Volker Hochschild

Glacier mass changes on the Tibetan Plateau 2003-2009 derived from ICESat laser altimetry measurements

Glacier mass changes are a valuable indicator of climate variability and monsoon oscillation on the underexplored Tibetan Plateau. In this study data from the Ice Cloud and Elevation Satellite (ICESat) is employed to estimate elevation and mass changes of glaciers on the Tibetan Plateau between 2003 and 2009. In order to get a representative sample size of ICESat measurements, glaciers on the Tibetan Plateau were grouped into eight climatically homogeneous sub-regions. Most negative mass budgets of ? 0.77 ? 0.35?m?w.e.?a?1 were found for the Qilian Mountains and eastern Kunlun Mountains while a mass gain of + 0.37 ? 0.25?m?w.e.?a?1 was found in the westerly-dominated north-central part of the Tibetan Plateau. A total annual mass budget of ? 15.6 ? 10.1?Gt?a?1 was estimated for the eight sub-regions sufficiently covered by ICESat data which represents ?80% of the glacier area on the Tibetan Plateau. 13.9 ? 8.9?Gt?a?1 (or 0.04 ? 0.02?mm?a?1 sea-level equivalent) of the total mass budget contributed ?directly? to the global sea-level rise while 1.7 ? 1.9?Gt?a?1 drained into endorheic basins on the plateau.

Temporal and Spatial Aspects of Snow Distribution in the Nam Co Basin on the Tibetan Plateau from MODIS Data

Large areas of the Tibetan plateau are only covered by a sparse network of ground snow sampling stations, while the snow cover is highly heterogeneously distributed due to wind, topography etc. Nevertheless, the snow accumulation and spatial patterns play an important role in the hydrological cycle. It releases moisture during the dry spring period before the onset of the monsoon season. Widely used MODIS snow cover products have been available globally since 2002. The understanding of the temporal and spatial distribution of snow cover in a given region calls for a comprehensive data representation method. In this paper a method to visualize both spatial and temporal aspects of snow cover distribution from MODIS 8-day composite data is presented. It is based on RGB display of the snow cover data which is grouped according to season. The RGB syntheses of snow cover distribution (RSD) were generated for the Nam Co Basin in the central part of the Tibetan Plateau during the years of 2002–2009. An alternating pattern of monsoon and autumn snow cover was identified in the western part of the basin which corresponds to the biennial character of the variations of the Indian monsoon. Monsoon snow cover was found in RSD images for the years 2002, 2004 and 2008 whereas in years 2003 and 2009 the autumn snow cover is dominant. The eastern part of the basin does not follow this general pattern since it is affected by the so called “lake effect”, which is a snow fall induced by the passing of dry and cold westerlies over the lake surface during the winter months. The years 2002, 2006 and 2007 were identified as years with a particularly strong lake effect from the RSD images. Areas with permanent snow cover and areas that were snow free were both found to be relatively stable. Comparison of the lake effect at Nam Co with nearby Siling Co, where the lake effect is smaller or absent, suggests that the presence of an effective barrier on the opposite side of the lake is a prerequisite for the occurrence of the strong lake effect.

Remote Sensing of Snow Cover

Snow was easily identified in the first image obtained from the Television Infrared Operational Satellite-1 (TIROS-1) weather satellite in 1960 because the high albedo of snow presents a good contrast with most other natural surfaces. Subsequently, the National Oceanic and Atmospheric Administration (NOAA) began to map snow using satellite-borne instruments in 1966. Snow plays an important role in the Earth s energy balance, causing more solar radiation to be reflected back into space as compared to most snow-free surfaces. Seasonal snow cover also provides a critical water resource through meltwater emanating from rivers that originate from high-mountain areas such as the Tibetan Plateau. Meltwater from mountain snow packs flows to some of the world s most densely-populated areas such as Southeast Asia, benefiting over 1 billion people (Immerzeel et al., 2010). In this section, we provide a brief overview of the remote sensing of snow cover using visible and near-infrared (VNIR) and passive-microwave (PM) data. Snow can be mapped using the microwave part of the electromagnetic spectrum, even in darkness and through cloud cover, but at a coarser spatial resolution than when using VNIR data. Fusing VNIR and PM algorithms to produce a blended product offers synergistic benefits. Snow-water equivalent (SWE), snow extent, and melt onset are important parameters for climate models and for the initialization of atmospheric forecasts at daily and seasonal time scales. Snowmelt data are also needed as input to hydrological models to improve flood control and irrigation management.

