Dryland Vegetation Functional Response to Altered Rainfall Amounts and Variability Derived from Satellite Time Series Data

Vegetation productivity is an essential variable in ecosystem functioning. Vegetation dynamics of dryland ecosystems are most strongly determined by water availability and consequently by rainfall and there is a need to better understand how water limited ecosystems respond to altered rainfall amounts and variability. This response is partly determined by the vegetation functional response to rainfall (β) approximated by the unit change in annual vegetation productivity per unit change in annual rainfall. Here, we show how this functional response from 1983 to 2011 is affected by below and above average rainfall in two arid to semi-arid subtropical regions in West Africa (WA) and South West Africa (SWA) differing in interannual variability of annual rainfall (higher in SWA, lower in WA). We used a novel approach, shifting linear regression models (SLRs), to estimate gridded time series of β. The SLRs ingest annual satellite based rainfall as the explanatory variable and annual satellite-derived vegetation productivity proxies (NDVI) as the response variable. Gridded β values form unimodal curves along gradients of mean annual precipitation in both regions. β is higher in SWA during periods of below average rainfall (compared to above average) for mean annual precipitation <600 mm. In WA, β is hardly affected by above or below average rainfall conditions. Results suggest that this higher β variability in SWA is related to the higher rainfall variability in this region. Vegetation type-specific β follows observed responses for each region along rainfall gradients leading to region-specific responses for each vegetation type. We conclude that higher interannual rainfall variability might favour a more dynamic vegetation response to rainfall. This in turn may enhance the capability of vegetation productivity of arid and semi-arid regions to better cope with periods of below average rainfall conditions

Assessing the inter-relationship between vegetation productivity, rainfall, population and land cover over the Bani River Basin in Mali (West Africa)

This research investigated the inter-relationship between vegetation productivity, measured using the Normalized Difference Vegetation Index (NDVI), change in rainfall and population density in the context of perceived greening and degradation trends over the Bani River Basin (BRB). A 30-year (1982-2011), 8-km gridded rainfall data sets was produced by inverse distance weighted (IDW) interpolation of monthly data from 40 meteorological stations contained within the basin. Population data were retrieved from the National Population Statistic data base for 1987, 1997, and 2009. Rainfall and NDVI time-series trends were computed for the 30-year period and analysed. The relationship between rainfall and NDVI at pixel level, and NDVI and population densities was analysed using a Pearson correlation. Land Use and Land Cover (LULC) conversion rates were computed for the same period using multi-temporal 30-meter Landsat imagery; ground surveys for selected areas within the basin were used for further cross-verification. The computed NDVI trends revealed that, vegetation 'greening' trends are mostly associated with areas where natural vegetation is still well represented. Concurrent with increases in rainfall over the period analysed, this finding supports the hypothesis that re-greening observed in that area is the result of multi-decadal fluctuations in climate, rather than improved land management

What four decades of earth observation tell us about land degradation in the Sahel?

The assessment of land degradation and the quantification of its effects on land productivity have been both a scientific and political challenge. After four decades of Earth Observation (EO) applications, little agreement has been gained on the magnitude and direction of land degradation in the Sahel. The large number of EO datasets and methods associated with the complex interactions among biophysical and social drivers of ecosystem changes make it difficult to apply aggregated EO indices for these non-linear processes. Hence, while many studies stress that the Sahel is greening, others indicate no trend or browning. The different generations of sensors, the granularity of studies, the study period, the applied indices and the assumptions and/or computational methods impact these trends. Consequently, many uncertainties exist in regression models between rainfall, biomass and various indices that limit the ability of EO science to adequately assess and develop a consistent message on the magnitude of land degradation. We suggest several improvements: (1) harmonize time-series data, (2) promote knowledge networks, (3) improve data-access, (4) fill data gaps, (5) agree on scales and assumptions, (6) set up a denser network of long-term field-surveys and (7) consider local perceptions and social dynamics. To allow multiple perspectives and avoid erroneous interpretations, we underline that EO results should not be interpreted without contextual knowledge

