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NASA NTRS2004

Relief Evolution in Tectonically Active Mountain Ranges

The overall aims of this 3-yr project, as originally proposed were to: (1) investigate quantitatively the roles of fluvial and glacial erosion in the evolution of relief in mountainous regions, and (2) test rigorously the quality and accuracy of SRTM topographic data in areas of rugged relief - both the most challenging and of greatest interest to geomorphic, neotectonic, and hazards applications. Natural laboratories in both the western US and the Southern Alps of New Zealand were identified as most promising. The project has been both successful and productive, despite the fact that no SRTM data for our primary field sites in New Zealand were released on the time frame of the work effort. Given the delayed release of SRTM data, we pursued the scientific questions of the roles of fluvial and, especially, glacial erosion in the evolution of relief in mountainous regions using available digital elevation models (DEMs) for the Southern Alps of New Zealand (available at both 25m and 50m pixel sizes), and USGS 10m and 30m DEMs within the Western US. As emphasized in the original proposal, we chose the emphasis on the role of glacial modification of topographic relief because there has been little quantitative investigation of glacial erosion processes at landscape scale. This is particularly surprising considering the dramatic sculpting of most mid- and high-latitude mountain ranges, the prodigious quantities of glacially-derived sediment in terrestrial and marine basins, and the current cross-disciplinary interest in the role of denudational processes in orogenesis and the evolution of topography in general. Moreover, the evolution of glaciated landscapes is not only a fundamental problem in geomorphology in its own right, but also is at the heart of the debate over Late Cenozoic linkages between climate and tectonics.

Whipple, Kelin X.↗
NASA NTRS1997

Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico

A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the high-abundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.

Insect Vectors/growth & development↗
NASA NTRS2005

Effects of Bedrock Landsliding on Cosmogenically Determined Erosion Rates

The successful quantification of long-term erosion rates underpins our understanding of landscape. formation, the topographic evolution of mountain ranges, and the mass balance within active orogens. The measurement of in situ-produced cosmogenic radionuclides (CRNs) in fluvial and alluvial sediments is perhaps the method with the greatest ability to provide such long-term erosion rates. In active orogens, however, deep-seated bedrock landsliding is an important erosional process, the effect of which on CRN-derived erosion rates is largely unquantified. We present a numerical simulation of cosmogenic nuclide production and distribution in landslide-dominated catchments to address the effect of bedrock landsliding on cosmogenic erosion rates in actively eroding landscapes. Results of the simulation indicate that the temporal stability of erosion rates determined from CRN concentrations in sediment decreases with increased ratios of landsliding to sediment detachment rates within a given catchment area, and that larger catchment areas must be sampled with increased frequency of landsliding in order to accurately evaluate long-term erosion rates. In addition, results of this simulation suggest that sediment sampling for CRNs is the appropriate method for determining long-term erosion rates in regions dominated by mass-wasting processes, while bedrock surface sampling for CRNs is generally an ineffective means of determining long-term erosion rates. Response times of CRN concentrations to changes in erosion rate indicate that climatically driven cycles of erosion may be detected relatively quickly after such changes occur, but that complete equilibration of CRN concentrations to new erosional conditions may take tens of thousands of years. Simulation results of CRN erosion rates are compared with a new, rich dataset of CRN concentrations from the Nepalese Himalaya, supporting conclusions drawn from the simulation.

Niemi, Nathan↗
NASA NTRS2004

Quantifying Grassland-to-Woodland Transitions and the Implications for Carbon and Nitrogen Dynamics in the Southwest United States

