Towards mapping the late Quaternary vegetation change of Europe

The number of well-dated pollen diagrams in Europe has increased considerably over the last 30 years and many of them have been submitted to the European Pollen Database (EPD). This allows for the construction of increasingly precise maps of Holocene vegetation change across the continent. Chronological information in the EPD has been expressed in uncalibrated radiocarbon years, and most chronologies to date are based on this time scale. Here we present new chronologies for most of the datasets stored in the EPD based on calibrated radiocarbon years. Age information associated with pollen diagrams is often derived from the pollen stratigraphy itself or from other sedimentological information. We reviewed these chronological tie points and assigned uncertainties to them. The steps taken to generate the new chronologies are described and the rationale for a new classification system for age uncertainties is introduced. The resulting chronologies are fit for most continental-scale questions. They may not provide the best age model for particular sites, but may be viewed as general purpose chronologies. Taxonomic particularities of the data stored in the EPD are explained. An example is given of how the database can be queried to select samples with appropriate age control as well as the suitable taxonomic level to answer a specific research question. © 2013 The Author(s).

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.

Land degradation and vegetation distribution in Chott El Beida wetland, Algeria

The aim of this study is to explore the environmental factors that determine plant Community distribution in northeast Algeria. This paper provides a quantitative analysis of the vegetation-environment relationships for a study site in the Cholt El Beida wetland, a RAMSAR site in Setif, Algeria. Sixty vegetation plots were sampled and analysed using TWINSPAN and Detrended Correspondence Analysis (DCA) in order to identify the principal vegetation communities and determine the environmental gradients associated with these. 127 species belonging to 41 families and 114 genera were recorded. Six of the recorded species were endemic representing 4.7% of the total species. The richest families were Compositae, Gramineae, Cruciferae and Chenopodiaceae. Therophytes and hemicryptophytes were the most frequent life forms. the Mediterranean floristic element is dominant and is represented by 39 species. The samples were classified into four main community types. The principal DCA axes represent gradients of soil salinity, moisture and anthropogenic pressure. The use of classification in combination with ordination techniques resulted in a good discrimination between plant communities and a greater understanding of controlling environmental factors. The methodology adopted can be employed for improving baseline information on plant community ecology and distribution in often critically endangered Mediterranean wetland areas. (C) 2008 Elsevier Ltd. All rights reserved.

A GIS-based empirical model for vegetation prediction in Lefka Ori, Crete

The aim of the study was to establish and verify a predictive vegetation model for plant community distribution in the alti-Mediterranean zone of the Lefka Ori massif, western Crete. Based on previous work three variables were identified as significant determinants of plant community distribution, namely altitude, slope angle and geomorphic landform. The response of four community types against these variables was tested using classification trees analysis in order to model community type occurrence. V-fold cross-validation plots were used to determine the length of the best fitting tree. The final 9node tree selected, classified correctly 92.5% of the samples. The results were used to provide decision rules for the construction of a spatial model for each community type. The model was implemented within a Geographical Information System (GIS) to predict the distribution of each community type in the study site. The evaluation of the model in the field using an error matrix gave an overall accuracy of 71%. The user's accuracy was higher for the Crepis-Cirsium (100%) and Telephium-Herniaria community type (66.7%) and relatively lower for the Peucedanum-Alyssum and Dianthus-Lomelosia community types (63.2% and 62.5%, respectively). Misclassification and field validation points to the need for improved geomorphological mapping and suggests the presence of transitional communities between existing community types.

Environmental factors and vegetation composition, Lefka Ori Massif, Crete, S. Aegean

Aim The aim of this study was to explore the environmental factors that determine the spatial distribution of oro-mediterranean and alti-mediterranean plant communities in Crete. Location The paper provides a quantitative analysis of vegetation-environment relationships for two study areas within the Lefka Ori massif Crete, a proposed Natura 2000 site. Methods Eleven environmental variables were recorded: altitude, slope, aspect, percentage of bare rock, percentage of unvegetated ground, soil depth, pH, organic matter content and percentages of sand, silt and clay content. Classification of the vegetation was based on twinspan, while detrended correspondence analysis (DCA) and canonical correspondence analysis (CCA) were used to identify environmental gradients linked to community distribution. Results One hundred and twenty-five species were recorded from 120 plots located within the two study areas. Forty-seven of the recorded species are endemic, belonging to 35 families. Hemicryptophytes and chamaephytes were the most frequent, suggesting a typical oro-mediterranean life form spectrum. The samples were classified into five main community types and one transitional. The main gradients, identified by CCA, were altitude and surface cover type in the North-west site, while in the Central site the gradients were soil formation-development and surface cover type. Main conclusions The use of classification in combination with ordination techniques resulted in a good discrimination between plant communities and a greater understanding of controlling environmental factors. The methodology adopted can be employed for improving baseline information on plant community ecology and distribution in Mediterranean mountain zones.

