Global-scale characterization of turning points in arid and semi-arid ecosystem functioning

Aim: Changes in dryland ecosystem functioning are threatening the well‐being of human populations worldwide, and land degradation, exacerbated by climate change, contributes to biodiversity loss and puts pressures on sustainable livelihoods. Here, abrupt changes in ecosystem functioning [so‐called turning points (TPs)] were detected using time series of Earth observation data. Hotspot areas of high TP occurrence were identified, observed changes characterized and insights gained on potential drivers for these changes.Location: Arid and semi‐arid regions.Time period: 1982–2015.Methods: We used a time series segmentation technique (breaks for additive season and trend) to detect breakpoints in rain‐use efficiency as a means of analysing changes in ecosystem functioning. A new typology to characterize the detected changes was proposed and evaluated, at regional to local scales, for a set of case studies. Ancillary data on population and drought were used to provide insights on potential drivers of TP occurrence.Results: Turning points in ecosystem functioning were found in 13.6% (c. 2.1 × 106 km2) of global drylands. Turning point hotspots were primarily observed in North America, the Sahel, Central Asia and Australia. In North America, the majority of TPs (62.6%) were characterized by a decreasing trend in ecosystem functioning, whereas for the other regions, a positive reversal in ecosystem functioning was prevalent. Further analysis showed that: (a) both climatic and anthropogenic pressure influenced the occurrence of TPs in North America; (b) Sahelian grasslands were primarily characterized by drought‐induced TPs; and (c) high anthropogenic pressure coincided with the occurrence of TPs in Asia and Australia.Main conclusions: By developing a new typology targeting the categorization of abrupt and gradual changes in ecosystem functioning, we detected and characterized TPs in global drylands. This TP characterization is a first crucial step towards understanding the drivers of change and supporting better decision‐making for ecosystem conservation and management in drylands

Uncovering Dryland Woody Dynamics Using Optical, Microwave, and Field Data—Prolonged Above-Average Rainfall Paradoxically Contributes to Woody Plant Die-Off in the Western Sahel

Dryland ecosystems are frequently struck by droughts. Yet, woody vegetation is often able to recover from mortality events once precipitation returns to pre-drought conditions. Climate change, however, may impact woody vegetation resilience due to more extreme and frequent droughts. Thus, better understanding how woody vegetation responds to drought events is essential. We used a phenology-based remote sensing approach coupled with field data to estimate the severity and recovery rates of a large scale die-off event that occurred in 2014–2015 in Senegal. Novel low (L-band) and high-frequency (Ku-band) passive microwave vegetation optical depth (VOD), and optical MODIS data, were used to estimate woody vegetation dynamics. The relative importance of soil, human-pressure, and before-drought vegetation dynamics influencing the woody vegetation response to the drought were assessed. The die-off in 2014–2015 represented the highest dry season VOD drop for the studied period (1989–2017), even though the 2014 drought was not as severe as the droughts in the 1980s and 1990s. The spatially explicit Die-off Severity Index derived in this study, at 500 m resolution, highlights woody plants mortality in the study area. Soil physical characteristics highly affected die-off severity and post-disturbance recovery, but pre-drought biomass accumulation (i.e., in areas that benefited from above-normal rainfall conditions before the 2014 drought) was the most important variable in explaining die-off severity. This study provides new evidence supporting a better understanding of the “greening Sahel”, suggesting that a sudden increase in woody vegetation biomass does not necessarily imply a stable ecosystem recovery from the droughts in the 1980s. Instead, prolonged above-normal rainfall conditions prior to a drought may result in the accumulation of woody biomass, creating the basis for potentially large-scale woody vegetation die-off events due to even moderate dry spells

Quantification of vegetation response to climate anomalies through remote sensing: methodological aspects and applications

