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Measurements of the atmospheric boundary layer (ABL) structure were performed for three years (October 2017–August 2020) at the Russian observatory “Ice Base Cape Baranova” (79.280° N, 101.620° E) using SODAR (Sound Detection And Ranging). These measurements were part of the YOPP (Year of Polar Prediction) project “Boundary layer measurements in the high Arctic” (CATS_BL) within the scope of a joint German–Russian project. In addition to SODAR-derived vertical profiles of wind speed and direction, a suite of complementary measurements at the observatory was available. ABL measurements were used for verification of the regional climate model COSMO-CLM (CCLM) with a 5 km resolution for 2017–2020. The CCLM was run with nesting in ERA5 data in a forecast mode for the measurement period. SODAR measurements were mostly limited to wind speeds <12 m/s since the signal was often lost for higher winds. The SODAR data showed a topographical channeling effect for the wind field in the lowest 100 m and some low-level jets (LLJs). The verification of the CCLM with near-surface data of the observatory showed good agreement for the wind and a negative bias for the 2 m temperature. The comparison with SODAR data showed a positive bias for the wind speed of about 1 m/s below 100 m, which increased to 1.5 m/s for higher levels. In contrast to the SODAR data, the CCLM data showed the frequent presence of LLJs associated with the topographic channeling in Shokalsky Strait. Although SODAR wind profiles are limited in range and have a lot of gaps, they represent a valuable data set for model verification. However, a full picture of the ABL structure and the climatology of channeling events could be obtained only with the model data. The climatological evaluation showed that the wind field at Cape Baranova was not only influenced by direct topographic channeling under conditions of southerly winds through the Shokalsky Strait but also by channeling through a mountain gap for westerly winds. LLJs were detected in 37% of all profiles and most LLJs were associated with channeling, particularly LLJs with a jet speed ≥ 15 m/s (which were 29% of all LLJs). The analysis of the simulated 10 m wind field showed that the 99%-tile of the wind speed reached 18 m/s and clearly showed a dipole structure of channeled wind at both exits of Shokalsky Strait. The climatology of channeling events showed that this dipole structure was caused by the frequent occurrence of channeling at both exits. Channeling events lasting at least 12 h occurred on about 62 days per year at both exits of Shokalsky Strait.
A model-based temperature adjustment scheme for wintertime sea-ice production retrievals from MODIS
(2022)
Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020.
The state-of-the-art finite element software Plaxis 3D was applied in a real-world study site of the Turaida castle mound to investigate the slope stability of the mound and understand the mechanisms triggering landslides there. During the simulation, the stability of the castle mound was analysed and the most landslide-susceptible zones of hillslopes were determined. The 3D finite-element stability analysis has significant advantages over conventional 2D limit-equilibrium methods where locations of 2D stability sections are arbitrarily selected. Two modelling scenarios of the slope stability were elaborated considering deep-seated slides in bedrock and shallow landslides in the colluvial material of slopes. The model shows that shallow slides in colluvium are more probable. In the finite-element model, slope failure occurs along the weakest zone in colluvium, similarly to the situation observed in previous landslides in the study site. The physical basis of the model allows results to be obtained very close to natural conditions and delivers valuable insight in triggering mechanisms of landslides.
Low-level jets (LLJs) are climatological features in polar regions. It is well known that katabatic winds over the slopes of the Antarctic ice sheet are associated with strong LLJs. Barrier winds occurring, e.g., along the Antarctic Peninsula may also show LLJ structures. A few observational studies show that LLJs occur over sea ice regions. We present a model-based climatology of the wind field, of low-level inversions and of LLJs in the Weddell Sea region of the Antarctic for the period 2002–2016. The sensitivity of the LLJ detection on the selection of the wind speed maximum is investigated. The common criterion of an anomaly of at least 2 m/s is extended to a relative criterion of wind speed decrease above and below the LLJ. The frequencies of LLJs are sensitive to the choice of the relative criterion, i.e., if the value for the relative decrease exceeds 15%. The LLJs are evaluated with respect to the frequency distributions of height, speed, directional shear and stability for different regions. LLJs are most frequent in the katabatic wind regime over the ice sheet and in barrier wind regions. During winter, katabatic LLJs occur with frequencies of more than 70% in many areas. Katabatic LLJs show a narrow range of heights (mostly below 200 m) and speeds (typically 10–20 m/s), while LLJs over the sea ice cover a broad range of speeds and heights. LLJs are associated with surface inversions or low-level lifted inversions. LLJs in the katabatic wind and barrier wind regions can last several days during winter. The duration of LLJs is sensitive to the LLJ definition criteria. We propose to use only the absolute criterion for model studies.
