| 1. |
- Cai, Zhanzhang, et al.
(författare)
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Modelling Daily Gross Primary Productivity with Sentinel-2 Data in the Nordic Region-Comparison with Data from MODIS
- 2021
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 13:3
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Tidskriftsartikel (refereegranskat)abstract
- The high-resolution Sentinel-2 data potentially enable the estimation of gross primary productivity (GPP) at finer spatial resolution by better capturing the spatial variation in a heterogeneous landscapes. This study investigates the potential of 10 m resolution reflectance from the Sentinel-2 Multispectral Instrument to improve the accuracy of GPP estimation across Nordic vegetation types, compared with the 250 m and 500 m resolution reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS). We applied linear regression models with inputs of two-band enhanced vegetation index (EVI2) derived from Sentinel-2 and MODIS reflectance, respectively, together with various environmental drivers to estimate daily GPP at eight Nordic eddy covariance (EC) flux tower sites. Compared with the GPP from EC measurements, the accuracies of modelled GPP were generally high (R-2 = 0.84 for Sentinel-2; R-2 = 0.83 for MODIS), and the differences between Sentinel-2 and MODIS were minimal. This demonstrates the general consistency in GPP estimates based on the two satellite sensor systems at the Nordic regional scale. On the other hand, the model accuracy did not improve by using the higher spatial-resolution Sentinel-2 data. More analyses of different model formulations, more tests of remotely sensed indices and biophysical parameters, and analyses across a wider range of geographical locations and times will be required to achieve improved GPP estimations from Sentinel-2 satellite data.
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| 2. |
- Cai, Zhanzhang, et al.
(författare)
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Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data
- 2017
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 9:12
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 3. |
- Conradt, Tobias, et al.
(författare)
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Cross-sectoral impacts of the 2018–2019 Central European drought and climate resilience in the German part of the Elbe River basin
- 2023
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Ingår i: Regional Environmental Change. - : Springer Science and Business Media LLC. - 1436-378X .- 1436-3798. ; 23, s. 1-18
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Tidskriftsartikel (refereegranskat)abstract
- The 2018–2019 Central European drought was probably the most extreme in Germany since the early sixteenth century. We assess the multiple consequences of the drought for natural systems, the economy and human health in the German part of the Elbe River basin, an area of 97,175 km2 including the cities of Berlin and Hamburg and contributing about 18% to the German GDP. We employ meteorological, hydrological and socio-economic data to build a comprehensive picture of the drought severity, its multiple effects and cross-sectoral consequences in the basin. Time series of different drought indices illustrate the severity of the 2018–2019 drought and how it progressed from meteorological water deficits via soil water depletion towards low groundwater levels and river runoff, and losses in vegetation productivity. The event resulted in severe production losses in agriculture (minus 20–40% for staple crops) and forestry (especially through forced logging of damaged wood: 25.1 million tons in 2018–2020 compared to only 3.4 million tons in 2015–2017), while other economic sectors remained largely unaffected. However, there is no guarantee that this socio-economic stability will be sustained in future drought events; this is discussed in the light of 2022, another dry year holding the potential for a compound crisis. Given the increased probability for more intense and long-lasting droughts in most parts of Europe, this example of actual cross-sectoral drought impacts will be relevant for drought awareness and preparation planning in other regions.
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| 4. |
- Eklundh, Lars, et al.
(författare)
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An Optical Sensor Network for Vegetation Phenology Monitoring and Satellite Data Calibration
- 2011
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 11:8, s. 7678-7709
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Tidskriftsartikel (refereegranskat)abstract
- We present a network of sites across Fennoscandia for optical sampling of vegetation properties relevant for phenology monitoring and satellite data calibration. The network currently consists of five sites, distributed along an N-S gradient through Sweden and Finland. Two sites are located in coniferous forests, one in a deciduous forest, and two on peatland. The instrumentation consists of dual-beam sensors measuring incoming and reflected red, green, NIR, and PAR fluxes at 10-min intervals, year-round. The sensors are mounted on separate masts or in flux towers in order to capture radiation reflected from within the flux footprint of current eddy covariance measurements. Our computations and model simulations demonstrate the validity of using off-nadir sampling, and we show the results from the first year of measurement. NDVI is computed and compared to that of the MODIS instrument on-board Aqua and Terra satellite platforms. PAR fluxes are partitioned into reflected and absorbed components for the ground and canopy. The measurements demonstrate that the instrumentation provides detailed information about the vegetation phenology and variations in reflectance due to snow cover variations and vegetation development. Valuable information about PAR absorption of ground and canopy is obtained that may be linked to vegetation productivity.
