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

Assessing the impact of dams on riparian and deltaic vegetation using remotely-sensed vegetation indices and Random Forests modelling

Riparian and deltaic areas exhibit a high biodiversity and offer a number of ecosystem services but are often degraded by human activities. Dams, for example, alter the hydrologic and sediment regimes of rivers and can negatively affect riparian areas and deltas. In order to sustainably manage these ecosystems, it is, therefore, essential to assess and monitor the impacts of dams. To this end, site-assessments and in-situ measurements have commonly been used in the past, but these can be laborious, resource demanding and time consuming. Here, we investigated the impact of three dams on the riparian forest of the Nestos River Delta in Greece by employing multi-temporal satellite data. We assessed the evolution in the values of eight vegetation indices over 27 years, derived from 14 dates of Landsat data. We also employed a modelling approach, using a machine learning Random Forests model, to investigate potential linkages between the observed changes in the indices and a host of climatic and terrestrial predictor variables. Our results show that low density vegetation (0–25%) is more affected by the construction of the dams due to its proximity to anthropogenic influences and the effects of hydrologic regime alteration. In contrast, higher density vegetation cover (50–75%) appears to be largely unaffected, or even improving, due to its proximity to the river, while vegetation with intermediate coverage (25–49%) exhibits no clear trend in the Landsat-derived indices. The Random Forests model found that the most important parameters for the riparian vegetation (based on the Mean Decrease Gini and the Mean Decrease Accuracy) were the distance to the dams, the sea and the river. Our results suggest that management plans of riparian and deltaic areas need to incorporate and take into consideration new innovative management practices and monitoring studies that employ multi-temporal satellite data archives

Final Report: A Land Degradation and Desertification Appraisal System for South Africa (LanDDApp)

Land degradation and desertification (LDD) is a serious global threat to humans and the environment. Globally, 10-20% of drylands and 24% of the world’s productive lands are potentially degraded, which affects 1.5 billion people and reduces GDP by €3.4 billion. Large parts of southern African arid, semi-arid and sub-humid areas are considered to be undergoing severe degradation processes, such as forest degradation, deforestation and bush encroachment that affect up to a third of the area, leading to a decline in the ecosystem services provided to some of the continent’s poorest and most vulnerable communities. There is, therefore, a pressing need for an objective, repeatable, systematic and spatially explicit measure of land degradation over the region and this is why the main aim of the LanDDApp project was to develop an appraisal system for assessing LDD in the southern African region. According to some assessments, only in South Africa bush encroachment, i.e. the advancement of woody plants into grasslands, and the subsequent conversion of savannahs and open woodlands into shrublands, has rendered 1.1 million ha of savannah unusable, it threatens another 27 million ha (~17% of the country), and has reduced the grazing capacity throughout the region by up to 50%. For this reason, one of the key objectives of LanDDApp was to devise an accurate methodology for mapping and monitoring bush encroachment using open access Earth Observation (EO) data. The use of multi-temporal and multi-sensor data from both the dry and the wet seasons proved to be a highly successful approach. To describe and map changes in ecosystem functioning at the regional scale, LanDDApp also carried out time-series analyses of vegetation index data: a proxy for vegetation vigour. Spatio‐temporal patterns of change in two different vegetation indices covering 33 years from 1981–2014 were identified. Areas of diverging trends in the vegetation indices were linked to well‐known changes in land use and land cover, such as deforestation and bush encroachment. Moreover, the patterns of diverging vegetation index trends were used as a reference in evaluating the impacts of environmental changes related to trends in Net Primary Productivity and Rain Use Efficiency. Field visits to three diverse study sites were carried out to verify the results using a variety of cameras mounted on poles, fixed wing and octocopter Unmanned Aerial Vehicles (UAVs), as well as expert knowledge. The results indicate areas of localized land degradation where ecosystem functioning has been reducing. Degradation impacts were reflected as reductions in productivity that varied along a continuum from slight to severe, depending on the specific land use/cover. The results from LanDDApp are relevant to various local, regional, national and international stakeholders related with savannah LDD, from small communal to larger private farmers, NGOs related with helping local communities maintain sustainable livelihoods while protecting their environment, Provincial and Central Government Organisations, Universities from all affected countries in the southern African region, research organisations as well as SMEs working on mapping tools and UAV/EO technologies

