Mineral composite assessment of Kelkit River Basin in Turkey by means of remote sensing

Abstract

Utilizing remote sensing (RS) and geographic information systems (GIS) tools, mineral composite characteristics (ferrous minerals (FM), iron oxide (IO), and clay minerals (CM)) of the Kelkit River Basin (15913.07 km 2 ) in Turkey were investigated and mapped. Mineral composite (MC) index maps were produced from three LANDSAT-ETM+ satellite images taken in 2000. Resulting MC index maps were summarized in nine classes by using 'natural breaks' classification method in GIS. Employing bi-variety correlation analysis, relationships among index maps were investigated. According to the results, FM and IO index maps showed positive correlation, while CM index map is negatively correlated with FM and IO index maps. Negative correlations between iron and clay variables suggested that the dominant clay minerals of the study area might be smectite, illite, kaolinite, and chlorite, which have little or no iron content. Using field data for which their geographic coordinates had been determined by global positioning system (GPS), developed MC maps were verified, and found dependable for environmental and ecological modeling studies.

Fragmentación del paisaje empleando análisis multitemporal de imágenes de satélite Landsat TM y ETM+ en el municipio de Montelíbano, Córdoba-Colombia

“…La información referente a los índices, transformaciones, correcciones y análisis de fragmentación aplicados en las imágenes del artículo se pueden detallar en Chuvieco, (1990), Dogan (2009), Ferrero et al (2002), Hotelling (1933) El análisis de fragmentación se obtuvo a partir de herramientas de geoprocesamiento estadístico del software SAGA GIS, a través del módulo especializado de fragmentación para la evaluación de fenómenos ecológicos.…”

Assessment of Heavy Metal Contamination in Wetlands Soils Around an Industrial Area Using Combined GIS-Based Pollution Indices and Remote Sensing Techniques

“…Igeo and PI have become reliable, efficient and sensitive to better estimate the natural and anthropogenic pollution (El Hamzaoui et al, 2020;Kong et al, 2012;Kowalska et al, 2016). On the other hand, satellite data, such as Landsat, has the capabilities to provide consistent, accurate and suitable indirect predictive information's relating to spatial distribution of heavy metal contamination (Dogan, 2009). For instance, Enhanced vegetation index (EVI), Normalized difference vegetation index (NDVI), and Modified normalized difference water index (MNDWI), remains reliable and accurate indirect techniques applied to assess heavy metal contamination in the environment (Abdelaziz et al, 2018;Bijeesh & Narasimhamurthy, 2019).…”

Imagens Landsat-7/Etm+ E SRTM Como Ferramentas Auxiliares Na Identificação De Perfis Lateríticos – Exemplo Em Bom Jesus Da Penha (Mg).

“…A investigação dos perfis lateríticos com uso de Landsat-7/ETM+ foi alvo de uma série de trabalhos, como Soe et al (2008), Dogan (2009) e Madeira et al (2010). Aqui optou-se por aplicar as composições propostas por Andrews Deller ( 2006) para imagens de satélite Landsat-7/ETM+.…”