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Written by Luca Congedo, the Semi-Automatic Classification Plugin is a free plugin for QGIS (open source) that allows for the semi-automatic supervised classification of remote sensing images, providing tools to expedite the creation of ROIs (training areas) through region growing or multiple ROI creation. The spectral signatures of training areas can be automatically calculated and displayed in a spectral signature plot. It is possible to import spectral signatures from external sources. Also, a tool allows for the selection and download of spectral signatures from the USGS Spectral Library . Several tools are available for the pre processing phase (image clipping, Landsat conversion to reflectance), the classification process (Minimum Distance, Maximum Likelihood, Spectral Angle Mapping algorithms, and classification previews), and the post processing phase (conversion to vector, accuracy assessment, land cover change, classification report). The first version of the Semi-Automatic Classification Plugin was written by Luca Congedo for the Adapting to Climate Change in Coastal Dar es Salaam Project. For more information and tutorials visit the official site From GIS to Remote Sensing.
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... Freely available Multispectral (MS) Sentinel-2 satellites, at the medium-high spatial resolution, allow the advance of more detailed forest fire mapping [11]. Sentinel-2 images were employed under investigation through the semi-automatic classification plugin (SCP) [12] in Quantum Geographic Information System (QGIS). QGIS is a free and Open-Source Desktop GIS platform that permits to map creation, edit, spatial analysis which established on the Geographical Information Science (GIS) [13]. ...
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Forest fire far could be considered as one of the majors concerning environmental issues mainly in tropical climate regions. In Sri Lanka, forest plantations and "sparsely used croplands" are the further most vulnerable areas of a forest fire. The case study was based on the forest fire reported in the Ella Rock region in 2019. The remote sensing techniques were utilized for the analysis in the QGIS open-source environment through Semi-automatic Classification Plugin (SCP) and Sentinel-2 images employed as the key source of data. Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI) were initially applied on the pre and post images and then computed the difference of NBR (dNBR) and the difference of NDVI (dNDVI). Then through the change detection techniques extent of the fire and the severity levels were obtained. As revealed by the investigation 73.82 hectares of areas were burned due to the forest fire and 15.65% of the area was highlighted as a high severity of the burn. Moreover, NDVI and NBR significantly important in forest fire mapping also emphasized by the study. The unavailability of a complete database of the forest fire in Sri Lanka found as the major issue. Further, taking necessary actions to prevent forest fire a vital requirement of the current context.
... y los metadatos en formato.txt. Las cinco imágenes satelitales fueron procesadas en el software QGIS, según la metodología propuesta por Luca Congedo (2014), cuyo Semi-Automatic Classification Plugin (SCP) convierte matemáticamente los niveles de grises en TST, aplicando la corrección atmosférica y convirtiendo valores en Kelvin para grados Celsius. ...
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Resumen: Las investigaciones exploratorias sobre el comportamiento es-pacial del COVID-19 se han concentrado en las correlaciones entre las ta-sas de morbilidad y mortalidad y sus determinantes socio-económicos. Re-conociendo la indudable importancia de las condiciones climáticas y sus cambios estacionales en los brotes y difusión de la pandemia, se han co-rrelacionado también, con resultados diversos, con informaciones climá-ticas a escala de ciudades, países y regiones globales, sin explorar las ca-racterísticas diferenciales de los climas al interior de las ciudades y su relación espacial con las tasas de incidencia del COVID-19. El clima ur-bano de Santiago de Chile, representado en este caso por la distribución espacial de las Temperaturas de la Superficie Terrestre (TST) a través del año 2020, se asocia con las tasas de incidencia de la pandemia a escala de comunas o conjuntos de barrios, observando condiciones de injusticias so-cioambientales que son urgentes de resolver. Palabras clave: Comodificación y gentrificación de los climas urbanos, Temperaturas de la Superficie Terrestre, tasas de incidencia del COVID-19 por comunas, coronavirus, pandemia.
... Para analizar la temperatura superficial, se utilizaron imágenes satelitales de Landsat-7 (banda térmica 6), con tamaño de píxel de 60 m, por medio del Semi-Automatic Classification Plugin (SCP), desarrollado para el software QGIS versión 2, que convierte los diferentes niveles de gris en temperatura de superficie (Congedo 2014 ...
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Santiago de Chile, como la totalidad de las ciudades latinoamericanas, registra importantes diferencias socioclimáticas en su interior que resultan de la apropiación, privatización y mercantilización de los climas por la falta de una adecuada planificación y gestión urbana, así como del ejercicio de poder por parte del mercado inmobiliario; caracterizando un objeto de estudio propio de la geografía física crítica. Se comparan las condiciones de temperatura superficial, coberturas y usos del suelo, morfología urbana y simulaciones de ventilación, de barrios que representan los significativos niveles de segregación e injusticia ambiental de esta ciudad. Las condiciones de origen y difusión de la pandemia de Covid-19 se correlacionan espacialmente con tales diferencias climático-urbanas y con determinantes socioeconómicos que se han ido construyendo junto con la ciudad y que requieren de acciones públicas decididas y masivas para revertir el actual escenario de injusticia e insustentabilidad socioclimática.
