Zoltan Szantoi

Protection of plants from ambient ozone by applications of ethylenediurea (EDU): A meta-analytic review

A meta-analysis was conducted to quantitatively assess the effects of ethylenediurea (EDU) on ozone (O3) injury, growth, physiology and productivity of plants grown in ambient air conditions. Results indicated that EDU significantly reduced O3-caused visible injury by 76%, and increased photosynthetic rate by 8%, above-ground biomass by 7% and crop yield by 15% in comparison with non-EDU treated plants, suggesting that ozone reduces growth and yield under current ambient conditions. EDU significantly ameliorated the biomass and yield of crops and grasses, but had no significant effect on tree growth with an exception of stem diameter. EDU applied as a soil drench at a concentration of 200-400 mg/L has the highest positive effect on crops grown in the field. Long-term research on full-grown tree species is needed. In conclusion, EDU is a powerful tool for assessing effects of ambient [O3] on vegetation.

Drivers of extinction risk in African mammals: the interplay of distribution state, human pressure, conservation response and species biology

Although conservation intervention has reversed the decline of some species, our success is outweighed by a much larger number of species moving towards extinction. Extinction risk modelling can identify correlates of risk and species not yet recognized to be threatened. Here, we use machine learning models to identify correlates of extinction risk in African terrestrial mammals using a set of variables belonging to four classes: species distribution state, human pressures, conservation response and species biology. We derived information on distribution state and human pressure from satellite-borne imagery. Variables in all four classes were identified as important predictors of extinction risk, and interactions were observed among variables in different classes (e.g. level of protection, human threats, species distribution ranges). Species biology had a key role in mediating the effect of external variables. The model was 90% accurate in classifying extinction risk status of species, but in a few cases the observed and modelled extinction risk mismatched. Species in this condition might suffer from an incorrect classification of extinction risk (hence require reassessment). An increased availability of satellite imagery combined with improved resolution and classification accuracy of the resulting maps will play a progressively greater role in conservation monitoring.

Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features

Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge from remotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using high spatial resolution imagery and machine learning image classification algorithms for mapping heterogeneous wetland plant communities. This study addresses this void by analyzing whether machine learning classifiers such as decision trees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedge communities using high resolution aerial imagery and image texture data in the Everglades National Park, Florida. In addition to spectral bands, the normalized difference vegetation index, and first- and second-order texture features derived from the near-infrared band were analyzed. Classifier accuracies were assessed using confusion tables and the calculated kappa coefficients of the resulting maps. The results indicated that an ANN (multilayer perceptron based on back propagation) algorithm produced a statistically significantly higher accuracy (82.04 %) than the DT (QUEST) algorithm (80.48 %) or the maximum likelihood (80.56 %) classifier (α<0.05). Findings show that using multiple window sizes provided the best results. First-order texture features also provided computational advantages and results that were not significantly different from those using second-order texture features.

A tool for rapid post-hurricane urban tree debris estimates using high resolution aerial imagery

Abstract Coastal communities in the southeast United States have regularly experienced severe hurricane impacts. To better facilitate recovery efforts in these communities following natural disasters, state and federal agencies must respond quickly with information regarding the extent and severity of hurricane damage and the amount of tree debris volume. A tool was developed to detect downed trees and debris volume to better aid disaster response efforts and tree debris removal. The tool estimates downed tree debris volume in hurricane affected urban areas using a Leica Airborne Digital Sensor (ADS40) and very high resolution digital images. The tool employs a Sobel edge detection algorithm combined with spectral information based on color filtering using 15 different statistical combinations of spectral bands. The algorithm identified downed tree edges based on contrasts between tree stems, grass, and asphalt and color filtering was then used to establish threshold values. Colors outside these threshold values were replaced and excluded from the detection processes. Results were overlaid and an “edge line” was placed where lines or edges from longer consecutive segments and color values within the threshold were met. Where two lines were paired within a very short distance in the scene a polygon was drawn automatically and, in doing so, downed tree stems were detected. Tree stem diameter–volume bulking factors were used to estimate post-hurricane tree debris volumes. Images following Hurricane Ivan in 2005 and Hurricane Ike in 2008 were used to assess the error of the tool by comparing downed tree counts and subsequent debris volume estimates with post-hurricane photo-interpreted downed tree counts and actual field measured estimates of downed tree debris volume. The errors associated with the use of the tool and potential applications are also presented.

