scholarly journals Assessing biomass and primary production of microphytobenthos in depositional coastal systems using spectral information

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0246012
Author(s):  
Pascalle Jacobs ◽  
Jaime Pitarch ◽  
Jacco C. Kromkamp ◽  
Catharina J. M. Philippart
Keyword(s):  
Primary Production ◽  
Extreme Events ◽  
Environmental Conditions ◽  
Food Supply ◽  
Western Europe ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Benthic Microalgae ◽  
Benthic Primary Production ◽  
Coastal Systems

In depositional intertidal coastal systems, primary production is dominated by benthic microalgae (microphytobenthos) inhabiting the mudflats. This benthic productivity is supporting secondary production and supplying important services to humans including food provisioning. Increased frequencies of extreme events in weather (such as heatwaves, storm surges and cloudbursts) are expected to strongly impact the spatiotemporal dynamics of the microphytobenthos and subsequently their contribution to coastal food webs. Within north-western Europe, the years 2018 and 2019 were characterized by record-breaking summer temperatures and accompanying droughts. Field-calibrated satellite data (Sentinel 2) were used to quantify the seasonal dynamics of microphytobenthos biomass and production at an unprecedented spatial and temporal resolution during these years. We demonstrate that the Normalized Difference Vegetation Index (NDVI) should be used with caution in depositional coastal intertidal systems, because it may reflect import of remains of allochthonous pelagic productivity rather than local benthic biomass. We show that the reduction in summer biomass of the benthic microalgae cannot be explained by grazing but was most probably due to the high temperatures. The fivefold increase in salinity from January to September 2018, resulting from reduced river run-off during this exceptionally dry year, cannot have been without consequences for the vitality of the microphytobenthos community and its resistance to wind stress and cloud bursts. Comparison to historical information revealed that primary productivity of microphytobenthos may vary at least fivefold due to variations in environmental conditions. Therefore, ongoing changes in environmental conditions and especially extreme events because of climate change will not only lead to changes in spatiotemporal patterns of benthic primary production but also to changes in biodiversity of life under water and ecosystem services including food supply. Satellite MPB data allows for adequate choices in selecting coastal biodiversity conservation and coastal food supply.

scholarly journals Assessing biomass and primary production of microphytobenthos in depositional coastal systems using spectral information

2021 ◽  
Author(s):  
Pascalle Jacobs ◽  
Jaime Pitarch ◽  
Jacco C. Kromkamp ◽  
Catharina J.M. Philippart
Keyword(s):  
Primary Production ◽  
Extreme Events ◽  
Environmental Conditions ◽  
Food Supply ◽  
Benthic Microalgae ◽  
List Type ◽  
Benthic Primary Production ◽  
Coastal Systems ◽  
Benthic Biomass ◽  
Summer Temperatures

ABSTRACTIn depositional intertidal coastal systems, primary production is dominated by benthic microalgae (microphytobenthos) inhabiting the mudflats. This benthic productivity is supporting secondary production and supplying important services to humans including food provisioning. Increased frequencies of extreme events in weather (such as heatwaves, storm surges and cloudbursts) are expected to strongly impact the spatiotemporal dynamics of the microphytobenthos and subsequently their contribution to coastal food webs. Within north-western Europe, the years 2018 and 2019 were characterized by record-breaking summer temperatures and accompanying droughts. Field-calibrated satellite data (Sentinel 2) were used to quantify the seasonal dynamics of microphytobenthos biomass and production at an unprecedented spatial and temporal resolution during these years. We demonstrate that the Normalized Difference Vegetation Index (NDVI) should be used with caution in depositional coastal intertidal systems, because it may reflect import of remains of allochthonous pelagic productivity rather than local benthic biomass. We show that the reduction in summer biomass of the benthic microalgae cannot be explained by grazing but was most probably due to the high temperatures. The fivefold increase in salinity from January to September 2018, resulting from reduced river run-off during this exceptionally dry year, cannot have been without consequences for the vitality of the microphytobenthos community and its resistance to wind stress and cloud bursts. Comparison to historical information revealed that primary productivity of microphytobenthos may vary at least fivefold due to variations in environmental conditions. Therefore, ongoing changes in environmental conditions and especially extreme events because of climate change will not only lead to changes in spatiotemporal patterns of benthic primary production but also to changes in biodiversity of life under water and ecosystem services including food supply. Satellite MPB data allows for adequate choices in selecting coastal biodiversity conservation and coastal food supply.HIGHLIGHTSExpected seasonality changes require large-scale and high-resolution coastal dataNDVI of tidal flats reflects local benthic biomass and allochthonous phytoplanktonHigh summer temperatures reduced biomass and productivity of benthic microalgaeLong-term data revealed a five-fold variation in MPB biomass and productionSatellite MPB data allow for adequate conservation of coastal biodiversity

