Identifying Ecosystem Function Shifts in Africa Using Breakpoint Analysis of Long-Term NDVI and RUE Data

Time-series of vegetation greenness data, derived from Earth-observation imagery, have become a key source of information for studying large-scale environmental change. The ever increasing length of such series allows for a range of indicators to be derived and for increasingly complex analyses to be applied. This study presents an analysis of trends in vegetation productivity—measured using the Global Inventory Monitoring and Modelling System third generation (GIMMS3g) Normalised Difference Vegetation Index (NDVI) data—for African savannahs, over the 1982–2015 period. Two annual metrics were derived from the 34 year dataset: the monthly, smoothed NDVI (the aggregated growth season NDVI) and the associated Rain Use Efficiency (growth season NDVI divided by annual rainfall). These indicators were then used in a BFAST-based change-point analysis, allowing the direction of change over time to change and the detection of one major break in the time-series. We also analysed the role of land cover type and climate zone as associations of the observed changes. Both methods agree that vegetation greening was pervasive across African savannahs, although RUE displayed less significant changes than NDVI. Monotonically increasing trends were the most common trend type for both indicators. The continental scale of the greening may suggest global processes as key drivers, such as carbon fertilization. That NDVI trends were more dynamic than RUE suggests that a large component of vegetation trends is driven by precipitation variability. Areas of negative trends were conspicuous by their minimalism. However, some patterns were apparent. In the southern Sahel and West Africa, declining NDVI and RUE overlapped with intensive population and agricultural regions. Dynamic trend reversals, in RUE and NDVI, located in Angola, Zambia and Tanzania, coincide with areas where a long-term trend of forest degradation and agricultural expansion has recently given way to increases in woody biomass. Meanwhile in southern Africa, monotonic increases in RUE with varying NDVI trend types may be indicative of shrub encroachment. However, all these processes are small-scale relative to the GIMMS NDVI data, and reconciling these conflicting drivers is not a trivial task. Our study highlights the importance of considering multiple options when undertaking trend analyses, as different inputs and methods can reveal divergent patterns

Monitoring ecosystem dynamics in semi-arid environments using multi-sensor Earth-observation

Climate change and a growing human population are instigating major changes on the Earth’s surface. Monitoring and understanding these changes as they unfold is critical for society and the environment. Satellite remote sensing provides the only means of achieving this over large spatial and temporal scales, and major progress in the application of Earth-observation imagery has been made since the beginning of the space age in the mid-20th century. However, savannahs - dynamic systems comprised of shrubs, trees, and grass species - have proved challenging for EO-based monitoring. Yet, these ecosystems cover almost 25% of the Earth’s surface and are home to some of the poorest people on the planet. This thesis investigates the use of EO for monitoring ecosystem dynamics in African savannahs, focusing specially on woody cover and biomass provision. One of the most common Earth-observation (EO) based tools for monitoring vegetation is the Normalised Difference Vegetation Index (NDVI). A detailed review of the application of NDVI for monitoring land degradation was undertaken. This covered the historical context and ongoing debates around NDVI analyses, and highlighted key research gaps. NDVI was then used to map grass biomass for the Kruger National Park in South Africa, by combining in situ data with a downscaled NDVI dataset in a machine-learning framework. These predictions highlighted that the NDVI-biomass relationship is vulnerable to overfi�tting in space and time, due to spatial autocorrelation and a variable species composition, respectively. The NDVI was further explored at the continental scale using multiple time-series analyses. These revealed that a majority of African savannahs have only experienced vegetation greening in the 1982-2016 period. Areas of declining vegetation, or changes in the trend direction, were associated with phenological changes (i.e. a shrinking growth season), woodland degradation, or population increases. Finally, fractional woody vegetation cover was mapped for the Limpopo province of South Africa using Landsat spectral metrics and ALOS PALSAR radar imagery and a series of Random Forest regression models. The most accurate models combined multi-seasonal Landsat data and the radar layers. However, this was only marginally more accurate than just using dry and wet season metrics alone. When using a single season of imagery, the dry season preformed best. These results were reaffirmed for categorical savannah land-cover classifications, highlighting the importance of multi-sensor and multi-temporal data. The thesis contributes new insights for monitoring savannahs using EO imagery. By combining EO data with modern statistics and machine-learning methods novel insights to ecological and environmental issues can be gained. In the coming years, the increasing number of operational sensors and the volume of data collected will be of great benefit for environmental monitoring, especially in savannahs

