Abstract Snow albedo is an important climate parameter as it governs the amount of solar energy absorbed by the snow and can be considered a major contributor to the surface radiation budget. The present study deals with the estimation of temporal variation of snow albedo at the upper elevation zone of glaciated Mago Basin of Arunachal Pradesh in eastern Himalaya. Moderate Resolution Imaging Spectroradiometer (MODIS) Daily Snow Products (MOD10A1 and MYD10A1) at 500 m spatial resolution were used. Both the MODIS data for ten years (2003–13) and the Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) of the study area were downloaded from NASA DAAC of NSIDC. The percentage area under different snow types (dry snow, wet snow, firn and ice) was determined by masking the upper elevation zone of the DEM into the albedo images. The average monthly slopes show a decreasing trend in area (%) of dry snow and wet snow and an increasing trend for firn and ice. Dry snow and wet snow cover percentages were observed to be decreasing, whereas firn and ice cover showed an increasing trend for most of the months. Firn dominated the type of snow, followed by ice then wet snow; the smallest area (%) was that of dry snow for the study period.

Abstract During the period 28 February 2000–31 December 2013, the MODVOLC system ( http://modis.higp.hawaii.edu ) autonomously analysed almost 9 trillion (i.e. 9×10 12 ) pixels contained within almost 3 million MODIS images, searching for evidence of high-temperature thermal signatures associated with volcanic eruptions. Thermal unrest, mainly associated with active lava, be it in the form of flows, domes, lakes or confined to vents, was detected at 93 volcanoes during this period of time. The first part of this paper describes the physical basis and operational implementation of the MODVOLC algorithm. The second part presents data to detail the nature of the thermal emission from these 93 volcanoes over the past 14 years.

Abstract The Volcano Sensor Web (VSW) is a globe-spanning net of sensors and applications for detecting volcanic activity. Alerts from the VSW are used to trigger observations from space using the Earth Observing-1 ( EO-1 ) spacecraft. Onboard EO-1 is the Autonomous Sciencecraft Experiment (ASE) advanced autonomy software. Using ASE has streamlined spacecraft operations and has enabled the rapid delivery of high-level products to end-users. The entire process, from initial alert to product delivery, is autonomous. This facility is of great value as a rapid response is vital during a volcanic crisis. ASE consists of three parts: (1) Science Data Classifiers, which process EO-1 Hyperion data to identify anomalous thermal signals; (2) a Spacecraft Command Language; and (3) the Continuous Activity Scheduling Planning Execution and Replanning (CASPER) software that plans and replans activities, including downlinks, based on available resources and operational constraints. For each eruption detected, thermal emission maps and estimates of eruption parameters are posted to a website at the Jet Propulsion Laboratory, California Institute of Technology, in Pasadena, CA. Selected products are emailed to end-users. The VSW uses software agents to detect volcanic activity alerts generated from a wide variety of sources on the ground and in space, and can also be easily triggered manually.

Abstract FLOWGO is a one-dimensional model that tracks the thermorheological evolution of lava flowing down a channel. The model does not spread the lava but, instead, follows a control volume as it descends a line of steepest descent centred on the channel axis. The model basis is the Jeffreys equation for Newtonian flow, modified for a Bingham fluid, and a series of heat loss equations. Adjustable relationships are used to calculate cooling, crystallization and down-channel increases in viscosity and yield strength, as well as the resultant decrease in velocity. Here we provide a guide that allows FLOWGO to be set up in Excel. In doing so, we show how the model can be executed using a slope profile derived from Google™ Earth. Model simplicity and ease of source-term input from Google™ Earth means that this exercise allows (i) easy access to the model, (ii) quick, global application and (iii) use in a teaching role. Output is tested using measurements made for the 2010 eruption of Piton de la Fournaise (La Réunion Island). The model is also set up for rapid syneruptive hazard assessment at Piton de la Fournaise, as we show using the example of the response to the June 2014 eruption.