Intense, southward low-level winds are common in Nares Strait, between Ellesmere Island and northern Greenland. The steep topography along Nares Strait leads to channelling effects, resulting in an along-strait flow. This research study presents a 30-year climatology of the flow regime from simulations of the COSMO-CLM climate model. The simulations are available for the winter periods (November–April) 1987/88 to 2016/17, and thus, cover a period long enough to give robust long-term characteristics of Nares Strait. The horizontal resolution of 15 km is high enough to represent the complex terrain and the meteorological conditions realistically. The 30-year climatology shows that LLJs associated with gap flows are a climatological feature of Nares Strait. The maximum of the mean 10-m wind speed is around 12 m s-1 and is located at the southern exit of Smith Sound. The wind speed is strongly related to the pressure gradient. Single events reach wind speeds of 40 m s-1 in the daily mean. The LLJs are associated with gap flows within the narrowest parts of the strait under stably stratified conditions, with the main LLJ occurring at 100–250 m height. With increasing mountain Froude number, the LLJ wind speed and height increase. The frequency of strong wind events (>20 m s-1 in the daily mean) for the 10 m wind shows a strong interannual variability with an average of 15 events per winter. Channelled winds have a strong impact on the formation of the North Water polynya.
The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.
Climate change is expected to cause mountain species to shift their ranges to higher elevations. Due to the decreasing amounts of habitats with increasing elevation, such shifts are likely to increase their extinction risk. Heterogeneous mountain topography, however, may reduce this risk by providing microclimatic conditions that can buffer macroclimatic warming or provide nearby refugia. As aspect strongly influences the local microclimate, we here assess whether shifts from warm south-exposed aspects to cool north-exposed aspects in response to climate change can compensate for an upward shift into cooler elevations.
This study aims to estimate the cotton yield at the field and regional level via the APSIM/OZCOT crop model, using an optimization-based recalibration approach based on the state variable of the cotton canopy - the leaf area index (LAI), derived from atmospherically corrected Landsat-8 OLI remote sensing images in 2014. First, a local sensitivity and global analysis approach was employed to test the sensitivity of cultivar, soil and agronomic parameters to the dynamics of the LAI. After sensitivity analyses, a series of sensitive parameters were obtained. Then, the APSIM/OZCOT crop model was calibrated by observations over a two-year span (2006-2007) at the Aksu station, combined with these sensitive cultivar parameters and the current understanding of cotton cultivar parameters. Third, the relationship between the observed in-situ LAI and synchronous perpendicular vegetation indices derived from six Landsat-8 OLI images covering the entire growth stage was modelled to generate LAI maps in time and space. Finally, the Particle Swarm Optimization (PSO) and general-purpose optimization approach (based on Nelder-Mead algorithm) were used to recalibrate four sensitive agronomic parameters (row spacing, sowing density per row, irrigation amount and total fertilization) according to the minimization of the root-mean-square deviation (RMSE) between the simulated LAI from the APSIM/OZCOT model and retrieved LAI from Landsat-8 OLI remote sensing images. After the recalibration, the best simulated results compared with observed cotton yield were obtained. The results showed that: (1) FRUDD, FLAI and DDISQ were the major cultivar parameters suitable for calibrating the cotton cultivar. (2) After the calibration, the simulated LAI performed well with an RMSE and mean absolute error (MAE) of 0.45 and 0.33, respectively, in 2006 and 0.46 and 0.41, respectively, in 2007. The coefficient of determination between the observed and simulated LAI was 0.83 and 0.97, respectively, in 2006 and 2007. The Pearson- correlation coefficient was 0.913 and 0.988 in 2006 and 2007, respectively, with a significant positive correlation between the simulated and observed LAI. The difference between the observed and simulated yield was 776.72 kg/ha and 259.98 kg/ha in 2006 and 2007, respectively. (3) Cotton cultivation in 2014 was obtained using three Landsat-8 OLI images - DOY136 (May), DOY 168 (June) and DOY 200 (July) - based on the phenological differences in cotton and other vegetation types. (4) The yield estimation after the assimilation closely approximated the field-observed values, and the coefficient of determination was as high as 0.82, after recalibration of the APSIM/OZCOT model for ten cotton fields. The difference between the observed and assimilated yields for the ten fields ranged from 18.2 to 939.7 