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| 5. |
- Huang, Ke, et al.
(författare)
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The confounding effect of snow cover on assessing spring phenology from space : A new look at trends on the Tibetan Plateau
- 2021
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 0048-9697. ; 756
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Tidskriftsartikel (refereegranskat)abstract
- The Tibetan Plateau is the highest and largest plateau in the world, hosting unique alpine grassland and having a much higher snow cover than any other region at the same latitude, thus representing a “climate change hot-spot”. Land surface phenology characterizes the timing of vegetation seasonality at the per-pixel level using remote sensing systems. The impact of seasonal snow cover variations on land surface phenology has drawn much attention; however, there is still no consensus on how the remote sensing estimated start of season (SOS) is biased by the presence of preseason snow cover. Here, we analyzed SOS assessments from time series of satellite derived vegetation indices and solar-induced chlorophyll fluorescence (SIF) during 2003–2016 for the Tibetan Plateau. We evaluated satellite-based SOS with field observations and gross primary production (GPP) from eddy covariance for both snow-free and snow covered sites. SOS derived from SIF was highly correlated with field data (R2 = 0.83) and also the normalized difference phenology index (NDPI) performed well for both snow free (R2 = 0.77) and snow covered sites (R2 = 0.73). On the contrary, normalized difference vegetation index (NDVI) correlates only weakly with field data (R2 = 0.35 for snow free and R2 = 0.15 for snow covered sites). We further found that an earlier end of the snow season caused an earlier estimate of SOS for the Tibetan Plateau from NDVI as compared to NDPI. Our research therefore adds new evidence to the ongoing debate supporting the view that the claimed advance in land surface SOS over the Tibetan Plateau is an artifact from snow cover changes. These findings improve our understanding of the impact of snow on land surface phenology in alpine ecosystems, which can further improve remote sensing based land surface phenology assessments in snow-influenced ecosystems.
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| 6. |
- Jin, Hongxiao, et al.
(författare)
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A physically based vegetation index for improved monitoring of plant phenology
- 2014
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 152, s. 512-525
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Tidskriftsartikel (refereegranskat)abstract
- Using a spectral vegetation index (VI) is an efficient approach for monitoring plant phenology from remotely-sensed data. However, the quantitative biophysical meaning of most VIs is still unclear, and, particularly at high northern latitudes characterized by low green biomass renewal rate and snow-affected VI signals, it is difficult to use them for tracking seasonal vegetation growth and retrieving phenology. In this study we propose a physically-based new vegetation index for characterizing terrestrial vegetation canopy green leaf area dynamics: the plant phenology index (PPI). PPI is derived from the solution to a radiative transfer equation, is computed from red and near-infrared (NIR) reflectance, and has a nearly linear relationship with canopy green leaf area index (LAI), enabling it to depict canopy foliage density well. This capability is verified with stacked-leaf measurements, canopy reflectance model simulations, and field LAI measurements from international sites. Snow influence on PPI is shown by modeling and satellite observations to be less severe than on the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI), while soil brightness variations in general have moderate influence on PPI. Comparison of satellite-derived PPI to ground observations of plant phenology and gross primary productivity (GPP) shows strong similarity of temporal patterns over several Nordic boreal forest sites. The proposed PPI can thus serve as an efficient tool for estimating plant canopy growth, and will enable improved vegetation monitoring, particularly of evergreen needle-leaf forest phenology at high northern latitudes.
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| 7. |
- Jin, Hongxiao, et al.
(författare)
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Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index
- 2017
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 198, s. 203-212
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Tidskriftsartikel (refereegranskat)abstract
- Land surface phenology is frequently derived from remotely sensed data. However, over regions with seasonal snow cover, remotely-sensed land surface phenology may be dominated by snow seasonality, rather than showing true plant phenology. Overlooking snow influences may lead to inaccurate plant phenology estimation, and consequently to misinterpretation of climate-vegetation interactions. To address the problem we apply the recently developed plant phenology index (PPI) to Moderate Resolution Imaging Spectroradiometer (MODIS) data for estimating plant phenology metrics over northern Europe. We compare PPI-derived start and end of the growing season with ground observations by professionals (6 sites) and nonprofessional citizens (378 sites), with phenology metrics derived from gross primary productivity (GPP, 18 sites), and with data on the timing of snow cover. These data are also compared with land surface phenology metrics derived from the normalized difference vegetation index (NDVI) using the same MODIS data. We find that the PPI-retrieved plant phenology agrees with ground observations and GPP-derived phenology, and that the NDVI-derived phenology to a large extent agrees with the end-of-snowmelt for the start-of-season and the start-of-snowing for the end-of-season. PPI is thereby useful for more accurate estimation of plant phenology from remotely sensed data over northern Europe and other regions with seasonal snow cover.
