Journal of Applied Meteorology and Climatology, Volume 52, Issue 5 (May 2013)
Christopher D. Karstens, William A. Gallus Jr., Bruce D. Lee, and Catherine A. Finley
“In this study, aerial imagery of tornado damage is used to digitize the falling direction of trees (i.e., tree fall) along the 22 May 2011 Joplin, Missouri, and 27 April 2011 Tuscaloosa–Birmingham, Alabama, tornado tracks. Normalized mean patterns of observed tree fall from each tornado’s peak-intensity period are subjectively compared with results from analytical vortex simulations of idealized tornado-induced tree fall to characterize mean properties of the near-surface flow as depicted by the model. A computationally efficient method of simulating tree fall is applied that uses a Gumbel distribution of critical tree-falling wind speeds on the basis of the enhanced Fujita scale. Results from these simulations suggest that both tornadoes had strong radial near-surface winds. A few distinct tree-fall patterns are identified at various locations along the Tuscaloosa–Birmingham tornado track. Concentrated bands of intense tree fall, collocated with and aligned parallel to the axis of underlying valley channels, extend well beyond the primary damage path. These damage patterns are hypothesized to be the result of flow acceleration caused by channeling within valleys. Another distinct pattern of tree fall, likely not linked to the underlying topography, may have been associated with a rear-flank downdraft (RFD) internal surge during the tornado’s intensification stage. Here, the wind field was strong enough to produce tornado-strength damage well beyond the visible funnel cloud. This made it difficult to distinguish between tornado- and RFD-related damage and thus illustrates an ambiguity in ascertaining tornado-damage-path width in some locations.”
PNAS, 28 January 2013
Jeffrey Q. Chambers, Robinson I. Negron-Juarez, Daniel Magnabosco Marra, Alan Di Vittorio, Joerg Tews, Dar Roberts, Gabriel H. P. M. Ribeiro, Susan E. Trumbore, and Niro Higuchi
“Old-growth forest ecosystems comprise a mosaic of patches in different successional stages, with the fraction of the landscape in any particular state relatively constant over large temporal and spatial scales. The size distribution and return frequency of disturbance events, and subsequent recovery processes, determine to a large extent the spatial scale over which this old-growth steady state develops. Here, we characterize this mosaic for a Central Amazon forest by integrating field plot data, remote sensing disturbance probability distribution functions, and individual-based simulation modeling. Results demonstrate that a steady state of patches of varying successional age occurs over a relatively large spatial scale, with important implications for detecting temporal trends on plots that sample a small fraction of the landscape. Long highly significant stochastic runs averaging 1.0 Mg biomass⋅ha−1⋅y−1 were often punctuated by episodic disturbance events, resulting in a sawtooth time series of hectare-scale tree biomass. To maximize the detection of temporal trends for this Central Amazon site (e.g., driven by CO2 fertilization), plots larger than 10 ha would provide the greatest sensitivity. A model-based analysis of fractional mortality across all gap sizes demonstrated that 9.1–16.9% of tree mortality was missing from plot-based approaches, underscoring the need to combine plot and remote-sensing methods for estimating net landscape carbon balance. Old-growth tropical forests can exhibit complex large-scale structure driven by disturbance and recovery cycles, with ecosystem and community attributes of hectare-scale plots exhibiting continuous dynamic departures from a steady-state condition.”
International Journal of Geographical Information Science, published online 19 November 2012
Seyed M. Mussavi Rizi, Maciej M. Łatek, and Armando Geller
“We develop a new algorithm for population synthesis that fuses remote-sensing data with partial and sparse demographic surveys. The algorithm addresses non-binding constraints and complex sampling designs by translating population synthesis into a computationally efficient procedure for constrained network growth. As a case, we synthesize the rural population of Afghanistan, validate the algorithm with in-sample and out-of-sample tests, examine the variability of algorithm outputs over k-nearest neighbor manifolds, and show the responsiveness of our algorithm to additional data as a constraint on marginal population counts.”
PLoS Biology, 3(1): e3, 2004
Marc Ancrenaz, Olivier Gimenez, Laurentius Ambu, Karine Ancrenaz, Patrick Andau, Benoît Goossens, John Payne, Azri Sawang, Augustine Tuuga, and Isabelle Lackman-Ancrenaz
“Great apes are threatened with extinction, but precise information about the distribution and size of most populations is currently lacking. We conducted orangutan nest counts in the Malaysian state of Sabah (North Borneo), using a combination of ground and helicopter surveys, and provided a way to estimate the current distribution and size of the populations living throughout the entire state. We show that the number of nests detected during aerial surveys is directly related to the estimated true animal density and that a helicopter is an efficient tool to provide robust estimates of orangutan numbers.
