Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes

Global change has led to shifts in phenology, potentially disrupting species interactions such as plant–pollinator relationships. Advances in remote sensing techniques allow one to detect vegetation phenological diversity between different land use types, but it is not clear how this translates to o...

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Bibliographic Details
Main Authors: Misha Leong, George K. Roderick
Format: Article
Language:English
Published: PeerJ Inc. 2015-08-01
Series:PeerJ
Subjects:
EVI
Online Access:https://peerj.com/articles/1141.pdf
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spelling doaj-e0dda3227c9c4607a9857218a82d51a52020-11-24T20:59:49ZengPeerJ Inc.PeerJ2167-83592015-08-013e114110.7717/peerj.1141Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapesMisha Leong0George K. Roderick1Department of Entomology, California Academy of Sciences, San Francisco, CA, United States of AmericaDepartment of Environmental Science, Policy and Management, University of California, Berkeley, CA, United States of AmericaGlobal change has led to shifts in phenology, potentially disrupting species interactions such as plant–pollinator relationships. Advances in remote sensing techniques allow one to detect vegetation phenological diversity between different land use types, but it is not clear how this translates to other communities in the ecosystem. Here, we investigated the phenological diversity of the vegetation across a human-altered landscape including urban, agricultural, and natural land use types. We found that the patterns of change in the vegetation indices (EVI and NDVI) of human-altered landscapes are out of synchronization with the phenology in neighboring natural California grassland habitat. Comparing these findings to a spatio-temporal pollinator distribution dataset, EVI and NDVI were significant predictors of total bee abundance, a relationship that improved with time lags. This evidence supports the importance of differences in temporal dynamics between land use types. These findings also highlight the potential to utilize remote sensing data to make predictions for components of biodiversity that have tight vegetation associations, such as pollinators.https://peerj.com/articles/1141.pdfRemote sensingBeesEVINDVIUrbanMODIS
collection DOAJ
language English
format Article
sources DOAJ
author Misha Leong
George K. Roderick
spellingShingle Misha Leong
George K. Roderick
Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes
PeerJ
Remote sensing
Bees
EVI
NDVI
Urban
MODIS
author_facet Misha Leong
George K. Roderick
author_sort Misha Leong
title Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes
title_short Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes
title_full Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes
title_fullStr Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes
title_full_unstemmed Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes
title_sort remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes
publisher PeerJ Inc.
series PeerJ
issn 2167-8359
publishDate 2015-08-01
description Global change has led to shifts in phenology, potentially disrupting species interactions such as plant–pollinator relationships. Advances in remote sensing techniques allow one to detect vegetation phenological diversity between different land use types, but it is not clear how this translates to other communities in the ecosystem. Here, we investigated the phenological diversity of the vegetation across a human-altered landscape including urban, agricultural, and natural land use types. We found that the patterns of change in the vegetation indices (EVI and NDVI) of human-altered landscapes are out of synchronization with the phenology in neighboring natural California grassland habitat. Comparing these findings to a spatio-temporal pollinator distribution dataset, EVI and NDVI were significant predictors of total bee abundance, a relationship that improved with time lags. This evidence supports the importance of differences in temporal dynamics between land use types. These findings also highlight the potential to utilize remote sensing data to make predictions for components of biodiversity that have tight vegetation associations, such as pollinators.
topic Remote sensing
Bees
EVI
NDVI
Urban
MODIS
url https://peerj.com/articles/1141.pdf
work_keys_str_mv AT mishaleong remotesensingcapturesvaryingtemporalpatternsofvegetationbetweenhumanalteredandnaturallandscapes
AT georgekroderick remotesensingcapturesvaryingtemporalpatternsofvegetationbetweenhumanalteredandnaturallandscapes
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