Replacement of grasslands and savannas by shrublands and woodlands has been widely reported in tropical, temperate and high-latitude rangelands worldwide (Archer 1994). These changes in vegetation structure may reflect historical shifts in climate and land use; and are likely to influence biodiversity, productivity, above- and below ground carbon and nitrogen sequestration and biophysical aspects of land surface-atmosphere interactions. The goal of our proposed research is to investigate how changes in the relative abundance of herbaceous and woody vegetation affect carbon and nitrogen dynamics across heterogeneous savannas and shrub/woodlands. By linking actual land-cover composition (derived through spectral mixture analysis of AVIRIS, TM, and AVHRR imagery) with a process-based ecosystem model, we will generate explicit predictions of the C and N storage in plants and soils resulting from changes in vegetation structure. Our specific objectives will be to (1) continue development and test applications of spectral mixture analysis across grassland-to-woodland transitions; (2) quantify temporal changes in plant and soil C and N storage and turnover for remote sensing and process model parameterization and verification; and (3) couple landscape fraction maps to an ecosystem simulation model to observe biogeochemical dynamics under changing landscape structure and climatological forcings.

Wessman, Carol A.↗
NASA NTRS2006

ALPS: The Age-Layered Population Structure for Reducing the Problem of Premature Convergence

To reduce the problem of premature convergence we define a new attribute of an individual, its age, and propose the Age-Layered Population Structure (ALPS), in which age is used to restrict competition and breeding between members of the population. ALPS differs from a typical EA by segregating individuals into different age-layers by their age - a measure of how long the genetic material has been in the population - and by regularly replacing all individuals in the bottom layer with randomly generated ones. The introduction of new, randomly generated individuals at regular intervals results in an EA that is never completely converged and is always looking at new parts of the fitness landscape. By using age to restrict competition and breeding search is able to develop promising young individuals without them being dominated by older ones. We demonstrate the effectiveness of the ALPS algorithm on an antenna design problem in which evolution with ALPS produces antennas more than twice as good as does evolution with two other types of EAs. Further analysis shows that the ALPS model does allow the offspring of newly generated individuals to move the population out of mediocre local-optima to better parts of the fitness landscape.

Hornby, Gregory S.↗
NASA NTRS2006

Remote Sensing and Spatial Growth Modeling Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 80 percent of the world s population will live in cities. Directly aligned with the expansion of cities is urban sprawl. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes. A reduction in air quality over cities is a major result of these impacts. Strategies that can be directly or indirectly implemented to help remediate air quality problems in cities and that can be accepted by political decision makers and the general public are now being explored to help bring down air pollutants and improve air quality. The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how ozone and air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rationale decisions on urban growth and sustainability for the metropolitan area in the future.

Quattrochi, Dale A.↗
NASA NTRS2006

Spatial Growth Modeling and High Resolution Remote Sensing Data Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 60 percent of the world s population will live in cities. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes within an urban ecosystems perspective. A reduction in air quality over cities is a major result of these impacts. Because of its complexity, the urban landscape is not adequately captured in air quality models such as the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to a meteorological/air quality modeling system focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include business as usual and smart growth scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the CMAQ modeling schemes. Use of these data has been found to better characterize low density/suburban development as compared with USGS lkm land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the Georgia Environmental Protection Division to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rational decisions on urban growth and sustainability for the metropolitan area in the future.

Quattrochi, Dale A.↗
NASA NTRS2005

Loggers and Forest Fragmentation: Behavioral Models of Road Building in the Amazon Basin

Although a large literature now exists on the drivers of tropical deforestation, less is known about its spatial manifestation. This is a critical shortcoming in our knowledge base since the spatial pattern of land-cover change and forest fragmentation, in particular, strongly affect biodiversity. The purpose of this article is to consider emergent patterns of road networks, the initial proximate cause of fragmentation in tropical forest frontiers. Specifically, we address the road-building processes of loggers who are very active in the Amazon landscape. To this end, we develop an explanation of road expansions, using a positive approach combining a theoretical model of economic behavior with geographic information systems (GIs) software in order to mimic the spatial decisions of road builders. We simulate two types of road extensions commonly found in the Amazon basin in a region: showing the fishbone pattern of fragmentation. Although our simulation results are only partially successful, they call attention to the role of multiple agents in the landscape, the importance of legal and institutional constraints on economic behavior, and the power of GIs as a research tool.