scholarly journals Satellites reveal an increase in gross primary production in a greenlandic high arctic fen 1992–2008

2010 ◽  
Vol 7 (1) ◽  
pp. 1101-1129 ◽  
Author(s):  
T. Tagesson ◽  
M. Mastepanov ◽  
M. P. Tamstorf ◽  
L. Eklundh ◽  
P. Schubert ◽  
...  
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Gross Primary Production ◽  
Field Measurements ◽  
High Arctic ◽  
Research Area ◽  
Strong Increase ◽  
Co2 Uptake ◽  
Flux Measurements

Abstract. Arctic wetlands play a key role in the terrestrial carbon cycle. Recent studies have shown a greening trend and indicated an increase in CO2 uptake in boreal and sub- to low-arctic areas. Our aim was to combine satellite-based normalized difference vegetation index (NDVI) with ground-based flux measurements of CO2 to investigate a possible greening trend and potential changes in gross primary production (GPP) between 1992 and 2008 in a high arctic fen area. The study took place in Rylekaerene in the Zackenberg Research Area (74°28' N 20°34' W), located in the National park of North Eastern Greenland. We estimated the light use efficiency (ε) for the dominant vegetation types from field measured fractions of photosynthetic active radiation (FAPAR) and ground-based flux measurements of GPP. Measured FAPAR were correlated to satellite-based NDVI. The FAPAR-NDVI relationship in combination with ε was applied to satellite data to model GPP 1992–2008. The model was evaluated against field measured GPP. The model was a useful tool for up-scaling GPP and all basic requirements for the model were well met, e.g., FAPAR was well correlated to NDVI and modeled GPP was well correlated to field measurements. The studied high arctic fen area has experienced a strong increase in GPP between 1992 and 2008. The area has during this period also experienced a substantial increase in local air temperature. Consequently, the observed greening trend is most likely due to ongoing climatic change possibly in combination with CO2 fertilization, due to increasing atmospheric concentrations of CO2.

scholarly journals Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

2020 ◽  
Vol 12 (1) ◽  
pp. 190 ◽  
Author(s):  
Ruyin Cao ◽  
Yan Feng ◽  
Xilong Liu ◽  
Miaogen Shen ◽  
Ji Zhou
Keyword(s):  
Western Europe ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
High Latitudes ◽  
Large Spatial Scale ◽  
Arid Grasslands ◽  
Seasonal Snow Cover ◽  
Spring Snowmelt

Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.

scholarly journals Tracking the Autumn Migration of the Bar-Headed Goose (Anser indicus) with Satellite Telemetry and Relationship to Environmental Conditions

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Yaonan Zhang ◽  
Meiyu Hao ◽  
John Y. Takekawa ◽  
Fumin Lei ◽  
Baoping Yan ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Habitat Type ◽  
Autumn Migration ◽  
Qinghai Lake ◽  
Vegetation Coverage ◽  
Migration Routes ◽  
Mountainous Regions ◽  
Stopover Sites

The autumn migration routes of bar-headed geese captured before the 2008 breeding season at Qinghai Lake, China, were documented using satellite tracking data. To assess how the migration strategies of bar-headed geese are influenced by environmental conditions, the relationship between migratory routes, temperatures, and vegetation coverage at stopovers sites estimated with the Normalized Difference Vegetation Index (NDVI) were analyzed. Our results showed that there were four typical migration routes in autumn with variation in timing among individuals in start and end times and in total migration and stopover duration. The observed variation may be related to habitat type and other environmental conditions along the routes. On average, these birds traveled about 1300 to 1500 km, refueled at three to six stopover sites and migrated for 73 to 83 days. The majority of the habitat types at stopover sites were lake, marsh, and shoal wetlands, with use of some mountainous regions, and farmland areas.