Land degradation and vegetation distribution in Chott El Beida wetland, Algeria

The aim of this study is to explore the environmental factors that determine plant Community distribution in northeast Algeria. This paper provides a quantitative analysis of the vegetation-environment relationships for a study site in the Cholt El Beida wetland, a RAMSAR site in Setif, Algeria. Sixty vegetation plots were sampled and analysed using TWINSPAN and Detrended Correspondence Analysis (DCA) in order to identify the principal vegetation communities and determine the environmental gradients associated with these. 127 species belonging to 41 families and 114 genera were recorded. Six of the recorded species were endemic representing 4.7% of the total species. The richest families were Compositae, Gramineae, Cruciferae and Chenopodiaceae. Therophytes and hemicryptophytes were the most frequent life forms. the Mediterranean floristic element is dominant and is represented by 39 species. The samples were classified into four main community types. The principal DCA axes represent gradients of soil salinity, moisture and anthropogenic pressure. The use of classification in combination with ordination techniques resulted in a good discrimination between plant communities and a greater understanding of controlling environmental factors. The methodology adopted can be employed for improving baseline information on plant community ecology and distribution in often critically endangered Mediterranean wetland areas. (C) 2008 Elsevier Ltd. All rights reserved.

A GIS-based empirical model for vegetation prediction in Lefka Ori, Crete

The aim of the study was to establish and verify a predictive vegetation model for plant community distribution in the alti-Mediterranean zone of the Lefka Ori massif, western Crete. Based on previous work three variables were identified as significant determinants of plant community distribution, namely altitude, slope angle and geomorphic landform. The response of four community types against these variables was tested using classification trees analysis in order to model community type occurrence. V-fold cross-validation plots were used to determine the length of the best fitting tree. The final 9node tree selected, classified correctly 92.5% of the samples. The results were used to provide decision rules for the construction of a spatial model for each community type. The model was implemented within a Geographical Information System (GIS) to predict the distribution of each community type in the study site. The evaluation of the model in the field using an error matrix gave an overall accuracy of 71%. The user's accuracy was higher for the Crepis-Cirsium (100%) and Telephium-Herniaria community type (66.7%) and relatively lower for the Peucedanum-Alyssum and Dianthus-Lomelosia community types (63.2% and 62.5%, respectively). Misclassification and field validation points to the need for improved geomorphological mapping and suggests the presence of transitional communities between existing community types.

Can the study of natural vegetation succession assist in the control of soil erosion on abandoned croplands on the Loess Plateau, China?

In the Loess Plateau, China, arable cultivation of slope lands is common and associated with serious soil erosion. Planting trees or grass may control erosion, but planted species may consume more soil water and can threaten long-term ecosystem sustainability. Natural vegetation succession is an alternative ecological solution to restore degraded land, but there is a time cost, given that the establishment of natural vegetation, adequate to prevent soil erosion, is a longer process than planting. The aims of this study were to identify the environmental factors controlling the type of vegetation established on abandoned cropland and to identify candidate species that might be sown soon after abandonment to accelerate vegetation succession and establishment of natural vegetation to prevent soil erosion. A field survey of thirty-three 2 × 2–m plots was carried out in July 2003, recording age since abandonment, vegetation cover, and frequency of species together with major environmental and soil variables. Data were analyzed using correspondence analysis, classification tree analysis, and species response curves. Four vegetation types were identified and the data analysis confirmed the importance of time since abandonment, total P, and soil water in controlling the type of vegetation established. Among the dominant species in the three late-successional vegetation types, the most appropriate candidates for accelerating and directing vegetation succession were King Ranch bluestem (Bothriochloa ischaemum) and Lespedeza davurica (Leguminosae). These species possess combinations of the following characteristics: tolerance of low water and nutrient availability, fibrous root system and strong lateral vegetative spread, and a persistent seed bank.

