Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Mapping slope movements in Alpine environments using TerraSAR-X interferometric methods

Mapping slope movements in Alpine environments is an increasingly important task in the context of climate change and natural hazard management. We propose the detection, mapping and inventorying of slope movements using different interferometric methods based on TerraSAR-X satellite images. Differential SAR interferograms (DInSAR), Persistent Scatterer Interferometry (PSI), Short-Baseline Interferometry (SBAS) and a semi-automated texture image analysis are presented and compared in order to determine their contribution for the automatic detection and mapping of slope movements of various velocity rates encountered in Alpine environments. Investigations are conducted in a study region of about 6 km × 6 km located in the Western Swiss Alps using a unique large data set of 140 DInSAR scenes computed from 51 summer TerraSAR-X (TSX) acquisitions from 2008 to 2012. We found that PSI is able to precisely detect only points moving with velocities below 3.5 cm/yr in the LOS, with a root mean squared error of about 0.58 cm/yr compared to DGPS records. SBAS employed with 11 days summer interferograms increases the range of detectable movements to rates up to 35 cm/yr in the LOS with a root mean squared error of 6.36 cm/yr, but inaccurate measurements due to phase unwrapping are already possible for velocity rates larger than 20 cm/year. With the semi-automated texture image analysis the rough estimation of the velocity rates over an outlined moving zone is accurate for rates of “cm/day”, “dm/month” and “cm/month”, but due to the decorrelation of yearly TSX interferograms this method fails for the observation of slow movements in the “cm/yr” range

Remote Sensing of Mountain Glaciers and Related Hazards

Mountain glaciers are highly sensitive to temperature and precipitation fluctuations and active geomorphic agents in shaping the landforms of glaciated regions which are direct imprints of past glaciations, providing reliable evidence of the evolution of the past Cryosphere and contain important information on climatic variables. But most importantly, glaciers have aroused a lot of concern in terms of glacier area changes, thickness change, mass balance and their consequences on water resources as well as related hazards. The contribution of glacier mass loss to global sea-level rise and increasing number of glacier-related hazards are the most important and current socioeconomic concerns. Therefore, understanding the dynamics of the changes and constant monitoring of glaciers are essential for studying climate, water resource management and hydropower and also to predict and evade glacier-related hazards. The recent advances in the techniques of earth observations have proved as a boon for investigating glaciers and glacier-related hazards. Remote sensing technology enables extraction of glacier parameters such as albedo/reflectance/scattering, glacier area, glacier zones and facies, equilibrium line, glacier thickness, volume, mass balance, velocity and glacier topography. The present chapter explores the prospective of remote sensing technology for understanding and surveying glaciers formed at high, inaccessible mountains and glacier-induced hazards

DInSAR for a Regional Inventory of Active Rock Glaciers in the Dry Andes Mountains of Argentina and Chile with Sentinel-1 Data

The Dry Andes region of Argentina and Chile is characterized by a highly developed periglacial environment. In these arid or semi-arid regions, rock glaciers represent one of the main pieces of evidence of mountain creeping permafrost and water reserves in a solid state. However, their distribution, degree of activity, and response to global warming are not yet well understood. In this context, this work aims to show the potential of the Sentinel-1-based interferometric technique (DInSAR) to map active rock glaciers at a regional level. In particular, the paper presents an active rock glacier inventory for the study area, which covers approximately 40,000 km2, ranging from latitude 30°210S to 33°210S. A total of 2116 active rock glaciers have been detected, and their elevations show a high correlation with the west-east direction. This result was obtained by using only 16 interferometric pairs. Compared to other remote sensing classification techniques, the interferometric technique offers a means to measure surface displacement (active rock glacier). This results in a reliable classification of the degree of activity compared to other methods, based on geomorphological, geomorphometric, and/or ecological criteria. This work presents evidence of this aspect by comparing the obtained results with existing optical data-based inventories. We conclude that the combination of both types of sensors (radar and optical) is an appropriate procedure for active rock glacier inventories, as both mapping methodologies are complementary.Fil: Villarroel, Cristian Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales. Departamento de Geología; ArgentinaFil: Tamburini Beliveau, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rosario; ArgentinaFil: Forte, Ana Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; ArgentinaFil: Monserrat, Oriol. Agencia Espacial Europea; EspañaFil: Morvillo, Mónica Cristina. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentin

Space observation for climate change studies

Climate change is associated with earth radiation budget that depends upon in-comming solar radiation, surface albedo and radiative forcing by green house gases. Human activities are contributing to climate change by causing changes in Earth's atmosphere (greenhouse gases, aerosols) and biosphere (deforestation, urbanization, irrigation). Long term and precise measurements from calibrated global observation constellation is a vital component in climate system modelling. Space based records of biosphere, cryosphere, hydrosphere and atmosphere over more than three decades are providing important information on climate change. Space observations are an important source of climate variables due to multi scale simultaneous observation (local, regional, global) capability with temporal revisit in tune with requirements of land, ocean and atmospheric processes. Essential climatic variables that can be measured from space include atmosphere (upper air temperature, water vapour, precipitation, clouds, aerosols & GHGs etc.), ocean (sea ice, sea level, SST, salinity, ocean colour etc.) and land (snow, glacier, albedo, biomass, LAI/fAPAR, soil moisture etc.). India's Earth Observation Programme addresses various aspects of land, ocean and atmospheric applications. The present and planned missions such as Resourcesat-1, Oceansat-2, RISAT, Megha-Tropiques, INSAT-3D, SARAL, Resourcesat-2, Geo-HR Imager and I-STAG would help in understanding the issues related to climate changes. The paper reviews observational needs, space observation systems and studies that have been carried out at ISRO towards mapping/ detecting the indicators of climate change, monitoring the agents of climate change and understanding the impact of climate change, in national perspectives. Studies to assess glacier retreat, changes in polar ice cover, timberline change and coral bleaching are being carried out towards monitoring of climate change indicators. Spatial methane inventories from paddy rice, livestock and wetlands have been prepared and seasonal pattern of CO2, and CO have been analysed. Future challenges in space observations include design and placement of adequate and accurate multi-platform observational system to monitor all parameters related to various interaction processes and generation of long term calibrated climate data records pertaining to land ocean and atmosphere

The Use of Coincident Synthetic Aperture Radar and Visible Imagery to Aid in the Analysis of Photon-Counting Lidar Data Sets Over Complex Ice/Snow Surfaces

Qualitative and quantitative analysis of multi-sensor data is becoming increasingly useful as a method of improving our understanding of complex environments, and can be an effective tool in the arsenal to help climate scientists to predict sea level rise due to change in the mass balance of large glaciers in the Arctic and Antarctic. A novel approach to remote sensing of the continuously changing polar environment involves the use of coincident RADARSAT-2 synthetic aperture radar (SAR) imagery and Landsat 7 visible/near-infrared imagery, combined with digital elevation models (DEM) developed from Multiple Altimeter Beam Experimental Lidar (MABEL) data sets. MABEL is a scaled down model of the lidar altimeter that will eventually be flown on ICESat-2, and provides dense along-track and moderate slope (cross-track) elevation data over narrow (~198 m) aircraft transects. Because glacial terrain consists of steep slopes, crevices, glacial lakes, and outflow into the sea, accurate slope information is critical to our understanding of any changes that may be happening in the ice sheets. RADARSAT-2 operates in the C-band, at a wavelength of 5.55 cm, and was chosen partly for its ability to image the Earth under all atmospheric conditions, including clouds. The SAR images not only provide spatial context for the elevation data found using the lidar, but also offer key insights into the consistency of the snow and ice making up the glacier, giving us some idea of mean temperature and surface conditions on the ice sheet. Finally, Landsat 7 images provide us with information on the extent of the glacier, and additional understanding of the state of the glacial surface. To aid in the analysis of the three data sets, proper preparation of each data set must first be performed. For the lidar data, this required the development of a new data reduction technique, based on statistical analysis, to reduce the number of received photons to those representing only the surface return. Accordingly, the raw SAR images require calibration, speckle reduction, and geocorrection, before they can be used. Landsat 7 bands are selected to provide the most contrast between rock, snow, and other surface features, and compiled into a three-band red, green, blue (RGB) image. By qualitatively analyzing images and data taken only a short time apart using multiple imaging modalities, we are able to accurately compare glacial surface features to elevation provided by MABEL, with the goal of increasing our understanding of how the glacier is changing over time. Quantitative analysis performed throughout this thesis has indicated that there is a strong correlation between top-of-the-atmosphere reflectance (Landsat 7), σ,0-calibrated HH and HV polarized backscatter coefficients (RADARSAT-2), elevation (MABEL), and various surface features and glacial zones on the ice sheet. By comparing data from unknown or mixed surfaces to known quantities scientists can effectively estimate the type of glacial zone the area of interest occurs in. Climate scientists can then use this data, along with long-term digital elevations models, as a measure of predicting climate change