Multisensoral Topsoil Mapping in the Semiarid Lake Manyara Region, Northern Tanzania

This study pursues the mapping of the distribution of topsoils and surface substrates of the Lake Manyara area of northern Tanzania. The nine soil and lithological target classes were selected through fieldwork and laboratory analysis of soil samples. High-resolution WorldView-2 data, TerraSAR-X intensity data, medium-resolution ASTER spectral bands and indices, as well as ENVISAT ASAR intensity and SRTM-X-derived topographic parameters served as input features. Objects were derived from image segmentation. The classification of the image objects was conducted applying a nonlinear support vector machine approach. With the recursive feature elimination approach, the most input-relevant features for separating the target classes were selected. Despite multiple target classes, an overall accuracy of 71.9% was achieved. Inaccuracies occurred between classes with high CaCO3 content and between classes of silica-rich substrates. The incorporation of different input feature datasets improved the classification accuracy. An in-depth interpretation of the classification result was conducted with three soil profile transects.

Integrated observations of lake ice at Nam Co on the Tibetan Plateau from 2001 to 2009

Both MODIS multi-spectral bands and AMSR-E Tb data can identify lake ice. However, the MODIS LST product does not seem to be reliable for ice monitoring on Nam Co (Fig. 3b). This needs to be investigated further. It also indicates that the 10.7 GHz horizontal polarization Tb provides the optimum linear relation (correlation coefficient: 0.86; error: less than 10cm) for ice thickness estimation. Using the reflectivity threshold from MODIS bands 1 and 2 data at Nam Co, it shows that the date of ice freeze onset, the ice-on period, and the first appearance of open water changes by 4.57, 1.88 and 3.65 days per year (d/a), respectively, and the complete disappearance of ice occurs earlier by 1.12 d/a when looking at the period during 2000 to 2009. As a result, the duration of lake ice is shortened by 5.68 d/a over the whole Nam Co. After analyzing temperature data from the Nam Co and DamXung weather stations, it seems that monthly averaged temperature and the duration of lake ice, as well as the date of ice freeze onset, are strongly correlated. The increase of monthly averaged temperature is the main reason for lake ice change during 2000–2009. Lake ice first appears at the East lake shore, along the bank, then slowly freezes to the West, and finally to the central section of the lake, while open water first appears at the Northwest and Southwest lake shores, then ice melts from the West to the East.

Energiewende im Quartier – Ein Ansatz im Reallabor

Zu Beginn des 21. Jahrhunderts lebten erstmalig mehr als 50 % der Menschen in Stadten, welche zwischen 60 und 80 % der weltweit benotigten Energie verbrauchen. Die Transformation der Stadte hin zu nachhaltigen Gesellschaften mit nachhaltigen Energiesystemen wird aber nicht nur durch die gebaute Umwelt, Technologien und Politik gepragt, sondern v. a. durch Systeminnovationen. Diese Transformation wird im ‚Energielabor Tubingen‘- Projekt mit einem Fokus auf die Energiewende auf Quartiersebene fur die Stadt Tubingen untersucht. Dies wird in sogenannten Realexperimenten durchgefuhrt, d. h. Experimente unter teilweise kontrollierten Bedingungen mit dem Ziel, neues Wissen zu erhalten. Ziel des Energielabors ist es, gemeinsam mit den Burger(inne)n die Energiewende in Tubingen voranzutreiben, neue Masnahmen zu erproben und zu beforschen. Wo dabei die Herausforderungen liegen und wie es zur Veranderung des Alltagshandelns der Burger(innen) und dadurch zu einer praktischen Umsetzung einer nachhaltigen Energiewende kommt, um die Quartiere damit in Bewegung zu bringen, sind unsere zentralen Fragen.

Identifying Droughts Affecting Agriculture in Africa Based on Remote Sensing Time Series between 2000–2016: Rainfall Anomalies and Vegetation Condition in the Context of ENSO

Droughts are amongst the most destructive natural disasters in the world. In large regions of Africa, where water is a limiting factor and people strongly rely on rain-fed agriculture, droughts have frequently led to crop failure, food shortages and even humanitarian crises. In eastern and southern Africa, major drought episodes have been linked to El Nino-Southern Oscillation (ENSO) events. In this context and with limited in-situ data available, remote sensing provides valuable opportunities for continent-wide assessment of droughts with high spatial and temporal resolutions. This study aimed to monitor agriculturally relevant droughts over Africa between 2000–2016 with a specific focus on growing seasons using remote sensing-based drought indices. Special attention was paid to the observation of drought dynamics during major ENSO episodes to illuminate the connection between ENSO and droughts in eastern and southern Africa. We utilized Tropical Rainfall Measuring Mission (TRMM)-based Standardized Precipitation Index (SPI) with 0 . 25 ∘ resolution and Moderate-resolution Imaging Spectroradiometer (MODIS)-derived Vegetation Condition Index (VCI) with 500 m resolution as indices for analysing the spatio-temporal patterns of droughts. We combined the drought indices with information on the timing of site-specific growing seasons derived from MODIS-based multi-annual average of Normalized Difference Vegetation Index (NDVI). We proved the applicability of SPI-3 and VCI as indices for a comprehensive continental-scale monitoring of agriculturally relevant droughts. The years 2009 and 2011 could be revealed as major drought years in eastern Africa, whereas southern Africa was affected by severe droughts in 2003 and 2015/2016. Drought episodes occurred over large parts of southern Africa during strong El Nino events. We observed a mixed drought pattern in eastern Africa, where areas with two growing seasons were frequently affected by droughts during La Nina and zones of unimodal rainfall regimes showed droughts during the onset of El Nino. During La Nina 2010/2011, large parts of cropland areas in Somalia (88%), Sudan (64%) and South Sudan (51%) were affected by severe to extreme droughts during the growing seasons. However, no universal El Nino- or La Nina-related response pattern of droughts could be deduced for the observation period of 16 years. In this regard, we discussed multi-year atmospheric fluctuations and characteristics of ENSO variants as further influences on the interconnection between ENSO and droughts. By utilizing remote sensing-based drought indices focussed on agricultural zones and periods, this study attempts to contribute to a better understanding of spatio-temporal patterns of droughts affecting agriculture in Africa, which can be essential for implementing strategies of drought hazard mitigation.