Analysis of full-waveform LiDAR data for classification of an orange orchard scene

Full-waveform laser scanning data acquired with a Riegl LMS-Q560 instrument were used to classify an orange orchard into orange trees, grass and ground using waveform parameters alone. Gaussian decomposition was performed on this data capture from the National Airborne Field Experiment in November 2006 using a custom peak-detection procedure and a trust-region-reflective algorithm for fitting Gauss functions. Calibration was carried out using waveforms returned from a road surface, and the backscattering coefficient c was derived for every waveform peak. The processed data were then analysed according to the number of returns detected within each waveform and classified into three classes based on pulse width and c. For single-peak waveforms the scatterplot of c versus pulse width was used to distinguish between ground, grass and orange trees. In the case of multiple returns, the relationship between first (or first plus middle) and last return c values was used to separate ground from other targets. Refinement of this classification, and further sub-classification into grass and orange trees was performed using the c versus pulse width scatterplots of last returns. In all cases the separation was carried out using a decision tree with empirical relationships between the waveform parameters. Ground points were successfully separated from orange tree points. The most difficult class to separate and verify was grass, but those points in general corresponded well with the grass areas identified in the aerial photography. The overall accuracy reached 91%, using photography and relative elevation as ground truth. The overall accuracy for two classes, orange tree and combined class of grass and ground, yielded 95%. Finally, the backscattering coefficient c of single-peak waveforms was also used to derive reflectance values of the three classes. The reflectance of the orange tree class (0.31) and ground class (0.60) are consistent with published values at the wavelength of the Riegl scanner (1550 nm). The grass class reflectance (0.46) falls in between the other two classes as might be expected, as this class has a mixture of the contributions of both vegetation and ground reflectance properties.

Ecophysiological and bioclimatic foundations for a global plant functional classification

Question: What plant properties might define plant functional types (PFTs) for the analysis of global vegetation responses to climate change, and what aspects of the physical environment might be expected to predict the distributions of PFTs? Methods: We review principles to explain the distribution of key plant traits as a function of bioclimatic variables. We focus on those whole-plant and leaf traits that are commonly used to define biomes and PFTs in global maps and models. Results: Raunkiær's plant life forms (underlying most later classifications) describe different adaptive strategies for surviving low temperature or drought, while satisfying requirements for reproduction and growth. Simple conceptual models and published observations are used to quantify the adaptive significance of leaf size for temperature regulation, leaf consistency for maintaining transpiration under drought, and phenology for the optimization of annual carbon balance. A new compilation of experimental data supports the functional definition of tropical, warm-temperate, temperate and boreal phanerophytes based on mechanisms for withstanding low temperature extremes. Chilling requirements are less well quantified, but are a necessary adjunct to cold tolerance. Functional traits generally confer both advantages and restrictions; the existence of trade-offs contributes to the diversity of plants along bioclimatic gradients. Conclusions: Quantitative analysis of plant trait distributions against bioclimatic variables is becoming possible; this opens up new opportunities for PFT classification. A PFT classification based on bioclimatic responses will need to be enhanced by information on traits related to competition, successional dynamics and disturbance.

Automating condition monitoring of vegetation on railway trackbeds and embankments

Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment. Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body. A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways. The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process. Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms. The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.

Utilizing Remote Sensing Imagery to Monitor Vegetation Change within World Heritage Sites

ABSTRACT World Heritage sites provide a glimpse into the stories and civilizations of the past. There are currently 1007 unique World Heritage properties with 779 being classified as cultural sites, 197 as natural sites, and 31 falling into the categories of both cultural and natural sites (UNESCO & World Heritage Centre, 1992-2015). However, of these 1007 World Heritage sites, at least 46 are categorized as in danger and this number continues to grow. These unique and irreplaceable sites are exceptional because of their universality. Consequently, since World Heritage sites belong to all the people of the world and provide inspiration and admiration to all who visit them, it is our responsibility to help preserve these sites. The key form of preservation involves the individual monitoring of each site over time. While traditional methods are still extremely valuable, more recent advances in the field of geographic and spatial technologies including geographic information systems (GIS), laser scanning, and remote sensing, are becoming more beneficial for the monitoring and overall safeguarding of World Heritage sites. Through the employment and analysis of more accurately detailed spatial data, World Heritage sites can be better managed. There is a strong urgency to protect these sites. The purpose of this thesis is to describe the importance of taking care of World Heritage sites and to depict a way in which spatial technologies can be used to monitor and in effect preserve World Heritage sites through the utilization of remote sensing imagery. The research conducted in this thesis centers on the Everglades National Park, a World Heritage site that is continually affected by changes in vegetation. Data used include Landsat satellite imagery that dates from 2001-2003, the Everglades' boundaries shapefile, and Google Earth imagery. In order to conduct the in-depth analysis of vegetation change within the selected World Heritage site, three main techniques were performed to study changes found within the imagery. These techniques consist of conducting supervised classification for each image, incorporating a vegetation index known as Normalized Vegetation Index (NDVI), and utilizing the change detection tool available in the Environment for Visualizing Images (ENVI) software. With the research and analysis conducted throughout this thesis, it has been shown that within the three year time span (2001-2003), there has been an overall increase in both areas of barren soil (5.760%) and areas of vegetation (1.263%) with a decrease in the percentage of areas classified as sparsely vegetated (-6.987%). These results were gathered through the use of the maximum likelihood classification process available in the ENVI software. The results produced by the change detection tool which further analyzed vegetation change correlate with the results produced by the classification method. As well, by utilizing the NDVI method, one is able to locate changes by selecting a specific area and comparing the vegetation index generated for each date. It has been found that through the utilization of remote sensing technology, it is possible to monitor and observe changes featured within a World Heritage site. Remote sensing is an extraordinary tool that can and should be used by all site managers and organizations whose goal it is to preserve and protect World Heritage sites. Remote sensing can be used to not only observe changes over time, but it can also be used to pinpoint threats within a World Heritage site. World Heritage sites are irreplaceable sources of beauty, culture, and inspiration. It is our responsibility, as citizens of this world, to guard these treasures.