kg/ha. The RMSE and MAE between the assimilated and observed yield was 417.5 and 303.1 kg/ha, respectively. These findings provide scientific evidence for the feasibility of coupled remote sensing and APSIM/OZCOT model at the field level. (5) Upscaling from field level to regional level, the assimilation algorithm and scheme are both especially important. Although the PSO method is very efficient, the computational efficiency is also the shortcoming of the assimilation strategy on a regional scale. Comparisons between the PSO and general-purpose optimization method (based on the Nelder-Mead algorithm) were implemented from the RSME, LAI curve and computational time. The general-purpose optimization method (based on the Nelder-Mead algorithm) was used for the regional assimilation between remote sensing and the APSIM/OZCOT model. Meanwhile, the basic unit for regional assimilation was also determined as cotton field rather than pixel. Moreover, the crop growth simulation was also divided into two phases (vegetative growth and reproductive growth) for regional assimilation. (6) The regional assimilation at the vegetative growth stage between the remote sensing derived and APSIM/OZCOT model-simulated LAI was implemented by adjusting two parameters: row spacing and sowing density per row. The results showed that the sowing density of cotton was higher in the southern part than in the northern part of the study area. The spatial pattern of cotton density was also consistent with the reclamation from 2001 to 2013. Cotton fields after early reclamation were mainly located in the southern part while the recent reclamation was located in the northern part. Poor soil quality, lack of irrigation facilities and woodland belts of cotton fields in the northern part caused the low density of cotton. Regarding the row spacing, the northern part was larger than the southern part due to the variation of two agronomic modes from military and private companies. (7) The irrigation and fertilization amount were both used as key parameters to be adjusted for regional assimilation during the reproductive growth period. The result showed that the irrigation per time ranged from 58.14 to 89.99 mm in the study area. The spatial distribution of the irrigation amount is higher in the northern part while lower in southern study area. The application of urea fertilization ranged from 500.35 to 1598.59 kg/ha in the study area. The spatial distribution of fertilization was lower in the northern part and higher in the southern part. More fertilization applied in the southern study area aims to increase the boll weight and number for pursuing higher yields of cotton. The frequency of the RSME during the second assimilation was mainly located in the range of 0.4-0.6 m2/m2. The estimated cotton yield ranged from 1489 to 8895 kg/ha. The spatial distribution of the estimated yield is also higher in the southern part than the northern study area.
High-resolution projections of the future climate are required to assess climate change realistically at a regional scale. This is in particular important for climate change impact studies since global projections are much too coarse to represent local conditions adequately. A major concern is thereby the change of extreme values in a warming climate due to their severe impact on the natural environment, socio-economical systems and the human health. Regional climate models (RCMs) are, however, able to reproduce much of those local features. Current horizontal resolutions are about 18-25km, which is still too coarse to directly resolve small-scale processes such as deep-convection. For this reason, projections of a possible future climate were simulated in this study with the regional climate model COSMO-CLM at horizontal resolutions of 4.5km and 1.3km for the region of Saarland-Lorraine-Luxemburg and Rhineland-Palatinate for the first time. At a horizontal scale of about 1km deep-convection is treated explicitly, which is expected to improve particularly the simulation of convective summer precipitation and a better resolved orography is expected to improve near surface fields such as 2m temperature. These simulations were performed as 10-year long time-slice experiments for the present climate (1991"2000), the near future (2041"2050) and the end of the century (2091"2100). The climate change signals of the annual and seasonal means and the change of extremes are analysed with respect to precipitation and 2m temperature and a possible added value due to the increased resolution is investigated. To assess changes in extremes, extreme indices have been applied and 10- and 20-year return levels were estimated by "peak-over-threshold" models. Since it is generally known that model output of RCMs should not directly be used for climate change impact studies, the precipitation and temperature fields were bias-corrected with several quantile-matching methods. Among them is a new developed parametric method which includes an