Editorial: Land degradation assessment with earth observation

For decades now, land degradation has been identified as one of the most pressing problems facing the planet. Alarming estimates are often published by the academic community and intergovernmental organisations claiming that a third of the Earth’s land surface is undergoing various degradation processes and almost half of the world’s population is already residing in degraded lands. Moreover, as land degradation directly affects vegetation biophysical processes and leads to changes in ecosystem functioning, it has a knock-on effect on habitats and, therefore, on numerous species of flora and fauna that become endangered or/and extinct

Multi-temporal Soil Erosion Modelling over the Mt Kenya Region with Multi-Sensor Earth Observation Data

Accelerated soil erosion is the principal cause of soil degradation across the world. In Africa, it is seen as a serious problem creating negative impacts on agricultural production, infrastructure and water quality. Regarding the Mt Kenya region, specifically, soil erosion is a serious threat mainly due to unplanned and unsustainable practices linked to tourism, agriculture and rapid population growth. The soil types roughly correspond with different altitudinal zones and are generally very fertile due to their volcanic origin. Some of them have been created by eroding glaciers while others are due to millions of years of fluvial erosion. The soils on the mountain are easily eroded once exposed: when vegetation is removed, the soil quickly erodes down to bedrock by either animals or humans, as tourists erode paths and local people clear large swaths of forested land for agriculture, mostly illegally. It is imperative, therefore, that a soil erosion monitoring system for the Mt Kenya region is in place in order to understand the magnitude of, and be able to respond to, the increasing number of demands on this renewable resource. In this paper, we employ a simple regional-scale soil erosion modelling framework based on the Thornes model and suggest an operational methodology for quantifying and monitoring water runoff and soil erosion using multi-sensor and multi-temporal remote sensing data in a GIS framework. We compare the estimates of this study with general data on the severity of soil erosion over Kenya and with measured rates of soil loss at different locations over the area of study. The results show that the measured and estimated rates of erosion are generally similar and within the same order of magnitude. They also show that, over the last years, erosion rates are increasing in large parts of the region at an alarming rate, and that mitigation measures are needed to reverse the negative effects of uncontrolled socio-economic practices

Modelling land cover change in a Mediterranean environment using Random Forests and a multi-layer neural network model

© 2016 IEEE.The present study seeks to identify the changes that have taken place in the Mediterranean island of Lesvos (Greece) between 1995 and 2007 in the seven main land cover types of the island. We also attempt to predict the changes that will occur by the year 2019. Three Landsat 5 TM summer scenes were used spanning 12 years. A combination of Random Forests (RF) classification with expert rules was then applied for achieving high overall classification accuracies (95%, 94% and 91%, respectively). The 1995 and 2001 classified data were then used to train a multi-layer perceptron neural network (MLPNN) model and predict land cover for the year 2007. Seven possible transitions were included in the MLPNN model which was trained with the 1995 and 2001 classified data successfully: accuracy rate of 93% after 5000 iterations. The quantity of change in each transition was modelled through Markov chain analysis. The modelling results for 2019 provide an anticipated prediction for the end of the decade: economic activity will remain centred to the agricultural sector, as crops and olive groves will expand. A rather unanticipated prediction is the significant increase in the area of forests

Enhancing NDVI-Based Biomass Models Through Feature Selection and Spatiotemporal Cross-Validation

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 spatiotemporal 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 36 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 ha-1, 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 spatiotemporal predictions, and offers strategies for their detection and mitigation