Ground-based Network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes

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

doi:10.1016/j.rse.2012.03.012

Cited by 182 publications

(125 citation statements)

References 71 publications

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“…Figure 5(a) shows a marked difference in NDVI minima values between the FPH/CON class and the FPH/DEC class in the reference data set used, explaining the RF classifier's output. This difference in minima is also observed between ground based NDVI series collected from deciduous (broadleaved) and evergreen (coniferous and broadleaved) forests in France [49]. This study also showed marked differences in maximum values which is not so evident from our reference plots.…”

Section: Resultssupporting

confidence: 73%

Exploring the Use of MODIS NDVI-Based Phenology Indicators for Classifying Forest General Habitat Categories

Clerici¹,

Weissteiner²

2012

Remote Sensing

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Abstract:The cost effective monitoring of habitats and their biodiversity remains a challenge to date. Earth Observation (EO) has a key role to play in mapping habitat and biodiversity in general, providing tools for the systematic collection of environmental data. The recent GEO-BON European Biodiversity Observation Network project (EBONE) established a framework for an integrated biodiversity monitoring system. Underlying this framework is the idea of integrating in situ with EO and a habitat classification scheme based on General Habitat Categories (GHC), designed with an Earth Observation-perspective. Here we report on EBONE work that explored the use of NDVI-derived phenology metrics for the identification and mapping of Forest GHCs. Thirty-one phenology metrics were extracted from MODIS NDVI time series for Europe. Classifications to discriminate forest types were performed based on a Random Forests™ classifier in selected regions. Results indicate that date phenology metrics are generally more significant for forest type discrimination. The achieved class accuracies are generally not satisfactory, except for coniferous forests in homogeneous stands (77-82%). The main causes of low classification accuracies were identified as (i) the spatial resolution of the imagery (250 m) which led to mixed phenology signals; (ii) the GHC scheme classification design, which allows for parcels of heterogeneous covers, and (iii) the low number of the training samples available from field surveys. A mapping strategy integrating EO-based phenology with vegetation height information is expected to be more effective than a purely phenology-based approach.

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Section: Resultssupporting

confidence: 73%

Exploring the Use of MODIS NDVI-Based Phenology Indicators for Classifying Forest General Habitat Categories

Clerici¹,

Weissteiner²

2012

Remote Sensing

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“…Sensor-based data provide measurements that range from the local scale of site based observations to the global extent of satellite missions (Garrity et al, 2011;Huemmrich et al, 1999;Jenkins et al, 2007;Soudani et al, 2012). A key challenge in the interpretation of phenology derived from sensor time series is determining how they relate to plant biological events that an observer would recognize.…”

Section: S T Klosterman Et Al: Evaluating Remote Sensing Of Decidumentioning

confidence: 99%

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

Klosterman

Hufkens²,

Gray³

et al. 2014

Biogeosciences

288

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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“…Many studies have employed remote sensing time-series to extract and monitor the timing of plant phenological events [13,15,[26][27][28][29]. Since these studies have used different smoothing methods to estimate phenology, it is hard to draw any firm conclusions about which methods are the most suitable.…”

Section: Introductionmentioning

confidence: 99%

Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data

et al. 2017

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Many time-series smoothing methods can be used for reducing noise and extracting plant phenological parameters from remotely-sensed data, but there is still no conclusive evidence in favor of one method over others. Here we use moderate-resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI) to investigate five smoothing methods: Savitzky-Golay fitting (SG), locally weighted regression scatterplot smoothing (LO), spline smoothing (SP), asymmetric Gaussian function fitting (AG), and double logistic function fitting (DL). We use ground tower measured NDVI (10 sites) and gross primary productivity (GPP, 4 sites) to evaluate the smoothed satellite-derived NDVI time-series, and elevation data to evaluate phenology parameters derived from smoothed NDVI. The results indicate that all smoothing methods can reduce noise and improve signal quality, but that no single method always performs better than others. Overall, the local filtering methods (SG and LO) can generate very accurate results if smoothing parameters are optimally calibrated. If local calibration cannot be performed, cross validation is a way to automatically determine the smoothing parameter. However, this method may in some cases generate poor fits, and when calibration is not possible the function fitting methods (AG and DL) provide the most robust description of the seasonal dynamics.

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Ground-based Network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes

Cited by 182 publications

References 71 publications

Exploring the Use of MODIS NDVI-Based Phenology Indicators for Classifying Forest General Habitat Categories

Exploring the Use of MODIS NDVI-Based Phenology Indicators for Classifying Forest General Habitat Categories

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

Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data

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