Michael P. Bishop

Remote Sensing of Glaciers in Afghanistan and Pakistan

Glaciers in Afghanistan and Pakistan are parts of an Asian “critical region” having significant roles in rising sea level, local and regional water resources, natural hazards, and geopolitical stability. The two countries lack fundamental and reliable quantitative information regarding glacier fluctuations. As part of the Global Land Ice Measurements from Space (GLIMS) project, we used satellite imagery and field observations to assess a relatively large number of glaciers in both countries. In Afghanistan, many glaciers have systematically been observed to be retreating and downwasting. Many glaciers have lost significant ice mass and have evolved into numerous smaller individual ice masses. Furthermore, the glaciers around the Kohi Bandakha massif in southern Badakshan Province are significantly more debris covered than other regions in Afghanistan. In Pakistan, the situation is more complex, as many glacier termini are variably stationary, advancing, or retreating. There appears to be a spatial trend with more retreating glaciers in the western Hindu Kush. To the east we observe more advancing glaciers and surging glaciers associated with an increase in precipitation. These observations suggest that glacier response to climate forcing is very different in Pakistan compared with conditions in the central and eastern Himalaya.

Digital Terrain Modeling and Glacier Topographic Characterization

The Earth’s topography results from dynamic interactions involving climate, tectonics, and surface processes. In this chapter our main interest is in describing and illustrating how satellite-derived DEMs (and other DEMs) can be used to derive information about glacier dynamical changes. Along with other data that document changes in glacier area, these approaches can provide useful measurements of, or constraints on glacier volume balance and—with a little more uncertainty related to the density of lost or gained volume—mass balance. Topics covered include: basics on DEM generation using stereo image data (whether airborne or spaceborne), the use of ground control points and available software packages, postprocessing, and DEM dataset fusion; DEM uncertainties and errors, including random errors and biases; various glacier applications including derivation of relevant geomorphometric parameters and modeling of topographic controls on radiation fields; and the important matters of glacier mapping, elevation change, and mass balance assessment. Altimetric data are increasingly important in glacier studies, yet challenges remain with availability of high-quality data, the current lack of standardization for methods for requiring, processing, and representing digital elevation data, and the identification and quantification of DEM error and uncertainty.

Theoretical Foundations of Remote Sensing for Glacier Assessment and Mapping

The international scientific community is actively engaged in assessing ice sheet and alpine glacier fluctuations at a variety of scales. The availability of stereoscopic, multitemporal, and multispectral satellite imagery from the optical wavelength regions of the electromagnetic spectrum has greatly increased our ability to assess glaciological conditions and map the cryosphere. There are, however, important issues and limitations associated with accurate satellite information extraction and mapping, as well as new opportunities for assessment and mapping that are all rooted in understanding the fundamentals of the radiation transfer cascade. We address the primary radiation transfer components, relate them to glacier dynamics and mapping, and summarize the analytical approaches that permit transformation of spectral variation into thematic and quantitative parameters. We also discuss the integration of satellite-derived information into numerical modeling approaches to facilitate understandings of glacier dynamics and causal mechanisms.

Geospatial Science and Technology for Understanding the Complexities of the Critical Zone

Abstract The Critical Zone is undergoing constant change due to climatic, lithospheric, and anthropogenic forcing factors. It is widely recognized that an integrated multidisciplinary approach to studying the Critical Zone is required to identify key variables, characterize process mechanics, study scale dependencies, and model various system dynamics. Geospatial technologies that include the fields of remote sensing, geomorphometry, and geocomputation will play an ever increasing role in studying the highly coupled complex systems of the Critical Zone. We provide fundamental background information on the role of geospatial technology for data collection, information extraction, and numerical modeling of landscape conditions; present a new spatio-temporal surface irradiance model, and highlight the use of a geocomputational approach that represents expert knowledge; integrates surface biophysical and topographic information, and provides a framework for studying landscape–subsurface relationships. Our results demonstrate the significance of geospatial technologies for the study and prediction of parameters and concepts that characterize the complexities of the Critical Zone.