... These images were obtained close to collection date (10 May and 22 October 2018), on the tile T22KGV, under cloud-free conditions (cloud cover less than 20%). Atmospheric correction was performed using the Semi-Automatic Classification Plugin Documentation plugin [45], available in QGIS version 3.6 Noosa software. NDVI was calculated using near-infrared spectral bands (NIR, band 8) and red spectral bands (RED, band 4), being computed by: NDVI = (NIR − RED)/(NIR + RED) [41]. ...
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Soil is the principal habitat and reservoir of fungi that act on ecological processes vital for life on Earth. Understanding soil fungal community structures and the patterns of species distribution is crucial, considering climatic change and the increasing anthropic impacts affecting nature. We evaluated the soil fungal diversity in southeastern Brazil, in a transitional region that harbors patches of distinct biomes and ecoregions. The samples originated from eight habitats, namely: semi-deciduous forest, Brazilian savanna, pasture, coffee and sugarcane plantation, abandoned buildings, owls’ and armadillos’ burrows. Forty-four soil samples collected in two periods were evaluated by metagenomic approaches, focusing on the high-throughput DNA sequencing of the ITS2 rDNA region in the Illumina platform. Normalized difference vegetation index (NDVI) was used for vegetation cover analysis. NDVI values showed a linear relationship with both diversity and richness, reinforcing the importance of a healthy vegetation for the establishment of a diverse and complex fungal community. The owls’ burrows presented a peculiar fungal composition, including high rates of Onygenales, commonly associated with keratinous animal wastes, and Trichosporonales, a group of basidiomycetous yeasts. Levels of organic matter and copper influenced all guild communities analyzed, supporting them as important drivers in shaping the fungal communities’ structures.
... The procedure is described in the following sections. At first, the spectral radiance at the sensor's aperture L λ (Wm −2 sr −1 um −1 ) is measured from DN (Equation (1)) [34]: ...
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The main objective of this study was to develop empirical models from Landsat 5 TM data to monitor nutrient (total phosphorus: TP), organic matter (biological oxygen demand: BOD), and algal chlorophyll (chlorophyll-a: CHL-a). Instead of traditional monitoring techniques, such models could be substituted for water quality assessment in aquatic systems. A set of models were generated relating surface reflectance values of four bands of Landsat 5 TM and in-situ data by multiple linear regression analysis. Radiometric and atmospheric corrections improved the satellite image quality. A total of 32 compositions of different bands of Landsat 5 TM images were considered to find the correlation coefficient (r) with in-situ measurement of TP, BOD, and CHL-a levels collected from five sampling sites in 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a correlate well with Landsat 5 TM band reflectance values. TP (r = −0.79) and CHL-a (r = −0.79) showed the strongest relations with B1 (Blue). In contrast, BOD showed the highest correlation with B1 (Blue) (r = −0.75) and B1*B3/B4 (Blue*Red/Near-infrared) (r = −0.76). Considering the r values, significant bands and their compositions were identified and used to generate linear equations. Such equations for Landsat 5 TM could detect TP, BOD, and CHL-a with accuracies of 67%, 65%, and 72%, respectively. The developed empirical models were then applied to all study sites on the Paldang Reservoir to monitor spatio-temporal distributions of TP, BOD, and CHL-a for the month of September using Landsat 5 TM images of the year 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a decreased from 2001 to 2006 and 2010. However, S3 and S4 still have water quality issues and are influenced by climatic and anthropogenic factors, which could significantly affect reservoir drinking water quality. Overall, the present study suggested that the Landsat 5 TM may be appropriate for estimating and monitoring water quality parameters in the reservoir.
... Since this article describes the method of simplified semi-automatic calculation, the QGIS Semi-Automatic Classification Plugin (SCP) [20] was chosen as a tool for simplified image processing and automation of a significant part of the image decoding process. This extension allows to perform all necessary corrections, as well as calculate the emissivity coefficient using supervised signature classification. ...
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This paper describes a simplified method of mapping of the ur-ban environment surface to obtain a map of the thermal anomalies distribu-tion and study the structure of the urban heat island. Those maps allow evaluating or planning the urban microclimate optimization methods and studying the effect of land cover type on the site temperature. The article discusses the processing of five satellite images for the summer and winter from 2002 to 2019. We propose a simpler and more automated processing of thermal images for Landsat 7 and Landsat 8. The stages of automatic atmospheric correction according to the DOS1 method and calculation of the emissivity with surface classification are considered. Image processing was carried out in the QGIS software package using the Semiautomatic Classification Plugin extension. As a result, thermal anomalies in Chelya-binsk were localized and a comparison of the thermal map for the specific region before and after urbanization was made.