Carbon Fertilisation is the Primary Driver of Shrub Encroachment in South African Savannahs

Woody plant encroachment has been documented for savannahs and grasslands in nearly all continents. Yet the drivers of this process remain unclear, with a range of local and global factors postulated. The traditional ecological narrative dictates that shrub encroachment is a localised phenomenon, resulting from poor land management regimes. The most frequently proposed mechanisms are overgrazing and suppression of fire, both of which are common management techniques in sub-Saharan Africa. More recently, increased focus has been directed at the role of global factors in woody cover dynamics. As savannah woody cover is constrained by both total and wet season rainfall, changes in precipitation regime have been proposed as drivers of shrub encroachment. This theory has been supported by small-scale field experiments showing shrubs disproportionally benefiting from increases in rainfall frequency, amount, and variability. A further potential global factor is the ongoing rise in atmospheric CO2 concentrations since the industrial revolution. A theoretical understanding of water limitations to woody cover in savannahs makes it reasonable to assume that CO2-driven increases would be concentrated in water-limited environments. This has been observed across South Africa using aerial photography and globally using satellite-derived Rain Use Efficiency (RUE). Here, we combine satellite-derived fractional woody cover maps with a suite of potentially explanatory variables, to elucidate on the potential drivers and mechanisms of woody cover change, in South African savannahs. The study area consists of the Limpopo and North West Provinces in northern South Africa. These municipalities cover a plurality of the savannah biome within South Africa (193,200 km2, 49% of the total savannah area), in addition to containing 33,830 km2 of grassland. More specifically, we test the three, abovementioned, competing hypotheses on the drivers of woody encroachment for South African savannahs. Our modelling framework was developed using Generalised Additive Models (GAM). We collated a series of 11 variables that have a hypothetical basis for explaining woody cover changes. These variables can be grouped into three categories: rainfall-derived, human, and natural factors. Fractional woody cover changes were mapped using Landsat-derived % woody cover layers, based on the methodology developed in Higginbottom et al. (2018, ISPRS Journal Ph&RS, 139, 88-102). In summary, two five-year epochs (1984-1988 and 2008-2012) of Landsat imagery were used to generate pixel-level seasonal spectral variability metrics, at 120 m resolution. Reducing the pixel resolutions improves the classification accuracies, and is more suited for observing overall trends. High-resolution imagery were classified into woody/non woody masks, and used as training data for a Random Forest regression for the fractional cover of each 120m-pixel. The Random Forest model was applied to the Landsat metric stacks to generate the two epochal maps. We then calculated both the absolute percentage change, and the relative percentage change in woody cover between the two maps. The fitted models had R2 values of 0.39 for absolute change and 0.41 for relative change. The results show that the modelled variables most closely matched the a-priori responses of the carbon fertilisation hypothesis. In recent years, this explanation has been postulated by studies using a variety of methods to account for the observed woody encroachment. Further work in this arena is still necessary, particularly where the data sources are sub-optimal. Land-use history and rainfall dynamics are especially difficult to quantify and would require further investigation. Furthermore, additional factors, such as reactive nitrogen deposition and mega-fauna extinctions, are likely to be relevant but where not included in our models. If carbon fertilisation is the key driver of shrub encroachment in savannahs, it would raise concerns for future environmental change: as CO2 levels continue to rise more savannahs and grassland are likely to experience an increase in woody cover levels, which has been linked to savannah land degradation