Arima, Eugenio Y.↗
NASA NTRS2007

Impacts of Urbanization in the Coastal Tropical City of San Juan, Puerto Rico

Urban sprawl in tropical locations is rapidly accelerating and it is more evident in islands where a large percentage of the population resides along the coasts. This paper focuses on the analysis of the impacts of land use and land cover for urbanization in the tropical coastal city of San Juan, in the Caribbean island of Puerto Rico. A mesoscale numerical model, the Regional Atmospheric Modeling System (RAMS), is used to study the impacts of land use for urbanization in the environment including specific characteristics of the urban heat island in the San Juan Metropolitan Area (SJMA), one of the most noticeable urban cores of the Caribbean. The research also makes use of the observations obtained during the airborne San Juan Atlas Mission. Surface and rawinsonde data from the mission are used to validate the atmospheric model yielding satisfactory results. Airborne high resolution remote sensing data are used to update the model's surface characteristics in order to obtain a more accurate and detailed configuration of the SJMA and perform a climate impact analysis based on land cover/land use (LCLU) changes. The impact analysis showed that the presence of the urban landscape of San Juan has an impact reflected in higher air temperatures over the area occupied by the city, with positive values of up to 2.5 C, for the simulations that have specified urban LCLU indexes in the model's bottom boundary. One interesting result of the impact analysis was the finding of a precipitation disturbance shown as a difference in total accumulated rainfall between the present urban landscape and with a potential natural vegetation, apparently induced by the presence of the urban area. Results indicate that the urban enhanced cloud formation and precipitation development occur mainly downwind of the city, including the accumulated precipitation. This spatial pattern can be explained by the presence of a larger urbanized area in the southwest sector of the city, and of the approaching northeasterly trade winds. No significant impacts were found in the sea breeze patterns of the city.

Comarazamy, Daniel E.↗
NASA NTRS2007

Impacts of Urbanization in the Coastal Tropical City of San Juan, Puerto Rico

Urban sprawl in tropical locations is rapidly accelerating and it is more evident in islands where a large percentage of the population resides along the coasts. This paper focuses on the analysis of the impacts of land use and land cover for urbanization in the tropical coastal city of San Juan, in the Caribbean island of Puerto Rico. A mesoscale numerical model, the Regional Atmospheric Modeling System (RAMS), is used to study the impacts of land use for urbanization in the environment including specific characteristics of the urban heat island in the San Juan Metropolitan Area (SJMA), one of the most noticeable urban cores of the Caribbean. The research also makes use of the observations obtained during the airborne San Juan Atlas Mission. Surface and raw insonde data from the mission are used to validate the atmospheric model yielding satisfactory results. Airborne high resolution remote sensing data are used to update the model's surface characteristics in order to obtain a more accurate and detailed configuration of the SJMA and perform a climate impact analysis based on land cover/land use (LCLU) changes. The impact analysis showed that the presence of the urban landscape of San Juan has an impact reflected in higher air temperatures over the area occupied by the city, with positive values of up to 2.5 degrees C, for the simulations that have specified urban LCLU indexes in the model's bottom boundary. One interesting result of the impact analysis was the finding of a precipitation disturbance shown as a difference in total accumulated rainfall between the present urban landscape and with a potential natural vegetation, apparently induced by the presence of the urban area. Results indicate that the urban-enhanced cloud formation and precipitation development occur mainly downwind of the city, including the accumulated precipitation. This spatial pattern can be explained by the presence of a larger urbanized area in the southwest sector of the city, and of the approaching northeasterly trade winds.

