| 1. |
- Abdi, Hakim, et al.
(författare)
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The supply and demand of net primary production in the Sahel
- 2014
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Ingår i: Environmental Research Letters. - : IOP Publishing. - 1748-9326. ; 9:9, s. 11-094003
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Tidskriftsartikel (refereegranskat)abstract
- Net primary production (NPP) is the principal source of energy for ecosystems and, by extension, human populations that depend on them. The relationship between the supply and demand of NPP is important for the assessment of socio-ecological vulnerability. We present an analysis of the supply and demand of NPP in the Sahel using NPP estimates from the MODIS sensor and agri-environmental data from FAOSTAT. This synergistic approach allows for a spatially explicit estimation of human impact on ecosystems. We estimated the annual amount of NPP required to derive food, fuel and feed between 2000 and 2010 for 22 countries in sub-Saharan Africa. When comparing annual estimates of supply and demand of NPP, we found that demand increased from 0.44 PgC to 1.13 PgC, representing 19% and 41%, respectively, of available supply due to a 31% increase in the human population between 2000 and 2010. The demand for NPP has been increasing at an annual rate of 2.2% but NPP supply was near-constant with an inter-annual variability of approximately 1.7%. Overall, there were statistically significant (p < 0.05) increases in the NPP of cropland (+6.0%), woodland (+6.1%) and grassland/savanna (+9.4%), and a decrease in the NPP of forests (−0.7%). On the demand side, the largest increase was for food (20.4%) followed by feed (16.7%) and fuel (5.5%). The supply-demand balance of NPP is a potentially important tool from the standpoint of sustainable development, and as an indicator of stresses on the environment stemming from increased consumption of biomass.
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| 2. |
- Connolly, J., et al.
(författare)
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Detecting recent disturbance on montane blanket bogs in the Wicklow Mountains, Ireland, using the MODIS enhanced vegetation index
- 2011
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Ingår i: International Journal of Remote Sensing. - : Informa UK Limited. - 1366-5901 .- 0143-1161. ; 32:9, s. 2377-2393
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Tidskriftsartikel (refereegranskat)abstract
- Irish peat soils are extensive, covering approximately 14-20% of the national land area. They contain between 53% and 62% of the national soil organic carbon stock. Montane blanket bog covers approximately 25% or 242650ha of the total peatland area in Ireland and is the dominant peatland type covering the upland area of Wicklow. Blanket bogs are very sensitive systems and have experienced much disturbance in Ireland due to overgrazing, burning, drainage, forestry and turf cutting. It has been estimated that disturbance of blanket bog, on a national area basis, ranges from 74% to 82% and in Wicklow is 57%. Disturbance can be detrimental to stocks of soil organic carbon in peatlands. Monitoring disturbance in peatlands, which tend to cover large, remote areas, is difficult and expensive using conventional surveying methods. Satellite remote sensing offers a way to gather data for these areas. In this paper a method of determining the probability of disturbance is presented. This method uses the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) in combination with univariate image differencing along with thresholding and binary logistic regression. A probability map was produced depicting the geospatial patterns and pressures on the peatland soil organic carbon stock in Wicklow. Peat soils in higher and steeper areas were more disturbed and the primary disturbance in between 2000 and 2005 was fire. Lower, flatter areas did not experience as much disturbance probably because they are wetter. The consumer's and producer's accuracy for the map was 76% and 42%, respectively.
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| 3. |
- Fensholt, Rasmus, et al.
(författare)
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Greenness in semi-arid areas across the globe 1981-2007 - an Earth Observing Satellite based analysis of trends and drivers
- 2012
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 121, s. 144-158
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Tidskriftsartikel (refereegranskat)abstract
- Semi-arid areas, defined as those areas of the world where water is an important limitation for plant growth, have become the subject of increased interest due to the impacts of current global changes and sustainability of human lifestyles. While many ground-based reports of declining vegetation productivity have been published over the last decades, a number of recent publications have shown a nuanced and, for some regions, positive picture. With this background, the paper provides an analysis of trends in vegetation greenness of semi-arid areas using AVHRR GIMMS from 1981 to 2007. The vegetation index dataset is used as a proxy for vegetation productivity and trends are analyzed for characterization of changes in semi-arid vegetation greenness. Calculated vegetation trends are analyzed with gridded data on potential climatic constraints to plant growth to explore possible causes of the observed changes. An analysis of changes in the seasonal variation of vegetation greenness and climatic drivers is conducted for selected regions to further understand the causes of observed inter-annual vegetation changes in semi-arid areas across the globe. It is concluded that semi-arid areas, across the globe, on average experience an increase in greenness (0.015 NDVI units over the period of analysis). Further it is observed that increases in greenness are found both in semi-arid areas where precipitation is the dominating limiting factor for plant production (0.019 NDVI units) and in semi-arid areas where air temperature is the primarily growth constraint (0.013 NDVI units). Finally, in the analysis of changes in the intra-annual variation of greenness it is found that seemingly similar increases in greenness over the study period may have widely different explanations. This implies that current generalizations, claiming that land degradation is ongoing in semi-arid areas worldwide, are not supported by the satellite based analysis of vegetation greenness. (c) 2012 Elsevier Inc. All rights reserved.