Pervasive Greening of African Savannahs from 1982 - 2016

Trends in vegetation greenness, measured using the Normalised Difference Vegetation Index (NDVI), are one of the most ubiquitous tools for inferring ecological change at large spatial scales. The NDVI has long been a favoured metric, owing to its simple calculation, correlation with various ecological attributes, and transferability between sensors. In particular, the Advanced Very High Resolution Radiometer (AVHRR) - derived Global Inventory Monitoring and Modelling System (GIMMS) dataset has been used extensively for environmental change purposes. The regions most studied using GIMMS-NDVI data are the drylands of Africa. These areas are particularly well suited for this form of analysis, due to the limited historical coverage of other Earth-observation archives in the region, with the exception of South Africa. Furthermore, NDVI becomes less sensitive to vegetation in dense canopies (NDVI > 0.7), but is relatively responsive to the low biomass levels found in savannahs and grasslands. Accordingly, a considerable amount of earlier work on the use of NDVI for monitoring environmental change focussed on the Sahel region. This study investigates the NDVI dynamics of African savannahs using a variety of time-series analysis techniques and NDVI-derived metrics. The overarching aim is to understand how vegetation dynamics have occurred and evolved during the 1982-2016 period and the ecological implications of these changes. Towards this aim, we generate two NDVI time-series from the GIMMS dataset: the annual maximum and aggregate sum values, hereafter NDVImax and NDVIsum, respectively. These series are used as inputs into monotonic linear and breakpoint regression models. The slopes and any associated breakpoints of these models are classified and examined as indicators of large-scale ecological change. We further processed the GIMMS data to maximum monthly composites and discarded the months for the incomplete 1981 year, resulting in a 34-year (408 months) time-series. When gaps remained after compositing, values were estimated by linear interpolation across months. We calculated the median NDVI using the monthly time-series, and discarded pixels with a value of less than 0.15 or greater than 0.8 from further analysis. This removed areas with very low vegetation cover (e.g the Sahara and Namib deserts) and dense forests (e.g. the Congo and Guinean forests). The monthly time-series was aggregated into two annual metrics: NDVImax and NDVIsum. This resulted in two 34-year annual time series, to be used as inputs for the trend analyses. We applied a standard linear regression model on both annual NDVI time-series. Firstly, a standard linear model was implemented with no breaks quantified. Secondly, a model allowing for two structural changes was applied. Breakpoints were determined first by an ordinary-least squares moving sum (MOSUM) test. When breaks were detected, the number was estimated by the Bayesian Information Criterion (BIC), and timings were set based on the residual sum of squares (RSS). The slopes resulting from the linear models were grouped into three categories: greening, no change, or browning, after removing pixels with insignificant trends. By comparing the trend classifications from the NDVI-max and NDVI-sum models, six combinations were possible. To classify the breakpoint outputs the following procedures were applied. Firstly, all segments with a length less than seven years or resulting from an insignificant break were discounted. Secondly, the remaining components were classified into greening, browning or no change based on the slope value. Finally, as only two breaks were allowed, a maximum of three segments were deemed possible. According to our results, greening pixels comprised 75% of the NDVIsum slopes, and 80% of the NDVImax, whereas browning was identified in 25% and 20% of pixels, respectively. Removing trends that did not meet a P < 0.05 significance threshold eliminated some of these pixels. Pronounced increases in NDVI were observed in the Sahel and southern Africa. Browning was concentrated in east Africa, Angola, Zambia, and Mozambique, with dispersed and isolated patches in the northern Sahel. Results also show that, regardless of NDVI metric, a large majority of African drylands, across all regions, have experienced only greening trends in the 1982-2015 period: a minimum of seven years’ increase and no seven-year decrease in the NDVI metrics. Conversely, few areas displayed only browning trends (a minimum of seven years’ decrease with no increase). These pixels were geographically clustered in the central Sahel, Angola-Zambia-Mozambique, and Tanzania. Patches of trend reversals (e.g. browning to greening) were present, although not geographically extensive. In summary, the overarching trends of African savannahs across our study period is of vegetation greening. This has occurred across all regions, even when different precipitation patterns have occurred. This would indicate the role of pan-continental driver(s). There are numerous ongoing trends which are beneficial for plant growth that could contribute to this, such as elevated CO2 levels, nitrogen deposition, and increased temperatures. Areas of browning predominantly occurred in areas where either population growth had been high, or where phenological change has curtailed the growth season