scholarly journals Herbivory Rate on Woody Species of the Caatinga and NDVI as Indicators of Plant Stress (Taxa de Herbivoria em Espécies Arbóreas da Caatinga e o Uso do Índice de Vegetação por Diferença Normalizada (NDVI) como Indicador de Estresse em Planta)

2012 ◽  
Vol 4 (5) ◽  
pp. 909
Author(s):  
Jarcilene Almeida-Cortez ◽  
Mateus Dantas de Paula ◽  
Martin Duarte de Oliveira ◽  
Cátia Inês Rodrigues dos Santos
Keyword(s):  
Primary Production ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Stress ◽  
Woody Species ◽  
Abiotic Conditions ◽  
Close Relationship ◽  
Negative Effect ◽  
Semi Arid Region

Espécies de plantas distribuídas em uma paisagem são submetidas a um mosaico de condições abióticas que podem ter efeito negativo sobre o desenvolvimento (stress geração) e expô-las à predação por herbívoros. Esse estresse pode causar adicionalmente assimetria foliar e uma redução na produção primária. A taxa fotossintética, relacionada com a produtividade da planta, pode ser medida por índices espectrais, tais como o NDVI (índice de vegetação da diferença normalizada), calculado a partir de imagens de satélite. No presente trabalho, testou-se a hipótese de que ambientes com baixa produtividade primária (NDVI baixo) irá possuir maior assimetria foliar e maiores taxas de herbivoria. Os resultados mostram que na região de Caatinga semi árida de Pernambuco, Brasil, a folha de assimetria diminui com valores mais elevados de NDVI, indicando uma estreita relação entre esta medida da planta e o índice espectral. Por outro lado, a correlação entre herbivoria e produção primária ou assimetria foliar não foi significativa, sugerindo que os herbívoros vão além da simples seleção de indivíduos mais estressados. Palavras-Chave: Assimetria flutuante, herbivoria, NDVI   Taxa de Herbivoria em Espécies Arbóreas da Caatinga e o Uso do Índice de Vegetação por Diferença Normalizada (NDVI) como Indicador de Estresse em Planta   ABSTRACT Plant species distributed on a landscape are submitted to a mosaic of abiotic conditions that may have a negative effect on development (generating stress) and expose them to predation by herbivores. This stress can cause additionally leaf asymmetry and a reduction on primary production. The photosynthetic rate, related to plant productivity, can be measured by spectral indexes, such as the NDVI (normalized difference vegetation index), calculated from satellite images. In the present work, we test the hypothesis that environments with low primary productivity (low NDVI) will possess larger leaf asymmetry and higher herbivory rates. Our results show that in the Caatinga semi-arid region of Pernambuco, Brazil, the leaf asymmetry reduces with higher NDVI values, indicating a close relationship between this plant measure and the spectral index. On the other side, the correlation between herbivory and primary production or leaf asymmetry was not significant, suggesting that herbivores go beyond just selecting more stressed individuals.   Keywords: Leaf asymmetry, NDVI, herbivory

Primary Production and Respiration in Pelagic and Benthic Communities at Two Intertidal Sites in the Upper Bay of Fundy

1983 ◽  
Vol 40 (S1) ◽  
pp. s229-s243 ◽  
Author(s):  
B. T. Hargrave ◽  
N. J. Prouse ◽  
G. A. Phillips ◽  
P. A. Neame
Keyword(s):  
Primary Production ◽  
Benthic Communities ◽  
Late Summer ◽  
Benthic Microalgae ◽  
Phytoplankton Production ◽  
Bay Of Fundy ◽  
Intertidal Sediments ◽  
Benthic Primary Production ◽  
Benthic Respiration ◽  
Undisturbed Sediments

