Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images

Identification of tree crowns from remote sensing requires detailed spectral information and submeter spatial resolution imagery. Traditional pixel-based classification techniques do not fully exploit the spatial and spectral characteristics of remote sensing datasets. We propose a contextual and probabilistic method for detection of tree crowns in urban areas using a Markov random field based super resolution mapping (SRM) approach in very high resolution images. Our method defines an objective energy function in terms of the conditional probabilities of panchromatic and multispectral images and it locally optimizes the labeling of tree crown pixels. Energy and model parameter values are estimated from multiple implementations of SRM in tuning areas and the method is applied in QuickBird images to produce a 0.6 m tree crown map in a city of The Netherlands. The SRM output shows an identification rate of 66% and commission and omission errors in small trees and shrub areas. The method outperforms tree crown identification results obtained with maximum likelihood, support vector machines and SRM at nominal resolution (2.4 m) approaches.     

Magnetic studies at Starunia paleontological and hydrocarbon bearing site (Carpathians,Ukraine)

The Starunia oil-ozokerite deposit occurs in the Boryslav-Pokuttya Unit of the Carpathian Foredeep, which is the main oil- and gas-bearing part of the Ukrainian Carpathians. Starunia is of great interest in studying the relationship between the magnetic properties of rocks, soils and hydrocarbons due to extensive surface microseeps yielding oil and gas, mineral water, and clay pulp containing hydrocarbons. We identified a local negative magnetic anomaly (30–35 nT) with a width of about 700 m within the MAG1 profile. The magnetic high is associated with the area of the largest mud volcanoes in the Starunia structure. Magnetic susceptibility of the soil was measured on a site with three distinct landscape features: a patch of forest with phaeozem and mass-specific susceptibility (χ) of 20–45 × 10^?8 m³/kg for the surface topsoil; an area near the volcano and Nadia-1 well with visible hydrocarbon microseepage at the surface and the topsoil showing no visible evidence of hydrocarbon presence with χ = 20–50 × 10^?8 m³/kg; and a patch of lowland with gleysols and χ = 10–20 × 10^?8 m³/kg. Hydrocarbon-containing clays and soils from the alluvial sediments of the Velyky Lukavets River and bedrock clays near the Nadia-1 well demonstrated high χ values (up to 250–440 × 10^?8 m³/kg).     

Discriminating the invasive species, ‘Lantana’ using vegetation indices

Invasive species have been the focus of environmentalists due to their undesired impact on the ecosystem. Spread of Lantana (Lantana camara L.), an invasive plant species, has been found in diverse geophysical environments causing a threat to the native flora. Various eradication programmes have been attempted such as burning, chemical sprays, bio-control agents and physical plugging mechanism for removing such invasive species in India. The efforts and success of these programmes need to be augmented with a correct, quick and cost effective technique of mapping in order to locate them, understand their spatial extent and hence make the process comprehensive. Also Lantana’s appearance as dense vegetation patches in remote sensing data causes problems for estimating forest canopy density. Remote sensing provides a possible solution in qualitatively and quantitatively evaluating terrestrial surface vegetation cover using spectral measure-ments. This research paper addresses issues and techniques adopted to detect and extract Lantana, and can be used for various applications in forestry as well as in eradication programmes. This study attempted to understand the appropriate band combination using Landsat data and generating vegetation indices in order to extract Lantana patches in an accurate manner. Twenty nine different vegetation indices were analyzed for their effectiveness in differentiating Lantana from other classes. The study showed that SAVI (Soil Adjusted Vegetation Index) is most favorable in discriminating Lantana followed by Perpendicular Vegetation Index-3 in the optimum bio-window (February to April).