1. |
- Abdi, Abdulhakim, et al.
(author)
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Evaluating Water Controls on Vegetation Growth in the Semi-Arid Sahel Using Field and Earth Observation Data
- 2017
-
In: Remote Sensing. - : MDPI AG. - 2072-4292. ; 9:3
-
Journal article (peer-reviewed)abstract
- Water loss is a crucial factor for vegetation in the semi-arid Sahel region of Africa. Global satellite-driven estimates of plant CO2 uptake (gross primary productivity, GPP) have been found to not accurately account for Sahelian conditions, particularly the impact of canopy water stress. Here, we identify the main biophysical limitations that induce canopy water stress in Sahelian vegetation and evaluate the relationships between field data and Earth observation-derived spectral products for up-scaling GPP. We find that plant-available water and vapor pressure deficit together control the GPP of Sahelian vegetation through their impact on the greening and browning phases. Our results show that a multiple linear regression (MLR) GPP model that combines the enhanced vegetation index, land surface temperature, and the short-wave infrared reflectance (Band 7, 2105–2155 nm) of the moderate-resolution imaging spectroradiometer satellite sensor was able to explain between 88% and 96% of the variability of eddy covariance flux tower GPP at three Sahelian sites (overall = 89%). The MLR GPP model presented here is potentially scalable at a relatively high spatial and temporal resolution. Given the scarcity of field data on CO2 fluxes in the Sahel, this scalability is important due to the low number of flux towers in the region.
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2. |
- Abel, Christin, et al.
(author)
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Contrasting ecosystem vegetation response in global drylands under drying and wetting conditions
- 2023
-
In: Global Change Biology. - 1354-1013. ; 29:14, s. 3954-3969
-
Journal article (peer-reviewed)abstract
- Increasing aridity is one major consequence of ongoing global climate change and is expected to cause widespread changes in key ecosystem attributes, functions, and dynamics. This is especially the case in naturally vulnerable ecosystems, such as drylands. While we have an overall understanding of past aridity trends, the linkage between temporal dynamics in aridity and dryland ecosystem responses remain largely unknown. Here, we examined recent trends in aridity over the past two decades within global drylands as a basis for exploring the response of ecosystem state variables associated with land and atmosphere processes (e.g., vegetation cover, vegetation functioning, soil water availability, land cover, burned area, and vapor-pressure deficit) to these trends. We identified five clusters, characterizing spatiotemporal patterns in aridity between 2000 and 2020. Overall, we observe that 44.5% of all areas are getting dryer, 31.6% getting wetter, and 23.8% have no trends in aridity. Our results show strongest correlations between trends in ecosystem state variables and aridity in clusters with increasing aridity, which matches expectations of systemic acclimatization of the ecosystem to a reduction in water availability/water stress. Trends in vegetation (expressed by leaf area index [LAI]) are affected differently by potential driving factors (e.g., environmental, and climatic factors, soil properties, and population density) in areas experiencing water-related stress as compared to areas not exposed to water-related stress. Canopy height for example, has a positive impact on trends in LAI when the system is stressed but does not impact the trends in non-stressed systems. Conversely, opposite relationships were found for soil parameters such as root-zone water storage capacity and organic carbon density. How potential driving factors impact dryland vegetation differently depending on water-related stress (or no stress) is important, for example within management strategies to maintain and restore dryland vegetation.
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3. |
- Abel, Christin, et al.
(author)
-
Improved characterization of dryland degradation using trends in vegetation/ rainfall sequential linear regression (SERGS-TREND)
- 2018
-
In: 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. - 9781538671498 - 9781538671504 ; 2018-July, s. 2988-2991
-
Conference paper (peer-reviewed)abstract
- Land degradation in drylands has been investigated extensively over recent decades and several remote sensing based techniques attempt to decouple the human influence from the natural climate variability, but are contested in literature. We introduce a novel approach termed SeRGS-TREND that is designed to monitor land degradation by suppressing the impact from climate variability and highlight vegetation disturbances may it be human or climate-induced. SeRGS-TREND is based on the interpretation of the slope of a linear regression analysis within a sequentially moving window along the temporal axis of the time series of remote sensing data. The use of a moving window increases the probability of a statistically significant linear vegetation-rainfall relationship (VRR), which in turn provides an improved statistical basis for the results produced and thereby confidence in the assessment of degradation. We test and compare SeRGS-TREND and the commonly used RESTREND by simulating different degradation scenarios and find that SeRGS reveals both, more significant and more exact information about degradation events (e.g. starting and end point) while keeping the VRR correlation coefficients high, thus rendering results more reliable.
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4. |
- Abel, Christin, et al.
(author)
-
The human–environment nexus and vegetation–rainfall sensitivity in tropical drylands
- 2020
-
In: Nature Sustainability. - : Springer Science and Business Media LLC. - 2398-9629. ; 4, s. 25-32
-
Journal article (peer-reviewed)abstract
- Global climate change is projected to lead to an increase in both the areal extent and degree of aridity in the world’s drylands. At the same time, the majority of drylands are located in developing countries where high population densities and rapid population growth place additional pressure on the ecosystem. Thus, drylands are particularly vulnerable to environmental changes and large-scale environmental degradation. However, little is known about the long-term functional response of vegetation to such changes induced by the interplay of complex human–environmental interactions. Here we use time series of satellite data to show how vegetation productivity in relation to water availability, which is a major aspect of vegetation functioning in tropical drylands, has changed over the past two decades. In total, one-third of tropical dryland ecosystems show significant (P < 0.05) changes in vegetation–rainfall sensitivity with pronounced differences between regions and continents. We identify population as the main driver of negative changes, especially for developing countries. This is contrasted by positive changes in vegetation–rainfall sensitivity in richer countries, probably resulting from favourable climatic conditions and/or caused by an intensification and expansion of human land management. Our results highlight geographic and economic differences in the relationship between vegetation–rainfall sensitivity and associated drivers in tropical drylands, marking an important step towards the identification, understanding and mitigation of potential negative effects from a changing world on ecosystems and human well-being.
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5. |
- Abel, Christin, et al.
(author)
-
Towards improved remote sensing based monitoring of dryland ecosystem functioning using sequential linear regression slopes (SeRGS)
- 2019
-
In: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 224, s. 317-332
-
Journal article (peer-reviewed)abstract
- We present a method for remote sensing based monitoring of changes in dryland ecosystem functioning based on the assumption that an altered vegetation rainfall relationship (VRR) indicates changes in vegetation biophysical processes, potentially leading to changes in ecosystem functioning. We describe the VRR through a linear regression between integrated rainfall and vegetation productivity (using NDVI as a proxy) within a combined spatio-temporal window, sequentially moved over the study area and along the temporal axis of a time series. The trend in the slope values derived from such a sequential linear regression, termed SeRGS, thus represents a measure of change in the VRR. Scenarios of land degradation, defined here as a reduction in biological productivity, which may be caused by either climatic or anthropogenic factors are simulated for the period 1970–2016 from CRU rainfall and modelled NDVI data to test and evaluate the performance of the SeRGS method in detecting degradation, and compare it against the well-known RESTREND method. We found that SeRGS showed (1) overall more pronounced trends and higher significance levels (p ≤ 0.01) in detecting degradation events and (2) an improved statistical basis for the calculation of trends in the VRR (expressed by high coefficients of determination throughout the period of analysis), which was found to increase the validity of the results produced. Through the implementation of the temporal moving window the effect of inter-annual rainfall variability on vegetation productivity was effectively reduced, thereby enabling a more exact and reliable identification of the timing of degradation events (e.g. start, maximum and end of degradation) by using a time series breakpoint analysis (BFAST). Finally, the SeRGS method was applied using real data for Senegal (seasonally integrated MODIS NDVI and CHIRPS rainfall data 2000–2016) and we discuss patterns and trends. This study provides the theoretical basis for an improved assessment of changes in dryland ecosystem functioning, which is of relevance to land degradation monitoring targeting loss of vegetation productivity.
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6. |
- Abel, Christin, et al.
(author)
-
Vegetation resistance to increasing aridity when crossing thresholds depends on local environmental conditions in global drylands
- 2024
-
In: Communications Earth and Environment. - 2662-4435. ; 5:1
-
Journal article (peer-reviewed)abstract
- The crossing of aridity thresholds triggers abrupt changes in multiple functional and structural ecosystem attributes across global drylands. While we understand the consequences associated with aridity thresholds, the key factors influencing dryland vegetation resistance when crossing them remain unclear. Here, we used field observations from 58 dryland sites across five continents and satellite remote sensing data (2000-2022) to show that plant richness, soil moisture dynamics and texture, and bare soil fraction are important variables contributing to vegetation resistance. Additionally, drought history (frequency and magnitude of past droughts) is important in interaction with plant richness and soil texture. Interestingly, plant species richness was negatively related to vegetation resistance, except in areas with higher drought history and in grasslands. Our results highlight that vegetation resistance depends on local environmental conditions. Enhancing our understanding of the factors important for vegetation resistance is an important step towards dryland conservation efforts and sustainable management strategies.
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7. |
- Agbohessou, Yélognissè, et al.