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| 8. |
- Jin, Hongxiao, et al.
(författare)
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Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application
- 2021
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13:10
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Tidskriftsartikel (refereegranskat)abstract
- Miniature hyperspectral and thermal cameras onboard lightweight unmanned aerial vehicles (UAV) bring new opportunities for monitoring land surface variables at unprecedented fine spatial resolution with acceptable accuracy. This research applies hyperspectral and thermal imagery from a drone to quantify upland rice productivity and water use efficiency (WUE) after biochar application in Costa Rica. The field flights were conducted over two experimental groups with bamboo biochar (BC1) and sugarcane biochar (BC2) amendments and one control (C) group without biochar application. Rice canopy biophysical variables were estimated by inverting a canopy radiative transfer model on hyperspectral reflectance. Variations in gross primary productivity (GPP) and WUE across treatments were estimated using light-use efficiency and WUE models respectively from the normalized difference vegetation index (NDVI), canopy chlorophyll content (CCC), and evapotranspiration rate. We found that GPP was increased by 41.9 +/- 3.4% in BC1 and 17.5 +/- 3.4% in BC2 versus C, which may be explained by higher soil moisture after biochar application, and consequently significantly higher WUEs by 40.8 +/- 3.5% in BC1 and 13.4 +/- 3.5% in BC2 compared to C. This study demonstrated the use of hyperspectral and thermal imagery from a drone to quantify biochar effects on dry cropland by integrating ground measurements and physical models.
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| 9. |
- Jin, Hongxiao, et al.
(författare)
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Higher vegetation sensitivity to meteorological drought in autumn than spring across European biomes
- 2023
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Ingår i: Communications Earth and Environment. - 2662-4435. ; 4
-
Tidskriftsartikel (refereegranskat)abstract
- Europe has experienced severe drought events in recent decades, posing challenges to understand vegetation responses due to diverse vegetation distribution, varying growth stages, different drought characteristics, and concurrent hydroclimatic factors. To analyze vegetation response to meteorological drought, we employed multiple vegetation indicators across European biomes. Our findings reveal that vegetation sensitivity to drought increases as the canopy develops throughout the year, with sensitivities from −0.01 in spring to 0.28 in autumn and drought-susceptible areas from 18.5 to 57.8% in Europe. Soil water shortage exacerbates vegetation-drought sensitivity temporally, while its spatial impact is limited. Vegetation-drought sensitivity strongly correlates with vapor pressure deficit and partially with atmospheric CO2 concentration. These results highlight the spatiotemporal variations in vegetation-drought sensitivities and the influence of hydroclimatic factors. The findings enhance our understanding of vegetation response to drought and the impact of concurrent hydroclimatic factors, providing valuable sub-seasonal information for water management and drought preparedness.
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| 10. |
- Jin, Hongxiao, et al.
(författare)
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In Situ Calibration of Light Sensors for Long-Term Monitoring of Vegetation
- 2015
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - 0196-2892. ; 53:6, s. 3405-3416
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Tidskriftsartikel (refereegranskat)abstract
- Light sensors are increasingly used to monitor vegetation growing status by measuring reflectance or transmittance in multispectral or photosynthetically active radiation (PAR) bands. The measurements are then used to estimate vegetation indices or the fraction of absorbed PAR (FPAR) in a continuous and long-term manner and to serve as inputs to environmental monitoring and calibration/validation data for satellite remote sensing. However, light-sensor calibration is often overlooked or not properly attended to, which leads to difficulties when comparing the measurement results across sites and through time. In this paper, we investigate a practical and accurate user-level in situ calibration method in daylight. The calibration of a sensor pair is made for measuring either bihemispherical reflectance or hemispherical-conical reflectance, which are the two most common ground-based spectral measurements. Procedures and considerations are suggested for user calibration. We also provide a method for calibrating and measuring a single-sensor reflectance-derived Normalized Difference Vegetation Index (NDVI) from red and near-infrared bands. The calibration error propagation is analyzed, and the induced uncertainties in vegetation reflectance and in the NDVI are evaluated. The analysis and field measurements show that the NDVI estimated from a user calibration factor can be as accurate as, or even more accurate than, the manufacturer's calibration. The in situ calibration described here remedies the situation where reflectance for large field-of-view sensors cannot be always estimated from the manufacturer's calibration. The method developed in this paper may help improve the reliability of long-term field spectral measurements and contributes to the near-surface remote sensing of vegetation.