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| 4. |
- Jamali, Sadegh, et al.
(författare)
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Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel
- 2014
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 141, s. 79-89
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Tidskriftsartikel (refereegranskat)abstract
- Over the last few decades, increasing rates of change in the structure and function of ecosystems have been brought about by human modification of land cover, of which a major component is vegetation. Metrics derived from linear regression models applied to high temporal resolution satellite data are commonly used to estimate rates of vegetation change. This approach implicitly assumes that vegetation changes gradually and linearly, which may not always be the case. In order to account for non-linear change in annual observations of vegetation from satellites, we test and apply a polynomial fitting-based scheme to annual GIMMS (Global Inventory Modeling and Mapping Studies)–NDVI (Normalized Difference Vegetation Index) observations for North Africa (including the Sahel) for the period 1982–2006. The scheme divides vegetation change into cubic, quadratic, linear, and “concealed” trend behaviors, the latter indicating that while no net change in vegetation amount has occurred over the period, the curve exhibits at least one minimum or/and maximum indicating that the vegetation has undergone change during the elapsed time period. Our results show that just over half the study area (51.9%) exhibit trends that are statistically significant, with a dominance of positive linear trends (22.2%) that are distributed in an east-west band across the Sahel, thus confirming previous studies. Non-linear trends occur much less frequently and are more widely scattered. Nevertheless, they tend to cluster within or on the outskirts of zones of linear trend, underscoring their importance for detecting anomalous change features. We also show that the ratio of linear vs. non-linear trends tends to be associated with different land cover types/land cover change estimates, many of which reflect biome-level controls on vegetation dynamics. However, more local drivers related to direct human impact, such as urbanization, cannot be ruled out. Our change detection approach retains the more complex signatures embedded in long-term time series by preserving details about change rates, therefore allowing for a more subtle interpretation of change trajectories on a case-by-case basis. The fitting method is entirely automated and does not require the judicious selection of thresholds. However, while polynomials can give a better fit, they like linear models are based on assumptions, and may sometimes lead to oversimplification or miss short-term variations. Our method can help to contribute more accurate information to one of the major goals of the burgeoning field of land change science, namely to observe and monitor land changes underway throughout the world.
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| 5. |
- Jamali, Sadegh, et al.
(författare)
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Comparing parametric and non-parametric approaches for estimating trends in multi-year NDVI
- 2012
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Konferensbidrag (refereegranskat)abstract
- The aim of this study is to systematically compare parametric and non-parametric techniques for analyzing trends in annual NDVI derived from NOAA AVHRR sensor in order to examine how trend type and departure from normality assumptions affect the accuracy of detecting long-term change. To generate annual data, the mean NDVI of a four-month long ‘green’ season was computed for fifteen sites (located in Africa, Spain, Italy, Sweden, and Iraq) from the GIMMS product for the periods 1982-2006. Trends in these time series were then estimated by Ordinary Least-Squares (OLS) regression (parametric) and the combined Mann-Kendall test with Theil-Sen slope estimator (non-parametric), and compared using slope value and statistical significance measures. We also estimated optimal polynomial model for the annual NDVI, by using Akaike Information Criterion (AIC), to determine the trend type at each site. Results indicate that slopes and their statistical significances obtained from the two approaches at sites with low degree polynomials (mostly linear) and steep monotonic (gradually increasing or decreasing) trends compare favourably with one another. At sites with weak linear slopes, the two approaches had similar results as well. Exceptions include sites with abrupt step-like changes resulting in departures from linearity and consequently high degree polynomials where the least-squares method outperformed the Mann-Kendall Theil-Sen method. In sum, we conclude that OLS is superior for detecting NDVI trends using annual data though further investigation using other techniques is recommended.
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| 6. |
- Jamali, Sadegh, et al.