(author)
-
To what extent are greenhouse-gas emissions offset by trees in a Sahelian silvopastoral system?
- 2023
-
In: Agricultural and Forest Meteorology. - 0168-1923. ; 343
-
Journal article (peer-reviewed)abstract
- To assess the extent to which trees in a semi-arid silvopastoral system (SPS) can offset the greenhouse-gas (GHG) emissions of the system's livestock, this study used two process-based models (STEP-GENDEC-N2O and DynACof) to simulate 9 years of agricultural activity and resulting emissions in a SPS that has been operating in sahelian Senegal. STEP-GENDEC-N2O simulated soil N2O and CO2 fluxes, plus growth of the herbaceous layer, while DynACof focused on the tree layer. Outputs from the models included simulated time series of vegetative growth, water fluxes, and emissions. This output was validated through the use of published data, and measurements that were made at the SPS. Overall, the outputs from STEP-GENDEC-N2O agreed well with validation data for water fluxes, soil N, soil C, herbaceous biomass, and N2O emissions. Good agreement was also found between the measured fluxes of the SPS ecosystem, and the simulated values that were generated by combining STEP-GENDEC-N2O's simulations (of the herbaceous layer's heterotrophic respiration, autotrophic respiration, and gross primary productivity (GPP)) with DynACof's simulations of the tree layer's autotrophic respiration and GPP. Among the insights gained from the simulations was that in this SPS's sandy soils, nitrification was the dominant process that leads to N2O emissions. Our results show that the trees, at their current density (81 ha−1) offset 18 % to 41 % of the GHG emissions from livestock. With further development, the model set-up can be used for estimating the GHG offset at other tree densities, and will be useful for guiding future policies regarding climate-change adaptation and mitigation in the management of the Sahel's SPSs.
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8. |
- Ahlström, Anders, et al.
(author)
-
Primary productivity of managed and pristine forests in Sweden
- 2020
-
In: Environmental Research Letters. - : IOP Publishing. - 1748-9318 .- 1748-9326. ; 15:9
-
Journal article (peer-reviewed)abstract
- Land use is affecting 70% of global ecosystems and their functioning. Forest management is a regionally dominant land use and affects forest ecosystems by changing both structure and functioning, but its impact on primary productivity is not well known. Here we investigated the effect of forest management on primary productivity by comparing managed secondary forests with relatively pristine unmanaged primary forests in Sweden. As proxy for primary productivity we used the satellite-based vegetation index NIRv which has been shown to be closely and linearly related to primary productivity. We produced a digital map of 390 primary forests across Sweden, and extracted NIRv over these and surrounding secondary forests forming spatially proximate pairs. By comparing the primary and secondary forests NIRv in the pairs we found that secondary forests on average show higher NIRv, but the highest values were found in primary forests. The difference in NIRv between pairs is related to their difference in mean stand age, and at equal stand age the NIRv of primary forests is higher than in their paired secondary forests. Overall, management leads to increased NIRv through regeneration of forests stands that reduce their mean age. However, primary forests show higher NIRv when controlling for age, despite being found on higher altitudes and on steeper slopes with lower soil moisture, which suggests that forest management other than regeneration is not increasing primary productivity of Swedish forests.
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9. |
- Ardö, Jonas, et al.
(author)
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MODIS EVI-based net primary production in the Sahel 2000–2014
- 2018
-
In: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 65, s. 35-45
-
Journal article (peer-reviewed)abstract
- Africa is facing resource problems due to increasing demand combined with potential climate-induced changes in supply. Here we aim to quantify resources in terms of net primary production (NPP [g C m−2 yr−1]) of vegetation in the Sahel region for 2000–2014.Using time series of the enhanced vegetation index (EVI) from MODIS, NPP was estimated for the Sahel region with a 500 × 500 m spatial resolution and 8-day temporal resolution. The estimates were based on local eddy covariance flux measurements from six sites in the Sahel region and the carbon use efficiency originating from a dynamic vegetation model.No significant NPP change was found for the Sahel as a region but, for sub-regions, significant changes, both increasing and decreasing, were observed. Substantial uncertainties related to NPP estimates and the small availability of evaluation data makes verification difficult. The simplicity of the methodology used, dependent on earth observation only, is considered an advantage.
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10. |
- Bigaignon, Laurent, et al.
(author)
-
Understanding N2O emissions in african ecosystems : Assessments from a semi-arid savanna grassland in senegal and sub-tropical agricultural fields in Kenya
- 2020
-
In: Sustainability (Switzerland). - : MDPI AG. - 2071-1050. ; 12:21
-
Journal article (peer-reviewed)abstract
- This study is based on the analysis of field-measured nitrous oxide (N2 O) emissions from a Sahelian semi-arid grassland site in Senegal (Dahra), tropical humid agricultural plots in Kenya (Mbita region) and simulations using a 1D model designed for semi arid ecosystems in Dahra. This study aims at improving present knowledge and inventories of N2 O emissions from the African continent. N2 O emissions were larger at the agricultural sites in the Mbita region (range: 0.0 ± 0.0 to 42.1 ± 10.7 ngN m−2 s−1) than at the Dahra site (range: 0.3 ± 0 to 7.4 ± 6.5 ngN m−2 s−1). Soil water and nitrate (NO3−) contents appeared to be the most important drivers of N2 O emissions in Dahra at the seasonal scale in both regions. The seasonal pattern of modelled N2 O emissions is well represented, though the model performed better during the rainy season than between the rainy and dry seasons. This study highlighted that the water-filled pore space threshold recognised as a trigger for N2 O emissions should be reconsidered for semi-arid ecosystems. Based on both measurements and simulated results, an annual N2 O budget was estimated for African savanna/grassland and agricultural land ranging between 0.17–0.26 and 1.15–1.20 TgN per year, respectively.
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11. |
- Boke-Olén, Niklas, et al.
(author)
-
Analyzing savannah vegetation phenology with remotely sensed data, lagged time-series models and phenopictures
- 2016
-
Conference paper (peer-reviewed)abstract
- It is predicted that savannah regions will see changes in precipitation patterns due to current climate change pro-jections. The change will most likely affect leaf phenology which controls net primary production. It is thereforeimportant to; 1) study those changes and its drivers, 2) to be able to correctly model the changes to vegetationphenology due to climate change. To our knowledge there is no existing global savannah phenology model thatcan capture both the phenological events and the vegetation state between the events. We therefore, investigate howday length, mean annual precipitation and soil moisture affects and controls the vegetation phenology of savannahs(using MODIS NDVI as a proxy for phenological state) with a lagged time series model for global application. Wefurthermore use phenological pictures (phenopictures) to investigate savannah tree and grass phenology. Phenopic-tures are pictures taken with a digital time-lapse camera with the purpose of recording and studying phenologicalevents. We used climate data from 15 flux towers sites located in 4 continents together with normalized differencevegetation index from MODIS for the model development. Two of the sites located in Africa were further ana-lyzed using phenopictures. The developed model identified all three considered variables as usable for modellingof savannah leaf phenology but showed some inconsistent result for some of the sites indicating the difficultiesin creating a simple common model that works equally well across sites. We attribute some of these difficultiesto site specific differences (e.g. grazing or tree and grass ratio) that the simplified model did not consider. Butwe expect it to on average give the cross-validated result (r2= 0.6, RMSE = 0.1) when applied to other savannahareas. The preliminary analysis of the phenological pictures with respect to tree and grass to some extent supportthis by showing differences in the start of the leaves development in the beginning of the season. However, thisdiffered between the two studied sites which further highlights the difficulties in creating a common model thatworks equally well for individual sites.
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12. |
- Boke-Olén, Niklas, et al.
(author)
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Estimating and analyzing savannah phenology with a lagged time series model
- 2016
-
In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 11:4
-
Journal article (peer-reviewed)abstract
- Savannah regions are predicted to undergo changes in precipitation patterns according to current climate change projections. This change will affect leaf phenology, which controls net primary productivity. It is of importance to study this since savannahs play an important role in the global carbon cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r2 of 0.6 and root mean squared error of 0.1. We therefore expect similar average results when applying the model to other savannah areas and further expect that it could be used to estimate the productivity of savannah regions.
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13. |
- Chen, Yuwen, et al.