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| 11. |
- Jin, Hongxiao, et al.
(författare)
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New satellite-based estimates show significant trends in spring phenology and complex sensitivities to temperature and precipitation at northern European latitudes
- 2019
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Ingår i: International journal of biometeorology. - : Springer. - 0020-7128 .- 1432-1254. ; 63:6, s. 763-775
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Tidskriftsartikel (refereegranskat)abstract
- Recent climate warming has altered plant phenology at northern European latitudes, but conclusions regarding the spatial patterns of phenological change and relationships with climate are still challenging as quantitative estimates are strongly diverging. To generate consistent estimates of broad-scale spatially continuous spring plant phenology at northern European latitudes (>50 degrees N) from 2000 to 2016, we used a novel vegetation index, the plant phenology index (PPI), derived from MODerate-resolution Imaging Spectroradiometer (MODIS) data. To obtain realistic and strong estimates, the phenology trends and their relationships with temperature and precipitation over the past 17years were analyzed using a panel data method. We found that in the studied region the start of the growing season (SOS) has on average advanced by 0.30dayyear(-1). The SOS showed an overall advancement rate of 2.47day degrees C-1 to spring warming, and 0.18daycm(-1) to decreasing precipitation in spring. The previous winter and summer temperature had important effects on the SOS but were spatially heterogeneous. Overall, the onset of SOS was delayed 0.66day degrees C-1 by winter warming and 0.56day degrees C-1 by preceding summer warming. The precipitation in winter and summer influenced the SOS in a relatively weak and complex manner. The findings indicate rapid recent phenological changes driven by combined seasonal climates in northern Europe. Previously unknown spatial patterns of phenological change and relationships with climate drivers are presented that improve our capacity to understand and foresee future climate effects on vegetation.
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| 12. |
- Jin, Hongxiao
(författare)
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Remote sensing phenology at European northern latitudes - From ground spectral towers to satellites
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Plant phenology exerts major influences on carbon, water, and energy exchanges between atmosphere and ecosystems, provides feedbacks to climate, and affects ecosystem functioning and services. Great efforts have been spent in studying plant phenology over the past decades, but there are still large uncertainties and disputations in phenology estimation, trends, and its climate sensitivities. This thesis aims to reduce these uncertainties through analyzing ground spectral sampling, developing methods for in situ light sensor calibration, and exploring a new spectral index for reliable retrieval of remote sensing phenology and climate sensitivity estimation at European northern latitudes. The ground spectral towers use light sensors of either nadir or off-nadir viewing to measure reflected radiation, yet how plants in the sensor view contribute differently to the measured signals, and necessary in situ calibrations are often overlooked, leading to great uncertainties in ground spectral sampling of vegetation. It was found that the ground sampling points in the sensor view follow a Cauchy distribution, which is further modulated by the sensor directional response function. We proposed in situ light sensor calibration methods and showed that the user in situ calibration is more reliable than manufacturer’s lab calibration when our proposed calibration procedures are followed. By taking the full advantages of more reliable and standardized reflectance, we proposed a plant phenology vegetation index (PPI), which is derived from a radiative transfer equation and uses red and near infrared reflectance. PPI shows good linearity with canopy green leaf area index, and is correlated with gross primary productivity, better than other vegetation indices in our test. With suppressed snow influences, PPI shows great potentials for retrieving phenology over coniferous-dominated boreal forests. PPI was used to retrieve plant phenology from MODIS nadir BRDF-adjusted reflectance at European northern latitudes for the period 2000-2014. We estimated the trend of start of growing season (SOS), end of growing season (EOS), length of growing season (LOS), and the PPI integral for the time span, and found significant changes in most part of the region, with an average rate of -0.39 days·year-1 in SOS, 0.48 days·year-1 in EOS, 0.87 days·year-1 in LOS, and 0.79%·year-1 in the PPI integral over the past 15 years. We found that the plant phenology was significantly affected by climate in most part of the region, with an average sensitivity to temperature: SOS at -3.43 days·°C-1, EOS at 1.27 days·°C-1, LOS at 3.16 days·°C-1, and PPI integral at 2.29 %·°C-1, and to precipitation: SOS at 0.28 days∙cm-1, EOS at 0.05 days∙cm-1, LOS at 0.04 days∙cm-1, and PPI integral at -0.07%∙cm-1. These phenology variations were significantly related to decadal variations of atmospheric circulations, including the North Atlantic Oscillation and the Arctic Oscillation. The methods developed in this thesis can help to improve the reliability of long-term field spectral measurements and to reduce uncertainties in remote sensing phenology retrieval and climate sensitivity estimation.
