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

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

Land Cover Dynamics and Mangrove Degradation in the Niger Delta Region

The Niger Delta Region is the largest river delta in Africa and features the fifth largest mangrove forest on Earth. It provides numerous ecosystem services to the local populations and holds a wealth of biodiversity. However, due to the oil and gas reserves and the explosion of human population it is under threat from overexploitation and degradation. There is a pressing need for an accurate assessment of the land cover dynamics in the region. The limited previous efforts have produced controversial results, as the area of western Africa is notorious for the gaps in the Landsat archive and the lack of cloud-free data. Even fewer studies have attempted to map the extent of the degraded mangrove forest system, reporting low accuracies. Here, we map the eight main land cover classes over the NDR using spectral-temporal metrics from all available Landsat data centred around three epochs. We also test the performance of the classification when L-band radar data are added to the Landsat-based metrics. To further our understanding of the land cover change dynamics, we carry out two additional assessments: a change intensity analysis for the entire NDR and, focusing specifically on the mangrove forest, we analyse the fragmentation of both the healthy and the degraded mangrove land cover classes. We achieve high overall classification accuracies in all epochs (~79% for 1988, and 82% for 2000 and 2013) and are able to map the degraded mangroves accurately, for the first time, with user’s accuracies between 77% and 87% and producer’s accuracies consistently above 82%. Our results show that mangrove forests, lowland rainforests, and freshwater forests are reporting net and highly intense losses (mangrove net loss: ~500 km2; woodland net loss: ~1400 km2), while built-up areas have almost doubled in size (from 1990 km2 in 1988 to 3730 km2 in 2013). The mangrove forests are also consistently more fragmented, with the opposite effect being observed for the degraded mangroves in more recent years. Our study provides a valuable assessment of land cover dynamics in the NDR and the first ever accurate estimates of the extent of the degraded mangrove forest and its fragmentation

Landsat time series reveal forest loss and woody encroachment in the Ngorongoro Conservation Area, Tanzania

The Ngorongoro Conservation Area (NCA) of Tanzania, is globally significant for biodiversity conservation due to the presence of iconic fauna, and, since 1959 has been managed as a unique multiple land-use areas to mutually benefit wildlife and indigenous residents. Understating vegetation dynamics and ongoing land cover change processes in protected areas is important to protect biodiversity and ensure sustainable development. However, land cover changes in savannahs are especially difficult, as changes are often long-term and subtle. Here, we demonstrate a Landsat-based monitoring strategy incorporating (i) regression-based unmixing for the accurate mapping of the fraction of the different land cover types, and (ii) a combination of linear regression and the BFAST trend break analysis technique for mapping and quantifying land cover changes. Using Google Earth Pro and the EnMap-Box software, the fractional cover of the main land cover types of the NCA were accurately mapped for the first time, namely bareland, bushland, cropland, forest, grassland, montane heath, shrubland, water and woodland. Our results show that the main changes occurring in the NCA are the degradation of upland forests into bushland: we exemplify this with a case study in the Lerai Forest; and found declines in grassland and co-incident increases in shrubland in the Serengeti Plains, suggesting woody encroachment. These changes threaten the wellbeing of livestock, the livelihoods of resident pastoralists and of the wildlife dependent on these grazing areas. Some of the land cover changes may be occurring naturally and caused by herbivory, rainfall patterns and vegetation succession, but many are linked to human activity, specifically, management policies, tourism development and the increase in human population and livestock. Our study provides for the first time much needed and highly accurate information on long-term land cover changes in the NCA that can support the sustainable management and conservation of this unique UNESCO World Heritage Site

Improving performance of index insurance using crop models and phenological monitoring

Extreme weather events cause considerable damage to livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyze to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (APSIM) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors, and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments

A Calanais myth and an alignment of the east stone-row with both the rising of the Pleiades and crossovers of Venus at sunrise on the summer solstices

Acknowledgements My thanks to Stefan Sagrott of Historic Environment Scotland for his help in obtaining Patrick Ashmore’s data and to David Forrest, School of Geographical and Earth Sciences, University of Glasgow, for providing a copy of David Tait’s map of Calanais.Peer reviewedPublisher PD

Rethinking 'risk' and self-management for chronic illness

Self-management for chronic illness is a current high profile UK healthcare policy. Policy and clinical recommendations relating to chronic illnesses are framed within a language of lifestyle risk management. This article argues the enactment of risk within current UK self-management policy is intimately related to neo-liberal ideology and is geared towards population governance. The approach that dominates policy perspectives to ‘risk' management is critiqued for positioning people as rational subjects who calculate risk probabilities and act upon them. Furthermore this perspective fails to understand the lay person's construction and enactment of risk, their agenda and contextual needs when living with chronic illness. Of everyday relevance to lay people is the management of risk and uncertainty relating to social roles and obligations, the emotions involved when encountering the risk and uncertainty in chronic illness, and the challenges posed by social structural factors and social environments that have to be managed. Thus, clinical enactments of self-management policy would benefit from taking a more holistic view to patient need and seek to avoid solely communicating lifestyle risk factors to be self-managed