Distribution and Size of the 16 Major Orangutan Populations Identified during the Surveys in Sabah, Malaysia, Borneo
“Our results reveal that with a total estimated population size of about 11,000 individuals, Sabah is one of the main strongholds for orangutans in North Borneo. More than 60% of orangutans living in the state occur outside protected areas, in production forests that have been through several rounds of logging extraction and are still exploited for timber. The role of exploited forests clearly merits further investigation for orangutan conservation in Sabah.”
Journal of Maps, Volume 8, Issue 4, December 2012
Magdalini Pleniou, Fotios Xystrakis, Panayotis Dimopoulos, and Nikos Koutsias
“Maps depicting the spatially explicit fire history of an area, including variables such as fire frequency and fire return interval, are important tools promoting the understanding of processes associated with wildfires (fire ignition and spread), the assessment of the impacts of wildland fires on landscape dynamics, and decisions on appropriate management practices. Remote sensing is a cost- and time-effective alternative to automatically assess a vast amount of spatial information and produce various thematic maps. The aim of this study was to reconstruct the recent fire history of Attica region (Greece), in a spatially explicit mode by means of remote sensing techniques using a series of Landsat images acquired from 1984 to 2011. The results show that the fire scar perimeters were captured with high accuracy. Regression modelling shows that the differences between the area burned estimated from satellite data and that recorded by the forest service can be explained (86.3% of the variance) by the number of satellite images used (standardized coefficient 0.752) followed by the date of the first image (standardized coefficient 0.705). The use of satellite data as the basic source of information alongside automated classification methods should be promoted for the creation of fire history maps. The latter is further supported when considering the long history of data capture from Landsat satellites, which provide a huge, global historical archive of repeat images of the Earth’s surface.”
Environmental Health Perspectives, 120:1727–1732 (2012)
Seung-Jae Lee, Marc L. Serre, Aaron van Donkelaar, Randall V. Martin, Richard T. Burnett, and Michael Jerrett
“Background: A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data.
“Objective: We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation.
Map of the United States indicating the month of the year when the monthly average PM2.5 concentration was highest; circles indicate individual monitoring sites.
“Methods: We developed a space–time geostatistical kriging model to predict PM2.5 over the continental United States and compared resulting predictions to estimates derived from satellite retrievals.
“Results: The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates.
“Conclusions: We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.”
Journal of Coastal Research, Volume 28, Issue 6, November 2012
Fadi Chaaban, Hanan Darwishe, Yvonne Battiau-Queney, Barbara Louche, Eric Masson, Jamal El Khattabi, and Erick Carlier
“Coastal areas are naturally dynamic with changes occurring over periods of time. When the shoreline moves landward, coastal erosion becomes a serious problem, and the rate of change has to be calculated. Coastline retreat is one of the best indicators of coastal erosion. Here, the geographic information systems (GIS) platform (ArcGIS 9.3.1; Esri software) is used to study the long-term (last 59 years) shoreline change in the area of Hardelot-Plage and Sainte Cécile–Plage (a north–south 14-km-long beach), in northern France. The primary aim of this study was to develop a methodology for calculating shoreline change using ArcGIS Modelbuilder and aerial photographs. Changes in 14 coastlines over the course of 59 years (from 1946 to 2005) were digitized and represented in ArcGIS 9.3.1 platform. Two hundred and ninety-two transects perpendicular to the shoreline were used to estimate coastal erosion and deduce the recession rate.
“The Modelbuilders (two models) created and used in this work are generic models that can be used for geoprocessing linear features. One model can be used to ascertain the intersection between linear features (transects and shorelines), adding a new field to the attribute table and calculating the geometry of the intersection points. A second model can be used to add a new field to the attribute table and calculate the distance on the transect lines between the linear reference feature and other linear features, in this study between the baseline (established adjacent to the series of shoreline positions) and the shoreline. The results show that the shoreline change rates between 1947 and 2005 along the Hardelot and Sainte Cécile coasts are generally negative; 82.2% of transects have values less than zero (i.e., retreat) and outside of the error margin (±10 m). Nevertheless the shoreline change shows successive phases of advance and retreat over the same period.”
International Journal of Health Geographics 11:51, 20 November 2012
Elainne Christine Gomes, Onicio Batista Leal-Neto, Jones Albuquerque, Hernande Pereira Silva and Constança Simões Barbosa
“Background: In Brazil, schistosomiasis mansoni infection is an endemic disease that mainly affects the country’s rural populations who carry out domestic and social activities in rivers and water accumulations that provide shelter for the snails of the disease. The process of rural migration to urban centers and the disorderly occupation of natural environments by these populations from endemic areas have favored expansion of schistosomiasis to locations that had been considered to be disease-free. Based on environmental changes that have occurred in consequent to an occupation and urbanization process in the locality of Porto de Galinhas, the present study sought to identify the relationship between those chances, measure by remote-sensing techniques, and establish a new endemic area for schistosomiasis on the coast of Pernambuco State – Brazil.