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| 13. |
- Junttila, Sofia, et al.
(författare)
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Estimating local-scale forest GPP in Northern Europe using Sentinel-2: Model comparisons with LUE, APAR, the plant phenology index, and a light response function
- 2023
-
Ingår i: Science of Remote Sensing. - : Elsevier BV. - 2666-0172. ; 7
-
Tidskriftsartikel (refereegranskat)abstract
- Northern forest ecosystems make up an important part of the global carbon cycle. Hence, monitoring local-scale gross primary production (GPP) of northern forest is essential for understanding climatic change impacts on terrestrial carbon sequestration and for assessing and planning management practices. Here we evaluate and compare four methods for estimating GPP using Sentinel-2 data in order to improve current available GPP es-timates: four empirical regression models based on either the 2-band Enhanced Vegetation Index (EVI2) or the plant phenology index (PPI), an asymptotic light response function (LRF) model, and a light-use efficiency (LUE) model using the MOD17 algorithm. These approaches were based on remote sensing vegetation indices, air temperature (Tair), vapor pressure deficit (VPD), and photosynthetically active radiation (PAR). The models were parametrized and evaluated using in-situ data from eleven forest sites in North Europe, covering two common forest types, evergreen needleleaf forest and deciduous broadleaf forest. Most of the models gave good agreement with eddy covariance-derived GPP. The VI-based regression models performed well in evergreen needleleaf forest (R2 = 0.69-0.78, RMSE = 1.97-2.28 g C m 2 d 1, and NRMSE = 9-11.0%, eight sites), whereas the LRF and MOD17 performed slightly worse (R2 = 0.65 and 0.57, RMSE = 2.49 and 2.72 g C m 2 d 1, NRMSE = 12 and 13.0%, respectively). In deciduous broadleaf forest all models, except the LRF, showed close agreements with the observed GPP (R2 = 0.75-0.80, RMSE = 2.23-2.46 g C m 2 d 1, NRMSE = 11-12%, three sites). For the LRF model, R2 = 0.57, RMSE = 3.21 g C m 2 d 1, NRMSE = 16%. The results highlighted the necessity of improved models in evergreen needleleaf forest where the LUE approach gave poorer results., The simplest regression model using only PPI performed well beside more complex models, suggesting PPI to be a process indicator directly linked with GPP. All models were able to capture the seasonal dynamics of GPP well, but underesti-mation of the growing season peaks were a common issue. The LRF was the only model tending to overestimate GPP. Estimation of interannual variability in cumulative GPP was less accurate than the single-year models and will need further development. In general, all models performed well on local scale and demonstrated their feasibility for upscaling GPP in northern forest ecosystems using Sentinel-2 data.
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| 14. |
- Köppl, Christian Josef, et al.
(författare)
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Hyperspectral reflectance measurements from UAS under intermittent clouds: Correcting irradiance measurements for sensor tilt
- 2021
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 267, s. 1-1
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Tidskriftsartikel (refereegranskat)abstract
- One great advantage of optical hyperspectral remote sensing from unmanned aerial systems (UAS) compared to satellite missions is the possibility to fly and collect data below clouds. The most typical scenario is flying below intermittent clouds and under turbulent conditions, which causes tilting of the platform. This study aims to advance hyperspectral imaging from UAS in most weather conditions by addressing two challenges: (i) the radiometric and spectral calibrations of miniaturized hyperspectral sensors; and (ii) tilting effects on measured downwelling irradiance. We developed a novel method to correct the downwelling irradiance data for tilting effects. It uses a hybrid approach of minimizing measured irradiance variations for constant irradiance periods and spectral unmixing, to calculate the spectral diffuse irradiance fraction for all irradiance measurements within a flight. It only requires the platform's attitude data and a standard incoming light sensor. We demonstrated the method at the Palo Verde National Park wetlands in Costa Rica, a highly biodiverse area. Our results showed that the downwelling irradiance correction method reduced systematic shifts caused by a change in flight direction of the UAS, by 87% and achieving a deviation of 2.78% relative to a on ground reference in terms of broadband irradiance. High frequency (< 3 s) irradiance variations caused by high-frequency tilting movements of the UAS were reduced by up to 71%. Our complete spectral and radiometric calibration and irradiance correction can significantly remove typical striped illumination artifacts in the surface reflectance-factor map product. The possibility of collecting precise hyperspectral reflectance-factor data from UAS under varying cloud cover makes it more operational for environmental monitoring or precision agriculture applications, being an important step in advancing hyperspectral imaging from UAS.