Primary production by microalgae on intertidal sediments during ebb tide at two sites in Cumberland and Minas Basin, Bay of Fundy, amounted to 47–83 g C∙m−2∙yr−1 Phytoplankton production measured during flood tide over intertidal sediments in Cumberland Basin varied from 4–10 g C∙m−2∙yr−1 with respiration in the water column between 6 and 12 g∙C∙m−2∙yr−1 depending on concentrations of suspended matter. Respiration by undisturbed sediments (47–62 g C∙m−2∙yr−1) was measured at both locations to estimate aerobic metabolic consumption of organic matter.Maximum rates of benthic primary production occurred during early and late summer at both locations but Values at the Cumberland Basin sites were two to three times greater than those observed in Minas Basin; Chlorophyll a in surface sediments was also lower at the stations in Minas Basin where coarser grained deposits reflect extensive sediment transport. Annual benthic respiration at the two stations in Cumberland Basin, however, was only slightly greater than that at four stations in Minas Basin. Spartina marshes, phytoplankton, and benthic microalgae may provide supplies of organic matter for aerobic consumption in these intertidal sediments which are more similar than are measures of benthic primary production.Key words: benthic microalgae, primary production, intertidal community metabolism, Bay of Fundy

Predictability of the Normalized Difference Vegetation Index in Kenya and Potential Applications as an Indicator of Rift Valley Fever Outbreaks in the Greater Horn of Africa

2006 ◽  
Vol 19 (9) ◽  
pp. 1673-1687 ◽  
Author(s):  
Matayo Indeje ◽  
M. Neil Ward ◽  
Laban J. Ogallo ◽  
Glyn Davies ◽  
Maxx Dilley ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Rift Valley ◽  
Rift Valley Fever ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Horn Of Africa ◽  
Valley Fever ◽  
Wide Range ◽  
Potential Applications ◽  
Greater Horn Of Africa

Abstract In this paper the progress made in producing predictions of the Normalized Difference Vegetation Index (NDVI) over Kenya in the Greater Horn of Africa (GHA) for the October–December (OND) season is discussed. Several studies have identified a statistically significant relationship between rainfall and NDVI in the region. Predictability of seasonal rainfall by global climate models (GCMs) during the OND season over the GHA has also been established as being among the best in the world. Information was extracted from GCM seasonal prediction output using statistical transformations. The extracted information was then used in the prediction of NDVI. NDVI is a key variable for management of various climate-sensitive problems. For example, it has been shown to have the potential to predict environmental conditions associated with Rift Valley Fever (RVF) viral activity and this is referred to throughout the paper as a motivation for the study. RVF affects humans and livestock and is particularly economically important in the GHA. The establishment of predictability for NDVI in this paper is therefore part of a methodology that could ultimately generate information useful for managing RVF in livestock in the GHA. It has been shown that NDVI can be predicted skillfully over the GHA with a 2–3-month lead time. Such information is crucial for tailoring forecast information to support RVF monitoring and prediction over the region, as well as many other potential applications (e.g., livestock forage estimation). More generally, the Famine Early Warning System (FEWS), a project of the U.S. Agency for International Development (USAID) and the National Aeronautics and Space Administration (NASA) and other specialized technical centers routinely use NDVI images to monitor environmental conditions worldwide. The high predictability for NDVI established in this paper could therefore supplement the routine monitoring of environmental conditions for a wide range of applications.

scholarly journals A global spatially Continuous Solar Induced Fluorescence (CSIF) dataset using neural networks

2018 ◽  
Author(s):  
Yao Zhang ◽  
Joanna Joiner ◽  
Seyed Hamed Alemohammad ◽  
Sha Zhou ◽  
Pierre Gentine
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Gross Primary Production ◽  
Terrestrial Ecosystems ◽  
Temporal Variations ◽  
Clear Sky ◽  
Spatio Temporal ◽  
The Individual ◽  
Sky Conditions

Abstract. Satellite-retrieved Solar Induced Chlorophyll Fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurements footprints also hinder the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially continuous SIF (CSIF) datasets at moderate spatio-temporal resolutions (0.05 degree 4-day) during 2001–2016, one for clear-sky conditions and the other one in all-sky conditions. The clear-sky instantaneous CSIF (CSIFclear-inst) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIFall-daily) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite observed SIF and CSIF is mostly caused by the environmental down-regulation on SIFyield, the ratio between OCO-2 SIF and CSIFclear-inst can be an effective indicator of drought stress that is more sensitive than normalized difference vegetation index and enhanced vegetation index. By comparing CSIFall-daily with gross primary production (GPP) estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of GPP-SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two continuous SIF datasets and the derived GPP-SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.

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