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

ABSTRACT: 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.

SUBMITTER: Leong M

PROVIDER: S-EPMC4540021 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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

Leong Misha M Roderick George K GK

PeerJ 20150804

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 fou ...[more]

PMID: 26290795

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Dataset Information

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

Publications

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

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