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Statements

Subject Item
n2:RIV%2F68407700%3A21110%2F13%3A00208781%21RIV14-MSM-21110___
rdf:type
skos:Concept n15:Vysledek
rdfs:seeAlso
http://www.tandfonline.com/doi/full/10.1080/01431161.2013.853896#.UnjRwo1VRhF
dcterms:description
Remote sensing is a useful tool for detecting landscape fragmentation, typically by creating land-use maps from remotely sensed images acquired at different dates. Nonetheless, classification may present a number of drawbacks since it degrades the information content of images leading to the loss of continuous information about fragmentation processes. For exploratory purposes, methods to detect landscape change based on continuous information should not require any a-priori assumptions about landscape characteristics. Accordingly, Fourier transforms may represent the best algorithmic solution. In this paper, we describe a Fourier transform tool developed in a free and open-source environment to detect potential fragmentation over the landscape. We briefly introduce Fourier transforms applied to remotely sensed imagery by further showing their potential application with an empirical example. We argue that Fourier transforms represent a straightforward approach for detecting spatial fragmentation of the landscape, on the strength of their potential to detect trends in increase or decrease of complexity/heterogeneity of the landscape in an objective manner. To our knowledge, this is the first open-source tool for analysing fragmentation of the landscape in multitemporal series based on Fourier transforms, which guarantees a high robustness and reproducibility of the applied algorithms. Remote sensing is a useful tool for detecting landscape fragmentation, typically by creating land-use maps from remotely sensed images acquired at different dates. Nonetheless, classification may present a number of drawbacks since it degrades the information content of images leading to the loss of continuous information about fragmentation processes. For exploratory purposes, methods to detect landscape change based on continuous information should not require any a-priori assumptions about landscape characteristics. Accordingly, Fourier transforms may represent the best algorithmic solution. In this paper, we describe a Fourier transform tool developed in a free and open-source environment to detect potential fragmentation over the landscape. We briefly introduce Fourier transforms applied to remotely sensed imagery by further showing their potential application with an empirical example. We argue that Fourier transforms represent a straightforward approach for detecting spatial fragmentation of the landscape, on the strength of their potential to detect trends in increase or decrease of complexity/heterogeneity of the landscape in an objective manner. To our knowledge, this is the first open-source tool for analysing fragmentation of the landscape in multitemporal series based on Fourier transforms, which guarantees a high robustness and reproducibility of the applied algorithms.
dcterms:title
Fourier transforms for detecting multitemporal landscape fragmentation by remote sensing Fourier transforms for detecting multitemporal landscape fragmentation by remote sensing
skos:prefLabel
Fourier transforms for detecting multitemporal landscape fragmentation by remote sensing Fourier transforms for detecting multitemporal landscape fragmentation by remote sensing
skos:notation
RIV/68407700:21110/13:00208781!RIV14-MSM-21110___
n15:predkladatel
n20:orjk%3A21110
n3:aktivita
n4:I
n3:aktivity
I
n3:cisloPeriodika
24
n3:dodaniDat
n14:2014
n3:domaciTvurceVysledku
n16:9478094
n3:druhVysledku
n8:J
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n13:predkladatel
n3:idSjednocenehoVysledku
75610
n3:idVysledku
RIV/68407700:21110/13:00208781
n3:jazykVysledku
n10:eng
n3:klicovaSlova
GIS; GRASS; Open source; Remote sensing; landscape fragmentation
n3:klicoveSlovo
n6:landscape%20fragmentation n6:GRASS n6:Remote%20sensing n6:GIS n6:Open%20source
n3:kodStatuVydavatele
GB - Spojené království Velké Británie a Severního Irska
n3:kontrolniKodProRIV
[505072C40749]
n3:nazevZdroje
International Journal of Remote Sensing
n3:obor
n11:DE
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
6
n3:rokUplatneniVysledku
n14:2013
n3:svazekPeriodika
34
n3:tvurceVysledku
Landa, Martin Rocchini, D. Ricotta, C. Metz, M. Frigeri, A. Neteler, M.
n3:wos
000327237000016
s:issn
0143-1161
s:numberOfPages
10
n18:doi
10.1080/01431161.2013.853896
n12:organizacniJednotka
21110