extension for extreme values and is hence expected to improve the correction. In addition, the impact of the bias-correction on the climate change signals and on the extreme value statistics was investigated. The results reveal a significant warming of the annual mean by about +1.7 -°C until 2041"2050 and +3.7 -°C until 2091"2100, but considerably stronger signals of up to +5 -°C in summer in the Rhine Valley. Furthermore, the daily variability increases by about +0.8 -°C in summer but decreases by about -0.8 -°C in winter. Consequently, hot extremes increase moderately until the mid of the century but strongly thereafter, in particular in the Rhine Valley. Cold extremes warm continuously in the complete domain in the next 100 years but strongest in mountainous areas. The change signals with regard to annual precipitation are of the order -±10% but not significant. Significant, however, are a predicted increase of +32% of the seasonal precipitation in autumn until 2041"2050 and a decrease of -28% in summer until 2091-2100. No significant changes were found for days with intensities > 20 mm/day, but the results indicate that extremes with return periods ≤2 years increase as well as the frequency and duration of dry periods. The bias-corrections amplified positive signals but dampened negative signals and considerably reduced the power of detection. Moreover, absolute values and frequencies of extremes were altered by the correction but change signals remained approximately constant. The new method outperformed other parametric methods, in particular with regard to extreme value correction and related extreme indices and return levels. Although the bias correction removed systematic errors, it should be treated as an additional layer of uncertainty in climate change studies. Finally, the increased resolution of 1.3km improved predominantly the representation of temperature fields and extremes in terms of spatial heterogeneity. The benefits for summer precipitation were not as clear due to a severe dry-bias in summer, but it could be shown that in principle the onset and intensity of convection improves. This work demonstrates that climate change will have severe impacts in this investigation area and that in particular extremes may change considerably. An increased resolution provides thereby an added value to the results. These findings encourage further investigations, for other variables as for example near-surface wind, which will be more feasible with growing computing resources. These analyses should, however, be repeated with longer time series, different RCMs and anthropogenic scenarios to determine the robustness and uncertainty of these results more extensively.
The reduction of information contained in model time series through the use of aggregating statistical performance measures is very high compared to the amount of information that one would like to draw from it for model identification and calibration purposes. It is readily known that this loss imposes important limitations on model identification and -diagnostics and thus constitutes an element of the overall model uncertainty as essentially different model realizations with almost identical performance measures (e.g. r-² or RMSE) can be generated. In three consecutive studies the present work proposes an alternative approach towards hydrological model evaluation based on the application of Self-Organizing Maps (SOM; Kohonen, 2001). The Self-Organizing Map is a type of artificial neural network and unsupervised learning algorithm that is used for clustering, visualization and abstraction of multidimensional data. It maps vectorial input data items with similar patterns onto contiguous locations of a discrete low-dimensional grid of neurons. The iterative training of the SOM causes the neurons to form a discrete, data-compressed representation of the high-dimensional input data. Using appropriate visualization techniques, information on distributions, patterns and relationships in complex data sets can be extracted. Irrespective of their potential, SOM applications have earned very little attention in hydrological modelling compared to other artificial neural network techniques. Therefore, the aim of the present work is to demonstrate that the application of Self-Organizing Maps has very high potential to address fundamental issues of model evaluation: It is shown that the clustering and classification of model time series by means of SOM can provide useful insights into model behaviour. In combination with the diagnostic properties of Signature Indices (Gupta et al., 2008; Yilmaz et al., 2008) SOM provides a novel tool for interpreting the model parameters in the hydrological context and identifying parameter sets that simultaneously meet multiple objectives, even if the corresponding model realizations belong to different models. Moreover, the presented studies and reviews also encourage further studies on the application of SOM in hydrological modelling.
