Towers, Chambers & UAVs: Exploring the drivers of carbon sink strength at a temperate peatland

2020 ◽  
Author(s):  
Gillian Simpson ◽  
Carole Helfter ◽  
Caroline Nichol ◽  
Tom Wade
Keyword(s):  
Vegetation Index ◽  
New Technologies ◽  
Carbon Sink ◽  
Meteorological Data ◽  
Sink Strength ◽  
Spatial And Temporal Scales ◽  
Environmental Controls ◽  
Spatial Coverage ◽  
Normalised Difference Vegetation Index ◽  
The Impact

<p>Peatlands are terrestrial carbon sinks of global significance, storing an estimated one-third of global soil carbon. Net Ecosystem Exchange (NEE) of carbon dioxide (CO<sub>2</sub>) can however vary substantially on seasonal and inter-annual timescales, with some peatlands switching from a sink to a source of CO<sub>2</sub>. Complex and sometimes competing processes, such as meteorology and phenology, regulate a peatland’s net carbon sink strength. Understanding seasonal and inter-annual variability in NEE requires studying these environmental controls at multiple spatial and temporal scales. The role of vegetation in regulating NEE can be particularly difficult to ascertain at the finer timescales (e.g. seasonal) and at sites with abundant plant diversity, non-uniform distribution and complex micro-topography, such as peatlands. Vegetation surveys are traditionally conducted every few years and, because of this, they might not capture the shorter-term variations that can result from meteorological anomalies such as drought. New technologies, such as Unmanned Aerial Vehicles (UAVs), offer novel opportunities to improve the temporal resolution and spatial coverage of traditional vegetation survey approaches. UAVs are a more flexible, often cheaper alternative to satellite products, which can be used to collect data at the sub-centimetre scale. Such high resolution is particularly valuable in peatland environments, which typically display strong heterogeneity at the micro-site level (< 0.5 m). We employ UAV surveys with a Parrot Sequoia multispectral camera to map vegetation and track its phenology using vegetation indices such as the Normalised Difference Vegetation Index (NDVI) over the course of two growing seasons at a temperate Scottish peatland. By combining this multispectral data with in-situ NEE measurements (closed chambers and eddy-covariance) and meteorological data, this project aims to quantify the impact of weather and phenology on carbon balance at the site. An improved understanding of these two drivers of peatland carbon cycling will allow for better prediction of the impact of climate change at the site.</p>

A fusion of UAVs, digital cameras and micrometeorology to quantify the link between phenology and the carbon balance of a temperate peatland

2021 ◽  
Author(s):  
Gillian Simpson ◽  
Carole Helfter ◽  
Caroline Nichol ◽  
Tom Wade
Keyword(s):  
Climate Change ◽  
Carbon Balance ◽  
Vegetation Index ◽  
Natural Capital ◽  
Carbon Sink ◽  
Sink Strength ◽  
Environmental Controls ◽  
Spatial Coverage ◽  
Normalised Difference Vegetation Index ◽  
The Impact

<p>Peatland ecosystems are historical carbon sinks of global importance, whose management and restoration are becoming an increasingly popular approach to reach climate change targets via natural capital. However, the Net Ecosystem Exchange (NEE) of carbon dioxide (CO<sub>2</sub>) can exhibit substantial variability on seasonal and inter-annual timescales, with some peatlands shifting from being a sink to a source of CO<sub>2 </sub>between years. This variability is due to the complex interaction between factors such as meteorology and phenology, which are both known to control a peatland’s net carbon sink strength. An improved understanding of these two drivers of peatland carbon cycling is needed to allow for better prediction of the impact of climate change on these ecosystems. This task requires us to study these environmental controls at multiple spatial and temporal scales. The role of vegetation in regulating NEE however, can be difficult to determine over shorter timescales (e.g. seasonal) and especially in peatland landscapes, which typically display strong spatial heterogeneity at the microsite scale (< 0.5 m). Digital phenology cameras (PhenoCams) and Unmanned Aerial Vehicles (UAVs), offer novel opportunities to improve the temporal resolution and spatial coverage of traditional vegetation survey approaches. UAVs in particular are a more flexible, often cheaper alternative to satellite products, and can be used to collect data at the sub-centimetre scale. We employ PhenoCam imagery and UAV surveys with a Parrot Sequoia multispectral camera to map vegetation and track its phenology using vegetation indices such as the Normalised Difference Vegetation Index (NDVI) over the course of two growing seasons at Auchencorth Moss, a Scottish temperate peatland. By combining this digital camera imagery with in-situ NEE measurements (closed chambers and eddy-covariance) and meteorological data, we seek to quantify the impact of weather and phenology on carbon balance at the site.</p>