Implications of management strategies and vegetation change in the Mount St. Helens blast zone

The 1980 eruptions of Mount St. Helens provided an excellent opportunity for scientists to investigate the recovery of vegetation communities following a major geologic disturbance. An important and often overlooked aspect in these studies is the human factor in recovery processes, and specifically, the different management approaches taken towards re-establishment of vegetation on lands under the control of various owners. This study examines vegetation changes throughout the 1980 blast zone using a time series of Landsat-derived Normalized Difference Vegetation Index (NDVI) images and change detection methods to assess the changes over 25 years, from 1980 to 2005, as a function of human management combined with ecological factors. This long-term tracking of change indicates that differences in the speed of vegetation re-establishment and consequent rates of change substantially reflect human involvement and varying management strategies.

Mapping orangutan habitat and agricultural areas using Landsat OLI imagery augmented with unmanned aircraft system aerial photography

ABSTRACT Conservation of the Sumatran orangutans’ (Pongo abelii) habitat is threatened by change in land use/land cover (LULCC), due to the logging of its native primary forest habitat, and the primary forest conversion to oil palm, rubber tree, and coffee plantations. Frequent LULCC monitoring is vital to rapid conservation interventions. Due to the costs of high-resolution satellite imagery, researchers are forced to rely on cost-free sources (e.g. Landsat), those, however, provide images at a moderate-to-low resolution (e.g. 15–250 m), permitting identification only general LULC classes, and limit the detection of small-scale deforestation or degradation. Here, we combine Landsat imagery with very high-resolution imagery obtained from an unmanned aircraft system (UAS). The UAS imagery was used as ‘drone truthing’ data to train image classification algorithms. Our results show that UAS data can successfully be used to help discriminate similar land-cover/use classes (oil palm plantation vs. reforestation vs. logged forest) with consistently high identification of over 75% on the generated thematic map, where the oil palm detection rate was as high as 89%. Because UAS is employed increasingly in conservation proWjects, this approach can be used in a large variety of them to improve land-cover classification or aid-specific mapping needs.

Small room for compromise between oil palm cultivation and primate conservation in Africa

Significance Although oil palm cultivation represents an important source of income for many tropical countries, its future expansion is a primary threat to tropical forests and biodiversity. In this context, and especially in regions where industrial palm oil production is still emerging, identifying “areas of compromise,” that is, areas with high productivity and low biodiversity importance, could be a unique opportunity to reconcile conservation and economic growth. We applied this approach to Africa, by combining data on oil palm suitability with primate distribution, diversity, and vulnerability. We found that such areas of compromise are very rare throughout the continent (0.13 Mha), and that large-scale expansion of oil palm cultivation in Africa will have unavoidable, negative effects on primates. Despite growing awareness about its detrimental effects on tropical biodiversity, land conversion to oil palm continues to increase rapidly as a consequence of global demand, profitability, and the income opportunity it offers to producing countries. Although most industrial oil palm plantations are located in Southeast Asia, it is argued that much of their future expansion will occur in Africa. We assessed how this could affect the continent’s primates by combining information on oil palm suitability and current land use with primate distribution, diversity, and vulnerability. We also quantified the potential impact of large-scale oil palm cultivation on primates in terms of range loss under different expansion scenarios taking into account future demand, oil palm suitability, human accessibility, carbon stock, and primate vulnerability. We found a high overlap between areas of high oil palm suitability and areas of high conservation priority for primates. Overall, we found only a few small areas where oil palm could be cultivated in Africa with a low impact on primates (3.3 Mha, including all areas suitable for oil palm). These results warn that, consistent with the dramatic effects of palm oil cultivation on biodiversity in Southeast Asia, reconciling a large-scale development of oil palm in Africa with primate conservation will be a great challenge.