The urban heat island in the city of Poznań as derived from Landsat 5 TM

Deforestation is expanding and accelerating into the remaining areas of undisturbed forest, and the quality of the remaining forests is declining today. Assessing the climatic impacts of deforestation can help to rectify this alarming situation. In this paper, how historical deforestation may affect global climate through interactive ocean and surface albedo is examined using an Earth system model of intermediate complexity (EMIC). Control and anomaly integrations are performed for 1000 years. In the anomaly case, cropland is significantly expanded since AD 1700. The response of climate in deforested areas is not uniform between the regions. In the background of a global cooling of 0.08 °C occurring with cooler surface air above 0.4 °C across 30° N to 75° N from March to September, the surface albedo increase has a global cooling effect in response to global-scale replacement of forests by cropland, especially over northern mid-high latitudes. The northern mid-latitude (30° N–60° N) suffers a prominent cooling in June, suggesting that this area is most sensitive to cropland expansion through surface albedo. Most regions show a consistent trend between the overall cooling in response to historical deforestation and its resulting cooling due to surface albedo anomaly. Furthermore, the effect of the interactive ocean on shaping the climate response to deforestation is greater than that of prescribed SSTs in most years with a maximum spread of 0.05 °C. This difference is more prominent after year 1800 than that before due to the more marked deforestation. These findings show the importance of the land cover change and the land surface albedo, stressing the necessity to analyze other biogeophysical processes of deforestation using interactive ocean.     

A GCM Simulation of Heat Waves, Dry Spells, and Their Relationships to Circulation

Heat waves and dry spells are analyzed (i) at eightstations in south Moravia (Czech Republic), (ii) inthe control ECHAM3 GCM run at the gridpoint closest tothe study area, and (iii) in the ECHAM3 GCM run fordoubled CO₂ concentrations (scenario A) at thesame gridpoint (heat waves only). The GCM outputs arevalidated both against individual station data andareally representative values. In the control run, theheat waves are too long, appear later in the year,peak at higher temperatures and their numbers areunder- (over-) estimated in June and July (in August).The simulated dry spells are too long, and the annualcycle of their occurrence is distorted.Mid-tropospheric circulation, and heat waves and dryspells are linked much less tightly in the controlclimate than in the observed. Since mid-troposphericcirculation is simulated fairly successfully, wesuggest the hypothesis that either the air-masstransformation and local processes are too strong inthe model or the simulated advection is too weak. Inthe scenario A climate, the heat waves become a commonphenomenon: warming of 4.5 °C in summer(difference between scenario A and control climates)induces a five-fold increase in the frequency oftropical days and an immense enhancement of extremityof heat waves. The results of the study underline theneed for (i) a proper validation of the GCM outputbefore a climate impact study is conducted and (ii)translation of large-scale information from GCMs intolocal scales using downscaling and stochasticmodelling techniques in order to reduce GCMs' biases.     

Mapping of aggregated floodplain plant communities using image fusion of CASI and LiDAR data

Combined optical and laser altimeter data offer the potential to map and monitor plant communities based on their spectral and structural characteristics. A problem unresolved is, however, that narrowly defined plant communities, i.e. plant communities at a low hierarchical level of classification in the Braun-Blanquet system, often cannot be linked directly to remote sensing data for vegetation mapping. We studied whether and how a floristic dataset can be aggregated into a few major discrete, mappable classes without substantial loss of ecological meaning. Multi-source airborne data (CASI and LiDAR) and floristic field data were collected for a floodplain along the river Waal in the Netherlands. Mapping results based on floristic similarity alone did not achieve highest levels of accuracy. Ordination of floristic data showed that terrain elevation and soil moisture were the main underlying environmental drivers shaping the floodplain vegetation, but grouping of plant communities based on their position in the ordination space is not always obvious. Combined ordination-based grouping with floristic similarity clustering led to syntaxonomically relevant aggregated plant assemblages and yielded highest mapping accuracies.     

Attitude estimation based on fusion of gyroscopes and single antenna GPS for small UAVs under the influence of vibration

An attitude estimation method is presented for small unmanned aerial vehicles (UAVs) powered by a piston engine which is the major source of vibration. Vibration of the engine significantly degrades the accuracy of the inertial measurement unit, especially for low-cost sensors that are based on micro electro-mechanical system. Therefore, a vibration model for a small UAV is proposed in order to examine the influence of vibration on attitude estimation with different sensors. The model is derived based on spectrum analysis with short-time Fourier transform. The vibration is compared with the drift of the gyroscope in order to examine the impact on attitude estimation. An attitude estimation method that fuses the gyroscopes and single antenna global positioning system (GPS) is proposed to mitigate the influence of engine vibration and gyroscope drift. The quaternion-based extended Kalman filter is implemented to fuse the sensors. This filter fuses the angular rates measured by the gyroscopes and the pseudo-attitude derived from the GPS velocity to estimate the attitude of the UAV. Simulations and experiment results indicate that the proposed method performs well both in short-term and long-term accuracy even though the gyroscopes are affected by drift and vibration noise, while the pseudo-attitude contains severe noise.     

Skyline-based registration of 3D laser scans

Acquisition and registration of terrestrial 3D laser scans is a fundamental task in mapping and modeling of cities in three dimensions. To automate this task marker-free registration methods are required. Based on the existence of skyline features, this paper proposes a novel method. The skyline features are extracted from panoramic 3D scans and encoded as strings enabling the use of string matching for merging the scans. Initial results of the proposed method in the old city center of Bremen are pre-sented.     

Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods

In recent years, a number of alternative methods have been proposed to predict forest canopy density from remotely sensed data. To date, however, it remains difficult to decide which method to use, since their relative performance has never been evaluated. In this study the performance of: (1) an artificial neural network, (2) a multiple linear regression, (3) the forest canopy density mapper and (4) a maximum likelihood classification method was compared for prediction of forest canopy density using a Landsat ETM+ image. Comparison of confusion matrices revealed that the regression model performed significantly worse than the three other methods. These results were based on a z-test for comparison of weighted kappa statistics, which is an appropriate statistic for analysis of ranked categories. About 89% of the variance of the observed canopy density was explained by the artificial neural networks, which outperformed the other three methods in this respect. Moreover, the artificial neural networks gave an unbiased prediction, while other methods systematically under or over predicted forest canopy density. The choice of biased method could have a high impact on canopy density inventories.     

Expanding global mapping of the foliage clumping index with multi-angular POLDER three measurements: Evaluation and topographic compensation

The clumping index measures the spatial aggregation (clumped, random and regular) of foliage elements. The global mapping of the clumping index with a limited eight-month multi-angular POLDER 1 dataset is expanded by integrating new, complete year-round observations from POLDER 3. We show that terrain-induced shadows can enhance bi-directional reflectance distribution function variation and negatively bias the clumping index (i.e. indicating more vegetation clumping) in rugged terrain. Using a global high-resolution digital elevation model, a topographic compensation function is devised to correct for this terrain effect. The clumping index reductions can reach up to 30% from the topographically non-compensated values, depending on terrain complexity and land cover type. The new global clumping index map is compared with an assembled set of field measurements from 32 different sites, covering four continents and diverse biomes.     

Do CRISTA Experiment/Campaign Data Represent a Typical Situation or Not?

Data from four A3 radio wave circuits in central Europe are used to analyse the representativeness of the ionization and gravity wave activity pattern in the lower ionosphere (85 – 100 km) in the CRISTA experiment interval (3 – 12 November 1994) for the given conditions. It has been found that the CRISTA experiment interval was run under conditions, which are highly representative both of October – November 1994 and autumn, low-moderate solar activity, the descending phase of solar cycle conditions, i.e. CRISTA measurements may be considered to provide values identical with, or close to climatological values (at least for central Europe, h = 85 – 100 km).     

Stability of source mechanisms inverted from P‐wave amplitude microseismic monitoring data acquired at the surface

We study the stability of source mechanisms inverted from data acquired at surface and near‐surface monitoring arrays. The study is focused on P‐wave data acquired on vertical components, as this is the most common type of acquisition. We apply ray modelling on three models: a fully homogeneous isotropic model, a laterally homogeneous isotropic model and a laterally homogeneous anisotropic model to simulate three commonly used models in inversion. We use geometries of real arrays, one consisting in surface receivers and one consisting in ‘buried’ geophones at the near‐surface. Stability was tested for two of the frequently observed source mechanisms: strike‐slip and dip‐slip and was evaluated by comparing the parameters of correct and inverted mechanisms. We assume these double‐couple source mechanisms and use quantitatively the inversion allowing non‐double‐couple components to measure stability of the inversion. To test the robustness we inverted synthetic amplitudes computed for a laterally homogeneous isotropic model and contaminated with noise using a fully homogeneous model in the inversion. Analogously amplitudes computed in a laterally homogeneous anisotropic model were inverted in all three models. We show that a star‐like surface acquisition array provides very stable inversion up to a very high level of noise in data. Furthermore, we reveal that strike‐slip inversion is more stable than dip‐slip inversion for the receiver geometries considered here. We show that noise and an incorrect velocity model may result in narrow bands of source mechanisms in Hudson's plots.     

Geophysical constraints on deep weathering and water storage potential in the Southern Sierra Critical Zone Observatory

The conversion of bedrock to regolith marks the inception of critical zone processes, but the factors that regulate it remain poorly understood. Although the thickness and degree of weathering of regolith are widely thought to be important regulators of the development of regolith and its water‐storage potential, the functional relationships between regolith properties and the processes that generate it remain poorly documented. This is due in part to the fact that regolith is difficult to characterize by direct observations over the broad scales needed for process‐based understanding of the critical zone. Here we use seismic refraction and resistivity imaging techniques to estimate variations in regolith thickness and porosity across a forested slope and swampy meadow in the Southern Sierra Critical Zone Observatory (SSCZO). Inferred seismic velocities and electrical resistivities image a weathering zone ranging in thickness from 10 to 35 m (average = 23 m) along one intensively studied transect. The inferred weathering zone consists of roughly equal thicknesses of saprolite (P‐velocity < 2 km s^?1) and moderately weathered bedrock (P‐velocity = 2–4 km s^?1). A minimum‐porosity model assuming dry pore space shows porosities as high as 50% near the surface, decreasing to near zero at the base of weathered rock. Physical properties of saprolite samples from hand augering and push cores are consistent with our rock physics model when variations in pore saturation are taken into account. Our results indicate that saprolite is a crucial reservoir of water, potentially storing an average of 3 m³ m^?2 of water along a forested slope in the headwaters of the SSCZO. When coupled with published erosion rates from cosmogenic nuclides, our geophysical estimates of weathering zone thickness imply regolith residence times on the order of 10⁵ years. Thus, soils at the surface today may integrate weathering over glacial–interglacial fluctuations in climate. Copyright © 2013 John Wiley & Sons, Ltd.