Ecosystems provide many crucial services, among which climate, water and erosion regulation, pollination, genetic resource conservation, wildlife habitat and products such as timber and fresh water. These services depend on ecosystem processes, such as biomass production and organic matter decomposition, which are on their turn dependent on biodiversity. A continuous and stable delivery of these ecosystem services is, however, threatened by the expected increase in average temperatures, the increased frequency and magnitude of climate extremes, and also by ecosystem degradation, land use conversion or change and biodiversity decline in many parts of the world. Within this context, it is of utmost importance to assess and monitor the stability of ecosystems and to understand the factors that may mediate ecosystem stability at large spatial scales. Ecosystem stability is often quantified through metrics defined on the ecosystem state variable, i.e. ecosystem resistance, resilience or variance. Resistance denotes the ability of the ecosystem to withstand a disturbance and resilience is related to the ability to recover after the disturbance has occurred. Resilience is often subdivided into two stability metrics: engineering resilience is defined as the speed at which the ecosystem returns to its original state, whereas ecological resilience denotes the amount of disturbance that is needed for the system to switch state. Finally, the variance is defined as the total variability of the ecosystem variable in face of environmental anomalies. The frequent and large scale assessment of satellite imagery provides an interesting asset to obtain and monitor ecosystem stability at large spatial scales. Time series of vegetation indices, such as the Normalised Difference Vegetation Index (NDVI) or Normalised Difference Water Index (NDWI), can be derived from these images and provide indicators of the vegetation condition, e.g. the vegetation greenness or water content. Yet, several problems, e.g. noise or the spatial heterogeneity of the disturbances, still hamper the reliable quantification of ecosystem stability. Therefore, the first part of this PhD provides methodologies to enhance the reliable assessment of ecosystem stability. Several types of noise and data characteristics, e.g. temporal resolution, time series length, and noise introduced by variable atmospheric conditions, may affect the stability metrics obtained from remote sensing time series. Therefore, a methodology has been described to quantify the impact of these noise and data characteristics on stability metrics, and assess the reliability of the time series. Next to noise, the spatial variability of disturbances may hamper the spatial comparison of stability metrics. A solution to this problem has been proposed by explicitly modelling the vegetation state indicator anomaly (e.g. NDVI anomaly) in function of past anomalies and the disturbance. Finally, the response of vegetation on environmental anomalies may differ over time. Vegetation may, for example, become more or less resistant or resilient to climate anomalies. This problem has first been theoretically described and subsequently been quantified using the magnitude and direction of stability change for several stability metrics in the case of Australia. Consequently, when comparing the stability of ecosystems, the proposed methodology allows to take into account not only data quality, and heterogeneity of the disturbances, but equally so their non-stationarity. The second part of the PhD provides insight in the factors that govern ecosystem stability. More specifically, it quantifies the effect of plant diversity and management on grassland stability. First, differences in stability between species-rich semi-natural and species-poor intensively managed agricultural grasslands in the Netherlands were assessed, and subsequently the relationship between plant diversity and stability of dune grasslands along the Dutch coast was studied. Both studies demonstrate that highly plant diverse grasslands tend to be less sensitive to droughts compared to lowly plant diverse grasslands. Yet, the semi-natural grasslands show to be less resilient than the intensively managed agricultural grasslands. Both studies highlight the importance of grassland plant diversity in the Netherlands, where it concerns maintaining a high resistance to droughts. Overall, this dissertation proposes three approaches or methodologies to enhance the reliability of large scale ecosystem stability assessment using remote sensing. In addition, these methodologies are used to study grassland stability in the Netherlands, and to upscale the relationship between diversity and grassland stability to droughts. This work therefor contributes to the reliable quantification, monitoring and understanding of ecosystem stability.nrpages: 161status: publishe

Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006

Within the context of climate change, it is of utmost importance to quantify the stability of ecosystems with respect to climate anomalies. It is well acknowledged that ecosystem stability may change over time. As these temporal stability changes may provide a warning for increased vulnerability of the system, this study provides a methodology to quantify and assess these temporal changes in vegetation stability. Within this framework, vegetation stability changes were quantified over Australia from 1982 to 2006 using GIMMS NDVI and climate time series (i.e., SPEI (Standardized Precipitation and Evaporation Index)). Starting from a stability assessment on the complete time series, we aim to assess: (i) the magnitude and direction of stability changes; and (ii) the similarity in these changes for different stability metrics, i.e., the standard deviation of the NDVI anomaly (SD), auto-correlation at lag one of the NDVI anomaly (AC) and the correlation of NDVI anomaly with SPEI (CS). Results show high variability in magnitude and direction for the different stability metrics. Large areas and types of Australian vegetation showed an increase in variability (SD) over time; however, vegetation memory (AC) decreased. The association of NDVI anomalies with drought events (CS) showed a mixed response: the association increased in the western part, while it decreased in the eastern part. This methodology shows the potential for quantifying vegetation responses to major climate shifts and land use change, but results could be enhanced with higher resolution time series data

Monitoring Woody Cover Dynamics in Tropical Dry Forest Ecosystems Using Sentinel-2 Satellite Imagery

status: Published onlin

Monitoring woody cover dynamics in tropical dry forest ecosystems using sentinel-2 satellite imagery

Dry forests in Sub-Saharan Africa are of critical importance for the livelihood of the local population given their strong dependence on forest products. Yet these forests are threatened due to rapid population growth and predicted changes in rainfall patterns. As such, large-scale woody cover monitoring of tropical dry forests is urgently required. Although promising, remote sensing-based estimation of woody cover in tropical dry forest ecosystems is challenging due to the heterogeneous woody and herbaceous vegetation structure and the large intra-annual variability in the vegetation due to the seasonal rainfall. To test the capability of Sentinel-2 satellite imagery for producing accurate woody cover estimations, two contrasting study sites in Ethiopia and Tanzania were used. The estimation accuracy of a linear regression model using the Normalised Difference Vegetation Index (NDVI), a Partial Least Squares Regression (PLSR), and a Random Forest regression model using both single-date and multi-temporal Sentinel-2 images were compared. Additionally, the robustness and site transferability of these methods were tested. Overall, the multi-temporal PLSR model achieved the most accurate and transferable estimations (R2 = 0.70, RMSE = 4.12%). This model was then used to monitor the potential increase in woody coverage within several reforestation projects in the Degua Tembien district. In six of these projects, a significant increase in woody cover could be measured since the start of the project, which could be linked to their initial vegetation, location and shape. It can be concluded that a PLSR model combined with Sentinel-2 satellite imagery is capable of monitoring woody cover in these tropical dry forest regions, which can be used in support of reforestation efforts.</p