A multisensor, multitemporal approach for monitoring herbaceous vegetation growth in the Amazon floodplain

The Amazon River floodplain is an important source of atmospheric CO2 and CH4. Aquatic herbaceous vegetation (macrophytes) have been shown to contribute significantly to floodplain net primary productivity (NPP) and methane emission in the region. Their fast growth rates under both flooded and dry conditions make herbaceous vegetation the most variable element in the Amazon floodplain NPP budget, and the most susceptible to environmental changes. The present study combines multitemporal Radarsat-1 and MODIS images to monitor spatial and temporal changes in herbaceous vegetation cover in the Amazon floodplain. Radarsat-1 images were acquired from Dec/2003 to Oct/2005, and MODIS daily surface reflectance products were acquired for the two cloud-free dates closest to each Radarsat-1 acquisition. An object-based, hierarchical algorithm was developed using the temporal SAR information to discriminate Permanent Open Water (OW), Floodplain (FP) and Upland (UL) classes at Level 1, and then subdivide the FP class into Woody Vegetation (WV) and Possible Macrophytes (PM) at Level 2. At Level 3, optical and SAR information were combined to discriminate actual herbaceous cover at each date. The resulting maps had accuracies ranging from 80% to 90% for Level 1 and 2 classifications, and from 60% to 70% for Level 3 classifications, with kappa values ranging between 0.7 and 0.9 for Levels 1 and 2 and between 0.5 and 0.6 for Level 3. All study sites had noticeable variations in the extent of herbaceous cover throughout the hydrological year, with maximum areas up to four times larger than minimum areas. The proposed classification method was able to capture the spatial pattern of macrophyte growth and development in the studied area, and the multitemporal information was essential for both separating vegetation cover types and assessing monthly variation in herbaceous cover extent.

Estimation of croplands using indicator kriging and fuzzy classification

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Multitemporal analysis of satellite imagery: implementation of a land evolution model in ALCS (ATSR LAND CLASSIFICATION SYSTEM)

In this report it was designed an innovative satellite-based monitoring approach applied on the Iraqi Marshlands to survey the extent and distribution of marshland re-flooding and assess the development of wetland vegetation cover. The study, conducted in collaboration with MEEO Srl , makes use of images collected from the sensor (A)ATSR onboard ESA ENVISAT Satellite to collect data at multi-temporal scales and an analysis was adopted to observe the evolution of marshland re-flooding. The methodology uses a multi-temporal pixel-based approach based on classification maps produced by the classification tool SOIL MAPPER ®. The catalogue of the classification maps is available as web service through the Service Support Environment Portal (SSE, supported by ESA). The inundation of the Iraqi marshlands, which has been continuous since April 2003, is characterized by a high degree of variability, ad-hoc interventions and uncertainty. Given the security constraints and vastness of the Iraqi marshlands, as well as cost-effectiveness considerations, satellite remote sensing was the only viable tool to observe the changes taking place on a continuous basis. The proposed system (ALCS – AATSR LAND CLASSIFICATION SYSTEM) avoids the direct use of the (A)ATSR images and foresees the application of LULCC evolution models directly to „stock‟ of classified maps. This approach is made possible by the availability of a 13 year classified image database, conceived and implemented in the CARD project (http://earth.esa.int/rtd/Projects/#CARD).The approach here presented evolves toward an innovative, efficient and fast method to exploit the potentiality of multi-temporal LULCC analysis of (A)ATSR images. The two main objectives of this work are both linked to a sort of assessment: the first is to assessing the ability of modeling with the web-application ALCS using image-based AATSR classified with SOIL MAPPER ® and the second is to evaluate the magnitude, the character and the extension of wetland rehabilitation.