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| 15. |
- Olsson, Per-Ola, et al.
(författare)
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Mapping the reduction in gross primary productivity in subarctic birch forests due to insect outbreaks
- 2017
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Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4189. ; 14, s. 1703-1719
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Tidskriftsartikel (refereegranskat)abstract
- It is projected that forest disturbances, such as insect outbreaks, will have an increasingly negative impact on forests with a warmer climate. These disturbance events can have a substantial impact on forests' ability to absorb atmospheric CO2, and may even turn forests from carbon sinks into carbon sources; hence, it is important to develop methods both to monitor forest disturbances and to quantify the impact of these disturbance events on the carbon balance. In this study we present a method to monitor insect-induced defoliation in a subarctic birch forest in northern Sweden, and to quantify the impact of these outbreaks on gross primary productivity (GPP). Since frequent cloud cover in the study area requires data with high temporal resolution and limits the use of finer spatial resolution sensors such as Landsat, defoliation was mapped with remote sensing data from the MODIS sensor with 250 m × 250 m spatial resolution. The impact on GPP was estimated with a light use efficiency (LUE) model that was calibrated with GPP data obtained from eddy covariance (EC) measurements from 5 years with undisturbed birch forest and 1 year with insect-induced defoliation. Two methods were applied to estimate the impact on GPP: (1) applying a GPP reduction factor derived from EC measured GPP to estimate GPP loss, and (2) running a LUE model for both undisturbed and defoliated forest and deriving the differences in modelled GPP. In the study area of 100 km2 the results suggested a substantial setback to the carbon uptake: an average decrease in regional GPP over the three outbreak years (2004, 2012, and 2013) was estimated to 15 ± 5 Gg C yr−1, compared to the mean regional GPP of 40 ± 12 Gg C yr−1 for the 5 years without defoliation, i.e. 38 %. In the most severe outbreak year (2012), 76 % of the birch forests were defoliated, and annual regional GPP was merely 50 % of GPP for years without disturbances. The study has generated valuable data on GPP reduction, and demonstrates a potential for mapping insect disturbance impact over extended areas.
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| 16. |
- Pascual, Didac, et al.
(författare)
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The missing pieces for better future predictions in subarctic ecosystems: A Torneträsk case study
- 2021
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Ingår i: Ambio. - : Springer. - 0044-7447 .- 1654-7209. ; 50:2, s. 375-392
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Forskningsöversikt (refereegranskat)abstract
- Arctic and subarctic ecosystems are experiencing substantial changes in hydrology, vegetation, permafrost conditions, and carbon cycling, in response to climatic change and other anthropogenic drivers, and these changes are likely to continue over this century. The total magnitude of these changes results from multiple interactions among these drivers. Field measurements can address the overall responses to different changing drivers, but are less capable of quantifying the interactions among them. Currently, a comprehensive assessment of the drivers of ecosystem changes, and the magnitude of their direct and indirect impacts on subarctic ecosystems, is missing. The Torneträsk area, in the Swedish subarctic, has an unrivalled history of environmental observation over 100 years, and is one of the most studied sites in the Arctic. In this study, we summarize and rank the drivers of ecosystem change in the Torneträsk area, and propose research priorities identified, by expert assessment, to improve predictions of ecosystem changes. The research priorities identified include understanding impacts on ecosystems brought on by altered frequency and intensity of winter warming events, evapotranspiration rates, rainfall, duration of snow cover and lake-ice, changed soil moisture, and droughts. This case study can help us understand the ongoing ecosystem changes occurring in the Torneträsk area, and contribute to improve predictions of future ecosystem changes at a larger scale. This understanding will provide the basis for the future mitigation and adaptation plans needed in a changing climate.
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| 17. |
- Porcar-Castell, A., et al.