Tropospheric ozone (O3) is known to have various detrimental effects on plants, such as visible leaf injury, reduced growth and premature senescence. Flux models offer the determination of the harmful ozone dose entering the plant through the stomata. This dose can then be related to phytotoxic effects mentioned above to obtain dose-response relationships, which are a helpful tool for the formulation of abatement strategies of ozone precursors. rnOzone flux models are dependant on the correct estimation of stomatal conductance (gs). Based on measurements of gs, an ozone flux model for two white clover clones (Trifolium repens L. cv Regal; NC-S (ozone-sensitive) and NC-R (ozone-resistant)) differing in their sensitivity to ozone was developed with the help of artificial neural networks (ANNs). White clover is an important species of various European grassland communities. The clover plants were exposed to ambient air at three sites in the Trier region (West Germany) during five consecutive growing seasons (1997 to 2001). The response parameters visible leaf injury and biomass ratio of NC-S/NC-R clone were regularly assessed. gs-measurements of both clones functioned as output of the ANN-based gs model, while corresponding climate parameters (i.e. temperature, vapour pressure deficit (VPD) and photosynthetic active radiation (PAR)) and various ozone concentration indices were inputs. The development of the model was documented in detail and various model evaluation techniques (e.g. sensitivity analysis) were applied. The resulting gs model was used as a basis for ozone flux calculations, which were related to above mentioned response parameters. rnThe results showed that the ANNs were capable of revealing and learning the complex relationship between gs and key meteorological parameters and ozone concentration indices. The dose-response relationships between ozone fluxes and visible leaf injury were reasonably strong, while those between ozone fluxes and NC-S/NC-R biomass ratio were fairly weak. The results were discussed in detail with respect to the suitability of the chosen experimental methods and model type.
It has been the overall aim of this research work to assess the potential of hyperspectral remote sensing data for the determination of forest attributes relevant to forest ecosystem simulation modeling and forest inventory purposes. A number of approaches for the determination of structural and chemical attributes from hyperspectral remote sensing have been applied to the collected data sets. Many of the methods to be found in the literature were up to now just applied to broadband multispectral data, applied to vegetation canopies other than forests, reported to work on the leaf level or with modelled data, not validated with ground truth data, or not systematically compared to other methods. Attributes that describe the properties of the forest canopy and that are potentially open to remote sensing were identified, appropriate methods for their retrieval were implemented and field, laboratory and image data (HyMap sensor) were acquired over a number of forest plots. The study on structural attributes compared statistical and physical approaches. In the statistical section, linear predictive models between vegetation indices derived from HyMap data and field measurements of structural forest stand attributes were systematically evaluated. The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify leaf area index and crown volume with good accuracy. For broadband multispectral data, the accuracy was generally lower. The physically-based approach used the invertible forest reflectance model (INFORM), a combination of well established sub-models FLIM, SAIL and LIBERTY. The model was inverted with HyMap data using a neural network approach. In comparison to the statistical approach, it could be shown that the reflectance model inversion works equally well. In opposition to empirically derived prediction functions that are generally limited to the local conditions at a certain point in time and to a specified sensor type, the calibrated reflectance model can be applied more easily to different optical remote sensing data acquired over central European forests. The study on chemical forest attributes evaluated the information content of HyMap data for the estimation of nitrogen, chlorophyll and water concentration. A number of needle samples of Norway spruce were analysed for their total chlorophyll, nitrogen and water concentrations. The chemical data was linked to needle spectra measured in the laboratory and canopy spectra measured by the HyMap sensor. Wavebands selected in statistical models were often located in spectral regions that are known to be important for chlorophyll detection (red edge, green peak). Predictive models were applied on the HyMap image to compute maps of chlorophyll concentration and nitrogen concentration. Results of map overlay operations revealed coherence between total chlorophyll and zones of stand development stage and between total chlorophyll and zones of soil type. Finally, it can be stated that the hyperspectral remote sensing data generally contains more information relevant to the estimation of the forest attributes compared to multispectral data. Structural forest attributes, except biomass, can be determined with good accuracy from a hyperspectral sensor type like HyMap. Among the chemical attributes, chlorophyll concentration can be determined with good accuracy and nitrogen concentration with moderate accuracy. For future research, additional dimensions have to be taken into account, for instance through exploitation of multi-view angle data. Additionally, existing forest canopy reflectance models should be further improved.
