Spatiotemporal analysis of vegetation variability and its relationship with climate change in China

This paper investigated spatiotemporal dynamic pattern of vegetation, climate factor, and their complex relationships from seasonal to inter-annual scale in China during the period 1982–1998 through wavelet transform method based on GIMMS data-sets. First, most vegetation canopies demonstrated obvious seasonality, increasing with latitudinal gradient. Second, obvious dynamic trends were observed in both vegetation and climate change, especially the positive trends. Over 70% areas were observed with obvious vegetation greening up, with vegetation degradation principally in the Pearl River Delta, Yangtze River Delta, and desert. Overall warming trend was observed across the whole country (\u3e98% area), stronger in Northern China. Although over half of area (58.2%) obtained increasing rainfall trend, around a quarter of area (24.5%), especially the Central China and most northern portion of China, exhibited significantly negative rainfall trend. Third, significantly positive normalized difference vegetation index (NDVI)–climate relationship was generally observed on the de-noised time series in most vegetated regions, corresponding to their synchronous stronger seasonal pattern. Finally, at inter-annual level, the NDVI–climate relationship differed with climatic regions and their long-term trends: in humid regions, positive coefficients were observed except in regions with vegetation degradation; in arid, semiarid, and semihumid regions, positive relationships would be examined on the condition that increasing rainfall could compensate the increasing water requirement along with increasing temperature. This study provided valuable insights into the long-term vegetation–climate relationship in China with consideration of their spatiotemporal variability and overall trend in the global change process

On The Response Of The European Vegetation Phenology To Hydroclimatic Anomalies

Climate change is expected to alter vegetation and carbon cycle processes, with implications for ecosystems and feedback to regional and global climate. Notably, understanding the sensitivity of vegetation to the anomalies of precipitation and temperature over different land cover classes and the corresponding temporal response is essential for improved climate prediction. In this paper, we analyse vegetation response to hydroclimatic forcings using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) derived from SeaWiFS (1998-2002) and MERIS (2003-2011) sensors at 1 km resolution. Based on land cover and pixel-wise analysis, we quantify the extent of the dependence between the FAPAR, and ultimately the phenology, and the anomalies of precipitation and temperature over Europe. Statistical tests are performed to establish where this correlation may be regarded as statistically dependent. Further, we assess a statistical link between the climate variables and a set of phenological metrics defined from FAPAR measurement. Variation in the phenological response to the unusual values of precipitation and temperature can be interpreted as the result of balanced opposite effects of water and temperature on vegetation processes. Results suggest very different responses on different land cover classes and timing seasons. The degree of observed coupled behaviour also indicates that European phenology may be quite sensitive to the perturbations in precipitation and temperature regimes such as those induced by the climate change.JRC.H.7-Climate Risk Managemen

Multi-Scale Phenology of Temperate Grasslands: Improving Monitoring and Management With Near-Surface Phenocams

Grasslands of the Australian Southern Tablelands represent a patchwork of native and exotic systems, occupying a continuum of C3-dominated to C4-dominated grasslands where composition depends on disturbance factors (e.g., grazing) and climate. Managing these complex landscapes is both challenging and critical for maintaining the security of Australia's pasture industries, and for protecting the biodiversity of native remnants. Differentiating C3 from C4 vegetation has been a prominent theme in remote sensing research due to distinct C3/C4 seasonal productivity patterns (phenology) and high uncertainty about how C3/C4 vegetation will respond to a changing climate. Phenology is used in northern hemisphere ecosystems for a range of purposes but has not been widely adopted in Australia, where dynamic climate often results in non-repetitive seasonal vegetation patterns. We employed time-lapse cameras (phenocams) to study the phenology of twelve grassland areas dominated by cool season (C3) and warm season (C4), native or exotic grasses near Canberra, Australia. Our aims were to assess phenological characteristics of the functional types and to determine the drivers of phenological variability. We compared the fine-scale phenocam seasonal profiles with field sampling and MODIS/Landsat satellite products to assess paddock-to-landscape functioning. We found C3/C4 species dominance to be the primary driver of phenological differences among grassland types, with C3 grasslands demonstrating peak greenness in spring, and senescing rapidly in response to high summer temperatures. In contrast, C4 grasslands showed peak activity in Austral summer and autumn (January-March). Some sites displayed primary and secondary peaks dependent on rainfall and species composition. We found that the proportion of dead vegetation is an important biophysical driver of grassland phenology, as were grazing pressures and species-dependent responses to rainfall and temperature. The satellite and field datasets were in general agreement with the phenocam results. However, the higher temporal fidelity of the cameras captured changes in vegetation not observed in the coarser satellite or field results. Our phenocam data shows consistent periods of increasing and decreasing greenness over as little as 5 days. Applications for management of grasslands in temperate Australia include the identification of remnant native grasslands, tracking biosecurity issues, and assessing productivity responses to climate variability

