Web service for biodiversity estimation using remote sensing data

Popov, Mykhailo; Kussul, Nataliia; Stankevich, Sergey А.; Kozlova, Anna; Shelestov, Andrii; Kravchenko, Oleksii; Korbakov, Mykhailo; Skakun, Serhiy

doi:10.1080/17538940802483745

Cited by 6 publications

(8 citation statements)

References 3 publications

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“…La caracterización de ecosistemas con sensores remotos es una técnica ampliamente utilizada en la investigación biológica y biogeográfica. Diferentes modelos se diseñan para obtenerla distribución de los ecosistemas o de los elementos de la naturaleza de interés, sean especies, biomas, cobertura forestal, humedales, bioclimas, entre muchos otros, o los cambios que, con el paso del tiempo, ocurren en estos (Popov, Kussul, Stankevich, Kozlova, Shelestov, Kravchenko, Korbakov & Skakun, 2008;Ozdogan 2014).…”

Section: Introductionunclassified

Clasificación de ecosistemas objeto de conservación en la zona costera del pacífico sur de Costa Rica

Villegas-Arguedas¹

2017

RPyS

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Resumen. Se clasificaron los polígonos de ecosistemas mediante fotointerpretación y análisis multiespectral en la zona costera de dos distritos del sur de Costa Rica, con la finalidad de identificar sitios sujetos de actividades para la conservación de la biodiversidad. Entre mayo y octubre 2016, se delimitaron y corroboraron en campo dichos ecosistemas. Los bosques se establecieron mediante fotointerpretación y establecimiento de puntos de verificación. En el caso de terrenos de aptitud forestal, se verificaron con medidas de inclinación porcentual promedio y el empleo de un inclinómetro de lectura óptica marca Suunto PM-5/1520; el parámetro mínimo en terrenos fue de más de 60% de pendiente, adicionalmente, la pedregosidad y la profundidad del suelo. Los humedales se obtuvieron/dispusierona partir de la existencia de vegetación hidrófita, suelos inundados o inundables y fauna asociada. La verificación de cada capa de ecosistemas se estableció gracias al índice de concordancia de Kappa-Cohen que utiliza las coberturas del suelo como modelo predictivo contra una red de puntos de ubicación geoespacial (GPS) de comprobación de campo, donde concordancias superiores al 70% validan el modelo y la fotointerpretación. Se delimitaron 188 polígonos de cuatro tipos de ecosistemas, donde el bosque solo cubre 639.1 ha. El área total de estudio corresponde con 12 223.10 ha., el mayor ecosistema natural es el manglar con 2808.64 ha. No se clasificaron terrenos de aptitud forestal sin bosque, mientras que los humedales correspondieron a 39 polígonos con un área total de 138.40 ha; y las asociaciones vegetales a humedales o manglares (AVHM), a 31 polígonos que cubren un área de 629.84 ha. Se concluye que estos ecosistemas naturales son alternativas de desarrollo sostenible para las comunidades con base en la actividad turística. Es recomendable que el Estado y las comunidades promuevan una agenda conjunta de desarrollo que permita aprovechar las ventajas de los ecosistemas naturales, también, se sugiere que los ecosistemas sujetos de conservación se incluyan como parte del Patrimonio Natural del Estado.Abstract. The ecosystem polygons were classified by photointerpretation and multispectral analysis in the coastal zone of two districts of the southern part of Costa Rica to identify sites subject to activities for the conservation of biodiversity. Between May and October 2016, these ecosystems were delimited and corroborated in the field. Forests were established by photointerpretation and establishment of verification points, in the case of land of forest aptitude, they were verified with measures of average percentage inclination by means of an optical reading inclinometer Suunto PM-5/1520, land with more than 60% slope being the minimum parameter, in addition to stoniness and depth of the soil. Wetlands were determined from the existence of hydrophilic vegetation, flooded soils or flood areas and associated fauna. The verification of each ecosystem layer was determined from the Kappa-Cohen Concordance Index that uses the ground...

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Section: Introductionunclassified

Clasificación de ecosistemas objeto de conservación en la zona costera del pacífico sur de Costa Rica

Villegas-Arguedas¹

2017

RPyS

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“…In recent years, methods and information technologies directed towards the use of remote sensing methods for solving problems of various departments including those of the agricultural sector [6][7][8][9][10] are actively developed in Ukraine. This article describes an approach to the creation of a distributed information system of agricultural monitoring applicable at the levels of ministries and individual farms.…”

Section: Introductionmentioning

confidence: 99%

Geospatial information system for agricultural monitoring

et al. 2013

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This paper describes a distributed system for agricultural monitoring in Ukraine at two levels, namely, at ministerial level and at agricultural enterprise level. Crop monitoring is performed using data and products obtained by moderate and high-resolution remote sensing satellites. The system includes a geoportal with a Web interface and a desktop geographic information system (GIS) with additional functions of automatic data retrieval and business-logic analysis. The system is constructed using open-source software that conforms to OGC standards for geospatial information management.

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“…For this purpose, average composites of images were created. Using these composites and solar irradiation acquired from SRTM DEM v2, we estimated the biodiversity index using the fuzzy model (Popov et al 2008). The resulting product is a georeferenced file in GeoTIFF format showing biodiversity index over the given region.…”

Section: Land Biodiversity Assessmentmentioning

confidence: 99%

“…These factors include: landscape types, geographical latitude/altitude, climate conditions (such as mean daily temperatures, humidity), structure, and primary productivity of a vegetation mantle (Hansen and Rotella 1999). These factors can be estimated using EO data from space (Popov et al 2008). The workflow for biodiversity estimation consists of the following steps: data acquisition, data processing, and visualization.…”

Section: Land Biodiversity Assessmentmentioning

confidence: 99%

“…In the framework of the innovative project of the National Academy of Sciences of Ukraine, scientists from the Scientific Centre for Aerospace Research of the Earth (CASRE) and the SRI NASU-NSAU have jointly developed a Web service for land biodiversity assessment for the Pre-Black Sea region of Ukraine (Popov et al 2008) using EOS data products (King et al 2004).…”

Section: Land Biodiversity Assessmentmentioning

confidence: 99%

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Grid and sensor web technologies for environmental monitoring

2009

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In this paper, we focus on the description of the Grid infrastructure that is under the development at the Space Research Institute NASU-NSAU (SRI). We describe several real-world applications that are being solved using the Grid infrastructure: numeral weather prediction (NWP), flood monitoring, and biodiversity assessment. We also review issues regarding the integration of the Sensor Web and Grid technologies for flood applications.

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Web service for biodiversity estimation using remote sensing data

Cited by 6 publications

References 3 publications

Clasificación de ecosistemas objeto de conservación en la zona costera del pacífico sur de Costa Rica

Clasificación de ecosistemas objeto de conservación en la zona costera del pacífico sur de Costa Rica

Geospatial information system for agricultural monitoring

Grid and sensor web technologies for environmental monitoring

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