood classes. Applying the approach to the whole study area for the time period between 2009 and 2013 has resulted in the multi-temporal identification of 471 landslide objects. A quantitative accuracy assessment for two independent validation sites has revealed overall high mapping accuracy (Quality Percentage: 80%), proving the suitability of the developed approach for efficient spatiotemporal landslide mapping over large areas, representing an important prerequisite for objective landslide hazard and risk assessment at the regional scale

Combining Field and Imaging Spectroscopy to Map Soil Organic Carbon in a Semiarid Environment

Semiarid regions are especially vulnerable to climate change and human-induced land-use changes and are of major importance in the context of necessary carbon sequestration and ongoing land degradation. Topsoil properties, such as soil carbon content, provide valuable indicators to these processes, and can be mapped using imaging spectroscopy (IS). In semiarid regions, this poses difficulties because models are needed that can cope with varying land surface and soil conditions, consider a partial vegetation coverage, and deal with usually low soil organic carbon (SOC) contents. We present an approach that aims at addressing these difficulties by using a combination of field and IS to map SOC in an extensively used semiarid ecosystem. In hyperspectral imagery of the HyMap sensor, the influence of nonsoil materials, i.e., vegetation, on the spectral signature of soil dominated image pixels was reduced and a residual soil signature was calculated. The proposed approach allowed this procedure up to a vegetation coverage of 40% clearly extending the mapping capability. SOC quantities are predicted by applying a spectral feature-based SOC prediction model to image data of residual soil spectra. With this approach, we could significantly increase the spatial extent for which SOC could be predicted with a minimal influence of a vegetation signal compared to previous approaches where the considered area was limited to a maximum of, e.g., 10% vegetation coverage. As a regional example, the approach was applied to a 320 km2 area in the Albany Thicket Biome, South Africa, where land cover and landuse changes have occurred due to decades of unsustainable land management. In the generated maps, spatial SOC patterns were interpreted and linked to geomorphic features and land surface processes, i.e., areas of soil erosion. It was found that the chosen approach supported the extraction of soil-related spectral image information in the semiarid region with highly varying land cover. However, the quantitative prediction of SOC contents revealed a lack in absolute accuracy

Combining Field and Imaging Spectroscopy to Map Soil Organic Carbon in a Semiarid Environment

Semiarid regions are especially vulnerable to climate change and human-induced land-use changes and are of major importance in the context of necessary carbon sequestration and ongoing land degradation. Topsoil properties, such as soil carbon content, provide valuable indicators to these processes, and can be mapped using imaging spectroscopy (IS). In semiarid regions, this poses difficulties because models are needed that can cope with varying land surface and soil conditions, consider a partial vegetation coverage, and deal with usually low soil organic carbon (SOC) contents. We present an approach that aims at addressing these difficulties by using a combination of field and IS to map SOC in an extensively used semiarid ecosystem. In hyperspectral imagery of the HyMap sensor, the influence of nonsoil materials, i.e., vegetation, on the spectral signature of soil dominated image pixels was reduced and a residual soil signature was calculated. The proposed approach allowed this procedure up to a vegetation coverage of 40% clearly extending the mapping capability. SOC quantities are predicted by applying a spectral feature-based SOC prediction model to image data of residual soil spectra. With this approach, we could significantly increase the spatial extent for which SOC could be predicted with a minimal influence of a vegetation signal compared to previous approaches where the considered area was limited to a maximum of, e.g., 10% vegetation coverage. As a regional example, the approach was applied to a 320 km2 area in the Albany Thicket Biome, South Africa, where land cover and landuse changes have occurred due to decades of unsustainable land management. In the generated maps, spatial SOC patterns were interpreted and linked to geomorphic features and land surface processes, i.e., areas of soil erosion. It was found that the chosen approach supported the extraction of soil-related spectral image information in the semiarid region with highly varying land cover. However, the quantitative prediction of SOC contents revealed a lack in absolute accuracy

Spatially Explicit Estimation of Clay and Organic Carbon Content in Agricultural Soils Using Multi-Annual Imaging Spectroscopy Data

Information on soil clay and organic carbon content on a regional to local scale is vital for a multitude of reasons such as soil conservation, precision agriculture, and possibly also in the context of global environmental change. The objective of this study was to evaluate the potential of multi-annual hyperspectral images acquired with the HyMap sensor (450–2480 nm) during three flight campaigns in 2004, 2005, and 2008 for the prediction of clay and organic carbon content on croplands by means of partial least squares regression (PLSR). Supplementary, laboratory reflectance measurements were acquired under standardized conditions. Laboratory spectroscopy yielded prediction errors between 19.48 and 35.55 g kg−1 for clay and 1.92 and 2.46 g kg−1 for organic carbon. Estimation errors with HyMap image spectra ranged from 15.99 to 23.39 g kg−1 for clay and 1.61 to 2.13 g kg−1 for organic carbon. A comparison of parameter predictions from different years confirmed the predictive ability of the models. BRDF effects increased model errors in the overlap of neighboring flight strips up to 3 times, but an appropriated preprocessing method can mitigate these negative influences. Using multi-annual image data, soil parameter maps could be successively complemented. They are exemplarily shown providing field specific information on prediction accuracy and image data source