Comarazamy, Daniel E.↗
NASA NTRS2008

In the Shade of Affluence: The Inequitable Distribution of the Urban Heat Island

The urban heat island is an unintended consequence of humans building upon rural and native landscapes. We hypothesized that variations in vegetation and land use patterns across an urbanizing regional landscape would produce a temperature distribution that was spatially heterogeneous and correlated with the social characteristics of urban neighborhoods. Using biophysical and social data scaled to conform to US census geography, we found that affluent whites were more likely to live in vegetated and less climatically stressed neighborhoods likely to live in than low-income Latinos in Phoenix, Arizona. Affluent neighborhoods had cooler summer temperatures that reduced exposure to outdoor heat-related health risks, especially during a heat wave period. In addition to being warmer, poorer neighborhoods lacked critical resources in their physical and social environments to help them cope with extreme heat. Increased average temperatures due to climate change are expected to exacerbate the impacts of urban heat islands.

Harlan, Sharon L.↗
NASA NTRS2008

Mars Cameras Make Panoramic Photography a Snap

If you wish to explore a Martian landscape without leaving your armchair, a few simple clicks around the NASA Web site will lead you to panoramic photographs taken from the Mars Exploration Rovers, Spirit and Opportunity. Many of the technologies that enable this spectacular Mars photography have also inspired advancements in photography here on Earth, including the panoramic camera (Pancam) and its housing assembly, designed by the Jet Propulsion Laboratory and Cornell University for the Mars missions. Mounted atop each rover, the Pancam mast assembly (PMA) can tilt a full 180 degrees and swivel 360 degrees, allowing for a complete, highly detailed view of the Martian landscape. The rover Pancams take small, 1 megapixel (1 million pixel) digital photographs, which are stitched together into large panoramas that sometimes measure 4 by 24 megapixels. The Pancam software performs some image correction and stitching after the photographs are transmitted back to Earth. Different lens filters and a spectrometer also assist scientists in their analyses of infrared radiation from the objects in the photographs. These photographs from Mars spurred developers to begin thinking in terms of larger and higher quality images: super-sized digital pictures, or gigapixels, which are images composed of 1 billion or more pixels. Gigapixel images are more than 200 times the size captured by today s standard 4 megapixel digital camera. Although originally created for the Mars missions, the detail provided by these large photographs allows for many purposes, not all of which are limited to extraterrestrial photography.

Source record↗
NASA NTRS2009

Challenges with Deploying and Integrating Environmental Control and Life Support Functions in a Lunar Architecture with High Degrees of Mobility

Visions of lunar outposts often depict a collection of fixed elements such as pressurized habitats, in and around which human inhabitants spend the large majority of their surface stay time. In such an outpost, an efficient deployment of environmental control and life support equipment can be achieved by centralizing certain functions within one or a minimum number of habitable elements and relying on the exchange of gases and liquids between elements via atmosphere ventilation and plumbed interfaces. However, a rigidly fixed outpost can constrain the degree to which the total lunar landscape can be explored. The capability to enable widespread access across the landscape makes a lunar architecture with a high degree of surface mobility attractive. Such mobility presents unique challenges to the efficient deployment of environmental control and life support functions in multiple elements that may for long periods of time be operated independently. This paper describes some of those anticipated challenges.

Bagdigian, Robert M.↗
NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗
NASA NTRS2008

NASA's Earth Science Use of Commercially Availiable Remote Sensing Datasets: Cover Image

The cover image incorporates high resolution stereo pairs acquired from the DigitalGlobe(R) QuickBird sensor. It shows a digital elevation model of Meteor Crater, Arizona at approximately 1.3 meter point-spacing. Image analysts used the Leica Photogrammetry Suite to produce the DEM. The outside portion was computed from two QuickBird panchromatic scenes acquired October 2006, while an Optech laser scan dataset was used for the crater s interior elevations. The crater s terrain model and image drape were created in a NASA Constellation Program project focused on simulating lunar surface environments for prototyping and testing lunar surface mission analysis and planning tools. This work exemplifies NASA s Scientific Data Purchase legacy and commercial high resolution imagery applications, as scientists use commercial high resolution data to examine lunar analog Earth landscapes for advanced planning and trade studies for future lunar surface activities. Other applications include landscape dynamics related to volcanism, hydrologic events, climate change, and ice movement.

