Torbern Tagesson

Using earth observation-based dry season NDVI trends for assessment of changes in tree cover in the Sahel

The co-existence of trees and grasses is a defining feature of savannah ecosystems and landscapes. During recent decades, the combined effect of climate change and increased demographic pressure has led to complex vegetation changes in these ecosystems. A number of recent Earth observation (EO)-based studies reported positive changes in biological productivity in the Sahelian region in relation to increased precipitation, triggering an increased amount of herbaceous vegetation during the rainy season. However, this ‘greening of the Sahel’ may mask changes in the tree–grass composition, with a potential reduction in tree cover having important implications for the Sahelian population. Large-scale EO-based evaluation of changes in Sahelian tree cover is assessed by analysing long-term trends in dry season minimum normalized difference vegetation index (NDVImin) derived from three different satellite sensors: Système Pour l’Observation de la Terre (SPOT)-VEGETATION (VGT), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) dataset. To evaluate the reliability of using NDVImin as a proxy for tree cover in the Sahel, two factors that could potentially influence dry season NDVImin estimates were analysed: the total biomass accumulated during the preceding growing season and the percentage of burned area observed during the dry season. Time series of dry season NDVImin derived from low-resolution satellite time series were found to be uncorrelated to dry grass residues from the preceding growing season and to seasonal fire frequency and timing over most of the Sahel (88%), suggesting that NDVImin can serve as a proxy for assessing changes in tree cover. Good agreement (R2 = 0.79) between significant NDVImin trends (p < 0.05) derived from VGT and MODIS was found. Significant positive trends in NDVImin were registered by both MODIS and VGT dry season NDVImin time series over the Western Sahel, whereas trends based on GIMMS data were negative for the greater part of the Sahel. EO-based trends were generally not confirmed at the local scale based on selected study cases, partly caused by a temporal mismatch between data sets (i.e. different periods of analysis). Analysis of desert area NDVImin trends indicates less stable values for VGT and GIMMS data as compared with MODIS. This suggests that trends in dry season NDVImin derived from VGT and GIMMS should be used with caution as an indicator for changes in tree cover, whereas the MODIS data stream shows a better potential for tree-cover change analysis in the Sahel.

High-resolution satellite data reveal an increase in peak growing season gross primary production in a high-Arctic wet tundra ecosystem 1992–2008

Arctic ecosystems play a key role in the terrestrial carbon cycle. Our aim was to combine satellite-based normalized difference vegetation index (NDVI) with field measurements of CO2 fluxes to investigate changes in gross primary production (GPP) for the peak growing seasons 1992-2008 in Rylekaerene, a wet tundra ecosystem in the Zackenberg valley, north-eastern Greenland. A method to incorporate controls on GPP through satellite data is the light use efficiency (LUE) model, here expressed as GPP = epsilon(peak) x PAR(in) x FAPAR(green_peak); where epsilon(peak) was peak growing season light use efficiency of the vegetation, PARin was incoming photosynthetically active radiation, and FAPAR(green_peak) was peak growing season fraction of PAR absorbed by the green vegetation. The Speak was measured for seven different high-Arctic plant communities in the field, and it was on average 1.63 g CO2 MJ(-1). We found a significant linear relationship between FAPARgreen_peak measured in the field and satellite-based NDVI. The linear regression was applied to peak growing season NDVI 1992-2008 and derived FAPAR(green_peak) was entered into the LUE-model. It was shown that when several empirical models are combined, propagation errors are introduced, which results in considerable model uncertainties. The LUE-model was evaluated against field-measured GPP and the model captured field-measured GPP well (RMSE was 192 mg CO2 m(-2) h(-1)). The model showed an increase in peak growing season GPP of 42 mg CO2 m(-2) h(-1) y(-1) in Rylekaerene 1992-2008. There was also a strong increase in air temperature (0.15 degrees C y(-1)), indicating that the GPP trend may have been climate driven

Modelling of growing season methane fluxes in a high-Arctic wet tundra ecosystem 1997-2010 using in situ and high-resolution satellite data

Methane (CH4) fluxes 1997–2010 were studied by combining remotely sensed normalised difference water index (NDWI) with in situ CH4 fluxes from Rylekærene, a high-Arctic wet tundra ecosystem in the Zackenberg valley, north-eastern Greenland. In situ CH4 fluxes were measured using the closed-chamber technique. Regression models between in situ CH4 fluxes and environmental variables [soil temperature (Tsoil), water table depth (WtD) and active layer (AL) thickness] were established for different temporal and spatial scales. The relationship between in situ WtD and remotely sensed NDWI was also studied. The regression models were combined and evaluated against in situ CH4 fluxes. The models including NDWI as the input data performed on average slightly better [root mean square error (RMSE) =1.56] than the models without NDWI (RMSE=1.67), and they were better in reproducing CH4 flux variability. The CH4 flux model that performed the best included exponential relationships against temporal variation in T soil and AL, an exponential relationship against spatial variation in WtD and a linear relationship between WtD and remotely sensed NDWI (RMSE=1.50). There were no trends in modelled CH4 flux budgets between 1997 and 2010. Hence, during this period there were no trends in the soil temperature at 10 cm depth and NDWI.