(författare)
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Investigating temporal relationships between rainfall, soil moisture and MODIS-derived NDVI and EVI for six sites in Africa
- 2011
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Konferensbidrag (refereegranskat)abstract
- This study investigates temporal relationships between vegetation growth, rainfall, and soil moisture for six sites located in sub-Saharan and southern Africa for the period 2005-2009. Specifically, seasonal components of time series of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) composites from the Moderate Resolution Imaging Spectroradiometer (MODIS) and half-hourly in-situ rainfall and soil moisture data at different depths (5-200 cm) during the growing season were used in a lagged correlation analysis in order to understand how vegetation growth responds to rainfall and soil moisture across different sites. Results indicate that both vegetation indices are strongly related to soil moisture (EVI slightly stronger than NDVI) for the upper 1 m reaching maximum correlations when they lag soil moisture by 0-28 days. They respond to rainfall with a 24-32 day lag at the sub-Saharan sites, EVI slightly earlier than NDVI, but their response at the southern hemisphere sites is complex.
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| 7. |
- Kross, Angela, et al.
(författare)
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Estimating carbon dioxide exchange rates at contrasting northern peatlands using MODIS satellite data
- 2013
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 137, s. 234-243
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Tidskriftsartikel (refereegranskat)abstract
- Northern hemisphere peatlands play an important role in the global carbon (C) cycle, accounting for about 30% of global soil C and similar to 10-25% of global natural methane (CH4) emissions. Satellite remote sensing has the potential for extracting continuous information related to C exchange rates at regional and global extents, yet, few studies have focused on peatlands. In this study we examined the potential of moderate resolution imaging spectroradiometer (MODIS) vegetation indices (normalized difference vegetation index, NDVI and simple ratio, SR), MODIS light use efficiency (LUE) based gross primary production (GPP) and a MODIS derived phenological index (annual peak photosynthetic rate) for the estimation of eddy covariance (EC) flux-derived GPP and net ecosystem production (NEP) at four contrasting northern peatlands. At the four sites of this study MODIS NDVI and SR explained between 39% and 71%, and between 42% and 69% of the variation in EC-derived GPP, respectively; and between 25% and 53%, and between 29% and 39% of the variation in EC-derived NEP, respectively. The relationships were mostly consistent across sites and within sites, suggesting that data may be pooled across years and sites, which could simplify the prediction of gross and net C dioxide (CO2) uptake over large areas dominated by northern peatlands based on MODIS data. MODIS GPP explained between 68% and 89% of the variation in EC-derived GPP at the four study sites. The root mean square errors ranged between 0.62 and 1.16 g C m(-2) d(-1) and were similar to errors from ecosystem process model estimates reported in the literature. Annual peak MODIS GPP, NDVI and SR rates explained up to 50% of the variations in annual cumulative EC-derived GPP and NEP at two of the study sites. Our results show the potentials and limitations of MODIS data to monitor the C dynamics of northern peatlands; among the three studied approaches the MODIS LUE-based GPP approach showed better performance as a predictor of GPP and NEP. The other approaches (VIs and phenology) can provide important input data for LUE models. (c) 2013 Elsevier Inc. All rights reserved.
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| 8. |
- Kross, Angela S. E., et al.
(författare)
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Phenology and its role in carbon dioxide exchange processes in northern peatlands
- 2014
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Ingår i: Journal of Geophysical Research - Biogeosciences. - 2169-8953. ; 119:7, s. 1370-1384
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Tidskriftsartikel (refereegranskat)abstract
- Ecosystem phenology plays an important role in carbon exchange processes and can be derived from continuous records of carbon dioxide (CO2) exchange data. In this study we examined the potential use of phenological indices for characterizing cumulative annual CO2 exchange in four contrasting northern peatland ecosystems. We used the approach of Jonsson and Eklundh (2004) to derive a set of phenological indices based on the daily time series of gross primary production (GPP), ecosystem respiration (R-e), and net ecosystem production (NEP) measured in the four peatland sites. The main objectives of this study were (a) to examine the variation in phenological indices across sites and (b) to determine the relationships among phenological indices, environmental conditions, and cumulative annual CO2 exchange. The phenological index used to define the "start of the growing season" showed good potential for differentiation among sites based on their average annual site GPP. Sites with earlier growing seasons had the highest average annual site GPP. The "peak CO2 exchange rate" phenological index performed best in reflecting variations among sites and for estimating annual values of GPP, R-e, and NEP (Pearson correlation coefficients ranged between 0.77 and 0.99, p<0.05 for all.). The phenological indices and annual GPP, R-e, and NEP were sensitive to winter (January-March) and summer (July-September) temperature and precipitation, but correlations, though significant, were weak.