Assessing the impact of seasonal precipitation and temperature on vegetation in a grass-dominated rangeland

2014 ◽  
Vol 36 (2) ◽  
pp. 185 ◽  
Author(s):  
Fang Chen ◽  
Keith T. Weber
Keyword(s):  
Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Vegetation Growth ◽  
Factors Affecting ◽  
Growing Seasons ◽  
Normalised Difference Vegetation Index ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact ◽  
Precipitation And Temperature

Changes in vegetation are affected by many climatic factors and have been successfully monitored through satellite remote sensing over the past 20 years. In this study, the Normalised Difference Vegetation Index (NDVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, was selected as an indicator of change in vegetation. Monthly MODIS composite NDVI at a 1-km resolution was acquired throughout the 2004–09 growing seasons (i.e. April–September). Data describing daily precipitation and temperature, primary factors affecting vegetation growth in the semiarid rangelands of Idaho, were derived from the Surface Observation Gridding System and local weather station datasets. Inter-annual and seasonal fluctuations of precipitation and temperature were analysed and temporal relationships between monthly NDVI, precipitation and temperature were examined. Results indicated NDVI values observed in June and July were strongly correlated with accumulated precipitation (R2 >0.75), while NDVI values observed early in the growing season (May) as well as late in the growing season (August and September) were only moderately related with accumulated precipitation (R2 ≥0.45). The role of ambient temperature was also apparent, especially early in the growing season. Specifically, early growing-season temperatures appeared to significantly affect plant phenology and, consequently, correlations between NDVI and accumulated precipitation. It is concluded that precipitation during the growing season is a better predictor of NDVI than temperature but is interrelated with influences of temperature in parts of the growing season.

scholarly journals Small waterbodies reduce the carbon sink of a polygonal tundra landscape

2021 ◽  
Author(s):  
Lutz Beckebanze ◽  
Zoé Rehder ◽  
David Holl ◽  
Charlotta Mirbach ◽  
Christian Wille ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Carbon Budget ◽  
Carbon Sink ◽  
Open Water ◽  
Methane Emissions ◽  
The Arctic ◽  
Sink Strength ◽  
Small Waterbodies ◽  
Methane Fluxes ◽  
The Impact

Abstract. Arctic permafrost landscapes have functioned as a global carbon sink for millennia. These landscapes are very heterogeneous, and the omnipresent waterbodies are a carbon source within them. Yet, only a few studies focus on the impact of these waterbodies on the landscape carbon budget. We compare carbon dioxide and methane fluxes from small waterbodies to fluxes from the surrounding tundra using eddy covariance measurements from a tower located between a large pond and semi-terrestrial vegetated tundra. When taking the open-water areas of small waterbodies into account, the carbon dioxide sink strength of the landscape was reduced by 11 %. While open-water methane emissions were similar to the tundra emissions, some parts of the studied pond's shoreline exhibited much higher emissions, underlining the high spatial variability of methane emissions. We conclude that gas fluxes from small waterbodies can contribute significantly to the carbon budget of arctic tundra landscapes. Consequently, changes in arctic hydrology and the concomitant changes in the waterbody distribution may substantially impact the overall carbon budget of the Arctic.