On the Use of Airborne Imaging Spectroscopy Data for the Automatic Detection and Delineation of Surface Water Bodies

There is economical and ecological relevance for remote sensing applications of inland and coastal waters: The European Union Water Framework Directive (European Parliament and the Council of the European Union, 2000) for inland and coastal waters requires the EU member states to take actions in order to reach a good ecological status in inland and coastal waters by 2015. This involves characterization of the specific trophic state and the implementation of monitoring systems to verify the ecological status. Financial resources at the national and local level are insufficient to assess the water quality using conventional methods of regularly field and laboratory work only. While remote sensing cannot replace the assessment of all aquatic parameters in the field, it powerfully complements existing sampling programs and offers the base to extrapolate the sampled parameter information in time and in space. The delineation of surface water bodies is a prerequisite for any further remote sensing based analysis and even can by itself provide up-to-date information for water resource management, monitoring and modelling (Manavalan et al., 1993). It is further important in the monitoring of seasonally changing water reservoirs (e.g., Alesheikh et al., 2007) and of shortterm events like floods (Overton, 2005). Usually the detection and delineation of surface water bodies in optical remote sensing data is described as being an easy task. Since water absorbs most of the irradiation in the near-infrared (NIR) part of the electromagnetic spectrum water bodies appear very dark in NIR spectral bands and can be mapped by simply applying a maximum threshold on one of these bands (Swain & Davis, 1978: section 5-4). Many studies took advantage of this spectral behaviour of water and applied methods like single band density slicing (e.g., Work & Gilmer, 1976), spectral indices (McFeeters, 1996, Xu, 2006) or multispectral supervised classification (e.g., Frazier & Page, 2000, Lira, 2006). However, all of these methods have the drawback that they are not fully automated since the analyst has to select a scene-specific threshold (Ji et al., 2009) or training pixels. Moreover there are certain situations where these methods lead to misclassification. For instance, water constituents in turbid water as well as water bottom reflectance and sun glint can raise the reflectance spectrum of surface water even in the NIR spectral range up to a reflectance level which is typical for dark surfaces on land such as dark rocks (e.g., basalt, lava), bituminous roofing materials and in particular shadow regions. Consequently, Carleer & Wolff (2006) amongst others found the land cover classes water and shadow to be highly confused in image classifications. This problem especially occurs in environments where both, a high amount of shadow and water regions can exist, such as urban landscapes, mountainous landscapes or cliffy coasts as well as generally in images with water bodies and cloud shadows. In this investigation we focus on the development of a new surface water body detection algorithm that can be automatically applied without user knowledge and supplementary data on any hyperspectral image of the visible and near-infrared (VNIR) spectral range. The analysis is strictly focused on the VNIR part of the electromagnetic spectrum due to the growing number of VNIR imaging spectrometers. The developed approach consists of two main steps, the selection of potential water pixels (section 4.1) and the removal of false positives from this mask (sections 4.2 and 4.3). In this context the separation between water bodies and shadowed surfaces is the most challenging task which is implemented by consecutive spectral and spatial processing steps (sections 4.3.1 and 4.3.2) resulting in very high detection accuracies

Cognitive and behavioural but not motor impairment increases brain age in amyotrophic lateral sclerosis

Age is the most important single risk factor of sporadic amyotrophic lateral sclerosis. Neuroimaging together with machine-learning algorithms allows estimating individuals' brain age. Deviations from normal brain-ageing trajectories (so called predicted brain age difference) were reported for a number of neuropsychiatric disorders. While all of them showed increased predicted brain-age difference, there is surprisingly few data yet on it in motor neurodegenerative diseases. In this observational study, we made use of previously trained algorithms of 3377 healthy individuals and derived predicted brain age differences from volumetric MRI scans of 112 amyotrophic lateral sclerosis patients and 70 healthy controls. We correlated predicted brain age difference scores with voxel-based morphometry data and multiple different motoric disease characteristics as well as cognitive/behavioural changes categorized according to Strong and Rascovsky. Against our primary hypothesis, there was no higher predicted brain-age difference in the amyotrophic lateral sclerosis patients as a group. None of the motoric phenotypes/characteristics influenced predicted brain-age difference. However, cognitive/behavioural impairment led to significantly increased predicted brain-age difference, while slowly progressive as well as cognitive/behavioural normal amyotrophic lateral sclerosis patients had even younger brain ages than healthy controls. Of note, the cognitive/behavioural normal amyotrophic lateral sclerosis patients were identified to have increased cerebellar brain volume as potential resilience factor. Younger brain age was associated with longer survival. Our results raise the question whether younger brain age in amyotrophic lateral sclerosis with only motor impairment provides a cerebral reserve against cognitive and/or behavioural impairment and faster disease progression. This new conclusion needs to be tested in subsequent samples. In addition, it will be interesting to test whether a potential effect of cerebral reserve is specific for amyotrophic lateral sclerosis or can also be found in other neurodegenerative diseases with primary motor impairment