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| 9. |
- Kross, Angela, et al.
(författare)
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The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests
- 2011
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 115:6, s. 1564-1575
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Tidskriftsartikel (refereegranskat)abstract
- Satellite remote sensing has the potential to contribute to plant phenology monitoring at spatial and temporal scales relevant for regional and global scale studies. Historically, temporal composites of satellite data, ranging from 8 days to 16 days, have been used as a starting point for satellite-derived phenology data sets. In this study we assess how the temporal resolution of such composites affects the estimation of the start of season (SOS) by: 1) calibrating a relationship between satellite derived SOS with in situ leaf unfolding (LU) of trembling aspen (Populus tremuloides) across Canada and 2) quantifying the sensitivity of calibrated satellite SOS estimates and trends, over Canadian broadleaf forests, to the temporal resolution of NDVI data. SOS estimates and trends derived from daily NDVI data were compared to SOS estimates and trends derived from multiday NDVI composites that retain the exact date of the maximum NDVI value or that assume the midpoint of the multiday interval as the observation date. In situ observations of LU dates were acquired from the PlantWatch Canada network. A new Canadian database of cloud and snow screened daily 1-km resolution National Oceanic and Atmospheric Administration advanced very high resolution radiometer surface reflectance images was used as input satellite data. The mean absolute errors of SOS dates with respect to in situ LU dates ranged between 13 and 40 days. SOS estimates from NDVI composites that retain the exact date of the maximum NDVI value had smaller errors (similar to 13 to 20 days). The sensitivity analysis reinforced these findings: SOS estimates from NDVI composites that use the exact date had smaller absolute deviations from the LU date (0 to 5 days) than the SOS estimates from NDVI composites that use the midpoint (-2 to -27 days). The SOS trends between 1985 and 2007 were not sensitive to the temporal resolution or compositing methods. However, SOS trends at individual ecozones showed significant differences with the SOS trends from daily NDVI data (Taiga plains and the Pacific maritime zones). Overall, our results suggest that satellite based estimates of vegetation green-up dates should preferably use sub-sampled NDVI composites that include the exact observation date of the maximum NDVI to minimize errors in both. SOS estimates and SOS trend analyses. For trend analyses alone, any of the compositing methods could be used, preferably with composite intervals of less than 28 days. This is an important finding, as it suggests that existing long-term 10-day or 15-day NDVI composites could be used for SOS trend analyses over broadleaf forests in Canada or similar areas. Future studies will take advantage of the growing in situ phenology networks to improve the validation of satellite derived green-up dates. (C) 2011 Elsevier Inc. All rights reserved.
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| 10. |
- Lindeskog, Mats, et al.
(författare)
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Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa
- 2013
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Ingår i: Earth System Dynamics. - : Copernicus GmbH. - 2190-4979 .- 2190-4987. ; 4:2, s. 385-407
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Tidskriftsartikel (refereegranskat)abstract
- Dynamic global vegetation models (DGVMs) are important tools for modelling impacts of global change on ecosystem services. However, most models do not take full account of human land management and land use and land cover changes (LULCCs). We integrated croplands and pasture and their management and natural vegetation recovery and succession following cropland abandonment into the LPJ-GUESS DGVM. The revised model was applied to Africa as a case study to investigate the implications of accounting for land use on net ecosystem carbon balance (NECB) and the skill of the model in describing agricultural production and reproducing trends and patterns in vegetation structure and function. The seasonality of modelled monthly fraction of absorbed photosynthetically active radiation (FPAR) was shown to agree well with satellite-inferred normalised difference vegetation index (NDVI). In regions with a large proportion of cropland, the managed land addition improved the FPAR vs. NDVI fit significantly. Modelled 1991-1995 average yields for the seven most important African crops, representing potential optimal yields limited only by climate forcings, were generally higher than reported FAO yields by a factor of 2-6, similar to previous yield gap estimates. Modelled inter-annual yield variations during 1971-2005 generally agreed well with FAO statistics, especially in regions with pronounced climate seasonality. Modelled land-atmosphere carbon fluxes for Africa associated with land use change (0.07 PgC yr(-1) release to the atmosphere for the 1980s) agreed well with previous estimates. Cropland management options (residue removal, grass as cover crop) were shown to be important to the land-atmosphere carbon flux for the 20th century.
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