Assessment of vegetation cover dynamics in the agro-ecological Zones of Nigeria

The drastic vegetation loss of about 22% in 12 years across agro-ecological zones (AEZs) in Nigeria, which can be attributed to the impact of climate and anthropogenic activities on vegetation dynamics within the context of socio-economics, calls for concern. In this study, remotely sensed data from the European Space Agency Land Use/Land Cover dataset, TAMSAT rainfall data, and MODIS NDVI dataset were used to examine changes in vegetation dynamics and monitor vegetation health in the AEZs based on the Normalised Difference Vegetation Index (NDVI). The study showed that vegetation cover has been decreasing tremendously at an alarming rate in most of the zones, while different drivers (change agents) have been responsible for the losses. The analysis shows that, between 2003 and 2018, the forestland cover gained 9,768.88 km2 and lost 40,891.6 km2, with a total net decrease of 31,122.73 km2. Contribution to the net change in forestland is usually converted to wetland, urban, cropland and grassland, across the varied ecosystem. The need for upto-date and accurate land-cover information is key to developing an appropriate mitigation strategy within the context of socio-economic sustainability across AEZs in Nigeria.The drastic vegetation loss of about 22% in 12 years across agro-ecological zones (AEZs) in Nigeria, which can be attributed to the impact of climate and anthropogenic activities on vegetation dynamics within the context of socio-economics, calls for concern. In this study, remotely sensed data from the European Space Agency Land Use/Land Cover dataset, TAMSAT rainfall data, and MODIS NDVI dataset were used to examine changes in vegetation dynamics and monitor vegetation health in the AEZs based on the Normalised Difference Vegetation Index (NDVI). The study showed that vegetation cover has been decreasing tremendously at an alarming rate in most of the zones, while different drivers (change agents) have been responsible for the losses. The analysis shows that, between 2003 and 2018, the forestland cover gained 9,768.88 km2 and lost 40,891.6 km2, with a total net decrease of 31,122.73 km2. Contribution to the net change in forestland is usually converted to wetland, urban, cropland and grassland, across the varied ecosystem. The need for upto-date and accurate land-cover information is key to developing an appropriate mitigation strategy within the context of socio-economic sustainability across AEZs in Nigeria

Spatio-Temporal Modeling of Vegetation Change Dynamics in the Guinea Savannah Region of Nigeria using Remote Sensing and GIS Techniques

The use of Normalized Difference Vegetation Index (NDVI) time series over the last decades has increased our understanding of vegetation change dynamics from global to regional scale through quantitative analysis of inter-annual trends in NDVI and climatological parameters (rainfall and temperature). Change in land cover induced by human activities such as livestock grazing and deforestation for large-scale farming (subsistence and mechanized) has influenced the ecological pattern of the Guinea savannah region (GSR) of Nigeria, thereby resulting in loss of biodiversity and changes in vegetation cover. In the context of the GSR of Nigeria where agriculture still plays a major role in people’s economy, it is important to identify the relationship between climatic variables, vegetation productivity and human activities which can be used to understand the on-going transition processes. This study, therefore, examines the spatial and temporal relationship between NDVI and climate parameters, land use land cover change (LULCC) and the perspective of local people on vegetation change dynamics in the study region. In order to do this, bi-monthly NDVI3g time series datasets from Global Inventory Modeling and Mapping Studies (GIMMS), monthly rainfall datasets from Tropical Applications of Meteorology Satellite (TAMSAT), monthly temperature datasets from Climate Research Unit (CRU), national land use land cover (LULC) data of Nigeria from Forestry Management Evaluation & Coordination Unit (FORMECU), global land cover datasets from European Space Agency, Landsat imagery and socio-economic field data collection were used in order to understand vegetation change dynamics across the Guinea savannah regions of Nigeria. Time series analysis (TSA) was applied to both NDVI and climate data used in order to examine the temporal dynamics of vegetation cover change and to detect NDVI-climate relationship during the period from 1983 through 2011. Both parametric and non-parametric statistical models were employed for the assessment of long-term inter-annual trend on the decomposed time series datasets for the whole region (Guinea savannah region) and selected locations. In addition to the TSA, harmonic regression analysis was performed on NDVI and rainfall datasets in order to examine change in seasonality and phyto-phenological characteristics of vegetation. Detection of change in land use and land cover was done by extracting information from existing land cover datasets (ancillary datasets). CLASlite was used for the assessment of the extent of deforestation, while linkage between remotely sensed data and social science was carried out via field surveys based on questionnaires in order to understand the drivers of vegetation change. The study reveals that about 90 % of the Guinea savannah region show positive NDVI trends which indicate greening over time, while about 10 % of the region shows negative trends. This greening trends are closely related to regions where intensive agriculture is being practiced (also along inland valleys) while regions with negative trends show significant loss in woodlands (forest and shrublands) as well as herbaceous vegetation cover due to over-grazing by agro-pastoralism. The result confirms that there is a good relationship (statistically significant positive correlation) between rainfall and NDVI both on intra-annual and inter annual time scale for some selected locations in the study region (> 65 %), while negative statistical correlation exists between NDVI and temperature in the selected locations. This implies that vegetation growth (productivity) in the region is highly dependent on rainfall. The result of the harmonic regression analysis reveals a shift in the seasonal NDVI pattern, indicating an earlier start and a more prolonged growing season in 2011 than in 1983. This study proves significant change in LULC with evidence of an increase in the spatial extent of agricultural land (+ 30 %) and loss of woodlands (- 55 %) between 2000 and 2009 for Kogi State. The results of the socio-economic analysis (people’s perception) highlight that vegetation change dynamics in the study region are the resultant effects of increased anthropogenic activities rather than climatic variability. This study couples data from remote sensing and ground survey (socio-economics) for a better understanding of greening trend phenomena across the Guinea savannah region of Nigeria, thus filling the gap of inadequate information on environmental condition and human perturbation which is essential for proper land use management and vegetation monitoring