(author)
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Exploring the potential of transmittance vegetation indices for leaf functional traits retrieval
- 2023
-
In: GIScience and Remote Sensing. - : Informa UK Limited. - 1548-1603 .- 1943-7226. ; 60:1
-
Journal article (peer-reviewed)abstract
- Leaf functional traits are key indicators of plant functions useful for inferring complex plant processes, including their responses to environmental changes. Vegetation indices (VIs) composed of a few reflectance wavelengths hold the advantages of being relatively simple and effective and have been widely used within remote sensing to estimate leaf traits. However, the difference between the reflectance from the upper and lower part of the leaf suggests that leaf reflectance mainly provides one-sided information, constraining its ability to estimate leaf functional traits. Leaf transmittance, on the other hand, gives information about the whole leaf and has more potential to be sensitive to changes in leaf biochemistry. As transmittance-based VI is rare, this study aims to propose new transmittance-based VIs for accurate estimations of leaf traits. Three forms, i.e. the normalized difference VI, the simple ratio VI, and the difference VI were employed, and wavelength selection for transmittance-based and reflectance-based VIs were conducted, respectively. The applicability of these VIs for estimating four leaf functional traits (leaf chlorophyll (Cab), leaf carotenoids (Car), equivalent water thickness (EWT), and leaf mass per area (LMA)) were evaluated. Cross-validation using three datasets of field observations and sensitivity analysis showed that the VIs constructed using transmittance were relatively less affected by interferences from other leaf parameters, improving the estimation accuracy of Car, EWT, and LMA compared to their optimal reflectance counterparts (RMSE reduced by 2% to 15%, and MAE reduced by 7% to 20% for the pooled dataset). Our study revealed that the normalized difference VI based on transmittance showed considerable sensitivity to Car, EWT, and LMA, whereas the difference VI based on reflectance was effective in indicating Cab. The proposed transmittance-based VIs will aid remote monitoring of leaf traits and thereby plant adaptations and acclimation to changes in environmental conditions.
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14. |
- Chen, Yuwen, et al.
(author)
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Optimized estimation of leaf mass per area with a 3d matrix of vegetation indices
- 2021
-
In: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13:18
-
Journal article (peer-reviewed)abstract
- Leaf mass per area (LMA) is a key plant functional trait closely related to leaf biomass. Estimating LMA in fresh leaves remains challenging due to its masked absorption by leaf water in the short-wave infrared region of reflectance. Vegetation indices (VIs) are popular variables used to estimate LMA. However, their physical foundations are not clear and the generalization ability is limited by the training data. In this study, we proposed a hybrid approach by establishing a three-dimensional (3D) VI matrix for LMA estimation. The relationship between LMA and VIs was con-structed using PROSPECT-D model simulations. The three-VI space constituting a 3D matrix was divided into cubical cells and LMA values were assigned to each cell. Then, the 3D matrix retrieves LMA through the three VIs calculated from observations. Two 3D matrices with different VIs were established and validated using a second synthetic dataset, and two comprehensive experimental datasets containing more than 1400 samples of 49 plant species. We found that both 3D matrices allowed good assessments of LMA (R2 = 0.76 and 0.78, RMSE = 0.0016 g/cm2 and 0.0017 g/cm2, re-spectively for the pooled datasets), and their results were superior to the corresponding single Vis, 2D matrices, and two machine learning methods established with the same VI combinations.
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15. |
- Delon, Claire, et al.
(author)
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Modelling land–atmosphere daily exchanges of NO, NH3, and CO2 in a semi-arid grazed ecosystem in Senegal
- 2019
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In: Biogeosciences. - : Copernicus GmbH. - 1726-4189. ; , s. 2049-2077
-
Journal article (peer-reviewed)abstract
- Three different models (STEP–GENDEC–NOflux, Zhang2010, and Surfatm) are used to simulate NO, CO2, and NH3 fluxes at the daily scale for 2 years (2012–2013) in a semi-arid grazed ecosystem at Dahra (15∘24′10′′ N, 15∘25′56′′ W, Senegal, Sahel). Model results are evaluated against experimental results acquired during three field campaigns. At the end of the dry season, when the first rains re-wet the dry soils, the model STEP–GENDEC–NOflux simulates the sudden mineralization of buried litter, leading to pulses in soil respiration and NO fluxes. The contribution of wet season fluxes of NO and CO2 to the annual mean is respectively 51 % and 57 %. NH3 fluxes are simulated by two models: Surfatm and Zhang2010. During the wet season, air humidity and soil moisture increase, leading to a transition between low soil NH3 emissions (which dominate during the dry months) and large NH3 deposition on vegetation during wet months. Results show a great impact of the soil emission potential, a difference in the deposition processes on the soil and the vegetation between the two models with however a close agreement of the total fluxes. The order of magnitude of NO, NH3, and CO2 fluxes is correctly represented by the models, as well as the sharp transitions between seasons, specific to the Sahel region. The role of soil moisture in flux magnitude is highlighted, whereas the role of soil temperature is less obvious. The simultaneous increase in NO and CO2 emissions and NH3 deposition at the beginning of the wet season is attributed to the availability of mineral nitrogen in the soil and also to microbial processes, which distribute the roles between respiration (CO2 emissions), nitrification (NO emissions), volatilization, and deposition (NH3 emission/deposition). The objectives of this study are to understand the origin of carbon and nitrogen compounds exchanges between the soil and the atmosphere and to quantify these exchanges on a longer timescale when only a few measurements have been performed.
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16. |
- Dorigo, Wouter, et al.
(author)
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The International Soil Moisture Network : Serving Earth system science for over a decade
- 2021
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In: Hydrology and Earth System Sciences. - : Copernicus GmbH. - 1027-5606 .- 1607-7938. ; 25:11, s. 5749-5804
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Research review (peer-reviewed)abstract
- In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements . The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.
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17. |
- Dou, Yujie, et al.
(author)
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Reliability of using vegetation optical depth for estimating decadal and interannual carbon dynamics
- 2023
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In: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 285
-
Journal article (peer-reviewed)abstract
- Vegetation optical depth (VOD) from satellite passive microwave sensors has enabled monitoring of aboveground biomass carbon dynamics by building a relationship with static carbon maps over space and then applying this relationship to VOD time series. However, uncertainty in this relationship arises from changes in water stress, as VOD is mainly determined by vegetation water content, which varies at diurnal to interannual scales, and depends on changes in both biomass and relative moisture content. Here, we studied the reliability of using VOD from various microwave frequencies and temporal aggregation methods for estimating decadal biomass carbon dynamics at the global scale. We used the VOD diurnal variations to represent the magnitude of vegetation water content buffering caused by climatic variations for a constant amount of dry biomass carbon. This magnitude of VOD diurnal variations was then used to evaluate the likelihood of VOD decadal variations in reflecting decadal dry biomass carbon changes. We found that SMOS-IC L-VOD and LPDR X-VOD can be reliably used to estimate decadal carbon dynamics for 76.7% and 69.9% of the global vegetated land surface, respectively, yet cautious use is warranted for some areas such as the eastern Amazon rainforest. Moreover, the annual VOD aggregated from the 95% percentile of the nighttime VOD retrievals was proved to be the most suitable parameter for estimating decadal biomass carbon dynamics among the temporal aggregation methods. Finally, we validated the use of annual VOD for estimating interannual carbon dynamics by comparing VOD changes between adjacent years against eddy covariance estimations of gross primary production from flux sites over several land cover classes across the globe. Despite the large difference in spatial scales between them, the positive correlation obtained supports the capability of satellite VOD in quantifying interannual carbon dynamics.
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18. |
- Ernst, Yolandi, et al.
(author)
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The African Regional Greenhouse Gases Budget (2010–2019)
- 2024
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In: Global Biogeochemical Cycles. - 0886-6236. ; 38:4
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Journal article (peer-reviewed)abstract
- As part of the REgional Carbon Cycle Assessment and Processes Phase 2 (RECCAP2) project, we developed a comprehensive African Greenhouse gases (GHG) budget covering 2000 to 2019 (RECCAP1 and RECCAP2 time periods), and assessed uncertainties and trends over time. We compared bottom-up process-based models, data-driven remotely sensed products, and national GHG inventories with top-down atmospheric inversions, accounting also for lateral fluxes. We incorporated emission estimates derived from novel methodologies for termites, herbivores, and fire, which are particularly important in Africa. We further constrained global woody biomass change products with high-quality regional observations. During the RECCAP2 period, Africa's carbon sink capacity is decreasing, with net ecosystem exchange switching from a small sink of −0.61 ± 0.58 PgC yr−1 in RECCAP1 to a small source in RECCAP2 at 0.16 (−0.52/1.36) PgC yr−1. Net CO2 emissions estimated from bottom-up approaches were 1.6 (−0.9/5.8) PgCO2 yr−1, net CH4 were 77 (56.4/93.9) TgCH4 yr−1 and net N2O were 2.9 (1.4/4.9) TgN2O yr−1. Top-down atmospheric inversions showed similar trends. Land Use Change emissions increased, representing one of the largest contributions at 1.7 (0.8/2.7) PgCO2eq yr−1 to the African GHG budget and almost similar to emissions from fossil fuels at 1.74 (1.53/1.96) PgCO2eq yr−1, which also increased from RECCAP1. Additionally, wildfire emissions decreased, while fuelwood burning increased. For most component fluxes, uncertainty is large, highlighting the need for increased efforts to address Africa-specific data gaps. However, for RECCAP2, we improved our overall understanding of many of the important components of the African GHG budget that will assist to inform climate policy and action.
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19. |
- Gebremedhn, Haftay Hailu, et al.
