End Of Senescence Research Articles

Autumn leaf phenology: discrepancies between in situ observations and satellite data at urban and rural sites

ABSTRACTAutumn phenophases, such as leaf colouration (LC) and leaf fall (LF), have received considerably less attention than their spring counterparts (budburst and leaf unfolding) but are equally important determinants of the duration of the growing season and thus have a controlling influence on the carbon-uptake period. Here, we examined THE trends (1968–2016) in in situ observations of the timing of LC and LF from a suite of deciduous trees at three rural sites and one urban site in Ireland. Satellite-derived autumn phenological metrics including mid-senescence (MS) and end of senescence (ES) based on two-band enhanced vegetation index (EVI2) from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) from 1982 to 2016 at a spatial resolution of 5km2 were also examined. The aim of this study was to assess the effectiveness of satellite remote sensing in capturing autumn phenology as determined by in situ observations . Analysis of in situ data (1968–2016) revealed the urban site to be significantly different from the rural sites as LC and LF occurred later in the season and the duration of the autumn season (LF-LC) became shorter over time. These trends may be partly driven by the presence of artificial light in the city. On average (1982–2016), there was a 6-day delay in the timing of MS compared to LC and a much larger difference (21 days) between ES and LF. This resulted in a 31-day autumn duration as defined by satellite data compared to 16 days from in situ observations. Furthermore, there was little overlap in timing between LC and MS, and LF and ES at the rural sites only. Discrepancies between in situ and satellite data may be attributed to the satellite data integrating a much broader vegetation signal across a heterogeneous landscape than in situ observations of individual trees. Therefore, at present, satellite-derived autumn phenology may be more successful in capturing in situ observations across large homogeneous landscapes of similar vegetation types (e.g. forested areas) than in heterogeneous landscapes (e.g. small mixed farms, urban areas, etc.) as is the case in Ireland where the in situ observations of trees may not be reflective of the overall vegetation. Matching the scale of satellite data with in situ observations remains a challenging task but may, at least in part, be overcome by increasing the extent of observations to include a wider range of species and in future as satellite data become available at higher spatial and temporal resolutions.

Extreme warm temperatures alter forest phenology and productivity in Europe

Recent climate warming has shifted the timing of spring and autumn vegetation phenological events in the temperate and boreal forest ecosystems of Europe. In many areas spring phenological events start earlier and autumn events switch between earlier and later onset. Consequently, the length of growing season in mid and high latitudes of European forest is extended. However, the lagged effects (i.e. the impact of a warm spring or autumn on the subsequent phenological events) on vegetation phenology and productivity are less explored. In this study, we have (1) characterised extreme warm spring and extreme warm autumn events in Europe during 2003–2011, and (2) investigated if direct impact on forest phenology and productivity due to a specific warm event translated to a lagged effect in subsequent phenological events. We found that warmer events in spring occurred extensively in high latitude Europe producing a significant earlier onset of greening (OG) in broadleaf deciduous forest (BLDF) and mixed forest (MF). However, this earlier OG did not show any significant lagged effects on autumnal senescence. Needleleaf evergreen forest (NLEF), BLDF and MF showed a significantly delayed end of senescence (EOS) as a result of extreme warm autumn events; and in the following year's spring phenological events, OG started significantly earlier. Extreme warm spring events directly led to significant (p=0.0189) increases in the productivity of BLDF. In order to have a complete understanding of ecosystems response to warm temperature during key phenological events, particularly autumn events, the lagged effect on the next growing season should be considered.

Characterising the Land Surface Phenology of Europe Using Decadal MERIS Data

Land surface phenology (LSP), the study of the timing of recurring cycles of changes in the land surface using time-series of satellite sensor-derived vegetation indices, is a valuable tool for monitoring vegetation at global and continental scales. Characterisation of LSP and its spatial variation is required to reveal and predict ongoing changes in Earth system dynamics. This study presents and analyses the LSP of the pan-European continent for the last decade, considering three phenological metrics: onset of greenness (OG), end of senescence (EOS), and length of season (LS). The whole time-series of Multi-temporal Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data at 1 km spatial resolution was used to estimate the phenological metrics. Results show a progressive pattern in phenophases from low to high latitudes. OG dates are distributed widely from the end of December to the end of May. EOS dates range from the end of May to the end of January and the spatial distribution is generally the inverse of that of the OG. Shorter growing seasons (approximately three months) are associated with rainfed croplands in Western Europe, and forests in boreal and mountainous areas. Maximum LS values appear in the Atlantic basin associated with grasslands. The LSP maps presented in this study are supported by the findings of a previous study where OG and EOS estimates were compared to those of the pan-European phenological network at certain locations corresponding to numerous observations of deciduous tree plant species. Moreover, the spatio-temporal pattern of the OG and EOS produced close agreement with the dates of deciduous tree leaf unfolding and autumnal colouring, respectively (pseudo R-squared equal to 0.70 and 0.71 and root mean square error of six days (over 365 days)).

