Selecting appropriate variables for detecting grassland to cropland changes using high resolution satellite data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F18%3A76827" target="_blank" >RIV/60460709:41330/18:76827 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11310/18:10382092

Výsledek na webu

<a href="http://dx.doi.org/10.7717/peerj.5487" target="_blank" >http://dx.doi.org/10.7717/peerj.5487</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.7717/peerj.5487" target="_blank" >10.7717/peerj.5487</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Selecting appropriate variables for detecting grassland to cropland changes using high resolution satellite data

Popis výsledku v původním jazyce

Grassland is one of the most represented, while at the same time, ecologically endangered, land cover categories in the European Union. In view of the global climate change, detecting its change is growing in importance from both an environmental and a socio-economic point of view. A well-recognised tool for Land Use and Land Cover (LULC) Change Detection (CD), including grassland changes, is Remote Sensing (RS). An important aspect affecting the accuracy of change detection is finding the optimal indicators of LULC changes (i.e., variables). Inappropriately selected variables can produce inaccurate results burdened with a number of uncertainties. The aim of our study is to find the most suitable variables for the detection of grassland to cropland change, based on a pair of high resolution images acquired by the Landsat 8 satellite and from the vector database Land Parcel Identification System (LPIS). In total, 59 variables were used to create models using Generalised Linear Models (GLM), the qualit

Název v anglickém jazyce

Selecting appropriate variables for detecting grassland to cropland changes using high resolution satellite data

Popis výsledku anglicky

Grassland is one of the most represented, while at the same time, ecologically endangered, land cover categories in the European Union. In view of the global climate change, detecting its change is growing in importance from both an environmental and a socio-economic point of view. A well-recognised tool for Land Use and Land Cover (LULC) Change Detection (CD), including grassland changes, is Remote Sensing (RS). An important aspect affecting the accuracy of change detection is finding the optimal indicators of LULC changes (i.e., variables). Inappropriately selected variables can produce inaccurate results burdened with a number of uncertainties. The aim of our study is to find the most suitable variables for the detection of grassland to cropland change, based on a pair of high resolution images acquired by the Landsat 8 satellite and from the vector database Land Parcel Identification System (LPIS). In total, 59 variables were used to create models using Generalised Linear Models (GLM), the qualit

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20705 - Remote sensing

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

PeerJ

ISSN

2167-8359

e-ISSN

2167-8359

Svazek periodika

6

Číslo periodika v rámci svazku

e5487

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

20

Strana od-do

1-20

Kód UT WoS článku

000444048000001

EID výsledku v databázi Scopus

2-s2.0-85052879894