The relative importance of environmental factors on the interannual variability of carbon fluxes in the boreal forest

2020 ◽  
Author(s):  
Mariam El-Amine ◽  
Alexandre Roy ◽  
Pierre Legendre ◽  
Oliver Sonnentag
Keyword(s):  
Environmental Factors ◽  
Boreal Forest ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Carbon Sink ◽  
Growing Season ◽  
Carbon Uptake ◽  
Net Ecosystem Productivity ◽  
Ecosystem Productivity ◽  
Environmental Controls

<p>As climate change will cause a more pronounced rise of air temperature in northern high latitudes than in other parts of the world, it is expected that the strength of the boreal forest carbon sink will be altered. To better understand and quantify these changes, we studied the influence of different environmental controls (e.g., air and soil temperatures, soil water content, photosynthetically active radiation, normalized difference vegetation index) on the timing of the start and end of the boreal forest growing season and the net carbon uptake period in Canada. The influence of these factors on the growing season carbon exchanges between the atmosphere and the boreal forest were also evaluated. There is a need to improve the understanding of the role of the length of the growing season and the net carbon uptake period on the strength of the boreal forest carbon sink, as an extension of these periods might not necessarily result in a stronger carbon sink if other environmental factors are not optimal for carbon sequestration or enhance respiration.</p><p>Here, we used 31 site-years of observation over three Canadian boreal forest stands: Eastern, Northern and Southern Old Black Spruce in Québec, Manitoba and Saskatchewan, respectively. Redundancy analyses were used to highlight the environmental controls that correlate the most with the annual net ecosystem productivity and the start and end of the growing season and the net carbon uptake period. Preliminary results show that the timing at which the air temperature becomes positive correlates the most strongly with the start of the net carbon uptake period (r = 0.70, p < 0.001) and the start of the growing season (r = 0.55, p < 0.01). Although the increase of the normalized difference vegetation index also correlates with the start of these periods, a thorough examination of this result shows that the latter happens well before the former. No dependency between any environmental control and the end of the net carbon uptake period was identified. Also, the annual net ecosystem productivity is highly correlated with the length of the net carbon uptake period (r = 0.54, p < 0.01). Other environmental controls such as annual precipitations, the mean annual soil temperature or the maximum yearly normalized difference vegetation index have a smaller impact on the annual net ecosystem productivity. By extending the dataset to include forest stands that represent a wider climate and permafrost variability, we will examine the generalizability of these results.</p>

scholarly journals Analysis of meteorological variations on wheat yield and its estimation using remotely sensed data. A case study of selected districts of Punjab Province, Pakistan (2001-14)

2017 ◽  
Vol 12 (3) ◽  
Author(s):  
Rafia Mumtaz ◽  
Shahbaz Baig ◽  
Iram Fatima
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Crop Yield ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Least Square ◽  
Wheat Crop ◽  
Main Challenge ◽  
Normalised Difference Vegetation Index ◽  
The Impact

Land management for crop production is an essential human activity that supports life on Earth. The main challenge to be faced by the agriculture sector in coming years is to feed the rapidly growing population while maintaining the key resources such as soil fertility, efficient land use, and water. Climate change is also a critical factor that impacts agricultural production. Among others, a major effect of climate change is the potential alterations in the growth cycle of crops which would likely lead to a decline in the agricultural output. Due to the increasing demand for proper agricultural management, this study explores the effects of meteorological variation on wheat yield in Chakwal and Faisalabad districts of Punjab, Pakistan and used normalised difference vegetation index (NDVI) as a predictor for yield estimates. For NDVI data (2001-14), the NDVI product of Moderate Resolution Imaging spectrometer (MODIS) 16-day composites data has been used. The crop area mapping has been realised by classifying the satellite data into different land use/land covers using iterative self-organising (ISO) data clustering. The land cover for the wheat crop was mapped using a crop calendar. The relation of crop yield with NDVI and the impact of meteorological parameters on wheat growth and its yield has been analysed at various development stages. A strong correlation of rainfall and temperature was found with NDVI data, which determined NDVI as a strong predictor of yield estimation. The wheat yield estimates were obtained by linearly regressing the reported crop yield against the time series of MODIS NDVI profiles. The wheat NDVI profiles have shown a parabolic pattern across the growing season, therefore parabolic least square fit (LSF) has been applied prior to linear regression. The coefficients of determination (<em>R</em><sup>2</sup>) between the reported and estimated yield was found to be 0.88 and 0.73, respectively, for Chakwal and Faisalabad. This indicates that the method is capable of providing yield estimates with competitive accuracies prior to crop harvest, which can significantly aid the policy guidance and contributes to better and timely decisions.