Comparison of vegetation phenological metrics extracted from GIMMS NDVIg and MERIS MTCI data sets over China

Evaluating vegetation phenology is crucial for a better understanding of the effects of climate change on the terrestrial ecosystem. The scientific community has used various vegetation index data sets from different sensors to quantify vegetation phenology from regional to global scales. The normalized difference vegetation index (NDVI) related to photosynthetic activities is the most widely used index. Recently, a number of published articles have used the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) to measure vegetation phenology. MTCI can closely represent the red-edge position (REP). Unlike NDVI, MTCI is more sensitive to high values of chlorophyll content. However, the consistency of vegetation phenological metrics derived from MTCI and NDVI needs to be further explored. This study compared two phenological metrics, i.e. onset of greenness (OG) and end of senescence (ES), extracted from MERIS MTCI data and Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) first generation NDVI (NDVIg) data, which has the longest time records, at nine regions in China from 2003 to 2006. The results showed that the differences of OG and ES vary between different vegetation types, regions, and years, although both NDVI and MTCI time series capture the growth patterns well for most vegetation types. Compared to ES, the OG estimates are more consistent. NDVI yields in general later ES estimates than MTCI.

Moist deciduous forest identification using temporal MODIS data — A comparative study using fuzzy based classifiers

The two soft fuzzy based classifiers, Possibilistic c-Means (PCM) approach and Noise Clustering (NC) were compared for the Moist Deciduous Forest (MDF) identification from MODIS temporal data. Seven date temporal MODIS data were used to identify MDF and temporal Advanced Wide Field Sensor (AWiFS) data was used as reference data for testing. Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index 2 (EVI2) were used to generate the temporal spectral index datasets for both the MODIS and AWiFS. The parameter weighting exponent m for PCM and resolution parameter δ for NC were optimized. Results show that the optimized value of m for MDF is 2.1, while δ value is 3.6×104 for temporal MODIS data. For assessment of the accuracy AWiFS datasets were also optimized using entropy approach. The optimized dataset of AWiFS was then used for accuracy assessment of the soft classified outputs from MODIS using Fuzzy ERror Matrix (FERM). It was found from this study that, for PCM classifier the highest fuzzy overall accuracy of 97.44% was obtained using the SAVI for the temporal dataset ‘Five’ consisting to one scene of ‘Full greenness’, three scenes in ‘Intermediate frequency stage of Onset of Greenness (OG) and End of Senescence (ES) activity’ and the last image pertaining corresponds to the ‘Maximum frequency stage of OG activity’ as per phenology of MDF. Similarly, for NC classifier the highest fuzzy overall accuracy of 95.19% was obtained for the EVI2 with temporal dataset ‘Five’ consisting with two scene of ‘Full greenness’, two scenes in ‘Intermediate frequency stage of OG and ES activity’ and the last one corresponds to the ‘Maximum frequency stage of OG activity’as per phenology of MDF.

Fuzzy Based Approach for Moist Deciduous Forest Identification Using MODIS Temporal Data

Fuzzy based soft classification have been used immensely for handling the mixed pixel and hence to extract the single class of interest. The present research attempts to extract the moist deciduous forest from MODIS temporal data using the Possibilistic c-Means (PCM) soft classification approach. Temporal MODIS (7 dates) data were used to identify moist deciduous forest and temporal AWiFS (7 dates) data were used as reference data for testing. The Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Transformed Normalized Difference Vegetation Index (TNDVI) were used to generate the temporal vegetation indices for both the MODIS and the AWiFS datasets. It was observed from the research that the MODIS temporal NDVI data set1, which contain the minimum number of images and avoids the temporal images corresponding to the highest frequency stages of onset of greenness (OG) and end of senescence (ES) activity of moist deciduous forest have been found most suitable data set for identification of moist deciduous forest with the maximum fuzzy overall accuracy of 96.731 %.

Characterising the spatial pattern of phenology for the tropical vegetation of India using multi-temporal MERIS chlorophyll data

The annual growth cycles of terrestrial ecosystems are related to long-term regional/global climatic patterns. Understanding vegetation phenology and its spatio-temporal variation is required to reveal and predict ongoing changes in Earth system dynamics. The study attempts to characterize the phenology of the major tropical vegetation types in India, since such information is not yet available for India. Multi-temporal Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data were utilized to derive onset of greenness (OG) and end of senescence (ES) for four major tropical vegetation types. The study found that Fourier-smoothed results using the first four components revealed adequately the annual phenological variation of the natural vegetation types in India. From these smoothed data, inflection points were located iteratively through a spatio-temporal search, spanning over 18 months of 8-day composite data, per pixel such as to derive the OG and ES. The median OG and ES was extracted from the available annual results for the years 2003–04, 2004–05, 2005–06 and 2006–07. The GLC2000 land cover map (1 km spatial resolution) was utilized to determine the locations of the major vegetation types. The percentage of each vegetation type falling beneath a MTCI composite pixel (4.6 km spatial resolution) was calculated. MTCI composite pixels with homogeneity of ≥80% vegetative cover were used for examining pattern of phenology in different regions, different years and at different latitudes. The most common dates for the occurrence of OG for the tropical evergreen, semi-evergreen, moist-deciduous, and dry-deciduous vegetation types were found to be during February–April, January–April, March–May, and February–May, respectively. Similarly, for ES the most common dates were in February–April, January–April, February–April, and December–April, respectively. The phenological pattern was uniquely different for each vegetation type, as expected, and also differed with regions and latitudes. A general trend of early occurrence of OG in the lower latitudes was observed.