Terrain as a continuum can be categorized into landform units that exhibit common physical and morphological characteristics of land surface which may serve as a boundary condition for a wide range of application domains. However, heterogeneous views, definitions, and applications on landforms yield incompatible nomenclature that lacks interoperability. Yet, there is still room for developing methods for classification of land surface into landforms that can provide different disciplines with a basis of landscape description that is also commonsense to human insight.This study proposes a method of landform classification that reveals general geomorphometry of the landscape. A set of landform classes that are commonsense to human insight and relevant to various disciplines is adopted to generate landforms at the landscape scale. The proposed classification method is based on local geometry of the surface and the geomorphometric context in a higher level framework. A set of digital terrain models (DTMs) at relevant scale is utilized where local geometry is represented with morphometric DTMs, and the geomorphometric context is incorporated through 'relative terrain position' and 'terrain network.' 'Object-based image analysis' tools that have the ability to segment and classify DTMs into representative terrain objects and connect those objects in a multi-level hierarchy is utilized. Ambiguities in landforms both in attribute and geographical space are represented via fuzzy classification. The proposed method is applied to two different case areas to evaluate the efficiency and stability of the outcomes. Results reveal a reasonable amount of consistency where landform classes can be utilized as general or multi-purpose regarding some ambiguity that is already inherent in landforms.

Hyperspectral imaging provides high spectral resolution and thereby improved classification, detection, and recognition capabilities with respect to standard imaging systems. However, hyperspectral images generally have low spatial resolution, varying from a few to tens of meters, resulting from technical limitations such as platform data storing capacity and satelliteto-ground transmission bandwidth. Spectral unmixing provides information on pixels in terms of abundances of pure spectral signatures, without providing spatial distribution at subpixel level. Multisensor image fusion approaches can provide such information but require an additional image with higher spatial resolution that is acquired in similar conditions with the hyperspectral image. In this letter, a novel spatial resolution enhancement method using fully constrained least squares (FCLS) spectral unmixing and spatial regularization based on modified binary particle swarm optimization is proposed to achieve spatial resolution enhancement in hyperspectral images, without using an additional image with higher spatial resolution. The proposed method has a highly parallel nature with respect to its counterparts in the literature and is fit to be adapted to field-programmable gate array architecture. Index Terms-Binary particle swarm optimization (BPSO), hyperspectral imaging, spatial regularization, spectral unmixing.

ABSTRACT:Along with urbanization, sealing of vegetated land and evaporation surfaces by impermeable materials, lead to changes in urban climate. This phenomenon is observed as temperatures several degrees higher in densely urbanized areas compared to the rural land at the urban fringe particularly at nights, so-called Urban Heat Island. Urban Heat Island (UHI) effect is related with urban form, pattern and building materials so far as it is associated with meteorological conditions, air pollution, excess heat from cooling. UHI effect has negative influences on human health, as well as other environmental problems such as higher energy demand, air pollution, and water shortage.Urban Heat Island (UHI) effect has long been studied by observations of air temperature from thermometers. However, with the advent and proliferation of remote sensing technology, synoptic coverage and better representations of spatial variation of surface temperature became possible. This has opened new avenues for the observation capabilities and research of UHIs.In this study, "UHI effect and its relation to factors that cause it" is explored for İzmit city which has been subject to excess urbanization and industrialization during the past decades. Spatial distribution and variation of UHI effect in İzmit is analysed using Landsat 8 and ASTER day & night images of 2015 summer. Surface temperature data derived from thermal bands of the images were analysed for UHI effect. Higher temperatures were classified into 4 grades of UHIs and mapped both for day and night.Inadequate urban form, pattern, density, high buildings and paved surfaces at the expanse of soil ground and vegetation cover are the main factors that cause microclimates giving rise to spatial variations in temperatures across cities. These factors quantified as land surface/cover parameters for the study include vegetation index (NDVI), imperviousness (NDISI), albedo, solar insolation, Sky View Factor (SVF), building envelope, distance to sea, and traffic space density. These parameters that cause variation in intra-city temperatures were evaluated for their relationship with different grades of UHIs. Zonal statistics of UHI classes and variations in average value of parameters were interpreted. The outcomes that highlight local temperature peaks are proposed to the attention of the decision makers for mitigation of Urban Heat Island effect in the city at local and neighbourhood scale.

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Land management and planning is crucial for present and future use of land and the sustainability of land resources. Physical, biological and cultural characteristics of land can be used to define Land Management Units (LMUs) that aid in decision making for managing land and communicating information between different research and application domains. This study aims to describe the classification of ecologically relevant land units that are suitable for land management, planning and conservation purposes. Relying on the idea of strong correlation between landform and potential landcover, a conceptual model for creating Land Management Units (LMUs) from topographic data and biophysical information is presented. The proposed method employs a multi-level object-based classification of Digital Terrain Models (DTMs) to derive landform units. The sensitivity of landform units to changes in segmentation scale is examined, and the outcome of the landform classification is evaluated. Landform classes are then aggregated with landcover information to produce ecologically relevant landform/landcover assemblages. These conceptual units that constitute a framework of connected entities are finally enriched given available socio-economic information e.g., land use, ownership, protection status, etc. to generate LMUs. LMUs attached to a geographic database enable the retrieval of information at various levels to support decision making for land management at various scales. LMUs that are created present a basis for conservation and management in a biodiverse area in the Black Sea region of Turkey.