The validation of a model estimating the Leaf Area Index of grasslands in southern China

2013 ◽  
Vol 35 (3) ◽  
pp. 245 ◽  
Author(s):  
Chengming Sun ◽  
Zhengguo Sun ◽  
Tao Liu ◽  
Doudou Guo ◽  
Shaojie Mu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Southern China ◽  
Carbon Sink ◽  
Measurement Data ◽  
Light Transmittance ◽  
Area Index ◽  
Normalised Difference Vegetation Index

In order to estimate the leaf area index (LAI) over large areas in southern China, this paper analysed the relationships between normalised difference vegetation index (NDVI) and the vegetation light transmittance and the extinction coefficient based on the use of moderate resolution imaging spectroradiometer data. By using the improved Beer–Lambert Law, a model was constructed to estimate the LAI in the grassy mountains and slopes of southern China with NDVI as the independent variable. The model was validated with field measurement data from different locations and different years in the grassland mountains and slopes of southern China. The results showed that there was a good correlation between the simulated and observed LAI values, and the values of R2 achieved were high. The relative root mean squared error was between 0.109 and 0.12. This indicated that the model was reliable. The above results provided the theoretical basis for the effective management of the grassland resources in southern China and the effective estimation of grassland carbon sink.

Decoupling the effect of the forest pest damage from the effects of meteorology using space-borne remote sensing and modelling

2021 ◽  
Author(s):  
Hrvoje Marjanovic ◽  
Aniko Kern
Keyword(s):  
Climate Change ◽  
Greenhouse Gas Emission ◽  
Carbon Sink ◽  
Meteorological Data ◽  
Environmental Data ◽  
Forest Pest ◽  
Modis Ndvi ◽  
Pest Damage ◽  
Lace Bug ◽  
The Impact

&lt;p&gt;The EU&amp;#8217;s climate change mitigation plans of 55% reduction in greenhouse gas emission by 2030 and reaching climate-neutrality by 2050 rely significantly on maintaining and increasing the carbon sink in European forests. In addition to direct consequences of climate change and ageing forests, this sink is becoming threatened by the new invasive forest pests which can decrease forest productivity. The Oak lace bug (Corythucha arcuata, Say 1832), native to North America, is a new invasive species rapidly spreading since 2012 from the east to the west of Europe. The oak lace bug (OLB) after establishment in an area shows no signs of retreating and negatively affects the tree photosynthetic capacity by feeding on leaf sap. The consequences of such new and persistent pest, which are not imminently life-threatening to trees but are long-lasting, have yet to be determined.&lt;/p&gt;&lt;p&gt;In our study, we used remotely sensed MODIS NDVI (MOD09Q1), gridded meteorological data (FORESEE), soil water content (ERA5 Land), available national forest management and land cover data to develop methods for detecting the presence and the assessment of the impact of the OLB. The study was focused on the modelling tools to decouple the effects caused by the environmental variables from the pest damage on the measured NDVI. To this different NDVI models were created based on the Least Absolute Shrinkage and Selection Operator (LASSO) technique and the most influential periods, to support accurate forest pest detection. We investigated forests containing oak trees in the transboundary area of Hungary and Croatia. The results show that the LASSO technique is a promising tool in NDVI modelling using meteorological and environmental data. The performance of the models based on the Most Influential Periods (MIP) of the different variables showed just slightly worse results, although their application is more intuitive. In the case of the OLB, the damage assessment results with the LASSO and MIP methods showed that the pest-caused NDVI decrease in pure oak stands during the late August to early September period can be as much as -14.5% and -15.6%, respectively.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Asknowledgments:&lt;/p&gt;&lt;p&gt;The research has been supported by the Croatian Science Foundation project MODFLUX (HRZZ IP-2019-04-6325), by the Hungarian Scientific Research Fund (OTKA FK-128709) and by the J&amp;#225;nos Bolyai Research Scholarship of the Hungarian Academy of Sciences.&lt;/p&gt;

