| 1. |
- Abdi, Abdulhakim M., et al.
(författare)
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Biodiversity decline with increasing crop productivity in agricultural fields revealed by satellite remote sensing
- 2021
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Ingår i: Ecological Indicators. - : Elsevier BV. - 1470-160X. ; 130
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Tidskriftsartikel (refereegranskat)abstract
- Increasing land-use intensity is a main driver of biodiversity loss in farmland, but measuring proxies for land-use intensity across entire landscapes is challenging. Here, we develop a novel method for the assessment of the impact of land-use intensity on biodiversity in agricultural landscapes using remote sensing parameters derived from the Sentinel-2 satellites. We link crop phenology and productivity parameters derived from time-series of a two-band enhanced vegetation index with biodiversity indicators (insect pollinators and insect-pollinated vascular plants) in agricultural fields in southern Sweden, with contrasting land management (i.e. conventional and organic farming). Our results show that arable land-use intensity in cereal systems dominated by spring-sown cereals can be approximated using Sentinel-2 productivity parameters. This was shown by the significant positive correlations between the amplitude and maximum value of the enhanced vegetation index on one side and farmer reported yields on the other. We also found that conventional cereal fields had 17% higher maximum and 13% higher amplitude of their enhanced vegetation index than organic fields. Sentinel-2 derived parameters were more strongly correlated with the abundance and species richness of bumblebees and the richness of vascular plants than the abundance and species richness of butterflies. The relationships we found between biodiversity and crop production proxies are consistent with predictions that increasing agricultural land-use intensity decreases field biodiversity. The newly developed method based on crop phenology and productivity parameters derived from Sentinel-2 data serves as a proof of concept for the assessment of the impact of land-use intensity on biodiversity over cereal fields across larger areas. It enables the estimation of arable productivity in cereal systems, which can then be used by ecologists and develop tools for land managers as a proxy for land-use intensity. Coupled with spatially explicit databases on agricultural land-use, this method will enable crop-specific cereal productivity estimation across large geographical regions.
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| 2. |
- 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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| 3. |
- 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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| 4. |
- Cai, Zhanzhang
(författare)
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Vegetation Observation in the Big Data Era : Sentinel-2 data for mapping the seasonality of land vegetation
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Using satellite remote sensing data for observing vegetation seasonality is an important approach to estimate phenology and carbon uptake of land vegetation. The successful launch of Sentinel-2B in 2017 initiated full operation of the Sentinel-2 twin satellites, and they now provide 10 - 60 m spatial resolution satellite data at 5 days temporal resolution worldwide, releasing approximately 3.2 TB of image data per day. With Sentinel-2's huge amount of high spatial resolution and high temporal resolution data, Earth observation is facing new opportunities and challenges. To adapt to the characteristics of Sentinel-2 MSI data, the existing time-series analysis methods used for vegetation seasonality studies with regular time step data (e.g., from the MODIS sensor) require modification and improvements. In this thesis, a new time-series analysis method, based on the currently available methods, was developed for estimating vegetation seasonality from high spatial resolution Sentinel-2 data. The new method is applied to Sentinel-2 data to estimate vegetation phenology and photosynthetic carbon uptake, and the outputs are evaluated based on ground reference data and compared to MODIS products. By comparing with ground reference data (in-situ NDVI time-series, flux tower GPP time-series, and elevation), function fitting methods (e.g., double logistic function fitting) provide the most robust description of the seasonal dynamics for MODIS NDVI time-series among five tested smoothing methods. Based on this finding, we developed box constrained separable least squares fits to double logistic functions with seasonal shape priors, and tested the robustness of the method on six years of simulated Sentinel-2 data by use of MODIS data. The results show that the new method is flexible enough to simulate interannual variations and robust enough when data are sparse. The box constrained function fitting method applied to Sentinel-2 MSI 2-band Enhanced Vegetation Index (EVI2) data was further used to estimate vegetation phenology and gross primary productivity (GPP) across diverse Nordic vegetation types. The results indicate that daily EVI2 time-series derived from Sentinel-2 is more accurate than from MODIS, with an RMSE of 0.08 for Sentinel-2 and 0.13 for MODIS versus the ground spectral data. With reference to the dates of greenness rising estimated from digital cameras, the dates estimated from Sentinel-2 (RMSE: 8.1 days) are closer than those from MODIS (RMSE: 14.4 days). Sentinel-2 data also generate more phenological details along elevation gradients and land cover variations than MODIS. However, Sentinel-2 does not show any advantage in estimating GPP, when comparing with data from flux towers. The average error between the modelled GPP from Sentinel-2 EVI2 and the GPP derived from flux tower data was similar to that from MODIS. This result partly reflects inabilities in the flux tower data to resolve variation at the same high resolution as Sentinel-2, and further studies will be required to fully evaluate the capability of the sensor in this respect.In conclusion, the new method, box constrained separable least squares fits to double logistic functions with seasonal shape priors, is useful and computationally efficient for robustly reconstructing daily vegetation index time-series and estimating vegetation phenology from Sentinel-2 data. In addition, by applying the new method to Sentinel-2 data is useful for describing the spatial variation of GPP in the footprint area, although Sentinel-2 did not show improvements in estimating GPP compared with MODIS data. The developed time-series methods will be implemented in a subsequent version of the TIMESAT software package for processing of irregular time step data.