(författare)
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EUROSPEC: at the interface between remote-sensing and ecosystem CO2 flux measurements in Europe
- 2015
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Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4189. ; 12:20, s. 6103-6124
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Forskningsöversikt (refereegranskat)abstract
- Resolving the spatial and temporal dynamics of gross primary productivity (GPP) of terrestrial ecosystems across different scales remains a challenge. Remote sensing is regarded as the solution to upscale point observations conducted at the ecosystem level, using the eddy covariance (EC) technique, to the landscape and global levels. In addition to traditional vegetation indices, the photochemical reflectance index (PRI) and the emission of solar-induced chlorophyll fluorescence (SIF), now measurable from space, provide a new range of opportunities to monitor the global carbon cycle using remote sensing. However, the scale mismatch between EC observations and the much coarser satellite-derived data complicates the integration of the two sources of data. The solution is to establish a network of in situ spectral measurements that can act as bridge between EC measurements and remote sensing data. In situ spectral measurements have been already conducted for many years at EC sites, but using variable instrumentation, setups, and measurement standards. In Europe in particular, in situ spectral measurements remain highly heterogeneous. The goal of EUROSPEC Cost Action ES0930 was to promote the development of common measuring protocols and new instruments towards establishing best practices and standardization of in situ spectral measurements. In this review we describe the background and main tradeoffs of in situ spectral measurements, review the main results of EUROSPEC Cost Action, and discuss the future challenges and opportunities of in situ spectral measurements for improved estimation of local and global carbon cycle.
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| 18. |
- Sang, Yirong, et al.
(författare)
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Assessing topographic effects on forest responses to drought with multiple seasonal metrics from Sentinel-2
- 2024
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - 1569-8432. ; 128
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Tidskriftsartikel (refereegranskat)abstract
- Topography determines run-off direction, redistributes groundwater, and affects land surface solar radiation loads and the associated evaporative forcing, consequently, topography can modulate the impact of drought and heat waves on ecosystems. This topographic modulation effect, which typically occurs at the local scale, is often overlooked when assessing ecosystem drought responses using moderate-to-coarse spatial resolution satellite observations, such as the Moderate-resolution Imaging Spectroradiometer (MODIS) imagery. Sentinel-2 and Landsat imagery with finer resolution are suitable for monitoring changes at the local scale, however, studies relying on single vegetation metrics may fail to get a holistic picture of vegetation drought responses, particularly for forests that have complex physiological mechanisms. Here, we performed a comprehensive assessment of the topographic effects on coniferous forest responses to the severe 2018 drought in Scandinavia, using 6 vegetation seasonal metrics during 2017–2021 from the Sentienl-2 High-Resolution Vegetation Phenology and Productivity (HR-VPP) products. We found significant differences (p < 0.05) between sunny and shady aspects, between higher and lower elevations, and between steep and gentle slopes, regarding the maximum impact time, forest drought resistance, and resilience. Specifically, the sunny aspects and steep slopes were related to higher risks of delayed impacts and low resistance and resilience, and elevation and slope were more powerful in regulating the phenology shift and greening rate loss. We also identified different sensitivity in greenness and productivity to topographic effect and greater sensitivity of spring phenology to topographic differences as compared to autumn phenology. The study demonstrates vegetation drought responses represented by multiple seasonal metrics, reveals the prevalent topographic effects at the local scale, and quantifies the magnitudes of the effects with regional statistics.
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| 19. |
- Sobejano‐Paz, Verónica, et al.
(författare)
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Heat dissipation from photosynthesis contributes to maize thermoregulation under suboptimal temperature conditions
- 2023
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The extent to which plants thermoregulate to maintain relatively stable metabolic function in response to gradual and rapid temperature changes that jeopardize crop production is unclear. Maize thermoregulation was investigated based on leaf temperature (TL) measurements and its relationship with photochemistry and stomatal conductance (gs) under dry and wet soil scenarios. Seasonal climatology was simulated in a growth chamber according to Beijing’s climatology with extreme “hot days” based on historical maxima.Maize behaved as a limited homeotherm, an adaptive strategy to maintain photosynthesis around optimum temperatures (Topt). Plants on drier soil had lower thermoregulatory capacity, with reduced gs, photosynthesis and transpiration, which impacted final yields, despite acclimation with a higher Topt to sustained stress. On hot days thermoregulation was affected by heat stress and water availability, suggesting that strong and frequent heatwaves will reduce crop activity although increased temperatures could bring photosynthesis closer to Topt in the region.We propose a novel mechanism to explain thermoregulation from the contribution of heat dissipation via non-photochemical quenching (NPQ) to TL, supporting our hypothesis that NPQ acts as a negative feedback mechanism from photosynthesis by increasing TL in suboptimal conditions. These results could help to design adaptation strategies based on deficit irrigation.
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| 20. |
- Tian, Feng, et al.
