The paper presents a method for automatic detection and monitoring of small waterlogged areas in farmland, using multispectral satellite images and neural network classifiers. In the waterlogged areas, excess water significantly damages or completely destroys the plants, thus reducing the average crop yield. Automatic detection of (waterlogged) crops damaged by rising underground water is an im...

Using remotely sensed data to identify burnt forest areas produces fast, economical, and highly accurate results. Accordingly, in this study we investigate the capabilities of Göktürk-2, Turkey's national satellite, for mapping areas. We compare our results with those obtained from Landsat-8 Worldview-2 satellite images, which are frequently used The satellites compared, terms detecting areas, ...

Landscape attributes that vary with microtopography, such as active layer thickness (ALT), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestria...

Fire impacts many vegetated ecosystems across the world. The severity of a fire is major component in determining post-fire effects, including soil erosion, trace gas emissions, and the trajectory of recovery. In this study, we used imaging spectroscopy data combined with Multiple Endmember Spectral Mixture Analysis (MESMA), a form of spectral mixture analysis that accounts for endmember variab...

This paper describes a tentative model for how discrete memories transform into an interconnected conceptual network, or worldview, wherein relationships between memories are forged by way of abstractions. The model draws on Kauffman's theory of how an information-evolving system can emerge through the formation and closure of an autocatalytic network. Here, the information units are not cataly...