A SAR-Based Index for Landscape Changes in African Savannas

Change detection is one of the main applications in earth observation but currently there are only a few approaches based on radar imagery. Available techniques strongly focus on optical data. These techniques are often limited to static analyses of image pairs and are frequently lacking results which address the requirements of the user. Some of these shortcomings include integration of user’s expertise, transparency of methods, and communication of results in a comprehensive understandable way. This study introduces an index describing changes in the savanna ecosystem around the refugee camp Djabal, Eastern Chad, based on a time-series of ALOS PALSAR data between 2007 and 2017. Texture based land-use/land cover classifications are transferred to values of natural resources which include comprehensive pertinent expert knowledge about the contributions of the classes to environmental integrity and human security. Changes between the images are analyzed, within grid cells of one kilometer diameter, according to changes of natural resources and the variability of these changes. Our results show the highest resource availability for the year of 2008 but no general decline in natural resources. Largest loss of resources occurred between 2010 and 2011 but regeneration could be observed in the following years. Neither the settlements nor the wadi areas of high ecologic importance underwent significant changes during the last decade.

Combined use of SAR and optical data for environmental assessments around refugee camps in semiarid landscapes

Abstract. Over 15 million people were officially considered as refugees in the year 2012 and another 28 million as internally displaced people (IDPs). Natural disasters, climatic and environmental changes, violent regional conflicts and population growth force people to migrate in all parts of this world. This trend is likely to continue in the near future, as political instabilities increase and land degradation progresses. EO4HumEn aims at developing operational services to support humanitarian operations during crisis situations by means of dedicated geo-spatial information products derived from Earth observation and GIS data. The goal is to develop robust, automated methods of image analysis routines for population estimation, identification of potential groundwater extraction sites and monitoring the environmental impact of refugee/IDP camps. This study investigates the combination of satellite SAR data with optical sensors and elevation information for the assessment of the environmental conditions around refugee camps. In order to estimate their impact on land degradation, land cover classifications are required which target dynamic landscapes. We performed a land use / land cover classification based on a random forest algorithm and 39 input prediction rasters based on Landsat 8 data and additional layers generated from radar texture and elevation information. The overall accuracy was 92.9 %, while optical data had the highest impact on the final classification. By analysing all combinations of the three input datasets we additionally estimated their impact on single classification outcomes and land cover classes.

Paleoenvironmental Research in the Semiarid Lake Manyara Area, Northern Tanzania: A Synopsis

The Lake Manyara area is the focus of several paleo-archeological investigations. The Manyara basin is located approximately 70 km east of Olduvai Gorge, where important paleoanthropological artifacts are traced back to Homo habilis. In the Manyara basin itself, two hominin-bearing sites (0.78–0.633 Ma) and plenty of vertebrate bones and teeth as well as stone artifacts from different periods were discovered, especially close to the Makuyuni River. Different methodological approaches with a main emphasis on remote sensing were utilized to contribute to the understanding of the paleo-landscape development. In order to investigate the morphotectonic evolution of the study area, lineaments were detected from Synthetic Aperture Radar satellite scenes. The complex lacustrine development of the Lake Manyara and its paleo-stages was investigated by delineating the extent of paleo-lake sediments (older than 0.633 Ma) with multispectral ASTER data. In addition, lake terraces and shorelines on different levels (up to 80 m above today’s lake level) and an outlet to the neighboring Engaruka basin were detected by analyzing the backscattered intensity of TerraSAR-X data. The distribution of topsoils, identified from multisensory remote sensing datasets, indicates soil formation as well as erosional and depositional processes. The fossils and artifacts were then characterized, and their distribution probabilities were determined using a statistical model. The proposed methods contribute to a better understanding of the paleoenvironmental interrelations within the study area.