A spatially explicit land surface phenology data product for science, monitoring and natural resources management applications

Broich, Mark; Huete, Alfredo; Paget, Matt; Ma, Xuanlong; Tulbure, Mirela G.; Restrepo‐Coupe, Natalia; Evans, Bradley J.; Beringer, Jason; Devadas, Rakhesh; Davies, Kevin; Held, Alex

doi:10.1016/j.envsoft.2014.11.017

Cited by 69 publications

(39 citation statements)

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“…Although temporal changes and dynamics were not addressed within this study (in the interest of developing a robust and stable relationship between ground observations and satellite data), the knowledge on the established relationships is applied in an ongoing study monitoring woody dynamics in the Sahel area. The woody cover dataset is made publicly available (following the example of Broich et al, 2015) for download (information on the data access can be found in the supplementary material).…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, land use maps could adjust the model to differences in plant phenology between cropland, rangeland and forest areas. "Finally, to produce annual maps of woody cover changes over time, the method has to be robust against inter-annual fluctuations of satellite derived metrics (Broich et al, 2015) caused by rainfall dynamics, human disturbances (cutting, clearing), fires and especially dynamics of leaf density hiding the real trend in woody population changes. "…”

Section: Uncertainties Sources Of Error and Proposed Improvementsmentioning

confidence: 99%

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Woody plant cover estimation in drylands from Earth Observation based seasonal metrics

Brandt

Hiernaux

Tagesson

et al. 2016

Remote Sensing of Environment

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From in situ measured woody cover we develop a phenology driven model to estimate the canopy cover of woody species in the Sahelian drylands at 1 km scale. The model estimates the total canopy cover of all woody phanerophytes and the concept is based on the significant difference in phenophases of dryland trees, shrubs and bushes as compared to that of the herbaceous plants. Whereas annual herbaceous plants are only green during the rainy season and senescence occurs shortly after flowering towards the last rains, most woody plants remain photosynthetically active over large parts of the year. We use Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite pour l'Observation de la Terre (SPOT) -VEGETATION (VGT) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time series and test 10 metrics representing the annual FAPAR dynamics for their ability to reproduce in situ woody cover at 43 sites (163 observations between 1993 and 2013) in the Sahel. Both multi-year field data and satellite metrics are averaged to produce a steady map. Multiple regression models using the integral of FAPAR from the onset of the dry season to the onset of the rainy season, the start date of the growing season and the rate of decrease of the FAPAR curve achieve a cross validated r 2 /RMSE (in % woody cover) of 0.73/3.0 (MODIS) and 0.70/3.2 (VGT). The extrapolation to Sahel scale shows agreement between VGT and MODIS at an almost nine times higher woody cover than in the global tree cover product MOD44B which only captures trees of a certain minimum size. The derived woody cover map of the Sahel is made publicly available and represents an improvement of existing products and a contribution for future studies of drylands quantifying carbon stocks, climate change assessment, as well as parametrization of vegetation dynamic models.

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Section: Resultsmentioning

confidence: 99%

Section: Uncertainties Sources Of Error and Proposed Improvementsmentioning

confidence: 99%

Woody plant cover estimation in drylands from Earth Observation based seasonal metrics

Brandt

Hiernaux

Tagesson

et al. 2016

Remote Sensing of Environment

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“…The majority of the continent experiences a green-up, which is partially due to an increase in non-deciduous perennial vegetation [13]. These climatic changes, land cover changes and altered vegetation vulnerabilities are potential causes of non-stationary behavior [26][27][28], as illustrated in Figure 3. Furthermore, the limited and variable water availability, low NDVI saturation and relatively low cloud cover allow one to derive stability metrics with a relatively low model error from NDVI time series [20] and increase the likeliness that non-stationary behavior can be detected.…”