In past years, desertification and land degradation have been acknowledged as a major threat to human welfare world-wide, and their environmental and societal implications have sparked the formulation of the UN Convention to Combat Desertification (UNCCD). Any measure taken against desertification, or the design of dedicated early warning systems, must take into account both the spatial and temporal dimensions of process driving factors. Equally important, past and present reactions of ecosystems to physical and socio-economical disturbances or management interventions need to be understood. In this context, remote sensing and geoinformation processing support the required assessment, monitoring and modelling approaches, and hence provide an essential contribution to the scientific component of the struggle against desertification. Supported by DG Research of the European Commission, the Remote Sensing Department of the University of Trier convened RGLDD to promote scientific exchange between specialists working on the interface of remote sensing, geoinformation processing, desertification/land degradation research and its socio-economic implications. Although targeted at the scientific community, contributions with application perspectives were of crucial importance and both an overview of the current state of the art as well as operational opportunities were presented. Hosted at the Robert-Schuman Haus in Trier, the conference gained widespread attention and attracted an international audience from all parts of the world, which underlines the global dimension of land degradation and desertification processes. Based on a rigorous review of submitted abstracts, more than 100 contributions were accepted for oral and poster presentation, which are found in these proceedings edition in full paper form. Please note: This document is optimised for screen resolution, to receive a high-resolution version please contact the editors.
Two areas were selected to represent major process regimes of Mediterranean rangelands. In the County of Lagads (Greece), situated east of the city of Thessaloniki, livestock grazing with sheep and goats is a major factor of the rural economy. In suitable areas, it is complemented by agricultural use. The region of Ayora (Spain) is located west of the city of Valencia. It is one of regions most affected by fires in Spain. First of all, long time series of satellite data were compiled for both regions on the basis of Landsat sensors, which cover the time until 1976 (Ayora) and 1984 (Lagadas) with one image per year. Using a rigorous processing scheme, the data were geometrically and radiometrically corrected Specific attention was given to an exact sensor calibration, the radiometric intercalibration of Landsat-TM and "MSS. Proportional cover of photosynthetically active vegetation was identified as a suitable quantitative indicator for assessing the state of rangelands. Using Spectral Mixture Analysis (SMA) it was inferred for all data sets. The extensive data base procured this way enabled to map fire events in the Ayora area based on sequential diachronic sets and provide fire dates, perimeter as well as fire recurrence for each pixel. The increasing fire frequency in the past decades is in large parts attributed to the accelerated abandonment of the area that leads to an encroachment of shrublands and the accumulation of combustible biomass. On the basis of the fire mapping results, a spatial and temporal stratification of the data set allowed to asses plant recovery dynamics on the landscape level through linear trend analysis. The long history of fire events in the Mediterranean frequently leads to processes of auto-succession. Following an initial dominance of herbaceous vegetation this commonly leads to similar plant communities as the ones present before the fire. On a temporal axis, this results in typical exponential post-fire trajectories which could also be shown in this study. The analysis of driving factors for post-fire dynamics confirmed the importance of aspect and slope. Locations with lower amounts of solar irradiation and favourable water supply yielded faster recovery rates and higher post-fire vegetation cover levels. In most cases, the vegetation cover levels observed before the fire were not reached within the post-fire observation period. In the area of Lagadas, linear trend analysis and additional statistical parameters were used to infer a degradation index. This could be used to illustrate a complex pattern of stability, regeneration and degradation of vegetation cover. These different processes and states are found in close proximity and are clearly determined by topography and elevation. Following a sequence of analyses, it was found that in particular steep, narrow valleys show positive trends, while negative trends are more abundant on plain or gently undulating areas. Considering the local grazing regime, this spatial differentiation was related to the accessibility of specific locations. Subsequently, animal numbers on community level were used to calculate efficient stocking rates and assess the temporal development of their relation with vegetation cover. This calculation of temporal trajectories illustrated that only some communities show the expected negative relation. To the contrary, a positive relation or even changing relation patterns are observed. This signifies recent concentration and intensification processes in the grazing scheme, as a result of which animals are kept in sheds, where additional feedstuffs are provided. In these cases, free roaming of livestock animals is often confined to some hours every day, which explains the spatial preference of easily accessible areas by the shepherds. Beyond these temporal trends, it was analysed whether the grazing pattern is equally reflected in a spatial trend. Making use of available geospatial information layers, the efforts required to reach each location was expressed as a cost. Then, cost zones could be defined and woody vegetation cover as a grazing indicator could be inferred for the different zones. Animal sheds were employed as starting features for this piospheric analysis, which could be mapped from very high spatial resolution Quickbird image data. The result was a clearly structured gradient showing increasing woody vegetation cover with increasing cost distance. On the basis of these two pilot studies, the elements of a monitoring and interpretation framework identified at the beginning of the work were evaluated and a formal interpretation scheme was presented.