A combination of climate, tree diversity and local human disturbance determine the stability of dry Afromontane forests

Background: Anthropogenic disturbances are increasingly affecting the vitality of tropical dry forests. The future condition of this important biome will depend on its capability to resist and recover from these disturbances. So far, the temporal stability of dryland forests is rarely studied, even though identifying the important factors associated with the stability of the dryland forests could serve as a basis for forest management and restoration. Methodology: In a degraded dry Afromontane forest in northern Ethiopia, we explored remote sensing derived indicators of forest stability, using MODIS satellite derived NDVI time series from 2001 to 2018. Resilience and resistance were measured using the anomalies (remainders) after time series decomposition into seasonality, trend and remainder components. Growth stability was calculated using the integral of the undecomposed NDVI data. These NDVI derived stability indicators were then related to environmental factors of climate, topography, soil, tree species diversity, and local human disturbance, obtained from a systematic grid of field inventory plots, using boosted regression trees in R. Results: Resilience and resistance were adequately predicted by these factors with an R2 of 0.67 and 0.48, respectively, but the model for growth stability was weaker. Precipitation of the wettest month, distance from settlements and slope were the most important factors associated with resilience, explaining 51% of the effect. Altitude, temperature seasonality and humus accumulation were the significant factors associated with the resistance of the forest, explaining 61% of the overall effect. A positive effect of tree diversity on resilience was also important, except that the impact of species evenness declined above a threshold value of 0.70, indicating that perfect evenness reduced the resilience of the forest. Precipitation of the wettest month was the most important factor explaining 43.52% of the growth stability variation. Conclusion: A combination of climate, topographic factors and local human disturbance controlled the stability of the dry forest. Also tree diversity is an important stability component that should be considered in the management and restoration programs of such degraded forests. If local disturbances are alleviated the recovery time of dryland forests could be shortened, which is vital to maintain the ecosystem services these forests provide to local communities and global climate change.</p

Mapping European ecosystem change types in response to land-use change, extreme climate events, and land degradation

Extreme climate events and nonsustainable land use are important drivers altering the functioning of European ecosystems, resulting in loss of the services provided. Yet a consensus method for regular continental scale assessment of ecosystem condition in relation to land degradation (LD) is still lacking. Here, we propose a new remote sensing-based approach allowing for improved, repeated assessment of changing pressure on terrestrial ecosystems. On the basis of segmented trend analysis of water-use efficiency (WUE), a map of ecosystem change type (ECT) was produced over Europe for the period 1999 to 2013. Results were related to drought and change in land use and land cover and to known cases of soil degradation (LD case-studies). More than 30% of the European ecosystems experienced significant changes in WUE, of which more than 20% were categorized as abrupt. Large-scale positive reversals in WUE were observed over regions with increasing crop yield and intensification of wood production, whereas decreased WUE was observed over grassland areas coinciding with high farmland abandonment. Evidence of drought pressure on ecosystem functioning (EF) was observed, with abrupt changes in functioning observed during major European drought events. The ECTs also provided relevant information on the location and type of change in EF over the LD case studies. We conclude that mapping of gradual and abrupt changes in EF is expected to be valuable tool for ecosystem condition assessment that is essential for assessing the success of reaching the LD neutrality objectives set by the United Nations Convention to Combat Desertification

A model quantifying global vegetation resistance and resilience to short-term climate anomalies and their relationship with vegetation cover

© 2015 John Wiley & Sons Ltd. Aim: In order to mitigate the ecological, economical and social consequences of future climate change, we must understand and quantify the response of vegetation to short-term climate anomalies. There is currently no model that quantifies vegetation resistance and resilience at a global scale while simultaneously taking climate variability into account. The goals of this study were therefore to develop a standardized indicator of short-term vegetation resilience and resistance to drought and temperature anomalies, and to improve our understanding of vegetation resistance and resilience in drought-sensitive areas by linking metrics of vegetation stability to the percentage of tree cover, non-tree vegetation and bare soil. Location: Global. Methods: The deviation of vegetation behaviour from expectations was quantified using anomalies in the normalized difference vegetation index (NDVI) and modelled as a function of (1) past NDVI anomalies, (2) an instantaneous drought indicator and (3) temperature anomalies. Metrics of resistance and resilience were then extracted from the model and related to the percentages of bare soil, non-tree vegetation and tree cover. Results: Comparisons of the globally derived resilience and resistance metrics showed low resilience and low resistance to drought in semi-arid areas, low resistance to negative temperature anomalies in high-latitude areas, and low resistance to positive temperature anomalies in the Sahel and Australia. In drought-sensitive areas, resilience was highest for vegetation types with 3-20% bare soil and 5-15% tree cover. Main conclusions: Our ARx model is the first to simultaneously derive vegetation resistance and resilience metrics at a global scale, explicitly taking into account the spatial variability of short-term climate anomalies and data reliability. Its results highlight the impact of tree cover, non-tree vegetation and bare soil on vegetation resilience.status: publishe