Since the beginning of the U.S. space program, the National Aeronautics and Space Administration (NASA) has trained astronauts in basic earth science topics to support their observations of Earth's surface from low Earth orbit. From its roots in the Apollo geology training campaigns, we describe the evolution of astronaut Earth observation training across human spaceflight programs, with a focus on the training for space shuttle and International Space Station (ISS) missions. Astronauts' Earth observation experiences—both preflight training and interactions with scientists on the ground during spaceflight missions—provide relevant information for defining training requirements for future astronaut exploration missions on other planetary surfaces.

Robotic rovers can be used as advance scouts to significantly improve scientific and technical return of planetary surface exploration. Robotic scouting, or “robotic recon,” involves using a robot to collect ground-level data prior to human field activity. The data collected and knowledge acquired through recon can be used to refine traverse planning, reduce operational risk, and increase crew productivity. To understand how robotic recon can benefit human exploration, we conducted a series of simulated planetary robotic missions at analog sites. These mission simulations were designed to: (1) identify and quantify operational requirements for robotic recon in advance of human activity; (2) identify and quantify ground control and science team requirements for robotic recon; and (3) identify capability, procedure, and training requirements for human explorers to draw maximum benefit from robotic recon during vehicular traverses and on-foot extravehicular activities (EVA). Our studies indicate that robotic recon can be beneficial to crew, improving preparation, situational awareness, and productivity in the field. This is particularly true when traverse plans contain significant unknowns that can be resolved by recon, such as target access and station/activity priority. In this paper, we first present the assumptions and major questions related to robotic reconnaissance. We detail our system design, including the configuration of our recon robot, the ground data system used for operation, ground control organization, and operational time lines. Finally, we describe the design and results from an experiment to assess robotic recon, discuss lessons learned, and identify directions for future work.

A comprehensive field training curriculum was developed and tested during the 2006, 2008, 2009, and 2010 National Aeronautics and Space Administration (NASA) Spaceward Bound missions at the Mars Desert Research Station (MDRS). The curriculum was developed to train teachers and students in fundamentals of Moon and Mars analog station operations, logistics, field work, and scientific investigation. The curriculum is composed of background content, directions, lesson plans, suggestions, protocols, images, diagrams, figures, checklists, worksheets, experiments, field missions, and references. To date, 48 individuals have participated in Spaceward Bound missions at MDRS, and 18 have successfully tested the curriculum. Based on our analysis and student feedback, we conclude that the Spaceward Bound curriculum is highly useful in training teachers and students in aspects of astrobiology, field science, and Mars exploration, and that MDRS is an ideal location for its use.

We have organized ten National Aeronautics and Space Administration (NASA)–sponsored planetary volcanology field workshops on Hawai‘i since 1992, providing an opportunity for almost 140 NASA-funded graduate students, postdocs, and junior faculty to view basaltic volcano features up close in the company of both terrestrial and planetary volcanologists. Most of the workshops have been thematic, for example, concentrating on large structural features (rift zones and calderas) or lava flows, or features best viewed in high-spatial-resolution data, but they always include a broad set of topics. The workshops purposely involve long field days—an appreciation of scale is important for planetary scientists, particularly if they are or will be working with slow-moving rovers. Our goals are to give these young scientists a strong background in basaltic volcanology and provide the chance to view eruptive and volcano-structural features up close so that they can compare the appearance of these features in the field to their representations in state-of-the-art remote-sensing images, and relate them in turn to analogous planetary features. In addition, the workshop enables the participants to start a collection of field photographs and observations that they can use in future research and teaching. An added benefit is that the participants interact with each other, forging collaborations that we hope will persist throughout their careers.

The Big Island of Hawai‘i presents ample opportunities for young planetary volcanologists to gain firsthand field experience in the analysis of analogs to landforms seen on Mercury, Venus, the Moon, Mars, and Io. In this contribution, we focus on a subset of the specific features that are included in the planetary volcanology field workshops described in the previous chapter in this volume. In particular, we discuss how remote-sensing data and field localities in Hawai‘i can help a planetary geologist to gain expertise in the analysis of lava flows and lava flow fields, to understand the best sensor for a specific application, to recognize the ways in which different data sets can be used synergistically for remote interpretations of lava flows, and to gain a deeper appreciation for the spatial scale of features that might be imaged in the planetary context.