Assessing Land Degradation/Recovery in the African Sahel from Long-Term Earth Observation Based Primary Productivity and Precipitation Relationships

The ‘rain use efficiency’ (RUE) may be defined as the ratio of above-ground net primary productivity (ANPP) to annual precipitation, and it is claimed to be a conservative property of the vegetation cover in drylands, if the vegetation cover is not subject to non-precipitation related land degradation. Consequently, RUE may be regarded as means of normalizing ANPP for the impact of annual precipitation, and as an indicator of non-precipitation related land degradation. Large scale and long term identification and monitoring of land degradation in drylands, such as the Sahel, can only be achieved by use of Earth Observation (EO) data. This paper demonstrates that the use of the standard EO-based proxy for ANPP, summed normalized difference vegetation index (NDVI) (National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies 3rd generation (GIMMS3g)) over the year (ΣNDVI), and the blended EO/rain gauge based data-set for annual precipitation (Climate Prediction Center Merged Analysis of Precipitation, CMAP) results in RUE-estimates which are highly correlated with precipitation, rendering RUE useless as a means of normalizing for the impact of annual precipitation on ANPP. By replacing ΣNDVI by a ‘small NDVI integral’, covering only the rainy season and counting only the increase of NDVI relative to some reference level, this problem is solved. Using this approach, RUE is calculated for the period 1982–2010. The result is that positive RUE-trends dominate in most of the Sahel, indicating that non-precipitation related land degradation is not a widespread phenomenon. Furthermore, it is argued that two preconditions need to be fulfilled in order to obtain meaningful results from the RUE temporal trend analysis: First, there must be a significant positive linear correlation between annual precipitation and the ANPP proxy applied. Second, there must be a near-zero correlation between RUE and annual precipitation. Thirty-seven percent of the pixels in Sahel satisfy these requirements and the paper points to a range of different reasons why this may be the case

Vegetation dynamics in Southern Africa from NOAA-AVHRR and SPOT-VGT time series

Understanding vegetation dynamics in Southern Africa in relation to climatic variability supports better management of natural resources. This study investigates the response of vegetation to rainfall variability in Southern Africa, based on a long-term time series of satellite images from SPOT-VGT and NOAA-AVHRR sensors. Of course, the differences in sensor-platform combination will hinder direct combination of these datasets. Therefore, the approach was, to first use VGT pre-processing as a benchmark for AVHRR pre-processing. Combination of the two NDVI datasets was improved afterwards by degrading the spectrally more accurate dataset, i.e. to decrease the dynamical range of the better' sensor rather than to upgrade the data from the oldest sensor to the new one. Comparison of the two NDVI datasets showed that though the values are similar, the discrepancy between the NDVI values of the sensors is spatially dependent. Applying empirical adjustment functions that account for the difference in spectral band definition improved the similarity of the two datasets. The performance of this adjustment was spatially dependent, and the one performing consistently best for the entire area was applied on the dataset. The resulting NDVI dataset was then used to study the spatial and temporal patterns of ecosystem dynamics in Southern Africa, in response to rainfall variability. To do so, a method was proposed based on wavelet coherency between the time series. Both the strength and the time lag of the vegetation response to rainfall were investigated, as well as the variability of these indicators over time. Further, we investigated the dependency of the relationship between NDVI and rainfall on mean annual rainfall, soil type, vegetation and topography. Among these, mean annual rainfall and topography, both at the site and at the watershed level, have the largest impact on the NDVI response to rainfall. The large influence of the topography at the watershed level on the strength and the response time between vegetation growth and rainfall has clear implications for the analysis of trends in vegetation change.Doctorat en sciences (sciences géographiques) (GEOG 3)--UCL, 200

