Maintaining tree islands in the Florida Everglades: nutrient redistribution is the key

The Florida Everglades is an oligotrophic wetland system with tree islands as one of its most prominent landscape features. Total soil phosphorus concentrations on tree islands can be 6 to 100 times greater than phosphorus levels in the surrounding marshes and sloughs, making tree islands nutrient hotspots. Several mechanisms are believed to redistribute phosphorus to tree islands: subsurface water flows generated by evapotranspiration of trees, higher deposition rates of dry fallout, deposition of guano by birds and other animals, groundwater upwelling, and bedrock mineralization by tree exudates. A conceptual model is proposed, in which the focused redistribution of limiting nutrients, especially phosphorus, onto tree islands controls their maintenance and expansion. Because of increased primary production and peat accretion rates, the redistribution of phosphorus can result in an increase in both tree island elevation and size. Human changes to hydrology have greatly decreased the number and size of tree islands in parts of the Everglades. The proposed model suggests that the preservation of existing tree islands, and ultimately of the Everglades landscape, requires the maintenance of these phosphorus redistribution mechanisms.

Tree Island Response to Fire and Flooding in the Short-Hydroperiod Marl Prairie Grasslands of the Florida Everglades

Within the marl prairie grasslands of the Florida Everglades, USA, the combined effects of fire and flooding usually lead to very significant changes in tree island structure and composition. Depending on fire severity and post-fire hydroperiod, these effects vary spatially and temporally throughout the landscape, creating a patchy post-fire mosaic of tree islands with different successional states. Through the use of the Normalized Difference Vegetation Index (NDVI) and three predictor variables (marsh water table elevation at the time of fire, post-fire hydroperiod, and tree island size), along with logistic regression analysis, we examined the probability of tree island burning and recovering following the Mustang Corner Fire (May to June 2008) in Everglades National Park. Our data show that hydrologic conditions during and after fire, which are under varying degrees of management control, can lead to tree island contraction or loss. More specifically, the elevation of the marsh water table at the time of the fire appears to be the most important parameter determining the severity of fire in marl prairie tree islands. Furthermore, in the post-fire recovery phase, both tree island size and hydroperiod during the first year after the fire played important roles in determining the probability of tree island recovery, contraction, or loss.

Sources and mixing state of size-resolved elemental carbon particles in a European megacity: Paris

An Aerosol Time-Of-Flight Mass Spectrometer (ATOFMS) was deployed to investigate the size-resolved chemical composition of single particles at an urban background site in Paris, France, as part of the MEGAPOLI winter campaign in January/February 2010. ATOFMS particle counts were scaled to match coincident Twin Differential Mobility Particle Sizer (TDMPS) data in order to generate hourly size-resolved mass concentrations for the single particle classes observed. The total scaled ATOFMS particle mass concentration in the size range 150–1067 nm was found to agree very well with the sum of concurrent High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) and Multi-Angle Absorption Photometer (MAAP) mass concentration measurements of organic carbon (OC), inorganic ions and black carbon (BC) (R2 = 0.91). Clustering analysis of the ATOFMS single particle mass spectra allowed the separation of elemental carbon (EC) particles into four classes: (i) EC attributed to biomass burning (ECbiomass), (ii) EC attributed to traffic (ECtraffic), (iii) EC internally mixed with OC and ammonium sulfate (ECOCSOx), and (iv) EC internally mixed with OC and ammonium nitrate (ECOCNOx). Average hourly mass concentrations for EC-containing particles detected by the ATOFMS were found to agree reasonably well with semi-continuous quantitative thermal/optical EC and optical BC measurements (r2 = 0.61 and 0.65–0.68 respectively, n = 552). The EC particle mass assigned to fossil fuel and biomass burning sources also agreed reasonably well with BC mass fractions assigned to the same sources using seven-wavelength aethalometer data (r2 = 0.60 and 0.48, respectively, n = 568). Agreement between the ATOFMS and other instrumentation improved noticeably when a period influenced by significantly aged, internally mixed EC particles was removed from the intercomparison. 88% and 12% of EC particle mass was apportioned to fossil fuel and biomass burning respectively using the ATOFMS data compared with 85% and 15% respectively for BC estimated from the aethalometer model. On average, the mass size distribution for EC particles is bimodal; the smaller mode is attributed to locally emitted, mostly externally mixed EC particles, while the larger mode is dominated by aged, internally mixed ECOCNOx particles associated with continental transport events. Periods of continental influence were identified using the Lagrangian Particle Dispersion Model (LPDM) "FLEXPART". A consistent minimum between the two EC mass size modes was observed at approximately 400 nm for the measurement period. EC particles below this size are attributed to local emissions using chemical mixing state information and contribute 79% of the scaled ATOFMS EC particle mass, while particles above this size are attributed to continental transport events and contribute 21% of the EC particle mass. These results clearly demonstrate the potential benefit of monitoring size-resolved mass concentrations for the separation of local and continental EC emissions. Knowledge of the relative input of these emissions is essential for assessing the effectiveness of local abatement strategies.