Studying Interactions between Climate Variability and Vegetation Dynamic Using a Phenology Based Approach

In this paper we investigated if and how a signature of climate control on vegetation growth can be individualized at regional scale using time series of SPOT-VEGETATION NDVI and ECMWF meteorological data. Twelve regions characterized by dominant and stable cropland or grassland covers were selected in Europe and Africa. Our results show that the relationship between NDVI and meteorological parameters is highly complex and significantly vary trough the phenological cycle of the plants. Hence, interactions between vegetation dynamics and climate variability must be studied at a smaller time scale in order to identify properly the limiting factors to vegetation growth. Using NDVI metrics, vegetative phases (from green-up to maximum NDVI) and reproductive phases (from maximum NDVI to maturity) were identified for each region. Cross-correlation analysis revealed that, in most of the cases, the best scores of Pearson's r are obtained when we considered the vegetative phase (from green-up to maximum of NDVI) and the reproductive phase (from maximum of NDVI to maturity) separately. We also showed that climatic constraints identified using yearly proxies of climate and vegetation do not depict correctly or completely the climate control on vegetation development. In that sense the complexity of the climate-vegetation relationship, which is spatially and temporally variable, is well underlined in this study.JRC.H.7-Climate Risk Managemen

Improved Understanding of the Linkages and Interactions between Vegetation, Climate, Streamflow and Drought: Case Studies in Germany

Global climate change has significantly impacted the terrestrial ecosystems and water cycles over the past century. This dissertation aims to further improve our knowledge of the linkages and interactions between vegetation, climate, streamflow, and drought. First, the current study investigated long-term variations in vegetation and climatic variables and their scale-dependent relationships by using Rhineland-Palatinate (Southwest Germany) as a case study area. Based upon the monthly normalized difference vegetation index (NDVI), precipitation and temperature data for six different vegetation types in two precipitation regimes (low and high precipitation regimes) of Rhineland-Palatinate, the temporal trends in the original time series of these variables and their relationships were examined. In addition, the further objectives were to evaluate which time-scale is dominantly responsible for the trend production found in the original data and find out the certain time-scales that represent the strongest correlation between NDVI and climatic variables (i.e., precipitation and temperature). A combined approach using the discrete wavelet transform (DWT), Mann-Kendall (MK) trend test and correlation analysis was implemented to achieve these goals. The trend assessment in the original data shows that the monthly NDVI time series for all vegetation types in both precipitation regimes have upward trends, most of which are significant. The precipitation and temperature data for six vegetation types in two precipitation regimes present weak downward trends and significant increasing trends, respectively. The most important time-scales contributing to the trend production in the original NDVI data are the 2-month and 8-month events. For precipitation, the most influential ones are 2-month and 4-month scales. The 4-month periodic mode predominantly affects the trends in the original temperature data. The results indicate temperature is the primary driver influencing the vegetation variability over this study area, while there is a negative correlation between NDVI and precipitation for all vegetation types and precipitation regimes. For the scale-dependent relationships between NDVI and precipitation, the 2-month and 8-month scales generally present the strongest negative correlation. The most significant positive correlation between NDVI and temperature is obtained at the 8- and 16-month scales for most vegetation types. The results might be valuable for water resources management as well as agricultural and ecological development planning in Rhineland-Palatinate, and also offer a helpful reference for other regions with similar climate condition. Then, this study presented a detailed regional investigation of the probabilistic and multi-scale relationships between streamflow and hydroclimatic variables (precipitation, temperature and soil moisture) and the potential links to large-scale atmospheric circulations over Baden-Württemberg, Southwest Germany. First, the joint dependence structure between seasonal streamflow and hydroclimatic variables was established using copulas. On the basis of the joint dependence structure, this study estimated the probability (risk) of hydrological droughts and floods conditioned upon two different scenarios of hydroclimatic variables for different seasons over the study area. Then, it was evaluated how the relationships between hydroclimatic forcings and streamflow vary among different temporal scales using wavelet coherence. The results reveal that the strong positive coupling between streamflow and both precipitation and soil moisture occurs at most temporal scales, particularly at decadal scales, while the multi-scale relationships between temperature and streamflow are significantly weak compared to precipitation and soil moisture. The connections between streamflow variability and large-scale atmospheric circulations were explored by using composite analysis. Although the atmospheric circulation patterns vary in different seasons, it can be found that the high streamflow anomalies for most seasons over Baden-Württemberg are related to strong westerly atmospheric circulations that play an important role in favoring the warm and moist air from the North Atlantic Ocean towards the study area and thus enhancing the precipitation. Moreover, the low streamflow anomalies are generally linked to the northerly circulations that induce the movement of cold air from northern Europe towards this study area and thus result in the reduced precipitation. Finally, a general probabilistic prediction network was developed in this dissertation for hydrological drought examination and environmental flow assessment. This methodology is divided into three major components. First, the joint streamflow drought indicator (JSDI) was proposed to describe the hydrological dryness/wetness conditions based on the monthly streamflow data. The JSDI relies on a high-dimensional (12-d) multivariate probabilistic model to establish a joint distribution model. In the second part, the drought-based environmental flow assessment method was introduced, which provides dynamic risk-based information about how much flow (the environmental flow target) is required for drought recovery and its likelihood under different hydrological drought initial situations. The final part involves estimating the conditional probability of achieving the required environmental flow under different precipitation scenarios according to the joint dependence structure between streamflow and precipitation. Two catchments in Germany were used to examine the usefulness of this network. The results show that the JSDI can provide an overall assessment of hydrological dryness/wetness conditions and does well in identifying both drought onset and persistence. The method also allows quantitative prediction of targeted environmental flow that is required for hydrological drought recovery and evaluates the corresponding risk. In addition, the results confirm that the general network can estimate the conditional probability associated with the required flow under different precipitation scenarios. The presented methodology offers a promising tool for water supply planning and management and for environmental flow assessment. The network has no restrictions that would prevent it from being applied to other basins worldwide