(author)
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Grazing effects on vegetation dynamics in the savannah ecosystems of the Sahel
- 2023
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In: Ecological Processes. - 2192-1709. ; 12:1
-
Journal article (peer-reviewed)abstract
- Background: The savannah ecosystems of Sahel have experienced continuous and heavy grazing of livestock for centuries but still, their vegetation response to grazing pressure remains poorly understood. In this study, we analysed the herbaceous plant dynamics, measured by species diversity, composition, cover, and biomass in response to grazing pressure in the savannah ecosystems of Sahel. In Senegal, we selected four savannah sites represented with high, moderate, light and no grazing intensity levels. Transect survey methods were used for sampling the vegetation data within each of the sites. Species richness and composition were analysed using species accumulation curve and multivariate analyses. Furthermore, we used General Linear Models and a piecewise Structural Equation Model (pSEM) to examine the relationships between grazing intensity, vegetation cover, diversity and biomass. Results: The herbaceous species diversity and composition varied significantly among the different grazing intensity levels (p <0.001). The plant species composition shifted from the dominance of grass cover to the dominance of forb cover with increasing grazing pressure. Moreover, the attributes of species diversity, herbaceous biomass, and ground cover were higher on sites with low grazing than sites with high and moderate grazing intensity. Across all sites, species diversity was positively related to total biomass. The pSEM explained 37% of the variance in total biomass and revealed that grazing intensity negatively influenced total biomass both directly and indirectly through its negative influence on species diversity. Conclusions: Managing grazing intensity may lead to higher plant production and higher mixed forage establishment in the dryland savannah ecosystems. This information can be used to support land management strategies and promote sustainable grazing practices that balance the needs of livestock with the conservation of ecosystem health and biodiversity.
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20. |
- Getachew Mengistu, Anteneh, et al.
(author)
-
Sun-induced fluorescence and near-infrared reflectance of vegetation track the seasonal dynamics of gross primary production over Africa
- 2021
-
In: Biogeosciences. - : Copernicus GmbH. - 1726-4170 .- 1726-4189. ; 18:9, s. 2843-2857
-
Journal article (peer-reviewed)abstract
- The carbon cycle of tropical terrestrial vegetation plays a vital role in the storage and exchange of atmospheric CO2. But large uncertainties surround the impacts of land-use change emissions, climate warming, the frequency of droughts, and CO2 fertilization. This culminates in poorly quantified carbon stocks and carbon fluxes even for the major ecosystems of Africa (savannas and tropical evergreen forests). Contributors to this uncertainty are the sparsity of (micro-)meteorological observations across Africa's vast land area, a lack of sufficient ground-based observation networks and validation data for CO2, and incomplete representation of important processes in numerical models. In this study, we therefore turn to two remotely sensed vegetation products that have been shown to correlate highly with gross primary production (GPP): sun-induced fluorescence (SIF) and near-infrared reflectance of vegetation (NIRv). The former is available from an updated product that we recently published (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval – SIFTER v2), which specifically improves retrievals in tropical environments.A comparison against flux tower observations of daytime-partitioned net ecosystem exchange from six major biomes in Africa shows that SIF and NIRv reproduce the seasonal patterns of GPP well, resulting in correlation coefficients of >0.9 (N=12 months, four sites) over savannas in the Northern and Southern hemispheres. These coefficients are slightly higher than for the widely used Max Planck Institute for Biogeochemistry (MPI-BGC) GPP products and enhanced vegetation index (EVI). Similarly to SIF signals in the neighboring Amazon, peak productivity occurs in the wet season coinciding with peak soil moisture and is followed by an initial decline during the early dry season, which reverses when light availability peaks. This suggests similar leaf dynamics are at play. Spatially, SIF and NIRv show a strong linear relation (R>0.9; N≥250 pixels) with multi-year MPI-BGC GPP even within single biomes. Both MPI-BGC GPP and the EVI show saturation relative to peak NIRv and SIF signals during high-productivity months, which suggests that GPP in the most productive regions of Africa might be larger than suggested.
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21. |
- Gärtner, Antje, et al.
(author)
-
Temperature and Tree Size Explain the Mean Time to Fall of Dead Standing Trees across Large Scales
- 2023
-
In: Forests. - : MDPI. - 1999-4907. ; 14:5
-
Journal article (peer-reviewed)abstract
- AbstractDead standing trees (DSTs) generally decompose slower than wood in contact with the forest floor. In many regions, DSTs are being created at an increasing rate due to accelerating tree mortality caused by climate change. Therefore, factors determining DST fall are crucial for predicting dead wood turnover time but remain poorly constrained. Here, we conduct a re-analysis of published DST fall data to provide standardized information on the mean time to fall (MTF) of DSTs across biomes. We used multiple linear regression to test covariates considered important for DST fall, while controlling for mortality and management effects. DSTs of species killed by fire, insects and other causes stood on average for 48, 13 and 19 years, but MTF calculations were sensitive to how tree size was accounted for. Species’ MTFs differed significantly between DSTs killed by fire and other causes, between coniferous and broadleaved plant functional types (PFTs) and between managed and unmanaged sites, but management did not explain MTFs when we distinguished by mortality cause. Mean annual temperature (MAT) negatively affected MTFs, whereas larger tree size or being coniferous caused DSTs to stand longer. The most important explanatory variables were MAT and tree size, with minor contributions of management and plant functional type depending on mortality cause. Our results provide a basis to improve the representation of dead wood decomposition in carbon cycle assessments.
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22. |
- He, Chunmei, et al.
(author)
-
A new vegetation index combination for leaf carotenoid-to-chlorophyll ratio : minimizing the effect of their correlation
- 2023
-
In: International Journal of Digital Earth. - : Informa UK Limited. - 1753-8947 .- 1753-8955. ; 16:1, s. 272-288
-
Journal article (peer-reviewed)abstract
- The ratio of leaf carotenoid to chlorophyll (Car/Chl) is an indicator of vegetation photosynthesis, development and responses to stress. However, the correlation between Car and Chl, and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio. This study aims to investigate combinations of vegetation indices (VIs) to minimize the influence of Car-Chl correlation, thus being more sensitive to the variability in the ratio across vegetation species and sites. VIs sensitive to Car and Chl variability were combined into four candidates of combinations, using a simulated dataset from the PROSPECT model. The VI combinations were then tested using six simulated datasets with different Car-Chl correlations, and evaluated against four independent datasets. The ratio of the carotenoid triangle ratio index (CTRI) with the red-edge chlorophyll index (CIred-edge) was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability. Compared with published VIs and two machine learning algorithms, CTRI/CIred-edge also showed the optimal performance in the four field datasets. This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio, applicable for assessing vegetation physiology, phenology, and response to environmental stress. Trial registration:Clinical Trials Registry India identifier: CTRI/.
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23. |
- He, Chunmei, et al.
(author)
-
PROSPECT-GPR : Exploring spectral associations among vegetation traits in wavelength selection for leaf mass per area and water contents
- 2023
-
In: Science of Remote Sensing. - 2666-0172. ; 8
-
Journal article (peer-reviewed)abstract
- Leaf mass per area (LMA) and equivalent water thickness (EWT) are key indicators providing information on plant growth status and agricultural management, and their retrieval is commonly done through radiative transfer models (RTMs) such as the PROSPECT model. However, the PROSPECT model is frequently hampered by the ill-posed problem as a consequence of measurement and model uncertainties. Here, we propose a wavelength selection method to improve the inversion of EWT and LMA by integrating PROSPECT with a machine learning algorithm (Gaussian process regression (GPR); PROSPECT-GPR for short). The GPR model conducted sorting of wavelengths and the PROSPECT-D was used to determine the optimal number of characteristic wavelengths. The results demonstrated that the estimation of EWT (R2 = 0.80; RMSE = 0.0021) and LMA (R2 = 0.71; RMSE = 0.0021) using the proposed wavelengths and PROSPECT inversion all exhibited superior accuracy in comparison with those from previous studies. The efficacy of PROSPECT-GPR in exploring the spectral linkage among vegetation traits was demonstrated by selecting wavelengths associated with leaf structure parameter N and EWT (1368 nm) that turn out to contribute to the estimation of LMA. The findings lay a strong foundation for understanding the spectral linkage among vegetation traits, and the proposed wavelength selection method provides valuable insights for selecting informative spectral wavelengths for RTMs inversion and designing future remote sensors.
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24. |
- Horion, Stéphanie, et al.
(author)
-
Mapping European ecosystem change types in response to land-use change, extreme climate events, and land degradation
- 2019
-
In: Land Degradation and Development. - : Wiley. - 1085-3278 .- 1099-145X. ; 30:8, s. 951-963
-
Journal article (peer-reviewed)abstract
- Extreme climate events and nonsustainable land use are important drivers altering the functioning of European ecosystems, resulting in loss of the services provided. Yet a consensus method for regular continental scale assessment of ecosystem condition in relation to land degradation (LD) is still lacking. Here, we propose a new remote sensing-based approach allowing for improved, repeated assessment of changing pressure on terrestrial ecosystems. On the basis of segmented trend analysis of water-use efficiency (WUE), a map of ecosystem change type (ECT) was produced over Europe for the period 1999 to 2013. Results were related to drought and change in land use and land cover and to known cases of soil degradation (LD case-studies). More than 30% of the European ecosystems experienced significant changes in WUE, of which more than 20% were categorized as abrupt. Large-scale positive reversals in WUE were observed over regions with increasing crop yield and intensification of wood production, whereas decreased WUE was observed over grassland areas coinciding with high farmland abandonment. Evidence of drought pressure on ecosystem functioning (EF) was observed, with abrupt changes in functioning observed during major European drought events. The ECTs also provided relevant information on the location and type of change in EF over the LD case studies. We conclude that mapping of gradual and abrupt changes in EF is expected to be valuable tool for ecosystem condition assessment that is essential for assessing the success of reaching the LD neutrality objectives set by the United Nations Convention to Combat Desertification.