Floodplain forest carbon exchange – a micrometeorological point of view

2020 ◽  
Author(s):  
Natalia Kowalska ◽  
Georg Jocher ◽  
Ladislav Šigut ◽  
Marian Pavelka
Keyword(s):  
Forest Canopy ◽  
Forest Ecosystems ◽  
Carbon Sink ◽  
Forest Carbon ◽  
Floodplain Forest ◽  
Mean Annual Temperature ◽  
Sink Strength ◽  
Spatial And Temporal Variability ◽  
Carbon Exchange ◽  
The Impact

&lt;p&gt;Since the eddy covariance (EC) method became a key method for measurements of the energy and greenhouse gas exchange between ecosystems and the atmosphere, a large number of studies was conducted to understand the mechanisms driving the carbon exchange in forest ecosystems. In recent years, case studies further focused on testing and validating the applicability of the EC technique above forest ecosystems, also assessing the spatial and temporal variability of sub canopy fluxes. These studies led to the conclusion that there is a high probability of overestimating the forest carbon sink strength with EC measurements above the forest canopy only, as these measurements may miss respiration components from within and below the canopy due to insufficient mixing across the canopy. Additional below canopy EC measurements were suggested to tackle this problem and to get information about potential decoupling between below and above forest canopy air masses as well as potentially missing respiration components in the above canopy derived signal.&lt;/p&gt;&lt;p&gt;The overall goal of the study here is to derive an as detailed as possible understanding of the carbon exchange in Lan&amp;#382;hot floodplain forest with the help of concurrent EC measurements below and above the forest canopy. Lan&amp;#382;hot floodplain forest is situated 6.5 km north of the confluence of the Morava and Thaya rivers in Czech Republic (48.6815483 N, 16.9463317 E). The long-term average annual precipitation at this site is around 517 mm and the mean annual temperature is 9.5 &amp;#176;C. The average groundwater level is -2.7 m. Since a long time flooding occurs here very rarely, the last flooding event was in 2013. In addition, the site is hydrologically managed. Consequently, the water regime of the site changed over the years and represents nowadays relatively dry conditions for such type of ecosystem.&lt;/p&gt;&lt;p&gt;To reach our research goal we evaluate different single- and two-level filtering strategies of the above canopy derived carbon exchange values and the impact of these filterings on the annual ecosystem carbon exchange rates. Our hypothesis is that conventional single-level EC flux filtering strategies like the u&lt;sub&gt;*&lt;/sub&gt;-filtering might not be sufficient to fully capture the carbon exchange of the studied floodplain forest ecosystem. We further hypothesize that additional below canopy EC measurements are mandatory to achieve unbiased forest carbon exchange values with the EC technique.&lt;/p&gt;

scholarly journals Meteorology-normalized impact of the COVID-19 lockdown upon NO<sub>2</sub> pollution in Spain

2020 ◽  
Vol 20 (18) ◽  
pp. 11119-11141 ◽  
Author(s):  
Hervé Petetin ◽  
Dene Bowdalo ◽  
Albert Soret ◽  
Marc Guevara ◽  
Oriol Jorba ◽  
...  
Keyword(s):  
Hospital Admissions ◽  
Meteorological Data ◽  
Phase Iii ◽  
Anthropogenic Emissions ◽  
Substantial Part ◽  
Urban Background ◽  
Spatial And Temporal Scales ◽  
Mixing Ratios ◽  
Business As Usual ◽  
The Impact