Section: Study Areamentioning

confidence: 99%

Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006

et al. 2017

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Within the context of climate change, it is of utmost importance to quantify the stability of ecosystems with respect to climate anomalies. It is well acknowledged that ecosystem stability may change over time. As these temporal stability changes may provide a warning for increased vulnerability of the system, this study provides a methodology to quantify and assess these temporal changes in vegetation stability. Within this framework, vegetation stability changes were quantified over Australia from 1982 to 2006 using GIMMS NDVI and climate time series (i.e., SPEI (Standardized Precipitation and Evaporation Index)). Starting from a stability assessment on the complete time series, we aim to assess: (i) the magnitude and direction of stability changes; and (ii) the similarity in these changes for different stability metrics, i.e., the standard deviation of the NDVI anomaly (SD), auto-correlation at lag one of the NDVI anomaly (AC) and the correlation of NDVI anomaly with SPEI (CS). Results show high variability in magnitude and direction for the different stability metrics. Large areas and types of Australian vegetation showed an increase in variability (SD) over time; however, vegetation memory (AC) decreased. The association of NDVI anomalies with drought events (CS) showed a mixed response: the association increased in the western part, while it decreased in the eastern part. This methodology shows the potential for quantifying vegetation responses to major climate shifts and land use change, but results could be enhanced with higher resolution time series data.

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“…MODIS sensor data provides good temporal resolution for land surface phenology with a revisit time 1e2 days (Jones and Vaughan, 2010). Land surface phenology studies frequently utilise MODIS vegetation index data; either MODIS NDVI (Ahmad, 2013;Beck et al, 2006;Bian et al, 2010;Boyd et al, 2011;Caccamo et al, 2011;Eckert et al, 2015;Fontana et al, 2008;Hird and McDermid, 2009;Jin and Xu, 2013;le Maire et al, 2011;Marsden et al, 2010;Michishita et al, 2014;Schmidt et al, 2012) and/or MODIS EVI (Boyd et al, 2011;Broich et al, 2014Broich et al, , 2015Caccamo et al, 2011;Huete et al, 2006aHuete et al, , 2006bLi et al, 2014;Ma et al, 2013;Sakamoto et al, 2005;Zhang et al, 2006Zhang et al, , 2003Zhang et al, , 2014. MODIS data has also been used in the study of natural environments and eucalypt forests in Australia (Caccamo, 2012;Caccamo et al, 2011;Cleugh et al, 2007;Hill et al, 2006;Leuning et al, 2005;Ma et al, 2013;Pickett-Heaps et al, 2014;Schmidt et al, 2012), and in eucalypt plantations globally (le Maire et al, 2014;le Maire et al, 2011;le Maire et al, 2012;Lopes et al, 2009;Marsden et al, 2010).…”

Section: Observations Of Growthmentioning

confidence: 99%

“…In Australia, initiatives such as the Atlas of Living Australia (ALA) (Atlas of Living Australia (2015)) and Terrestrial Ecosystem Research Network (TERN) (Terrestrial Ecosystem Research Network, 2015) aggregate environmental data from multiple sources and present this online to a broader audience. Although TERN has recently added a phenology product based on MODIS data for the entire Australian continent for the period from 2000 to 2012 (Broich et al, 2015), accessing and using this data still requires specialist software and knowledge. In contrast, BeeBox allows beekeepers to produce phenological time series interactively for any region of interest using only a web-browser, and on any platform (PC, tablet, mobile).…”

Section: Introductionmentioning

confidence: 99%

A web-based application for beekeepers to visualise patterns of growth in floral resources using MODIS data

Arundel

Winter

Gui

et al. 2016

Environmental Modelling & Software

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A spatially explicit land surface phenology data product for science, monitoring and natural resources management applications

Cited by 69 publications

References 50 publications

Woody plant cover estimation in drylands from Earth Observation based seasonal metrics

Woody plant cover estimation in drylands from Earth Observation based seasonal metrics

Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006

A web-based application for beekeepers to visualise patterns of growth in floral resources using MODIS data

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