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25. |
- Huang, Ke, et al.
(author)
-
The confounding effect of snow cover on assessing spring phenology from space : A new look at trends on the Tibetan Plateau
- 2021
-
In: Science of the Total Environment. - : Elsevier BV. - 0048-9697. ; 756
-
Journal article (peer-reviewed)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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26. |
- Jamali, Sadegh, et al.
(author)
-
Global-Scale Patterns and Trends in Tropospheric NO2 Concentrations, 2005–2018
- 2020
-
In: Remote Sensing. - : MDPI AG. - 2072-4292. ; 12:21
-
Journal article (peer-reviewed)abstract
- Nitrogen dioxide (NO2) is an important air pollutant with both environmental and epidemiological effects. The main aim of this study is to analyze spatial patterns and temporal trends in tropospheric NO2 concentrations globally using data from the satellite-based Ozone Monitoring Instrument (OMI). Additional aims are to compare the satellite data with ground-based observations, and to find the timing and magnitude of greatest breakpoints in tropospheric NO2 concentrations for the time period 2005–2018. The OMI NO2 concentrations showed strong relationships with the ground-based observations, and inter-annual patterns were especially well reproduced. Eastern USA, Western Europe, India, China and Japan were identified as hotspot areas with high concentrations of NO2. The global average trend indicated slightly increasing NO2 concentrations (0.004 × 1015 molecules cm−2 y−1) in 2005–2018. The contribution of different regions to this global trend showed substantial regional differences. Negative trends were observed for most of Eastern USA, Western Europe, Japan and for parts of China, whereas strong, positive trends were seen in India, parts of China and in the Middle East. The years 2005 and 2007 had the highest occurrence of negative breakpoints, but the trends thereafter in general reversed, and the highest tropospheric NO2 concentrations were observed for the years 2017–2018. This indicates that the anthropogenic contribution to air pollution is still a major issue and that further actions are necessary to reduce this contribution, having a substantial impact on human and environmental health.
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27. |
- Johnston, Alice S.A., et al.
(author)
-
Temperature thresholds of ecosystem respiration at a global scale
- 2021
-
In: Nature Ecology and Evolution. - : Springer Science and Business Media LLC. - 2397-334X. ; 5:4, s. 487-494
-
Journal article (peer-reviewed)abstract
- Ecosystem respiration is a major component of the global terrestrial carbon cycle and is strongly influenced by temperature. The global extent of the temperature–ecosystem respiration relationship, however, has not been fully explored. Here, we test linear and threshold models of ecosystem respiration across 210 globally distributed eddy covariance sites over an extensive temperature range. We find thresholds to the global temperature–ecosystem respiration relationship at high and low air temperatures and mid soil temperatures, which represent transitions in the temperature dependence and sensitivity of ecosystem respiration. Annual ecosystem respiration rates show a markedly reduced temperature dependence and sensitivity compared to half-hourly rates, and a single mid-temperature threshold for both air and soil temperature. Our study indicates a distinction in the influence of environmental factors, including temperature, on ecosystem respiration between latitudinal and climate gradients at short (half-hourly) and long (annual) timescales. Such climatological differences in the temperature sensitivity of ecosystem respiration have important consequences for the terrestrial net carbon sink under ongoing climate change.
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28. |
- Junttila, Sofia, et al.
(author)
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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
-
In: Science of Remote Sensing. - : Elsevier BV. - 2666-0172. ; 7
-
Journal article (peer-reviewed)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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29. |
- Karkauskaite, Paulina, et al.
(author)
-
Evaluation of the plant phenology index (PPI), NDVI and EVI for start-of-season trend analysis of the Northern Hemisphere boreal zone
- 2017
-
In: Remote Sensing. - : MDPI AG. - 2072-4292. ; 9:5
-
Journal article (peer-reviewed)abstract
- Satellite remote sensing of plant phenology provides an important indicator of climate change. However, start of the growing season (SOS) estimates in Northern Hemisphere boreal forest areas are known to be challenged by the presence of seasonal snow cover and limited seasonality in the greenness signal for evergreen needleleaf forests, which can both bias and impede trend estimates of SOS. The newly developed Plant Phenology Index (PPI) was specifically designed to overcome both problems. Here we use Moderate Resolution Imaging Spectroradiometer (MODIS) data (2000-2014) to analyze the ability of PPI for estimating start of season (SOS) in boreal regions of the Northern Hemisphere, in comparison to two other widely applied indices for SOS retrieval: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). Satellite-based SOS is evaluated against gross primary production (GPP)-retrieved SOS derived from a network of flux tower observations in boreal areas (a total of 81 site-years analyzed). Spatiotemporal relationships between SOS derived from PPI, EVI and NDVI are furthermore studied for different boreal land cover types and regions. The overall correlation between SOS derived from VIs and ground measurements was rather low, but PPI performed significantly better (r = 0.50, p < 0.01) than EVI and NDVI which both showed a very poor correlation (r = 0.11, p = 0. 16 and r = 0.08, p = 0.24). PPI, EVI and NDVI overall produce similar trends in SOS for the Northern Hemisphere showing an advance in SOS towards earlier dates (0.28, 0.23 and 0.26 days/year), but a pronounced difference in trend estimates between PPI and EVI/NDVI is observed for different land cover types. Deciduous needleleaf forest is characterized by the largest advance in SOS when considering all indices, yet PPI showed less dramatic changes as compared to EVI/NDVI (0.47 days/year as compared to 0.62 and 0.74). PPI SOS trends were found to be higher for deciduous broadleaf forests and savannas (0.54 and 0.56 days/year). Taken together, the findings of this study suggest improved performance of PPI over NDVI and EVI in retrieval of SOS in boreal regions and precautions must be taken when interpreting spatio-temporal patterns of SOS from the latter two indices.
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30. |
- Lembrechts, Jonas J., et al.
(author)
-
Global maps of soil temperature
- 2022
-
In: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 28:9, s. 3110-3144
-
Journal article (peer-reviewed)abstract
- Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean=3.0±2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6±2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7±2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.
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31. |
- Lo, Adama, et al.
(author)
-
Dry season forage assessment across senegalese rangelands using earth observation data
- 2022
-
In: Frontiers in Environmental Science. - : Frontiers Media SA. - 2296-665X. ; 10
-
Journal article (peer-reviewed)abstract
- Strengthening of feed security in the Sahel is urgently needed given the climate change and growing human population. A prerequisite to this is sustainable use of rangeland forage resources for livestock. Many studies have focused on the assessment of rangeland resources during the rainy season, while only a few have focused on the dry season which is the longest and most demanding period for livestock in Sahelian rangelands. The objective of this study is to develop remote sensing-based models for estimating dry season forage vegetation mass. To that end, 29 vegetation indices calculated from each of the MODIS-MCD43A4 (500 m), Landsat-8 (30 m), and Sentinel-2 (10 m) satellite products were used and tested against in situ data collected during three field-measurement campaigns in 2021 at eleven monitoring sites across Senegalese rangelands. Four statistical models were tested, namely, random forest, gradient boosting machines, and simple linear and multiple linear regressions. The two main vegetation mass variables modeled from remote sensing imagery were the standing herbaceous and litter dry mass (BH) and total forage dry mass (BT) with a dry mass of woody plant leaves added to BH. Overall, Sentinel-2 data provided the best performance for the assessment of BH with multiple linear regression (R2 = 0.74; RMSE = 378 kg DM/ha) using NDI5 (Normalized Difference Index5), GRCI (Green Residue Cover Index), SRI (Simple Ratio Index), TCARI (Transformed Chlorophyll Absorption in Reflectance Index), and DFI (Dead Fuel Index) indices. For BT, the best model was also obtained from Sentinel-2 data, including RVI3 (Ratio Vegetation Index3) (R2 = 0.78; RMSE = 496 kg DM/ha). Results showed the suitability of combining the red, green, blue, NIR, SWIR1, and SWIR2 bands in monitoring forage availability during the dry season. Our study revealed that the spectral richness of the optical sensor systems Sentinel-2, Landsat-8, and MODIS-MCD43A4 allowed for accurate assessments of dry-season forage mass of semi-arid rangelands. Adding to this, the high spatial and temporal resolution of Sentinel-2 satellite imagery makes this a promising data source for timely monitoring. These findings can support the monitoring of the animal feed balance in Sahelian countries and contribute to enhancing the resilience of pastoralism toward feed shortage through early warning systems.
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32. |
- Madani, Nima, et al.