Downscaling GIMMS3g NDVI-based biomass predictions using empirical orthogonal teleconnections

The accurate mapping and quantification of above ground biomass (AGB) is required for a number of applications, including carbon accounting, fire and grazing management, amongst others. Accordingly, relating field measurements of AGB to satellite-derived indicators, most prominently the Normalised Difference Vegetation Index (NDVI) has been a feature of the remote sensing literature for over 30 years. Recently, there has been an increase in the use of machine learning methods and the incorporation of auxiliary environmental variables for spatio-temporal modelling. However, there is increasing evidence that these models may be vulnerable to artefacts of data structure, such as spatial autocorrelation and inappropriate auxiliary variables, which may hinder the development of accurate models. In this study, a robust methodology for the creation of moderate- resolution AGB estimates is presented. We obtained AGB data from an 18-year long dataset comprising 533 sites within the Kruger National Park of South Africa. We then generated a 1km-resolution NDVI product by downscaling the GIMMS 3g NDVI using Empirical Orthogonal Teleconnections (EOT) and the MODIS MYD13A2. AGB was then predicted based on a series of NDVI-metrics and auxiliary environmental variables in a Cubist regression model framework. Our analysis consisted of two components: i) a comparison of validation approaches, including a k-fold cross validation (CV) and multiple spatial/temporal CVs; and ii) a variable selection component, incorporating forward feature selections (FFS) on the above validation strategies. Prediction accuracies differed considerably, with the Root Mean Squared Error ranging from 1310 to 1844 kg ha1, depending on the variables and validation strategy employed. Errors were consistently higher with spatial or temporal validation strategies. Spatial overfitting was prominent in most models, which we attribute to spatial autocorrelation within the predictor variables. Comparatively, the NDVI-biomass relationship was highly variable between years, with unseen years being poorly modelled. This potentially results from changing species composition and moisture content on an annual basis. The FFS was effective at correcting these issues, where possible, by constructing models with appropriate variable combinations. For temporal models, the profile of auxiliary variables was increased leading to a more deterministic prediction approach. This study contributes to the growing literature highlighting the potential pitfalls of machine learning for spatio-temporal predictions, and offers strategies for their detection and mitigation

Deforestation dynamics in an endemic-rich mountain system: Conservation successes and challenges in West Java 1990–2015

While much has been published on recent rates of forest loss in the Sundaic lowlands, deforestation rates and patterns on Java’s endemic-rich mountains have been rather neglected. We used nearly 1000 Landsat images to examine spatio-altitudinal and temporal patterns of forest loss in montane West Java over the last 28 years, and the effectiveness of protected areas in halting deforestation over that period. Around 40% of forest has been lost since 1988, the bulk occurring pre-2000 (2.5% per annum), falling to 1% per annum post-2007. Most deforestation has occurred at lower altitudes (<1000 m above sea level), both as attrition of the edges of forested mountain blocks as well as the near-total clearance of lower-altitude forested areas. Deforestation within protected areas was rife pre-2000, but greatly decreased thereafter, almost ceasing post-2007 in protected areas of high International Union for Conservation of Nature (IUCN) status. While this trend is welcome, it must be stressed that the area of remaining forest is only 5234 km2, that most accessible lower-altitude forest has already disappeared, and that the extant montane forest is largely fragmented and isolated. The biological value of these forests is huge and without strong intervention we anticipate imminent loss of populations of taxa such as the Javan Slow Loris Nycticebus javanicus and Javan Green Magpie Cissa thalassina

High-resolution wetness index mapping: A useful tool for regional scale wetland management