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Our main objective in this research work is to propose operational and innovative approaches for automatic mapping of irrigated areas using high spatial resolution satellite images. These are based on two types of supervised machine learning, namely ensemble and deep learning
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La pandemia del COVID-19 obligó a los gobiernos nacionales a emprender acciones de aislamiento social, con el objetivo de frenar el avance del virus y sus brutales efectos sobre la salud y bienestar de la población. Este trabajo pretende analizar el comportamiento de este avance en Santiago de Chile, la capital del país ubicada en la llamada Región Metropolitana, que tiene más de siete millones de habitantes distribuidos en 38 comunas o municipios. Por medio de imágenes térmicas satelitales Landsat-7 es posible caracterizar las áreas más vegetadas de la ciudad, que se correlacionan espacialmente con los sectores de menores variación térmica y donde residen los grupos sociales de mayores ingresos económicos. Se cruzaron los valores de las temperaturas superficiales y las tasas de incidencia de la pandemia en cada comuna, para los meses de abril, mayo y julio de 2020. Los contagios surgieron en el mes de abril, a comienzos del otoño, en las comunas más ricas de la ciudad, ubicadas en el sector oriente, tales como Vitacura, Las Condes, Lo Barnechea y Providencia. Al mes siguiente, se extendieron en forma homogénea por toda la ciudad, y en el mes de julio, en el corazón de la estación de invierno, localizarse preferentemente en las comunas más pobres que alcanzaron las mayores tasas de incidencia, como La Granja, La Pintana, San Ramón, Renca y San Joaquín, con al menos 4500 contagios a cada 100 mil hab., que corresponde a 4,5% de sus poblaciones actuales. Persisten muchas incertidumbres sobre el comportamiento espacial del COVID-19 y sus factores causales, entre ellos el ambiente natural, representado por el clima urbano, y los determinantes socio-económicos de una ciudad caracterizada por profundas desigualdades socio-económicas y socio-ambientales.
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Spatial and temporal relationships between COVID-19 incidence rates and surface temperatures at neighborhoods scale in Santiago de Chiiel during 2020
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Remote Sensing Digital Image Analysis provides the non-specialist with an introduction to quantitative evaluation of satellite and aircraft derived remotely retrieved data. Each chapter covers the pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations. Problems conclude each chapter. This fourth edition has been developed to reflect the changes that have occurred in this area over the past several years.
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Historical background, fundamental concepts, statistical considerations and a case study emphasize the need for absolute precision in applying remotely sensed data. This book is a complete guide to assessing the accuracy of maps generated from remotely sensed data.
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Remote sensing of urban heat islands (UHIs) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST) – vegetation relationship. This study investigates the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance. This is based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002. The transformed ETM+ image was unmixed into three fraction images (green vegetation, dry soil, and shade) with a constrained least-square solution. These fraction images were then used for land cover classification based on a hybrid classification procedure that combined maximum likelihood and decision tree algorithms. Results demonstrate that LST possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with NDVI for all land cover types across the spatial resolution (30 to 960 m). Correlations reached their strongest at the 120-m resolution, which is believed to be the operational scale of LST, NDVI, and vegetation fraction images. Fractal analysis of image texture shows that the complexity of these images increased initially with pixel aggregation and peaked around 120 m, but decreased with further aggregation. The spatial variability of texture in LST was positively correlated with those in NDVI and in vegetation fraction. The interplay between thermal and vegetation dynamics in the context of different land cover types leads to the variations in spectral radiance and texture in LST. These variations are also present in the other imagery, and are responsible for the spatial patterns of urban heat islands. It is suggested that the areal measure of vegetation abundance by unmixed vegetation fraction has a more direct correspondence with the radiative, thermal, and moisture properties of the Earth's surface that determine LST.
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The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the ‘‘Spectral Image Processing System (SIPS)’’ using ‘‘IDL’’ (the Interactive Data Language) on UNIX‐based workstations. SIPS is designed to take advantage of the combination of high spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to interact with entire datasets in real‐time. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X‐windows‐based, user friendly, and provides ‘‘point and click’’ operation. SIPS is being used for multidisciplinary research concentrating on the use of physically‐based analysis methods to enhance scientific results from imging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).
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Effective May 5, 2003, Landsat-5 (L5) Thematic Mapper (TM) data processed and distributed by the U.S. Geological Survey (USGS) Earth Resources Observation System (EROS) Data Center (EDC) will be radiometrically calibrated using a new procedure and revised calibration parameters. This change will improve absolute calibration accuracy, consistency over time, and consistency with Landsat-7 (L7) Enhanced Thematic Mapper Plus (ETM+) data. Users will need to use new parameters to convert the calibrated data products to radiance. The new procedure for the reflective bands (1-5,7) is based on a lifetime radiometric calibration curve for the instrument derived from the instrument's internal calibrator, cross-calibration with the ETM+, and vicarious measurements. The thermal band will continue to be calibrated using the internal calibrator. Further updates to improve the relative detector-to-detector calibration and thermal band calibration are being investigated, as is the calibration of the Landsat-4 (L4) TM.