Underwood, Lauren W.↗
NASA NTRS2009

Landform Erosion and Volatile Redistribution on Ganymede and Callisto

We have been modeling landscape evolution on the Galilean satellites driven by volatile transport. Our work directly addresses some of the most fundamental issues pertinent to deciphering icy Galilean satellite geologic histories by employing techniques currently at the forefront of terrestrial, martian, and icy satellite landscape evolution studies [e.g., 1-6], including modeling of surface and subsurface energy and volatile exchanges, and computer simulation of long-term landform evolution by a variety of processes. A quantitative understanding of the expression and rates of landform erosion, and of volatile redistribution on landforms, is especially essential in interpreting endogenic landforms that have, in many cases, been significantly modified by erosion [e.g., 7-9].

Moore, Jeffrey Morgan↗
NASA NTRS2010

Fluvial Channel Networks as Analogs for the Ridge-Forming Unit, Sinus Meridiani, Mars

Fluvial models have been generally discounted as analogs for the younger layered rock units of Sinus Meridiani. A fluvial model based on the large fluvial fan provides a possibly close analog for various features of the sinuous ridges of the etched, ridge-forming unit (RFU) in particular. The close spacing of the RFU ridges, their apparently chaotic orientations, and their organization in dense networks all appear unlike classical stream channel patterns. However, drainage patterns on large fluvial fans low-angle, fluvial aggradational features, 100s of km long, documented worldwide by us provide parallels. Some large fan characteristics resemble those of classical floodplains, but many differences have been demonstrated. One major distinction relevant to the RFU is that channel landscapes of large fans can dominate large areas (1.2 million km2 in one S. American study area). We compare channel morphologies on large fans in the southern Sahara Desert with ridge patterns in Sinus Meridiani (fig 1). Stream channels are the dominant landform on large terrestrial fans: they may equate to the ubiquitous, sinuous, elongated ridges of the RFU that cover areas region wide. Networks of convergent/divergent and crossing channels may equate to similar features in the ridge networks. Downslope divergence is absent in channels of terrestrial upland erosional landscapes (fig. 1, left), whereas it is common to both large fans (fig. 1, center) and RFU ridge patterns (fig 1, right downslope defined as the regional NW slope of Sinus Meridiani). RFU ridge orientation, judged from those areas apparently devoid of impact crater control, is broadly parallel with the regional slope (arrow, fig. 1, right), as is mean orientation of major channels on large fans (arrow, fig. 1, center). High densities per unit area characterize fan channels and martian ridges reaching an order of magnitude higher than those in uplands just upstream of the terrestrial study areas fig. 1. In concert with several other regional features, these morphological similarities argue for the RFU as a possibly fluvial unit.

Wilkinson, M. J.↗
NASA NTRS2010

Resilience of Alaska's Boreal Forest to Climatic Change

This paper assesses the resilience of Alaska s boreal forest system to rapid climatic change. Recent warming is associated with reduced growth of dominant tree species, plant disease and insect outbreaks, warming and thawing of permafrost, drying of lakes, increased wildfire extent, increased postfire recruitment of deciduous trees, and reduced safety of hunters traveling on river ice. These changes have modified key structural features, feedbacks, and interactions in the boreal forest, including reduced effects of upland permafrost on regional hydrology, expansion of boreal forest into tundra, and amplification of climate warming because of reduced albedo (shorter winter season) and carbon release from wildfires. Other temperature-sensitive processes for which no trends have been detected include composition of plant and microbial communities, long-term landscape-scale change in carbon stocks, stream discharge, mammalian population dynamics, and river access and subsistence opportunities for rural indigenous communities. Projections of continued warming suggest that Alaska s boreal forest will undergo significant functional and structural changes within the next few decades that are unprecedented in the last 6000 years. The impact of these social ecological changes will depend in part on the extent of landscape reorganization between uplands and lowlands and on policies regulating subsistence opportunities for rural communities.

Chapin, F. S., III↗