Sarah J. Graves

Tree Species Abundance Predictions in a Tropical Agricultural Landscape with a Supervised Classification Model and Imbalanced Data

Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced dataset with many samples for common species and few samples for less common species. Despite the high prevalence of imbalanced datasets in multiclass species predictions, the effect on species prediction accuracy and landscape species abundance has not yet been quantified. First, we trained and assessed the accuracy of a support vector machine (SVM) model with a highly imbalanced dataset of 20 tropical species and one mixed-species class of 24 species identified in a hyperspectral image mosaic (350–2500 nm) of Panamanian farmland and secondary forest fragments. The model, with an overall accuracy of 62% ± 2.3% and F-score of 59% ± 2.7%, was applied to the full image mosaic (23,000 ha at a 2-m resolution) to produce a species prediction map, which suggested that this tropical agricultural landscape is more diverse than what has been presented in field-based studies. Second, we quantified the effect of class imbalance on model accuracy. Model assessment showed a trend where species with more samples were consistently over predicted while species with fewer samples were under predicted. Standardizing sample size reduced model accuracy, but also reduced the level of species over- and under-prediction. This study advances operational species mapping of diverse tropical landscapes by detailing the effect of imbalanced data on classification accuracy and providing estimates of tree species abundance in an agricultural landscape. Species maps using data and methods presented here can be used in landscape analyses of species distributions to understand human or environmental effects, in addition to focusing conservation efforts in areas with high tree cover and diversity.

Women are underrepresented on the editorial boards of journals in environmental biology and natural resource management

Despite women earning similar numbers of graduate degrees as men in STEM disciplines, they are underrepresented in upper level positions in both academia and industry. Editorial board memberships are an important example of such positions; membership is both a professional honor in recognition of achievement and an opportunity for professional advancement. We surveyed 10 highly regarded journals in environmental biology, natural resource management, and plant sciences to quantify the number of women on their editorial boards and in positions of editorial leadership (i.e., Associate Editors and Editors-in-Chief) from 1985 to 2013. We found that during this time period only 16% of subject editors were women, with more pronounced disparities in positions of editorial leadership. Although the trend was towards improvement over time, there was surprising variation between journals, including those with similar disciplinary foci. While demographic changes in academia may reduce these disparities over time, we argue journals should proactively strive for gender parity on their editorial boards. This will both increase the number of women afforded the opportunities and benefits that accompany board membership and increase the number of role models and potential mentors for early-career scientists and students.

Outer bark thickness decreases more with height on stems of fire-resistant than fire-sensitive Floridian oaks (Quercus spp.; Fagaceae)

UNLABELLED • PREMISE OF THE STUDY In ecosystems maintained by low-intensity surface fires, tree bark thickness is a determinant of fire-survival because it protects underlying tissues from heat damage. However, it has been unclear whether relatively thick bark i S: maintained at all heights or only near the ground where damage is most likely.• METHODS We studied six Quercus species from the red and white clades, with three species characteristic of fire-maintained savannas and three species characteristic of forests with infrequent fire. Inner and outer bark (secondary phloem and rhytidome, respectively) thicknesses were measured at intervals from 10 to 300 cm above the ground. We used linear mixed-effects models to test for relationships among height, habitat, and clade on relative thickness (stem proportion) of total, inner, and outer bark. Bark moisture and tissue density were measured for each species at 10 cm.• KEY RESULTS Absolute and relative total bark thickness declined with height, with no difference in height-related changes between habitat groups. Relative outer bark thickness showed a height-by-habitat interaction. There was a clade effect on relative thickness, but no interaction with height. Moisture contents were higher in inner than outer bark, and red oaks had denser bark than white oaks, but neither trait differed by habitat.• CONCLUSIONS Quercus species characteristic of fire-prone habitats invest more in outer bark near the ground where heat damage to outer tissues is most likely. Future investigations of bark should consider the height at which measurements are made and distinguish between inner and outer bark.

Impact of atmospheric correction and image filtering on hyperspectral classification of tree species using support vector machine

Abstract. Hyperspectral images can be used to identify savannah tree species at the landscape scale, which is a key step in measuring biomass and carbon, and tracking changes in species distributions, including invasive species, in these ecosystems. Before automated species mapping can be performed, image processing and atmospheric correction is often performed, which can potentially affect the performance of classification algorithms. We determine how three processing and correction techniques (atmospheric correction, Gaussian filters, and shade/green vegetation filters) affect the prediction accuracy of classification of tree species at pixel level from airborne visible/infrared imaging spectrometer imagery of longleaf pine savanna in Central Florida, United States. Species classification using fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) atmospheric correction outperformed ATCOR in the majority of cases. Green vegetation (normalized difference vegetation index) and shade (near-infrared) filters did not increase classification accuracy when applied to large and continuous patches of specific species. Finally, applying a Gaussian filter reduces interband noise and increases species classification accuracy. Using the optimal preprocessing steps, our classification accuracy of six species classes is about 75%.

Hyperspectral Measurement of Seasonal Variation in the Coverage and Impacts of an Invasive Grass in an Experimental Setting

Hyperspectral remote sensing can be a powerful tool for detecting invasive species and their impact across large spatial scales. However, remote sensing studies of invasives rarely occur across multiple seasons, although the properties of invasives often change seasonally. This may limit the detection of invasives using remote sensing through time. We evaluated the ability of hyperspectral measurements to quantify the coverage of a plant invader and its impact on senesced plant coverage and canopy equivalent water thickness (EWT) across seasons. A portable spectroradiometer was used to collect data in a field experiment where uninvaded plant communities were experimentally invaded by cogongrass, a non-native perennial grass, or maintained as an uninvaded reference. Vegetation canopy characteristics, including senesced plant material, the ratio of live to senesced plants, and canopy EWT varied across the seasons and showed different temporal patterns between the invaded and reference plots. Partial least square regression (PLSR) models based on a single season had a limited predictive ability for data from a different season. Models trained with data from multiple seasons successfully predicted invasive plant coverage and vegetation characteristics across multiple seasons and years. Our results suggest that if seasonal variation is accounted for, the hyperspectral measurement of invaders and their effects on uninvaded vegetation may be scaled up to quantify effects at landscape scales using airborne imaging spectrometers.

One-Class Gaussian Process for Possibilistic Classification Using Imaging Spectroscopy

With the greater availability of imaging spectrometer data, vegetation species classification in the presence of outlier and ambiguous spectra is an increasingly important and poorly addressed problem. At large scales, assuming that all test spectra are from one of the training classes is unrealistic. An attractive resolution of these problems is the possibility theory, which is an axiomatic system, like probability theory, that represents uncertain labels of outliers and ambiguities more flexibly. In this letter, two popular probabilistic classification algorithms, namely, the support vector machine (SVM) with Platt scaling (SVM-Platt) and the Gaussian process (GP) classifier (GPC), are evaluated and compared to a novel one-class GP (OCGP) possibilistic classifier. OCGP, unlike one-class classification with GP, another GP-based one-class classifier, finds the hyperparameters automatically. Experiments were conducted with two data sets. The OCGP outperformed SVM-Platt and GPC when tested with outlier spectra.

Evaluating similarity measures for hyperspectral classification of tree species at Ordway-Swisher Biological Station

In this manuscript, we investigate which similarity measure discriminates the most between reflectance spectra of two sets of labeled hyperspectral pixels. We find preliminary evidence that shared nearest neighbor, a secondary similarity measure unknown to hyperspectral community, relatively helps the separability of a primary similarity measure such as radial basis function.