Assessing vegetation response to climate variability at different time scales

To evaluate human impacts on forests and other carbon-storing ecosystems, temporal patterns of vegetation cover status must be explained in terms of their causal factors. More specifically, the impacts of climate variability on vegetation dynamics must be identified, quantified, and separated from the impacts of human disturbances such as land use changes. A generic approach is to combine remote sensing-derived information about the vegetation cover status with meteorological data for a corresponding period and study area. To date, the most common approach consists of correlation analyses between time series of Normalized Difference Vegetation Index (NDVI) and precipitation depths. Various authors have examined the strength and the lag of the correlation between monthly, seasonal or annual vegetation indices on the one hand, and rainfall depths on the other hand. Thereafter, the findings were spatially related to various environmental conditions, such as soil and vegetation types. We propose a number of methodological improvements related to the choice of variables, and the handling of periodic fluctuations at different time scales. Previous studies have utilized standardized NDVI anomalies and anomalies of precipitation accumulated over variable time spans. However, we found that off-site and lag effects are better accounted for by using anomalies of soil moisture content as an indicator of climate variability. Since the soil pore space can be regarded as a buffering reservoir for precipitation, we observed a more instantaneous and site specific correlation of soil moisture content with vegetation growth over a study area spanning East and Central Africa. Furthermore, the fraction of absorbed photosynthetically active radiation (fAPAR) is, as opposed to NDVI, a biophysical variable and is considered here to replace NDVI for further temporal analysis. Moreover, it is derived from multispectral satellite imagery, and its sensor-independent characteristics allow the compilation of a 30-year long time series. The methodology to quantify the response of vegetation to variable climate conditions must be capable of detecting the time scales at which fluctuations occur. Time series of soil moisture content and vegetation indices display a clear annual periodicity, arising from the annual cycles of precipitation and the growing seasons of dominant vegation types. As the objective is to understand climatic forcing beyond this seasonal response, all possible time scales must be accounted for in the analysis of the temporal signals of the explanatory variables. After the detection of trends in the time domain, spectral analysis of the temporal signals may reveal significant underlying periodic components, other than the annual cycle. As it is expected that the studied phenomena show changing behavior over time, the applied spectral decomposition methods must allow for flexibility in the basic periodic function. Therefore, time adaptive expansions of regular harmonic analysis are considered, and applied on univariate time series. Ultimately, bivariate analysis on a meteorological variable and a vegetation index will indicate which climatic fluctuations affect vegetation growth most.status: publishe

Monitoring of seasonal glacier mass balance over the European Alps using low-resolution optical satellite images

We explore a new method to retrieve seasonal glacier mass balances (MBs) from low-resolution optical remote sensing. We derive annual winter and summer snow maps of the Alps during 1998–2014 using SPOT/VEGETATION 1 km resolution imagery. We combine these seasonal snow maps with a DEM to calculate a ‘mean regional’ altitude of snow (Z) in a region surrounding a glacier. Then, we compare the interannual variation of Z with the observed winter/summer glacier MB for 55 Alpine glaciers over 1998–2008, our calibration period. We find strong linear relationships in winter (mean R 2 = 0.84) and small errors for the reconstructed winter MB (mean RMSE = 158 mm (w.e.) a−1). This is lower than errors generally assumed for the glaciological MB measurements (200–400 mm w.e. a−1). Results for summer MB are also satisfying (mean R 2 and RMSE, respectively, 0.74 and 314 mm w.e. a−1). Comparison with observed seasonal MB available over 2009–2014 (our evaluation period) for 19 glaciers in winter and 13 in summer shows good agreement in winter (RMSE = 405 mm w.e. a−1) and slightly larger errors in summer (RMSE = 561 mm w.e. a−1). These results indicate that our approach might be valuable for remotely determining the seasonal MB of glaciers over large regions.ISSN:0022-1430ISSN:1727-565

Quantifying climatic influence on vegetation time series

Vegetation monitoring by satellite sensors has delivered 30-year time series of vegetation cover images over large areas. Decomposition of a pixel’s Vegeta-tion Index (VI) time series reveals the underlying processes of vegetation cover change at various time scales. Ensemble Empirical Mode Decomposition (EEMD) is a data-adaptive technique to isolate the effects of non-stationary recurrent climatic variability. To recognize significant patterns in the detected components, we propose a local significance test. We tested the method’s accuracy and sensitivity on a set of synthetic time series that represent our knowledge of climatic phenomena and vegeta-tion dynamics. It was also demonstrated for a set of real VI time series over a study area in East and Central Africa.status: publishe