Kernel maps show that the risk of disease occurrence and transmission were concentrated in the locality of Salinas in 2010.
“Methods: To gather prevalence data, two parasitological census surveys were conducted (2000 and 2010) using the Kato-Katz technique. Two malacological surveys were also conducted in the same years in order to define the density and infection rate of the intermediate host. Based on these data, spatial analyses were done, resulting in maps of the risk of disease transmission. To ascertain the environmental changes that have occurred at the locality, images from the QuickBird satellite were analyzed, thus resulting in land use maps.
“Results: Over this 10-year period, the foci of schistosomiasis became more concentrated in the Salinas district. This area was considered to be at the greatest risk of schistosomiasis transmission and had the highest prevalence rates over this period. The study illustrated that this was the area most affected by the environmental changes resulting from the disorderly urbanization process, which gave rise to unsanitary environments that favored the establishment and maintenance of foci of schistosomiasis transmission, thereby consolidating the process of expansion and endemization of this parasitosis. ”
Remote Sensing, 2012, 4(3), 661-681
Sonia M. Ortiz, Johannes Breidenbach, Ralf Knuth and Gerald Kändler
“Climate change is a factor that largely contributes to the increase of forest areas affected by natural damages. Therefore, the development of methodologies for forest monitoring and rapid assessment of affected areas is required. Space-borne synthetic aperture radar (SAR) imagery with high resolution is now available for large-scale forest mapping and forest monitoring applications. However, a correct interpretation of SAR images requires an adequate preprocessing of the data consisting of orthorectification and radiometric calibration. The resolution and quality of the digital elevation model (DEM) used as reference is crucial for this purpose. Therefore, the primary aim of this study was to analyze the influence of the DEM quality used in the preprocessing of the SAR data on the mapping accuracy of forest types.
Application of the classification model in Biberach. (a) Map of deciduous- and
coniferous-dominated forest based on combined leaf-on and leaf-off TerraSAR-X images (acquired in 2008 and 2009) preprocessed with the ALS DTM; (b) official forest stand map (established 2007); (c) ortho-photographs (acquired on 2007).
“In order to examine TerraSAR-X images to map forest dominated by deciduous and coniferous trees, High Resolution SpotLight images were acquired for two study sites in southern Germany. The SAR images were preprocessed with a Shuttle Radar Topography Mission (SRTM) DEM (resolution approximately 90 m), an airborne laser scanning (ALS) digital terrain model (DTM) (5 m resolution), and an ALS digital surface model (DSM) (5 m resolution). The orthorectification of the SAR images using high resolution ALS DEMs was found to be important for the reduction of errors in pixel location and to increase the classification accuracy of forest types. SAR images preprocessed with ALS DTMs resulted in the highest classification accuracies, with kappa coefficients of 0.49 and 0.41, respectively. SAR images preprocessed with ALS DTMs resulted in greater accuracy than those preprocessed with ALS DSMs in most cases. The classification accuracy of forest types using SAR images preprocessed with the SRTM DEM was fair, with kappa coefficients of 0.23 and 0.32, respectively.Analysis of the radar backscatter indicated that sample plots dominated by coniferous trees tended to have lower scattering coefficients than plots dominated by deciduous trees. Leaf-off images were only slightly better suited for the classification than leaf-on images. The combination of leaf-off and leaf-on improved the classification accuracy considerably since the backscatter changed between seasons, especially in deciduous-dominated forest.”
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Remote Sensing, 2012, 4(4), published online 27 March 2012
Alex Hoffmann , Debbie Clifford , Josep Aulinas , James G. Carton , Florian Deconinck , Berivan Esen , Jakob Hüsing , Katharina Kern , Stephan Kox , David Krejci , Thomas Krings , Steffen Lohrey , Patrick Romano , Ricardo Topham, and Claudia Weitnauer
“We propose a new satellite mission to deliver high quality measurements of upper air water vapour. The concept centres around a LiDAR in limb sounding by occultation geometry, designed to operate as a very long path system for differential absorption measurements. We present a preliminary performance analysis with a system sized to send 75 mJ pulses at 25 Hz at four wavelengths close to 935 nm, to up to 5 microsatellites in a counter-rotating orbit, carrying retroreflectors characterized by a reflected beam divergence of roughly twice the emitted laser beam divergence of 15 µrad. This provides water vapour profiles with a vertical sampling of 110 m; preliminary calculations suggest that the system could detect concentrations of less than 5 ppm. A secondary payload of a fairly conventional medium resolution multispectral radiometer allows wide-swath cloud and aerosol imaging. The total weight and power of the system are estimated at 3 tons and 2,700 W respectively.
“This novel concept presents significant challenges, including the performance of the lasers in space, the tracking between the main spacecraft and the retroreflectors, the refractive effects of turbulence, and the design of the telescopes to achieve a high signal-to-noise ratio for the high precision measurements. The mission concept was conceived at the Alpbach Summer School 2010.”