Phenological Differences Between Understory and Overstory

Richardson, Andrew D.; O’Keefe, John

doi:10.1007/978-1-4419-0026-5_4

Cited by 106 publications

(56 citation statements)

References 51 publications

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“…e l s e v i e r . c o m / l o c a t e / r s e effects of regional warming and environmental changes on phenology (Stocker et al, 2013) for various scales, from local areas (Ho, Lee, Lee, & Jeong, 2006;Menzel, Sparks, Estrella, & Roy, 2006) to continental regions (Jeong et al, 2013;Schwartz, Ahas, & Aasa, 2006), and for vegetation types, from individual species (Jeong, Medvigy, Shevliakova, & Malyshev, 2012;Richardson & O'Keefe, 2009;Wolkovich et al, 2012) to biomes (Fisher & Mustard, 2007;Jeong, Medvigy, Shevliakova, & Malyshev, 2013;Zhu et al, 2012). Despite these previous studies, our ability to explain all facets of vegetation growth stages remains insufficient.…”

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

Nonlinear response of vegetation green-up to local temperature variations in temperate and boreal forests in the Northern Hemisphere

Park

Jeong

et al. 2015

Remote Sensing of Environment

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Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

Nonlinear response of vegetation green-up to local temperature variations in temperate and boreal forests in the Northern Hemisphere

Park

Jeong

et al. 2015

Remote Sensing of Environment

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“…Visual inspection of G CC curves indicated that Season Spotter estimates occur near inflection points where G CC values start decreasing rapidly (Supplementary Figure S6). Additionally, Season Spotter estimates for the harvard site were within a few days of the long-term mean date of autumn color change based on ground-based measures [32]. One challenge in using the 90% amplitude of G CC is that summertime G CC values slowly decrease, before decreasing more rapidly in autumn.…”

Section: Validating Vegetation Indicesmentioning

confidence: 99%

Season Spotter: Using Citizen Science to Validate and Scale Plant Phenology from Near-Surface Remote Sensing

et al. 2016

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Abstract:The impact of a rapidly changing climate on the biosphere is an urgent area of research for mitigation policy and management. Plant phenology is a sensitive indicator of climate change and regulates the seasonality of carbon, water, and energy fluxes between the land surface and the climate system, making it an important tool for studying biosphere-atmosphere interactions. To monitor plant phenology at regional and continental scales, automated near-surface cameras are being increasingly used to supplement phenology data derived from satellite imagery and data from ground-based human observers. We used imagery from a network of phenology cameras in a citizen science project called Season Spotter to investigate whether information could be derived from these images beyond standard, color-based vegetation indices. We found that engaging citizen science volunteers resulted in useful science knowledge in three ways: first, volunteers were able to detect some, but not all, reproductive phenology events, connecting landscape-level measures with field-based measures. Second, volunteers successfully demarcated individual trees in landscape imagery, facilitating scaling of vegetation indices from organism to ecosystem. And third, volunteers' data were used to validate phenology transition dates calculated from vegetation indices and to identify potential improvements to existing algorithms to enable better biological interpretation. As a result, the use of citizen science in combination with near-surface remote sensing of phenology can be used to link ground-based phenology observations to satellite sensor data for scaling and validation. Well-designed citizen science projects targeting improved data processing and validation of remote sensing imagery hold promise for providing the data needed to address grand challenges in environmental science and Earth observation.

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“…For mapping regions where there is heterogeneous vegetation cover, it is necessary to find the variables most effective for classification. Each vegetation type in each season, was representative of different vegetation, soil, and water conditions [44], so the phenology-based indices were found effective for classifying these vegetation cover types [15,18,69,70].…”

Section: Temporal Indices Of Land Cover Classes and Relevant Variablementioning

confidence: 99%

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

et al. 2016

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Abstract:Phenology-based multi-index with the random forest (RF) algorithm can be used to overcome the shortcomings of traditional deforestation mapping that involves pixel-based classification, such as ISODATA or decision trees, and single images. The purpose of this study was to investigate methods to identify specific types of deforestation in North Korea, and to increase the accuracy of classification, using phenological characteristics extracted with multi-index and random forest algorithms. The mapping of deforestation area based on RF was carried out by merging phenology-based multi-indices (i.e., normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference soil index (NDSI)) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) products and topographical variables. Our results showed overall classification accuracy of 89.38%, with corresponding kappa coefficients of 0.87. In particular, for forest and farm land categories with similar phenological characteristic (e.g., paddy, plateau vegetation, unstocked forest, hillside field), this approach improved the classification accuracy in comparison with pixel-based methods and other classes. The deforestation types were identified by incorporating point data from high-resolution imagery, outcomes of image classification, and slope data. Our study demonstrated that the proposed methodology could be used for deciding on the restoration priority and monitoring the expansion of deforestation areas.

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Phenological Differences Between Understory and Overstory

Cited by 106 publications

References 51 publications

Nonlinear response of vegetation green-up to local temperature variations in temperate and boreal forests in the Northern Hemisphere

Nonlinear response of vegetation green-up to local temperature variations in temperate and boreal forests in the Northern Hemisphere

Season Spotter: Using Citizen Science to Validate and Scale Plant Phenology from Near-Surface Remote Sensing

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

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