(author)
-
Below-surface water mediates the response of African forests to reduced rainfall
- 2020
-
In: Environmental Research Letters. - : IOP Publishing. - 1748-9318 .- 1748-9326. ; 15:3
-
Journal article (peer-reviewed)abstract
- Terrestrial ecosystem gross primary productivity (GPP) is the largest land-atmosphere carbon flux and the primary mechanism of photosynthetic fixation of atmospheric CO2 into plant biomass. Anomalous rainfall events have been shown to have a great impact on the global carbon cycle. However, less is known about the impact of these events on GPP, especially in Africa, where in situ observations are sparse. Here, we use a suite of satellite and other geospatial data to examine the responses of major ecosystems in Africa to anomalous rainfall events from 2003 to 2017. Our results reveal that higher-than-average groundwater storage in tropical ecosystems offsets the rainfall deficit during the dry years. While the inter-annual variations in GPP in semi-arid ecosystems are controlled by near surface soil water, deeper soil moisture and groundwater control the inter-annual variability of the GPP in dense tropical forests. Our study highlights the critical role of groundwater in buffering rainfall shortages and continued availability of near-surface water to plants through dry spells.
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33. |
- Madani, Nima, et al.
(author)
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The Impacts of Climate and Wildfire on Ecosystem Gross Primary Productivity in Alaska
- 2021
-
In: Journal of Geophysical Research: Biogeosciences. - 2169-8953. ; 126:6
-
Journal article (peer-reviewed)abstract
- The increase in wildfire occurrence and severity seen over the past decades in the boreal and Arctic biomes is expected to continue in the future in response to rapid climate change in this region. Recent studies documented positive trends in gross primary productivity (GPP) for Arctic boreal biomes driven by warming, but it is unclear how GPP trends are affected by wildfires. Here, we used satellite vegetation observations and environmental data with a diagnostic GPP model to analyze recovery from large fires in Alaska over the period 2000–2019. We confirmed earlier findings that warmer-than-average years provide favorable climate conditions for vegetation growth, leading to a GPP increase of 1 Tg C yr−1, contributed mainly from enhanced productivity in the early growing season. However, higher temperatures increase the risk of wildfire occurrence leading to direct carbon loss over a period of 1–3 years. While mortality related to severe wildfires reduce ecosystem productivity, post-fire productivity in moderately burned areas shows a significant positive trend. The rapid GPP recovery following fires reported here might be favorable for maintaining the region's net carbon sink, but wildfires can indirectly promote the release of long-term stored carbon in the permafrost. With the projected increase in severity and frequency of wildfires in the future, we expect a reduction of GPP and therefore amplification of climate warming in this region.
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34. |
- Martínez, Beatriz, et al.
(author)
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Evaluation of the LSA-SAF Gross Primary Production product derived from SEVIRI/MSG data (MGPP)
- 2020
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In: ISPRS Journal of Photogrammetry and Remote Sensing. - : Elsevier BV. - 0924-2716. ; 159, s. 220-236
-
Journal article (peer-reviewed)abstract
- The objective of this study is to describe a completely new 10-day gross primary production (GPP) product(MGPP LSA-411) based on data from the geostationary SEVIRI/MSG satellite within the LSA SAF (Land SurfaceAnalysis SAF) as part of the SAF (Satellite Application Facility) network of EUMETSAT.The methodology relies on the Monteith approach. It considers that GPP is proportional to the absorbedphotosynthetically active radiation APAR and the proportionality factor is known as the light use efficiency ε. Aparameterization of this factor is proposed as the product of a εmax, corresponding to the canopy functioningunder optimal conditions, and a coefficient quantifying the reduction of photosynthesis as a consequence ofwater stress. A three years data record (2015–2017) was used in an assessment against site-level eddy covariance(EC) tower GPP estimates and against other Earth Observation (EO) based GPP products. The site-level comparisonindicated that the MGPP product performed better than the other EO based GPP products with 48% ofthe observations being below the optimal accuracy (absolute error < 1.0 g m−2 day−1) and 75% of these databeing below the user requirement threshold (absolute error < 3.0 g m−2 day−1). The largest discrepanciesbetween the MGPP product and the other GPP products were found for forests whereas small differences wereobserved for the other land cover types. The integration of this GPP product with the ensemble of LSA-SAF MSG
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35. |
- Mastepanov, Mikhail, et al.
(author)
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Revisiting factors controlling methane emissions from high-Arctic tundra
- 2013
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In: Biogeosciences. - : Copernicus GmbH. - 1726-4189. ; 10:7, s. 5139-5158
-
Journal article (peer-reviewed)abstract
- The northern latitudes are experiencing disproportionate warming relative to the mid-latitudes, and there is growing concern about feedbacks between this warming and methane production and release from high-latitude soils. Studies of methane emissions carried out in the Arctic, particularly those with measurements made outside the growing season, are underrepresented in the literature. Here we present results of 5 yr (2006-2010) of automatic chamber measurements at a high-Arctic location in Zackenberg, NE Greenland, covering both the growing seasons and two months of the following freeze-in periods. The measurements show clear seasonal dynamics in methane emission. The start of the growing season and the increase in CH4 fluxes were strongly related to the date of snowmelt. Within each particular growing season, CH4 fluxes were highly correlated with the soil temperature (R-2 > 0.75), which is probably explained by high seasonality of both variables, and weakly correlated with the water table. The greatest variability in fluxes between the study years was observed during the first part of the growing season. Somewhat surprisingly, this variability could not be explained by commonly known factors controlling methane emission, i.e. temperature and water table position. Late in the growing season CH4 emissions were found to be very similar between the study years (except the extremely dry 2010) despite large differences in climatic factors (temperature and water table). Late-season bursts of CH4 coinciding with soil freezing in the autumn were observed during at least three years. The cumulative emission during the freeze-in CH4 bursts was comparable in size with the growing season emission for the year 2007, and about one third of the growing season emissions for the years 2009 and 2010. In all three cases the CH4 burst was accompanied by a corresponding episodic increase in CO2 emission, which can compose a significant contribution to the annual CO2 flux budget. The most probable mechanism of the late-season CH4 and CO2 bursts is physical release of gases accumulated in the soil during the growing season. In this study we discuss possible links between growing season and autumn fluxes. Multiannual dynamics of the subsurface CH4 storage pool are hypothesized to be such a link and an important driver of intearannual variations in the fluxes, capable of overruling the conventionally known short-term control factors (temperature and water table). Our findings suggest the importance of multiyear studies with a continued focus on shoulder seasons in Arctic ecosystems.
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36. |
- Mwakoba, Kharid, et al.
(author)
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Land use and land cover change in locally supported wildlife management areas in the Nyerere-Selous ecosystem, Tanzania
-
In: International Journal of Environmental Studies. - 0020-7233.
-
Journal article (peer-reviewed)abstract
- Worldwide, there is conversion of natural habitats to other land cover types. These are explained by changes in land use and climate change. In Tanzania, environmental degradation happens also within and around protected areas. To mitigate the effects, wildlife management areas (WMAs) surrounding the borders of protected areas in Tanzania have been promoted. They are based on the concept of community based natural resources management (CBNRM). These WMAs have attracted adverse attention because there is a lack of active local participation and of equitable benefits for local people. Unsustainable activities have been reported in these areas. To determine the extent of these activities in WMAs in the Nyerere-Selous ecosystem in Southeastern Tanzania, the researchers did a change detection analysis using Landsat images. Analysis showed that the conditions of the vegetation within WMAs were considerably improving. Although almost all other categories of land use/cover had increased, socio-economic activities had decreased by 25% since the establishment of WMAs in 2003. This decrease is a positive sign towards attaining the goals of CBNRM.
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37. |
- Pastorello, Gilberto, et al.
(author)
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The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
- 2020
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In: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 7:1
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Journal article (peer-reviewed)abstract
- The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
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38. |
- Rahimi, Jaber, et al.
(author)
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Modeling gas exchange and biomass production in West African Sahelian and Sudanian ecological zones
- 2021
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In: Geoscientific Model Development. - : Copernicus GmbH. - 1991-959X .- 1991-9603. ; 14:6, s. 3789-3812
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Journal article (peer-reviewed)abstract
- West African Sahelian and Sudanian ecosystems provide essential services to people and also play a significant role within the global carbon cycle. However, climate and land use are dynamically changing, and uncertainty remains with respect to how these changes will affect the potential of these regions to provide food and fodder resources or how they will affect the biosphere-atmosphere exchange of CO2. In this study, we investigate the capacity of a process-based biogeochemical model, LandscapeDNDC, to simulate net ecosystem exchange (NEE) and aboveground biomass of typical managed and natural Sahelian and Sudanian savanna ecosystems. In order to improve the simulation of phenology, we introduced soil-water availability as a common driver of foliage development and productivity for all of these systems. The new approach was tested by using a sample of sites (calibration sites) that provided NEE from flux tower observations as well as leaf area index data from satellite images (MODIS, MODerate resolution Imaging Spectroradiometer). For assessing the simulation accuracy, we applied the calibrated model to 42 additional sites (validation sites) across West Africa for which measured aboveground biomass data were available. The model showed good performance regarding biomass of crops, grass, or trees, yielding correlation coefficients of 0.82, 0.94, and 0.77 and root-mean-square errors of 0.15, 0.22, and 0.12gkggm-2, respectively. The simulations indicate aboveground carbon stocks of up to 0.17, 0.33, and 0.54gkggCgha-1gm-2 for agricultural, savanna grasslands, and savanna mixed tree-grassland sites, respectively. Carbon stocks and exchange rates were particularly correlated with the abundance of trees, and grass biomass and crop yields were higher under more humid climatic conditions. Our study shows the capability of LandscapeDNDC to accurately simulate carbon balances in natural and agricultural ecosystems in semiarid West Africa under a wide range of conditions; thus, the model could be used to assess the impact of land-use and climate change on the regional biomass productivity.
