Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements

Hmimina, Gabriel; Dufrêne, Éric; Pontailler, Jean-Yves; Delpierre, Nicolas; Aubinet, Marc; Caquet, Blandine; Grandcourt, A. de; Burban, Benoît; Fléchard, Christophe; Granier, André; Gross, Peggy; Heinesch, Bernard; Longdoz, Bernard; Moureaux, Christine; Ourcival, Jean‐Marc; Rambal, Serge; Lenouo, André; Soudani, Kamel

doi:10.1016/j.rse.2013.01.010

Cited by 371 publications

(239 citation statements)

References 49 publications

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“…A recent study by Hmimina et al (2013) also found good agreement between near-surface and satellite remote-sensing-derived estimates for the beginning of spring. However we found that data-driven estimates of later spring phenology from near-surface imagery, intended to represent the final stages of springtime leaf development, exhibited less correspondence to estimates derived from both visual assessments and satellite remote sensing.…”

Section: Comparison Of Phenocam Curve Fitting To Visual Assessment Anmentioning

confidence: 54%

“…However, recent results have shown that EVI from satellite remote sensing has a 2-to 3-week temporal bias, similar to GCC from tower-mounted cameras, with respect to the suite of leaf physiology measurements mentioned above (Keenan et al, 2014). Further, recent work indicates that bias between end of spring phenology at the near-surface and landscape scales may not be caused by differences in vegetation index; Hmimina et al (2013) found a similar late spring bias using NDVI from remote sensing and near-surface NDVI sensors. Camera fields of view are smaller than ground areas associated with satellite pixels.…”

Section: Comparison Of Phenocam Curve Fitting To Visual Assessment Anmentioning

confidence: 88%

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Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Klosterman

Hufkens²,

Gray³

et al. 2014

Biogeosciences

285

234

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Abstract. Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of fall, can be derived from sensorbased time series, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, and to perform digital image analysis for time-series-based estimation of phenophase transition dates. We then compare these results to remote sensing metrics of phenophase transition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to estimate phenology dates, and we quantify the statistical uncertainty of phenophase transition dates estimated using this method. Results show that the generalized sigmoid provides estimates of dates with less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing metrics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates derived from visual assessment of leaf-out, as well as satellite remote-sensing-derived estimates of the start of spring. However late spring and fall phenology metrics exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.

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Section: Comparison Of Phenocam Curve Fitting To Visual Assessment Anmentioning

confidence: 54%

Section: Comparison Of Phenocam Curve Fitting To Visual Assessment Anmentioning

confidence: 88%

Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Klosterman

Hufkens²,

Gray³

et al. 2014

Biogeosciences

285

234

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show abstract

“…Leaf senescence is also relatively more difficult to monitor because, in contrast to budburst (which occurs in a few days and involves morphological changes that are easy to detect), leaf senescence is a slow, continuous ensemble of processes, visually progressing from the leaf coloration induced by the degradation of chlorophylls to, ultimately, leaf fall. The recent development of satellitebased and ground-based imagery (Hmimina et al 2013;Keenan et al 2014) is expected to increase the level of research effort on the leaf senescence phase. Temperature and photoperiod are regularly mentioned as the most influential environmental cues in triggering leaf senescence, but their respective contributions have not been fully elucidated (see Estrella and Menzel 2006).…”

Section: Phenology Of Leaves and Reproductive Structuresmentioning

confidence: 99%

Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models

Delpierre

Vitasse

Chuine

et al. 2016

Annals of Forest Science

231

192

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Abstract& Key message We demonstrate that, beyond leaf phenology, the phenological cycles of wood and fine roots present clear responses to environmental drivers in temperate and boreal trees. These drivers should be included in terrestrial ecosystem models. & Context In temperate and boreal trees, a dormancy period prevents organ development during adverse climatic conditions. Whereas the phenology of leaves and flowers has received considerable attention, to date, little is known regarding the phenology of other tree organs such as wood, fine roots, fruits, and reserve compounds. & Aims Here, we review both the role of environmental drivers in determining the phenology of tree organs and the models used to predict the phenology of tree organs in temperate and boreal forest trees. & Results Temperature is a key driver of the resumption of tree activity in spring, although its specific effects vary among organs. There is no such clear dominant environmental cue involved in the cessation of tree activity in autumn and in the onset of dormancy, but temperature, photoperiod, and water stress appear as prominent factors. The phenology of a given organ is, to a certain extent, influenced by processes in distant organs. & Conclusion Inferring past trends and predicting future trends of tree phenology in a changing climate requires specific phenological models developed for each organ to consider the phenological cycle as an ensemble in which the environmental cues that trigger each phase are also indirectly involved in the subsequent phases. Incorporating such models into terrestrial ecosystem models (TEMs) would likely improve the accuracy of their predictions. The extent to which the coordination of the phenologies of tree organs will be affected in a changing climate deserves further research.

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“…SPOT-4 and SPOT-5 VEGETATION data time series have been effective in detecting variations in leaf phenology of deciduous broadleaved forest in different elevations, extracting a five year perpendicular vegetation index (PVI) and using a temporal unmixing method (Guyon et al 2011). A number of indices from MODIS or Landsat data, including Enhanced Vegetation Index (EVI), NDVI, Excess Green Index (ExG M ), and Normalized Difference Water Index (NDWI), were evaluated in several studies (Hmimina et al 2013;Hufkens et al 2012;White et al 2014). The optimized soil-adjusted vegetation index (OS-AVI), calculated from MODIS data, was more consistent than NDVI and EVI in characterizing Gross Primary Productivity (GPP) end in evergreen needleleaved forests, encouraging its broader use (Wu et al 2014).…”

Section: Forestry Monitoringmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements

Cited by 371 publications

References 49 publications

Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

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