Statistical Emulation for Environmental Sustainability Analysis

The potential effects of climate change on the environment and society are many. In order to effectively quantify the uncertainty associated with these effects, highly complex simulation models are run with detailed representations of ecosystem processes. These models are computationally expensive and can involve computer runs of several days for their outputs. Computationally cheaper models can be obtained from large ensembles of simulations using a statistical emulation. The purpose of this thesis is to construct cheaper computational models (emulators) from simulation outputs of Lund-Potsdam-Jena-managed Land (LPJmL) which is a dynamic global vegetation and crop model. This research work is part of a project called ERMITAGE. The project links together several key component models into a common framework to better understand how the management and interaction of land, water and the earth’s climate system could be improved. The thesis focuses specifically on emulation of major outputs from the LPJmL model; carbon fluxes (NPP, carbon loss due to heterotrophic respiration and fire carbon) and potential crop yields (cereal, rice, maize and oil crops). Future decadal changes in carbon fluxes and crop yields are modelled as linear functions of climate change and other relevant variables. The emulators are constructed using a combination of statistical techniques of stepwise least squares regression, principal component analysis, weighted least squares regression, censored regression and Gaussian process regression. Further modelling involves sensitivity analyses to identify the relative contribution of each input variable to the total output variance. This used the Sobol global sensitivity method. The data cover the period 2001-2100 and comprise climate scenarios of several GCMs and RCPs. Under cross validation the percentage of variance explained ranges from 52-96% for carbon fluxes, 60-88% for the rainfed crops and 62-93% for the irrigated crops, averaged over climate scenarios

The use of vegetation indices to study temporal variation in vegetation phenology