Wetland ecosystems are key habitats for carbon sequestration, biodiversity and ecosystem services, yet in many they localities have been subject to modification or damage. In recent years, there has been increasing focus on effective management and, where possible, restoration of wetlands. Whilst this is highly laudable, practical implementation is limited by the high costs and unpredictable rates of success. Accordingly, there is a need for spatial information to guide restoration, ideally at the regional scale that land managers operate. In this study, we use high-resolution Light Detection and Ranging (LiDAR)-derived elevation, in conjunction with regional soil and land cover maps, to model the wetness potential of an area of conservation importance in north-west England. We use the Compound Topographic Index (CTI) as a measure for the site-specific wetness and potential to be receptive to wetland restoration. The resulting model is in agreement with the regional-scale distribution of wetlands and is clearly influenced by the topographic and soil parameters. An assessment of three representative case studies highlights the small scale features that determine the potential wetness of an area. For each site, the model results conform to the expected patterns of wetness, highlighting restoration and management activity. Furthermore, areas showing high potential wetness that may be suitable for wetland habitat creation, are highlighted. The increasing availability of LiDAR data at regional and national scales will allow studies of this nature to be undertaken at previously unobtainable resolutions. Simple models, such as implemented here, benefit from explainability and relatability and have clear potential for use by managers and conservation agencies involved in wetland restoration

Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons

Savannahs are heterogeneous environments with an important role in supporting biodiversity and providing essential ecosystem services. Due to extensive land use/cover changes and subsequent land degradation, the provision of ecosystems services from savannahs has increasingly declined over recent years. Mapping the extent and the composition of savannah environments is challenging but essential in order to improve monitoring capabilities, prevent biodiversity loss and ensure the provision of ecosystem services. Here, we tested combinations of Sentinel-1 and Sentinel-2 data from three different seasons to optimise land cover mapping, focusing in the Ngorongoro Conservation Area (NCA) in Tanzania. The NCA has a bimodal rainfall pattern and is composed of a combination savannah and woodland landscapes. The best performing model achieved an overall accuracy of 86.3 ± 1.5% and included a combination of Sentinel-1 and 2 from the dry and short-dry seasons. Our results show that the optical models outperform their radar counterparts, the combination of multisensor data improves the overall accuracy in all scenarios and this is particularly advantageous in single-season models. Regarding the effect of season, models that included the short-dry season outperform the dry and wet season models, as this season is able to provide cloud free data and is wet enough to allow for the distinction between woody and herbaceous vegetation. Additionally, the combination of more than one season is beneficial for the classification, specifically if it includes the dry or the short-dry season. Combining several seasons is, overall, more beneficial for single-sensor data; however, the accuracies varied with land cover. In summary, the combination of several seasons and sensors provides a more accurate classification, but the target vegetation types should be taken into consideration

Choosing Motherhood : The complexities of pregnancy decision-making among young black women 'looked after' by the State

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. Copyright 2013 the Authors, published by Elsevier Ltd.Objective: This paper addresses the experiences of a group of young black teenage mothers looked after by the State, most of whom were also either migrants or asylum seekers. The paper explores the experience of discovery of pregnancy, attempts to seek professional help and the eventual decision to continue with the pregnancy. Design: An interpretative study with in-depth interviews. Settings: Interviews were carried out in the participants’ homes and focussed on their experiences of pregnancy decision-making. Participants: 15 young women (aged 16-19), from black minority ethnic groups, with a history of care (past or present), currently pregnant or mothers of a child no older than two years of age. Findings: All the pregnancies were unexpected: eight of the informants conceived as a result of rape and seven while in a relationship. All the young women chose motherhood over abortion despite their complex social and pregnancy background. Conclusions: The importance of social positioning of migrants in terms of the cluster of negative aspects and environmental disadvantage generally experienced by most immigrants in the host country is raised in this paper. Care practices of pregnant women with complex social factors were little observant of woman-centred care approaches.Peer reviewedFinal Published versio