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| 5. |
- Ganeva, Dessislava, et al.
(författare)
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In-situ start and end of growing season dates of major European crop types from France and Bulgaria at a field level
- 2023
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Ingår i: Data in Brief. - 2352-3409. ; 51
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Tidskriftsartikel (refereegranskat)abstract
- Crop phenology data offer crucial information for crop yield estimation, agricultural management, and assessment of agroecosystems. Such information becomes more important in the context of increasing year-to-year climatic variability. The dataset provides in-situ crop phenology data (first leaves emergence and harvest date) of major European crops (wheat, corn, sunflower, rapeseed) from seventeen field study sites in Bulgaria and two in France. Additional information such as the sowing date, area of each site, coordinates, method and equipment used for phenophase data estimation, and photos of the France sites are also provided. The georeferenced ground-truth dataset provides a solid base for a better understanding of crop growth and can be used to validate the retrieval of phenological stages from remote sensing data.
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| 6. |
- 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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| 7. |
- 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
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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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| 8. |
- 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
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Ingår i: Science of Remote Sensing. - : Elsevier BV. - 2666-0172. ; 7
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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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| 9. |
- Jönsson, Per, et al.
(författare)
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A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data
- 2018
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 10:4
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Tidskriftsartikel (refereegranskat)abstract
- Time series from Landsat and Sentinel-2 satellites have great potential for modeling vegetation seasonality. However, irregular time sampling and frequent data loss due to clouds, snow, and short growing seasons, makes this modeling a challenge. We describe a new method for modeling seasonal vegetation index dynamics from satellite time series data. The method is based on box constrained separable least squares fits to logistic model functions combined with seasonal shape priors. To enable robust estimates, we extract a base level (i.e., the minimum dormant season value) from the frequency distribution of clear-sky vegetation index values. A seasonal shape prior is computed from several years of data, and in the final fits local parameters are box constrained. More specifically, if enough data values exist in a certain time period, the corresponding local parameters determining the shape of the model function over this period are relaxed and allowed to vary freely. If there are no observations in a period, the corresponding local parameters are locked to the parameters of the shape prior. The method is flexible enough to model interannual variations, yet robust enough when data are sparse. We test the method with Landsat, Sentinel-2, and MODIS data over a forested site in Sweden, demonstrating the feasibility and potential of the method for operational modeling of growing seasons.
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| 10. |
- Kelly, Julia, et al.
(författare)
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Wildfire impacts on the carbon budget of a managed Nordic boreal forest
- 2024
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Ingår i: Agricultural and Forest Meteorology. - 0168-1923. ; 351
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Tidskriftsartikel (refereegranskat)abstract
- Wildfire is one of the most important disturbances affecting boreal forests. Most previous research on boreal forest fires has occurred in North American forests which have different fire regimes, tree species and are less intensively managed than their Eurasian counterparts. Recent extreme fire years have highlighted the vulnerability of the Nordic boreal forest to climatic shifts that are increasing forest fire frequency and severity. The Ljusdal fire (2018) was one of the largest wildfires in recorded history in Sweden. We established eddy covariance flux towers to track the impacts of this fire on the carbon balance of two Pinus sylvestris sites subject to different fire severities and forest management strategies 1–4 years post-fire. The ‘SLM’ site was a mature stand that experienced low-severity fire (trees survived) followed by salvage-logging and reseeding, whilst the ‘HY’ site was 10 years old when it experienced high-severity fire (all trees killed) then was replanted with seedlings. During the study period, both sites were net carbon sources at the annual scale. It took up to 4 years after the fire until the first day of net CO2 uptake was recorded at each site. We estimated that it will take 13 years (8, 21; mean ± 95 % confidence intervals) after the fire until the sites reach a neutral annual carbon balance. It will take up to 32 years (19, 53) at HY and 46 years (31, 70) at SLM to offset the carbon lost during and after the fire and salvage-logging. In addition, our measurements showed that more carbon was emitted in the first 4 years after the fire compared to the carbon lost from combustion during the fire. Quantifying carbon fluxes during the initial years after fire is therefore crucial for estimating the net impact of wildfire on the carbon budget of boreal forests.
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