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39. |
- Senty, Paul, et al.
(author)
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Fast Fusion of Sentinel-2 and Sentinel-3 Time Series over Rangelands
- 2024
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In: Remote Sensing. - 2072-4292. ; 16:11
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Journal article (peer-reviewed)abstract
- Monitoring ecosystems at regional or continental scales is paramount for biodiversity conservation, climate change mitigation, and sustainable land management. Effective monitoring requires satellite imagery with both high spatial resolution and high temporal resolution. However, there is currently no single, freely available data source that fulfills these needs. A seamless fusion of data from the Sentinel-3 and Sentinel-2 optical sensors could meet these monitoring requirements as Sentinel-2 observes at the required spatial resolution (10 m) while Sentinel-3 observes at the required temporal resolution (daily). We introduce the Efficient Fusion Algorithm across Spatio-Temporal scales (EFAST), which interpolates Sentinel-2 data into smooth time series (both spatially and temporally). This interpolation is informed by Sentinel-3’s temporal profile such that the phenological changes occurring between two Sentinel-2 acquisitions at a 10 m resolution are assumed to mirror those observed at Sentinel-3’s resolution. The EFAST consists of a weighted sum of Sentinel-2 images (weighted by a distance-to-clouds score) coupled with a phenological correction derived from Sentinel-3. We validate the capacity of our method to reconstruct the phenological profile at a 10 m resolution over one rangeland area and one irrigated cropland area. The EFAST outperforms classical interpolation techniques over both rangeland (−72% in the mean absolute error, MAE) and agricultural areas (−43% MAE); it presents a performance comparable to the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) (+5% MAE in both test areas) while being 140 times faster. The computational efficiency of our approach and its temporal smoothing enable the creation of seamless and high-resolution phenology products on a regional to continental scale.
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40. |
- Shi, Weibo, et al.
(author)
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Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery
- 2023
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In: Remote Sensing. - 2072-4292. ; 15:8
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Journal article (peer-reviewed)abstract
- Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution is very important to protect this highly sensitive ecological environment. Here, we combined unmanned aerial vehicle (UAV) imagery and convolutional neural networks (CNNs) to identify Faxon fir and explored the identification capabilities of multispectral (five bands) and red-green-blue (RGB) imagery under different months. For a case study area in Wanglang Nature Reserve, Southwest China, we acquired monthly RGB and multispectral images on six occasions over the growing season. We found that the accuracy of RGB imagery varied considerably (the highest intersection over union (IoU), 83.72%, was in April and the lowest, 76.81%, was in June), while the accuracy of multispectral imagery was consistently high (IoU > 81%). In April and October, the accuracy of the RGB imagery was slightly higher than that of multispectral imagery, but for the other months, multispectral imagery was more accurate (IoU was nearly 6% higher than those of the RGB imagery for June). Adding vegetation indices (VIs) improved the accuracy of the RGB models during summer, but there was still a gap to the multispectral model. Hence, our results indicate that the optimized time of the year for identifying Faxon fir using UAV imagery is during the peak of the growing season when using a multispectral imagery. During the non-growing season, RGB imagery was no worse or even slightly better than multispectral imagery for Faxon fir identification. Our study can provide guidance for optimizing observation plans regarding data collection time and UAV loads and could further help enhance the utility of UAVs in forestry and ecological research.
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41. |
- Ström, Lena, et al.
(author)
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Presence of Eriophorum scheuchzeri enhances substrate availability and methane emission in an Arctic wetland
- 2012
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In: Soil Biology & Biochemistry. - : Elsevier BV. - 0038-0717. ; 45, s. 61-70
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Journal article (peer-reviewed)abstract
- Here we present results from a field experiment in an Arctic wetland situated in Zackenberg, NE Greenland. During one growing season we investigated how dominance of the sedge Eriophorum scheuchzeri affected the below-ground concentrations of low molecular weight carbon compounds (LMWOC) and the fluxes of CO2 and CH4 in comparison to dominance of other sedges (Carex stans and Dupontia psilosantha). Three groups of LMWOC were analysed using liquid chromatography-ionspray tandem mass spectrometry, i.e., organic acids (OAs), amino acids (AAs) and simple carbohydrates (CHs). To identify the effect of plant composition the experiments were carried out in a continuous fen area with very little between species variation in environmental conditions, e.g., water-table and active layer thickness and soil temperature. The pool of labile LMWOC compounds in this Arctic fen was dominated by OAs, constituting between 75 and 83% of the total pore water pool of OAs. CHs and Ms. The dominant OA was acetic acid, an easily available substrate for methanogens, which constituted >= 85% of the OA pool. We estimated that the concentration of acetic acid found in pore water would support 2 -2.5 h of CH4 flux and an additional continuous input of acetic acid through root exudation that would support 1.3-1.5 h of CH4 flux. Thus, the results clearly points to the importance of a continuous input for acetoclastic methanogenesis to be sustainable. Additionally, Eriophorum had a very strong effect on parts of the carbon cycle in the Arctic fen. The mean seasonal CH4 flux was twice as high in Eriophorum dominated plots, most likely due to a 1.7 times higher concentration of OAs in these plots. Further, the ecosystem respiration was 1.3 times higher in Eriophorum dominated plots. In conclusion, the results offer further support to the importance of certain vascular plant species for the carbon cycle of wetland ecosystems. (C) 2011 Elsevier Ltd. All rights reserved.
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42. |
- Sun, Jia, et al.
(author)
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Leaf pigment retrieval using the PROSAIL model : Influence of uncertainty in prior canopy-structure information
- 2022
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In: Crop Journal. - : Elsevier BV. - 2095-5421 .- 2214-5141. ; 10:5, s. 1251-1263
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Journal article (peer-reviewed)abstract
- Leaf pigments are critical indicators of plant photosynthesis, stress, and physiological conditions. Inversion of radiative transfer models (RTMs) is a promising method for robustly retrieving leaf biochemical traits from canopy observations, and adding prior information has been effective in alleviating the “ill-posed” problem, a major challenge in model inversion. Canopy structure parameters, such as leaf area index (LAI) and average leaf inclination angle (ALA), can serve as prior information for leaf pigment retrieval. Using canopy spectra simulated from the PROSAIL model, we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll (Cab) and carotenoid (Car). The retrieval accuracies of the two pigments were increased by use of the priors of LAI (RMSE of Cab from 7.67 to 6.32 μg cm−2, Car from 2.41 to 2.28 μg cm−2) and ALA (RMSE of Cab from 7.67 to 5.72 μg cm−2, Car from 2.41 to 2.23 μg cm−2). However, this improvement deteriorated with an increase of additive and multiplicative uncertainties, and when 40% and 20% noise was added to LAI and ALA respectively, these priors ceased to increase retrieval accuracy. Validation using an experimental winter wheat dataset also showed that compared with Car, the estimation accuracy of Cab increased more or deteriorated less with uncertainty in prior canopy structure. This study demonstrates possible limitations of using prior information in RTM inversions for retrieval of leaf biochemistry, when large uncertainties are present.
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43. |
- Sun, Jia, et al.
(author)
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Optimizing LUT-based inversion of leaf chlorophyll from hyperspectral lidar data : Role of cost functions and regulation strategies
- 2021
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In: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 105
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Journal article (peer-reviewed)abstract
- Hyperspectral lidar (HSL) is a novel remote sensing technology that provides spectral information in addition to spatial features. This unprecedented data source leads to new possibilities for monitoring leaf biochemistry. Inversion of physically based radiative transfer models (RTMs) is a popular method for deriving leaf physiological traits due to its robustness and generalization capability. However, owing to the active nature of the HSL system, RTM inversion using the backscattered reflectance spectra may face new problems. Thus, optimization strategies for RTM inversion based on HSL measurements need to be studied. In this paper, several regulation strategies for lookup table (LUT)-based PROSPECT model inversions were explored for an HSL system. In particular, the influences of i) different cost functions, ii) multiple best solutions (1–1000), iii) different LUT sizes (100–100000), and iv) spectral domains for leaf chlorophyll (Chl) retrieval were analyzed. An evaluation against an experimental dataset of rice leaves indicated that i) least-squares estimation (LSE) provided better estimates than seven alternative cost functions when more than 200 solutions were taken; ii) accuracy in leaf Chl retrieval increased up until 200 solutions where after it stabilized; iii) the impact of LUT size became insignificant after 1000; and iv) the red edge was the spectral domain that had the largest impact on the inversion performance. The optimal performance of leaf Chl estimation reached R2 of 0.58 and RMSE of 0.69 between the z-scores from retrieved and measured leaf Chl. The practical application of combining RTM with HSL data will facilitate the detection of leaf-level biochemistry and advance research on terrestrial carbon cycle modeling.