Learning from Geometry

Subspaces and manifolds are two powerful models for high dimensional signals. Subspaces model linear correlation and are a good fit to signals generated by physical systems, such as frontal images of human faces and multiple sources impinging at an antenna array. Manifolds model sources that are not linearly correlated, but where signals are determined by a small number of parameters. Examples are images of human faces under different poses or expressions, and handwritten digits with varying styles. However, there will always be some degree of model mismatch between the subspace or manifold model and the true statistics of the source. This dissertation exploits subspace and manifold models as prior information in various signal processing and machine learning tasks.

A near-low-rank Gaussian mixture model measures proximity to a union of linear or affine subspaces. This simple model can effectively capture the signal distribution when each class is near a subspace. This dissertation studies how the pairwise geometry between these subspaces affects classification performance. When model mismatch is vanishingly small, the probability of misclassification is determined by the product of the sines of the principal angles between subspaces. When the model mismatch is more significant, the probability of misclassification is determined by the sum of the squares of the sines of the principal angles. Reliability of classification is derived in terms of the distribution of signal energy across principal vectors. Larger principal angles lead to smaller classification error, motivating a linear transform that optimizes principal angles. This linear transformation, termed TRAIT, also preserves some specific features in each class, being complementary to a recently developed Low Rank Transform (LRT). Moreover, when the model mismatch is more significant, TRAIT shows superior performance compared to LRT.

The manifold model enforces a constraint on the freedom of data variation. Learning features that are robust to data variation is very important, especially when the size of the training set is small. A learning machine with large numbers of parameters, e.g., deep neural network, can well describe a very complicated data distribution. However, it is also more likely to be sensitive to small perturbations of the data, and to suffer from suffer from degraded performance when generalizing to unseen (test) data.

From the perspective of complexity of function classes, such a learning machine has a huge capacity (complexity), which tends to overfit. The manifold model provides us with a way of regularizing the learning machine, so as to reduce the generalization error, therefore mitigate overfiting. Two different overfiting-preventing approaches are proposed, one from the perspective of data variation, the other from capacity/complexity control. In the first approach, the learning machine is encouraged to make decisions that vary smoothly for data points in local neighborhoods on the manifold. In the second approach, a graph adjacency matrix is derived for the manifold, and the learned features are encouraged to be aligned with the principal components of this adjacency matrix. Experimental results on benchmark datasets are demonstrated, showing an obvious advantage of the proposed approaches when the training set is small.

Stochastic optimization makes it possible to track a slowly varying subspace underlying streaming data. By approximating local neighborhoods using affine subspaces, a slowly varying manifold can be efficiently tracked as well, even with corrupted and noisy data. The more the local neighborhoods, the better the approximation, but the higher the computational complexity. A multiscale approximation scheme is proposed, where the local approximating subspaces are organized in a tree structure. Splitting and merging of the tree nodes then allows efficient control of the number of neighbourhoods. Deviation (of each datum) from the learned model is estimated, yielding a series of statistics for anomaly detection. This framework extends the classical {\em changepoint detection} technique, which only works for one dimensional signals. Simulations and experiments highlight the robustness and efficacy of the proposed approach in detecting an abrupt change in an otherwise slowly varying low-dimensional manifold.

Impact of protected forests on rural household fuel tree planting and deforestation: Chiro District, Ethiopia

This study aims at exploring the potential impact of forest protection intervention on rural households’ private fuel tree planting in Chiro district of eastern Ethiopia. The study results revealed a robust and significant positive impact of the intervention on farmers’ decisions to produce private household energy by growing fuel trees on their farm. As participation in private fuel tree planting is not random, the study confronts a methodological issue in investigating the causal effect of forest protection intervention on rural farm households’ private fuel tree planting through non-parametric propensity score matching (PSM) method. The protection intervention on average has increased fuel tree planting by 503 (580.6%) compared to open access areas and indirectly contributed to slowing down the loss of biodiversity in the area. Land cover/use is a dynamic phenomenon that changes with time and space due to anthropogenic pressure and development. Forest cover and land use changes in Chiro District, Ethiopia over a period of 40 years was studied using remotely sensed data. Multi temporal satellite data of Landsat was used to map and monitor forest cover and land use changes occurred during three point of time of 1972,1986 and 2012. A pixel base supervised image classification was used to map land use land cover classes for maps of both time set. The result of change detection analysis revealed that the area has shown a remarkable land cover/land use changes in general and forest cover change in particular. Specifically, the dense forest cover land declined from 235 ha in 1972 to 51 ha in 1986. However, government interventions in forest protection in 1989 have slowed down the drastic change of dense forest cover loss around the protected area through reclaiming 1,300 hectares of deforested land through reforestation program up to 2012.