Applicability of leaf area index products for boreal regions of Sweden

Leaf area index (LAI) of boreal ecosystems was estimated with optical instruments at the Laxemar and the Forsmark investigation areas in Sweden. The aim was to study relationships between LAI and normalized difference vegetation index (NDVI), and to evaluate the applicability of the moderate resolution imaging spectroradiometer (MODIS) LAI product for small boreal regions. Relationships between optically-estimated LAI and NDVI were significant for different forest types in Laxemar and for Forsmark, effective LAI was correlated to the NDVI for all sites. NDVI-estimated LAI was used for evaluating accuracy of the MODIS LAI product and the comparison showed no correlation in Forsmark, whereas they were correlated in Laxemar. MODIS LAI was, on average, 2.28 higher than NDVI-based LAI, and it showed larger scatter. Scale issues were the main explanation for the high MODIS LAI, since heterogeneous landscapes with open areas were seen as forest in the large pixels of the MODIS LAI product.

Estimating Net Primary Production of Swedish Forest Landscapes by Combining Mechanistic Modeling and Remote Sensing

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.

Estimating and analyzing savannah phenology with a lagged time series model

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.

Very high CO2 exchange fluxes at the peak of the rainy season in a West African grazed semi-arid savanna ecosystem

Abstract Africa is a sink of carbon, but there are large gaps in our knowledge regarding the CO2 exchange fluxes for many African ecosystems. Here, we analyse multi-annual eddy covariance data of CO2 exchange fluxes for a grazed Sahelian semi-arid savanna ecosystem in Senegal, West Africa. The aim of the study is to investigate the high CO2 exchange fluxes measured at the peak of the rainy season at the Dahra field site: gross primary productivity and ecosystem respiration peaked at values up to −48 μmol CO2 m−2 s−1 and 20 μmol CO2 m−2 s−1, respectively. Possible explanations for such high fluxes include a combination of moderately dense herbaceous C4 ground vegetation, high soil nutrient availability and a grazing pressure increasing the fluxes. Even though the peak net CO2 uptake was high, the annual budget of −229 ± 7 ± 49 g C m−2 y−1 (±random errors ± systematic errors) is comparable to that of other semi-arid savanna sites due the short length of the rainy season. An inter-comparison between the open-path and a closed-path infrared sensor indicated no systematic errors related to the instrumentation. An uncertainty analysis of long-term NEE budgets indicated that corrections for air density fluctuations were the largest error source (11.3% out of 24.3% uncertainty). Soil organic carbon data indicated a substantial increase in the soil organic carbon pool for the uppermost .20 m. These findings have large implications for the perception of the carbon sink/source of Sahelian ecosystems and its response to climate change.

Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone

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.

Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands

The African continent is facing one of the driest periods in the past three decades as well as continued deforestation. These disturbances threaten vegetation carbon (C) stocks and highlight the need for improved capabilities of monitoring large-scale aboveground carbon stock dynamics. Here we use a satellite dataset based on vegetation optical depth derived from low-frequency passive microwaves (L-VOD) to quantify annual aboveground biomass-carbon changes in sub-Saharan Africa between 2010 and 2016. L-VOD is shown not to saturate over densely vegetated areas. The overall net change in drylands (53% of the land area) was −0.05 petagrams of C per year (Pg C yr−1) associated with drying trends, and a net change of −0.02 Pg C yr−1 was observed in humid areas. These trends reflect a high inter-annual variability with a very dry year in 2015 (net change, −0.69 Pg C) with about half of the gross losses occurring in drylands. This study demonstrates, first, the applicability of L-VOD to monitor the dynamics of carbon loss and gain due to weather variations, and second, the importance of the highly dynamic and vulnerable carbon pool of dryland savannahs for the global carbon balance, despite the relatively low carbon stock per unit area.Low-frequency passive microwave data (L-VOD) allow quantification of biomass change in sub-Saharan Africa between 2010 and 2016, revealing climate-induced carbon losses, particularly in drylands.

Assessing drivers of vegetation changes in drylands from time series of earth observation data

This chapter summarizes methods of inferring information about drivers of global dryland vegetation changes observed from remote sensing time series data covering from the 1980s until present time. Earth observation (EO) based time series of vegetation metrics, sea surface temperature (SST) (both from the AVHRR (Advanced Very High Resolution Radiometer) series of instruments) and precipitation data (blended satellite/rain gauge) are used for determining the mechanisms of observed changes. EO-based methods to better distinguish between climate and human induced (land use) vegetation changes are reviewed. The techniques presented include trend analysis based on the Rain-Use Efficiency (RUE) and the Residual Trend Analysis (RESTREND) and the methodological challenges related to the use of these. Finally, teleconnections between global sea surface temperature (SST) anomalies and dryland vegetation productivity are illustrated and the associated predictive capabilities are discussed.