Abstract. The spread of the new coronavirus SARS-CoV-2 that causes COVID-19 forced the Spanish Government to implement extensive lockdown measures to reduce the number of hospital admissions, starting on 14 March 2020. Over the following days and weeks, strong reductions in nitrogen dioxide (NO2) pollution were reported in many regions of Spain. A substantial part of these reductions was obviously due to decreased local and regional anthropogenic emissions. Yet, the confounding effect of meteorological variability hinders a reliable quantification of the lockdown's impact upon the observed pollution levels. Our study uses machine-learning (ML) models fed by meteorological data along with other time features to estimate the “business-as-usual” NO2 mixing ratios that would have been observed in the absence of the lockdown. We then quantify the so-called meteorology-normalized NO2 reductions induced by the lockdown measures by comparing the estimated business-as-usual values with the observed NO2 mixing ratios. We applied this analysis for a selection of urban background and traffic stations covering the more than 50 Spanish provinces and islands. The ML predictive models were found to perform remarkably well in most locations, with an overall bias, root mean square error and correlation of +4 %, 29 % and 0.86, respectively. During the period of study, from the enforcement of the state of alarm in Spain on 14 March to 23 April, we found the lockdown measures to be responsible for a 50 % reduction in NO2 levels on average over all provinces and islands. The lockdown in Spain has gone through several phases with different levels of severity with respect to mobility restrictions. As expected, the meteorology-normalized change in NO2 was found to be stronger during phase II (the most stringent phase) and phase III of the lockdown than during phase I. In the largest agglomerations, where both urban background and traffic stations were available, a stronger meteorology-normalized NO2 change is highlighted at traffic stations compared with urban background sites. Our results are consistent with foreseen (although still uncertain) changes in anthropogenic emissions induced by the lockdown. We also show the importance of taking the meteorological variability into account for accurately assessing the impact of the lockdown on NO2 levels, in particular at fine spatial and temporal scales. Meteorology-normalized estimates such as those presented here are crucial to reliably quantify the health implications of the lockdown due to reduced air pollution.

scholarly journals Maximum, Minimum, and Daily Air Temperature Range in Orchards: What Do Observations Reveal?

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1279
Author(s):  
Ana Firanj Sremac ◽  
Branislava Lalic ◽  
Joan Cuxart ◽  
Milena Marcic
Keyword(s):  
Meteorological Data ◽  
Reanalysis Data ◽  
Study Data ◽  
Daily Temperature ◽  
Daily Temperature Range ◽  
Phenological Data ◽  
Temperature Range ◽  
Spatial Coverage ◽  
The Impact ◽  
Maximum Minimum

This study was designed to better understand vegetation’s impact on air maximum (Tmax), minimum (Tmin), and daily temperature range (DTR), as well as seasonality and variability. We selected a flat, under synoptic-scale, northern Serbian region with an operational network of automated weather stations (AWS) for the study. Data were collected directly from the eighteen AWSs placed in the orchard canopy during 2013–2018. Meteorological data, plant phenological data in the form of the BBCH scale, and orchards’ soil characteristics data were collected. Environmental factors influencing the temperature were classified as static (slow or unchangeable) and dynamic (fast-changing). The impact of both factors on maximum, minimum, and daily temperature range and its variability were analyzed. Results show that static factors (like soil texture) affect the annual variation of Tmax, Tmin, and DTR rather than its variability over the season. The dynamic factors, mainly coming from the plant’s phenology, substantially affected the seasonal variability of these variables. Studies like this suffer from missing data and sparse spatial coverage by the AWS network. Therefore, the alternatives of orchard micrometeorological data, nearest climatological station, and ERA5-Land reanalysis data are tested. Both data sets showcased limitations in their applicability, while reanalysis data deviated more from the in-situ measurements, both seasonally and regionally.

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