Monitoring vegetation dynamics and carbon stocks under variable climate conditions

Attempted international measures to reduce carbon emissions from deforestation and forest degradation (REDD) require accounting for both climatic and human impacts on vegetation, and a shift towards a whole-landscape approach. Remote sensing archives allow for an area-covering, spatio-temporally explicit analysis of vegetation dynamics on different scales. This research attempts to quantify and separate the effects of regional and local variability in weather and soil conditions on vegetation, via time series analysis of satellite imagery and weather records, image classification and field surveys. Carbon stocks are estimated from field data and linked to detected land use for up-scaling to a national carbon balance, thereby comparing a sectoral forest approach with a landscape approach.Posterstatus: publishe

A time series processing tool to extract climate-driven interannual vegetation dynamics using Ensemble Empirical Mode Decomposition (EEMD)

Interannual changes of vegetation are crucial in understanding ecosystem dynamics under global change. However, there is no automated tool to extract these interannual changes from remote sensing time series. To fill this gap, the Ensemble Empirical Mode Decomposition (EEMD) framework was refined and implemented to decompose time series of Normalized Difference Vegetation Index (NDVI) and reconstruct their interannual components. The performance of EEMD-based interannual NDVI detection was assessed using simulated time series, and its sensitivity to model and data parameters was determined to provide a basis for remote sensing applications. The sensitivity analysis highlighted application limitations for time series with low interannual to annual amplitude ratios and high irregularity in timing of growing seasons, as these factors have the strongest effects on the overall performance. However, within these limitations, the detected interannual components correspond well to simulated input components with respect to timing of episodes and composition of time scales. The applicability on real world NDVI time series was demonstrated by mapping the coupling between precipitation variability, interannual vegetation changes, and the El Niño Southern Oscillation and Indian Ocean Dipole phenomena for ecoregions in East and Central Africa. In most areas where precipitation was found sensitive to oceanic forcing, the EEMD detected vegetation changes matched the predicted response, except in dense forest ecosystems.publisher: Elsevier articletitle: A time series processing tool to extract climate-driven interannual vegetation dynamics using Ensemble Empirical Mode Decomposition (EEMD) journaltitle: Remote Sensing of Environment articlelink: http://dx.doi.org/10.1016/j.rse.2015.08.024 content_type: article copyright: Copyright © 2015 Elsevier Inc. All rights reserved.status: publishe

Assessing carbon stocks across land cover types in heterogeneous landscapes in Rwanda

Reducing land use based carbon emissions is an essential part of proposed climate change mitigation strategies in the ongoing climate debate. In order to achieve effective monitoring of the carbon that is stored in all parts of the landscape, detailed estimations of actual and historic carbon stocks must be performed across the major land use types. A field study in Rwanda was set up to survey the major landscapes and land use types, and to collect data on carbon stocks in biomass and soil. High resolution satellite imagery and background maps of land cover, topography and soils were the basic input for a preceding stratification of the landscape. First, additional observations of land cover were made along transects throughout Rwanda, to characterize the landscapes in terms of areal proportions of vegetative cover. Sample plots for carbon inventorying were laid out across the different land cover classes, to test a field protocol for the measurement and estimation of above-ground and below-ground carbon stocks. Measurements of tree height, tree diame-ter, canopy closure and basal area of woody species were used to estimate volumes of above-ground biomass. Soil samples were analyzed to determine the soil organic carbon content of the topsoil. An overview of the preliminary results and the remaining methodological obstacles will be presented, to draw conclusions for the further improvement of the measurement protocol and the sampling design.status: publishe

Correction of Directional Effects in VEGETATION NDVI Time-Series

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