(författare)
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Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe
- 2021
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 260
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Tidskriftsartikel (refereegranskat)abstract
- Vegetation phenology obtained from time series of remote sensing data is relevant for a range of ecological applications. The freely available Sentinel-2 imagery at a 10 m spatial resolution with a ~ 5-day repeat cycle provides an opportunity to map vegetation phenology at an unprecedented fine spatial scale. To facilitate the production of a Europe-wide Copernicus Land Monitoring Sentinel-2 based phenology dataset, we design and evaluate a framework based on a comprehensive set of ground observations, including eddy covariance gross primary production (GPP), PhenoCam green chromatic coordinate (GCC), and phenology phases from the Pan-European Phenological database (PEP725). We test three vegetation indices (VI) — the normalized difference vegetation index (NDVI), the two-band enhanced vegetation index (EVI2), and the plant phenology index (PPI) — regarding their capability to track the seasonal trajectories of GPP and GCC and their performance in reflecting spatial variabilities of the corresponding GPP and GCC phenometrics, i.e., start of season (SOS) and end of season (EOS). We find that for GPP phenology, PPI performs the best, in particular for evergreen coniferous forest areas where the seasonal variations in leaf area are small and snow is prevalent during wintertime. Results are inconclusive for GCC phenology, for which no index is consistently better than the others. When comparing to PEP725 phenology phases, PPI and EVI2 perform better than NDVI regarding the spatial correlation and consistency (i.e., lower standard deviation). We also link VI phenometrics at various amplitude thresholds to the PEP725 phenophases and find that PPI SOS at 25% and PPI EOS at 15% provide the best matches with the ground-observed phenological stages. Finally, we demonstrate that applying bidirectional reflectance distribution function correction to Sentinel-2 reflectance is a step that can be excluded for phenology mapping in Europe.
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| 21. |
- Vicente‑Serrano, Sergio M., et al.
(författare)
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Influence of the interannual variability of meteorological drought on the cross-interactions of ecological and hydrological drought in the central Spanish Pyrenees
- 2023
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Ingår i: GeoFocus. - 1578-5157. ; 31, s. 55-85
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Tidskriftsartikel (refereegranskat)abstract
- This paper analyzes the influence of the interannual variability of climatic drought on ecological and hydrological droughts for a basin in the central Spanish Pyrenees using variables derived from observations and hydro-ecological simulation in order to determine the possible connection between meteorological, ecological and hydrological drought considering a cascading approach and encompassing different variables that give insights into water availability in the basin (e.g., soil moisture, streamflow, reservoir storages and releases). Using different climatic, ecological and hydrological standardized drought indices, we show the greater role of meteorological droughts in hydrological systems than in ecological systems, and the small influence of vegetation activity and growth in explaining the interannual variability of water resources in the basin. By contrast,hydrological droughts are strongly affected by precipitation variability with relationships characterized by seasonal differences and the role of different time-scales in the standardized drought metrics.
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| 22. |
- Zhang, Wenxin, et al.
(författare)
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Convergence and divergence emerging in climatic controls of polynomial trends for northern ecosystem productivity over 2000–2018
- 2023
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 1879-1026 .- 0048-9697.
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Tidskriftsartikel (refereegranskat)abstract
- Southwest China has been the largest terrestrial carbon sink in China over the past 30 years, but has recently experienced a succession of droughts caused by high precipitation variability, potentially threatening vegetation productivity in the region. Yet, the impact of precipitation anomalies on the vegetation primary productivity is poorly known. We used an asymmetry index (AI) to explore possible asymmetric productivity responses to precipitation anomalies in Southwest China from 2003 to 2018, using a precipitation dataset, combined with gross primary productivity (GPP), net primary productivity (NPP), and vegetation optical depth (VOD) products. Our results indicate that the vegetation primary productivity of Southwest China shows a negative asymmetry, suggesting that the increase of vegetation primary productivity during wet years exceeds the decrease during dry years. However, this negative asymmetry of vegetation primary productivity was shifted towards a positive asymmetry during the period of analysis, suggesting that the resistance of vegetation to drought, has increased with the rise in the occurrence of drought events. Among the different biomes, grassland vegetation primary productivity had the highest sensitivity to precipitation anomalies, indicating that grasslands are more flexible than other biomes and able to adjust primary productivity in response to precipitation anomalies. Furthermore, our results showed that the asymmetry of vegetation primary productivity was influenced by the effects of temperature, precipitation, solar radiation, and anthropogenic and topographic factors. These findings improve our understanding of the response of vegetation primary productivity to climate change over Southwest China.
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