Práce se zabývá problematikou využití vegetačních indexů ke studiu časových změn vegetační fenologie. První část byla věnována detailnímu rozboru domácí i zahraniční literatury, která se zabývá pracemi zpracovanými v tomto oboru. Hlavními výzkumnými otázkami byly, jestli se změnil začátek, konec, délka a vrchol vegetačního období v průběhu zkoumaného období. Další výzkumnou oblastí bylo srovnání s pozemními fenologickými daty. Dalším cílem práce bylo tedy hledání závislostí vypočtených údajů fenologických proměnných z vegetačních indexů s fenologickými pozemními daty. Jako základní datová sada byla využita sada GIMMS, která distribuuje vegetační index NDVI. Další datové sady byly MERIS MTCI, data MODIS s vegetačními indexy NDVI, EVI a LAI. Výsledky analýzy vývoje vegetační fenologie vykazují trendy v posunech, nejvýrazněji u začátku vegetačního období, kde došlo k posunu do dřívější doby. Výsledky analýzy vegetačních dat DPZ s pozemními fenologickými daty ČHMÚ se odvíjely vždy podle konkrétní lesní fenologické stanice. Zajímavé byly výsledky u fenologické stanice Svobody nad Úpou, kde se shodovaly výsledky trendů směrnic u téměř všech datových sad. Srovnání křivek průběhu vegetačních indexů s pozemními daty odpovídalo nejvíce u vybraných stanic křivkám datových sad MCD LAI a MTCI MERIS. Nejlepší...1 ABSTRACT The work deals with the use of vegetation indices to study temporal variation in vegetation phenology. The first part was devoted to detailed analysis of domestic and foreign literature, which deals with the work processed in this field. The main research questions were if changed start, end and length of growing period during the analysis period. Other research theme was comparision with ground phenological data. Another objective of this work was search dependencies computed data phenological variables from vegetation indicies with phenological ground data. As a basic data set was used GIMMS set, which distributes the vegetation index NDVI. Other data sets were MERIS MTCI, data MODIS with vegetation indices NDVI, EVI a LAI. The results of analyzes of vegetation phenology show trends in most shifts at the beginning of growing season, where was a shift to an earlier time. Results of the analysis of vegetation remote sensing data with ground-based phenological data ČHMÚ were unfolding always according to the specific forest phenological stations. Interesting results were at the phenological station Svoboda nad Úpou, where the results of trends directives were consistent in almost all data sets. Comparison of process curves vegetation indicies with ground data corresponded most curves at selected...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografieFaculty of SciencePřírodovědecká fakult

Factors affecting recent vegetation change in north-east Libya

Over the last few decades global warming and human intervention have led to changes and deterioration in natural vegetation across the world. The Al Jabal Al Akhdar, in north east Libya, is one of those areas that have experienced changes in land cover. This region has environmental and economic importance in providing suitable habitat for wildlife and providing services for local communities and cities in the Libyan Desert. The overall aim of this thesis was to evaluate the factors which have affected vegetation cover change in the Al Jabal Al Akhdar region over the last 42 years. There were three key objectives to this research: (1) to assess changes in natural and semi-natural vegetation cover in the north-east of Libya using forty years of satellite image data, (2) to assess land cover change and the effects of human activities in the study area over a period of 42 years, (3) to assess the factors affecting vegetation change in the study area. A further objective was to assess climate change in the study area using the climate data which was available from three climatic stations as climate change may be responsible for vegetation cover change in the areas that have low human activity. To address these objectives, remote sensing techniques were used to assess vegetation cover change and the changes in human activity from 1972 to the present. Satellite images provide data that cannot be collected by traditional methods and provide a historical archive of what the landscape looked like in the past. This study used multi-temporal Landsat images, which are freely available, for the period from 1972 to the present and provide the key temporal record of vegetation change on the Earth. Vegetation Indices (NDVI, SAVI and EVI), derived from the spectral reflectance of leaves and canopies, were used to assess the changes in vegetation cover over time. Image classification was also used to characterise the nature of land cover change, in particular the impact of human intervention. A key finding related to Objective (1) was that some areas have experienced a statistically significant change in vegetation indices over the 42 years which was interpreted as a change in vegetation cover in the areas in question. A key conclusion related to Objective (2) was that land cover had changed in the study area over the period of study. The influence of human activities was exerted through increased land use and decreased areas of forest and shrubland in the region. The outputs of the above-mentioned objectives and the effects of climate change were used to assess Objective (3), to detect which factors caused vegetation cover change in the Al Jabal Al Akhdar region. The main factors causing vegetation change were the effects of human activities in the areas adjacent to human settlements, while in the sparsely populated areas in the south of the study area, vegetation cover changes may be related to recent climate change. In conclusion, although the number of available Landsat images used to delineate the changes in vegetation cover was limited, the methods used to interpret the images for vegetation indices and image classification were invaluable in determining important results for the objectives of the thesis. The results obtained from assessing vegetation cover and land cover change and patterns of changes are major steps towards filling the information gap and creating a database for monitoring land cover in the study area. This effort will contribute towards facilitating decision-making on mitigating the impact of land use dynamics on land cover as well as provide a basis for future research