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44. |
- Tagesson, Torbern, et al.
(author)
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A physiology-based Earth observation model indicates stagnation in the global gross primary production during recent decades
- 2021
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In: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 27:4, s. 836-854
-
Journal article (peer-reviewed)abstract
- Earth observation-based estimates of global gross primary production (GPP) are essential for understanding the response of the terrestrial biosphere to climatic change and other anthropogenic forcing. In this study, we attempt an ecosystem-level physiological approach of estimating GPP using an asymptotic light response function (LRF) between GPP and incoming photosynthetically active radiation (PAR) that better represents the response observed at high spatiotemporal resolutions than the conventional light use efficiency approach. Modelled GPP is thereafter constrained with meteorological and hydrological variables. The variability in field-observed GPP, net primary productivity and solar-induced fluorescence was better or equally well captured by our LRF-based GPP when compared with six state-of-the-art Earth observation-based GPP products. Over the period 1982–2015, the LRF-based average annual global terrestrial GPP budget was 121.8 ± 3.5 Pg C, with a detrended inter-annual variability of 0.74 ± 0.13 Pg C. The strongest inter-annual variability was observed in semi-arid regions, but croplands in China and India also showed strong inter-annual variations. The trend in global terrestrial GPP during 1982–2015 was 0.27 ± 0.02 Pg C year−1, and was generally larger in the northern than the southern hemisphere. Most positive GPP trends were seen in areas with croplands whereas negative trends were observed for large non-cropped parts of the tropics. Trends were strong during the eighties and nineties but levelled off around year 2000. Other GPP products either showed no trends or continuous increase throughout the study period. This study benchmarks a first global Earth observation-based model using an asymptotic light response function, improving simulations of GPP, and reveals a stagnation in the global GPP after the year 2000.
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45. |
- Tagesson, Torbern, et al.
(author)
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Applicability of leaf area index products for boreal regions of Sweden
- 2009
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In: International Journal of Remote Sensing. - : Informa UK Limited. - 1366-5901 .- 0143-1161. ; 30:21, s. 5619-5632
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Journal article (peer-reviewed)abstract
- Leaf area index (LAI) of boreal ecosystems were estimated with optical instruments at the Laxemar and the Forsmark investigation areas in Sweden. The aim was to study the relationship between LAI and the normalized difference vegetation index (NDVI) from Landsat-5 and SPOT and evaluate the applicability of the MODIS (Moderate Resolution Imaging Spectroradiometer) LAI product for small boreal regions. Relationships between ground-estimated LAI and NDVI were significant for coniferous, deciduous and mixed forest sites in Laxemar. For Forsmark, effective LAI was correlated to NDVI for all sites. LAI estimated from NDVI was also used for evaluating accuracy of the MODIS LAI product. The comparison showed no correlation between MODIS LAI and NDVI-based LAI in Forsmark whereas there was in Laxemar. MODIS LAI was on average 2.28 higher than NDVI-based LAI and it also showed larger scatter. Scale issues were the main explanation to high MODIS LAI, since the heterogeneous landscapes with open areas (given a value of zero in the NDVI estimates) was seen as forest in the large pixels of the MODIS LAI product. Therefore, we do not recommend using the MODIS LAI product in small boreal regional landscapes, such as the Forsmark and Laxemar investigation areas.
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46. |
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47. |
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48. |
- Tagesson, Torbern, et al.
(author)
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Dynamics in carbon exchange fluxes for a grazed semi-arid savanna ecosystem in West Africa
- 2015
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In: Agriculture, Ecosystems & Environment. - : Elsevier BV. - 1873-2305 .- 0167-8809. ; 205, s. 15-24
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Journal article (peer-reviewed)abstract
- The main aim of this paper is to study land-atmosphere exchange of carbon dioxide (CO2) for semi-arid savanna ecosystems of the Sahel region and its response to climatic and environmental change. A subsidiary aim is to study and quantify the seasonal dynamics in light use efficiency (epsilon) being a key variable in scaling carbon fluxes from ground observations using earth observation data. The net ecosystem exchange of carbon dioxide (NEE) 2010-2013 was measured using the eddy covariance technique at a grazed semi-arid savanna site in Senegal, West Africa. Night-time NEE was not related to temperature, confirming that care should be taken before applying temperature response curves for hot dry semi-arid regions when partitioning NEE into gross primary productivity (GPP) and ecosystem respiration (R-eco). Partitioning was instead done using light response curves. The values of epsilon ranged between 0.02 g carbon (C) MJ(-1) for the dry season and 2.27 g C MJ(-1) for the peak of the rainy season, and its seasonal dynamics was governed by vegetation phenology, photosynthetically active radiation, soil moisture and vapor pressure deficit (VPD). The CO2 exchange fluxes were very high in comparison to other semi-arid savanna sites; half-hourly GPP and R-eco peaked at -43 mu mol CO2 m(-2) s(-1) and 20 mu mol CO2 m(-2) s(-1), and daily GPP and R-eco peaked at -15 g C m(-2) and 12 g C m(-2), respectively. Possible explanations for the high CO2 fluxes are a high fraction of C4 species, alleviated water stress conditions, and a strong grazing pressure that results in compensatory growth and fertilization effects. We also conclude that vegetation phenology, soil moisture, radiation, VPD and temperature were major components in determining the seasonal dynamics of CO2 fluxes. Despite the height of the peak of the growing season CO2 fluxes, the annual C budget (average NEE: -271 g C m(-2)) were similar to that in other semi-arid ecosystems because the short rainy season resulted in a short growing season. Global circulation models project a decrease in rainfall, an increase in temperature and a shorter growing season for the western Sahel region, and the productivity and the sink function of this semi-arid ecosystem may thus be lower in the future. (C) 2015 Elsevier B.V. All rights reserved.
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49. |
- Tagesson, Torbern, et al.
(author)
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Ecosystem properties of semiarid savanna grassland in West Africa and its relationship with environmental variability
- 2015
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In: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 21:1, s. 250-264
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Journal article (peer-reviewed)abstract
- The Dahra field site in Senegal, West Africa, was established in 2002 to monitor ecosystem properties of semiarid savanna grassland and their responses to climatic and environmental change. This article describes the environment and the ecosystem properties of the site using a unique set of in situ data. The studied variables include hydroclimatic variables, species composition, albedo, normalized difference vegetation index (NDVI), hyperspectral characteristics (350-1800nm), surface reflectance anisotropy, brightness temperature, fraction of absorbed photosynthetic active radiation (FAPAR), biomass, vegetation water content, and land-atmosphere exchanges of carbon (NEE) and energy. The Dahra field site experiences a typical Sahelian climate and is covered by coexisting trees (similar to 3% canopy cover) and grass species, characterizing large parts of the Sahel. This makes the site suitable for investigating relationships between ecosystem properties and hydroclimatic variables for semiarid savanna ecosystems of the region. There were strong interannual, seasonal and diurnal dynamics in NEE, with high values of similar to-7.5g Cm(-2)day(-1) during the peak of the growing season. We found neither browning nor greening NDVI trends from 2002 to 2012. Interannual variation in species composition was strongly related to rainfall distribution. NDVI and FAPAR were strongly related to species composition, especially for years dominated by the species Zornia glochidiata. This influence was not observed in interannual variation in biomass and vegetation productivity, thus challenging dryland productivity models based on remote sensing. Surface reflectance anisotropy (350-1800nm) at the peak of the growing season varied strongly depending on wavelength and viewing angle thereby having implications for the design of remotely sensed spectral vegetation indices covering different wavelength regions. The presented time series of in situ data have great potential for dryland dynamics studies, global climate change related research and evaluation and parameterization of remote sensing products and dynamic vegetation models.
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50. |
- Tagesson, Torbern, et al.
(author)
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Estimating Net Primary Production of Swedish Forest Landscapes by Combining Mechanistic Modeling and Remote Sensing
- 2009
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In: Ambio: a Journal of Human Environment. - 0044-7447. ; 38:6, s. 316-324
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Journal article (peer-reviewed)abstract
- The aim of this study was to investigate a combination of satellite images of leaf area index (LAI) with process-based vegetation modeling for the accurate assessment of the carbon balances of Swedish forest ecosystems at the scale of a landscape. Monthly climatologic data were used as inputs in a dynamic vegetation model, the Lund Potsdam Jena-General Ecosystem Simulator. Model estimates of net primary production (NPP) and the fraction of absorbed photosynthetic active radiation were constrained by combining them with satellite-based LAI images using a general light use efficiency (LUE) model and the Beer-Lambert law. LAI estimates were compared with satellite-extrapolated field estimates of LAI, and the results were generally acceptable. NPP estimates directly from the dynamic vegetation model and estimates obtained by combining the model estimates with remote sensing information were, on average, well simulated but too homogeneous among vegetation types when compared with field estimates using forest inventory data.
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