Comprehensive UAV agricultural remote-sensing research at Texas A M University

Unmanned aerial vehicles (UAVs) have advantages over manned vehicles for agricultural remote sensing. Flying UAVs is less expensive, is more flexible in scheduling, enables lower altitudes, uses lower speeds, and provides better spatial resolution for imaging. The main disadvantage is that, at lower altitudes and speeds, only small areas can be imaged. However, on large farms with contiguous fields, high-quality images can be collected regularly by using UAVs with appropriate sensing technologies that enable high-quality image mosaics to be created with sufficient metadata and ground-control points. In the United States, rules governing the use of aircraft are promulgated and enforced by the Federal Aviation Administration (FAA), and rules governing UAVs are currently in flux. Operators must apply for appropriate permissions to fly UAVs. In the summer of 2015 Texas A&M Universitys agricultural research agency, Texas A&M AgriLife Research, embarked on a comprehensive program of remote sensing with UAVs at its 568-ha Brazos Bottom Research Farm. This farm is made up of numerous fields where various crops are grown in plots or complete fields. The crops include cotton, corn, sorghum, and wheat. After gaining FAA permission to fly at the farm, the research team used multiple fixed-wing and rotary-wing UAVs along with various sensors to collect images over all parts of the farm at least once per week. This article reports on details of flight operations and sensing and analysis protocols, and it includes some lessons learned in the process of developing a UAV remote-sensing effort of this sort.

Teaching Complex Concepts in the Geosciences by Integrating Analytical Reasoning With GIS

ABSTRACT Conceptual models have long served as a means for physical geographers to organize their understanding of feedback mechanisms and complex systems. Analytical reasoning provides undergraduate students with an opportunity to develop conceptual models based upon their understanding of surface processes and environmental conditions. This study describes the use of analytical reasoning by junior and senior undergraduate students to predict the expansion and contraction of the South Texas Sandsheet as an example of this instructional technique. Students perceived that the analytical reasoning approach was significantly better for understanding desert expansion and contraction compared with the traditional lecture. A preliminary assessment of an analytical reasoning approach to desertification is presented as an example of how this approach can be incorporated into undergraduate geoscience courses.

Radiative Transfer Modeling in the Cryosphere

Radiative transfer (RT) modeling plays a key role in interpreting the radiance measured by multispectral sensors. Glaciers respond to variations in solar irradiance. At-sensor radiance depends upon glacier surface material composition and intermixture of materials, solar and sensor geometry, and surface topography. To bridge the gap between investigative findings and spectral data, a physically based (i.e., based on first principles) linkage between properties of the observed surface and the measured electromagnetic signal should be established. Complementing the treatment of related subjects in Chap. 2 of this book by Bishop et al., in this chapter we show how RT theory can be adapted to derive radiative transfer equations (RTEs) that are commonly employed to properly describe the radiative field within, at the surface of, and above glaciers, debris fields, and glacier lakes. RTEs are derived using the basic principle of conservation of photons and are simplified to obtain equations that are more mathematically tractable. Such equations are numerically solved to compute quantities that are of interest in remote sensing (e.g., bidirectional reflectance factor, BRF, and spectral albedo) that are a function of the optical properties of the observed surface. Accurate modeling of the optical properties of single-material particles (e.g., ice or snow, water, lithic debris, and carbon soot) is critical to obtaining meaningful and accurate RT calculations. The common methods employed to determine single-scattering albedo and scattering phase function, for both single-type particles and mixtures, are discussed. In addition, although the basic conservation of photons holds for both glaciers and glacier lake water, we have marked a clear distinction between the equation of transfer for glacier surfaces and glacier lake water, as well as between the methods employed to describe their optical properties. The chapter also provides examples of RT-based calculations for both BRF and spectral albedo in scenarios typically found in the cryosphere. Five simulation sets show how remotely measurable quantities depend on the morphological and mineralogical properties of the medium (e.g., BRF for mixtures of snow and debris; spectral albedo variation for snow and carbon soot with varying grain size and particle concentration; and spectral variation of glacier lake water reflectance as a function of rock “flour” concentration).