Linkages between Atmospheric Circulation, Weather, Climate, Land Cover and Social Dynamics of the Tibetan Plateau

The Tibetan Plateau (TP) is an important landmass that plays a significant role in both regional and global climates. In recent decades, the TP has undergone significant changes due to climate and human activities. Since the 1980s anthropogenic activities, such as the stocking of livestock, land cover change, permafrost degradation, urbanization, highway construction, deforestation and desertification, and unsustainable land management practices, have greatly increased over the TP. As a result, grasslands have undergone rapid degradation and have altered the land surface which in turn has altered the exchange of heat and moisture properties between land and the atmosphere. But gaps still exist in our knowledge of land-atmosphere interactions in the TP and their impacts on weather and climate around the TP, making it difficult to understand the complete energy and water cycles over the region. Moreover, human, and ecological systems are interlinked, and the drivers of change include biophysical, economic, political, social, and cultural elements that operate at different temporal and spatial scales. Current studies do not holistically reflect the complex social-ecological dynamics of the Tibetan Plateau. To increase our understanding of this coupled human-natural system, there is a need for an integrated approach to rendering visible the deep interconnections between the biophysical and social systems of the TP. There is a need for an integrative framework to study the impacts of sedentary and individualized production systems on the health and livelihoods of local communities in the context of land degradation and climate change. To do so, there is a need to understand better the spatial variability and landscape patterns in grassland degradation across the TP. Therefore, the main goal of this dissertation is to contribute to our understanding of the changes over the land surface and how these changes impact the plateau\u27s weather, climate, and social dynamics. This dissertation is structured as three interrelated manuscripts, which each explore specific research questions relating to this larger goal. These manuscripts constitute the three primary papers of this dissertation. The first paper documents the significant association of surface energy flux with vegetation cover, as measured by satellite based AVHRR GIMMS3g normalized difference vegetation index (NDVI) data, during the early growing season of May in the western region of the Tibetan Plateau. In addition, a 1°K increase in the temperature at the 500 hPa level was observed. Based on the identified positive effects of vegetation on the temperature associated with decreased NDVI in the western region of the Tibetan Plateau, I propose a positive energy process for land-atmosphere associations. In the second paper, an increase in Landsat-derived NDVI, i.e., a greening, is identified within the TP, especially during 1990 to 2018 and 2000 to 2018 time periods. Larger median growing season NDVI change values were observed for the Southeast Tibet shrublands and meadows and Tibetan Plateau Alpine Shrublands and Meadows grassland regions, in comparison to the other three regions studied. Land degradation is prominent in the lower and intermediate hillslope positions in comparison to the higher relative topographic positions, and change is more pronounced in the eastern Southeast Tibet shrublands and meadows and Tibetan Plateau Alpine Shrublands and Meadows grasslands. Geomorphons were found to be an effective spatial unit for analysis of hillslope change patterns. Through the extensive literature review presented in third paper, this dissertation recommends using critical physical geography (CPG) to study environmental and social issues in the TP. The conceptual model proposed provides a framework for analysis of the dominant controls, feedback, and interactions between natural, human, socioeconomic, and governance activities, allowing researchers to untangle climate change, land degradation, and vulnerability in the Tibetan Plateau. CPG will further help improve our understanding of the exposure of local people to climate and socio-economic and political change and help policy makers devise appropriate strategies to combat future grassland degradation and to improve the lives and strengthen livelihoods of the inhabitants of the TP

Linear and segmented linear trend detection for vegetation cover using GIMMS normalized difference vegetation index data in semiarid regions of Nigeria

Quantitative analysis of trends in vegetation cover, especially in Kogi state, Nigeria, where agriculture plays a major role in the region’s economy, is very important for detecting long-term changes in the phenological behavior of vegetation over time. This study employs the use of normalized difference vegetation index (NDVI) [global inventory modeling and mapping studies 3g (GIMMS)] data from 1983 to 2011 with detailed methodological and statistical approach for analyzing trends within the NDVI time series for four selected locations in Kogi state. Based on the results of a comprehensive study of seasonalities in the time series, the original signals are decomposed. Different linear regression models are applied and compared. In order to detect structural changes over time a detailed breakpoint analysis is performed. The quality of linear modeling is evaluated by means of statistical analyses of the residuals. Standard deviations of the regressions are between 0.015 and 0.021 with R2 of 0.22–0.64. Segmented linear regression modeling is performed for improvement and a decreasing standard deviation of 33%–40% (0.01–0.013) and R2 up to 0.82 are obtained. The approach used in this study demonstrates the added value of long-term time series analyses of vegetation cover for the assessment of agricultural and rural development in the Guinea savannah region of Kogi state, Nigeria