HLA-DRB3/4/5 Matching Improves Outcome of Unrelated Hematopoietic Stem Cell Transplantation

The HLA-DRB3/4/5 loci are closely linked to the HLA-DRB1 gene. Mismatches in these loci occur with a frequency of about 8%–12% in otherwise 10/10 HLA-matched transplant pairs. There is preliminary evidence that these disparities may associate with increased acute graft-versus-host disease (GvHD) rates. The aim of this study was to analyze a large cohort of German patients and their donors for HLA-DRB3/4/5 compatibility and to correlate the HLA-DRB3/4/5 matching status with the outcome of unrelated hematopoietic stem cell transplantation (uHSCT). To this end, 3,410 patients and their respective donors were HLA-DRB3/4/5 and HLA-DPB1 typed by amplicon-based nextgeneration sequencing (NGS). All patients included received their first allogeneic transplant for malignant hematologic diseases between 2000 and 2014. Mismatches in the antigen recognition domain (ARD) of HLA-DRB3/4/5 genes were correlated with clinical outcome. HLA-DRB3/4/5 incompatibility was seen in 12.5% (n = 296) and 17.8% (n = 185) of the 10/10 and 9/10 HLA-matched cases, respectively. HLA-DRB3/4/5 mismatches in the ARD associated with a worse overall survival (OS), as shown in univariate (5-year OS: 46.1% vs. 39.8%, log-rank p = 0.038) and multivariate analyses [hazard ratio (HR) 1.25, 95% CI 1.02–1.54, p = 0.034] in the otherwise 10/10 HLAmatched subgroup. The worse outcome was mainly driven by a significantly higher nonrelapse mortality (HR 1.35, 95% CI 1.05–1.73, p = 0.017). In the 9/10 HLA-matched cases, the effect was not statistically significant. Our study results suggest that mismatches within the ARD of HLA-DRB3/4/5 genes significantly impact the outcome of otherwise fully matched uHSCT and support their consideration upon donor selection in the future

The Human Leukocyte Antigen-DPB1 Degree of Compatibility Is Determined by Its Expression Level and Mismatch Permissiveness: A German Multicenter Analysis

T-cell epitope matching according to the TCE3 algorithm classifies HLA-DPB1 mismatches in permissive and non-permissive. This classification has been shown to be predictive for mortality and acute GvHD (aGvHD) events in large international cohorts. We retrospectively genotyped HLA-DPB1 in 3523 patients transplanted in Germany between 2000 and 2014 and in their unrelated donors using an Illumina amplicon-NGS based assay. Aim of the study was to evaluate DP-compatibility beyond the established TCE3 algorithm by assessing the combined effect of several DP-mismatch parameters on post-transplant outcome. We implemented an extended DP-mismatch assessment model where TCE3, DP allotype expression with respect to rs9277534, mismatch vector and number of mismatches were conjointly taken into consideration. In this model, non-permissive HLA-DPB1 mismatches showed significantly increased aGvHD risk if they were accompanied by two HLA-DPB1 mismatches in GvH direction (HR: 1.46) or one mismatched highly expressed patient allotype (HR: 1.53). As previously reported, non-permissive HLA-DPB1 mismatches associated with a significantly higher risk of aGvHD and non-relapse mortality (HR 1.36 and 1.21, respectively), which in turn translated into worse GvHD and relapse free survival (HR 1.13). Effects on GvL and GvHD appeared strongest in GvH-directed non-permissive mismatches. Our study results support the consideration of additional HLA-DPB1 mismatch parameters along with the established TCE3 matching algorithm for refinement of future donor selection. In particular, our findings suggest that DP non-permissiveness associated with two HLA-DPB1 mismatches or at least on highly expressed mismatched patient allotype should be avoided

Reduction of Radiometric Miscalibration—Applications to Pushbroom Sensors

The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results in the assignment of incident electromagnetic radiation to digital numbers and reduces the striping caused by slightly different responses of the pixel detectors. However, due to uncertainties in the calibration some striping remains. This publication presents a new reduction framework that efficiently reduces linear and nonlinear miscalibrations by an image-driven, radiometric recalibration and rescaling. The proposed framework—Reduction Of Miscalibration Effects (ROME)—considering spectral and spatial probability distributions, is constrained by specific minimisation and maximisation principles and incorporates image processing techniques such as Minkowski metrics and convolution. To objectively evaluate the performance of the new approach, the technique was applied to a variety of commonly used image examples and to one simulated and miscalibrated EnMAP (Environmental Mapping and Analysis Program) scene. Other examples consist of miscalibrated AISA/Eagle VNIR (Visible and Near Infrared) and Hawk SWIR (Short Wave Infrared) scenes of rural areas of the region Fichtwald in Germany and Hyperion scenes of the Jalal-Abad district in Southern Kyrgyzstan. Recovery rates of approximately 97% for linear and approximately 94% for nonlinear miscalibrated data were achieved, clearly demonstrating the benefits of the new approach and its potential for broad applicability to miscalibrated pushbroom sensor data