Filtern
Erscheinungsjahr
Dokumenttyp
Sprache
- Englisch (18) (entfernen)
Schlagworte
- Fernerkundung (18) (entfernen)
With the ongoing trend towards deep learning in the remote sensing community, classical pixel based algorithms are often outperformed by convolution based image segmentation algorithms. This performance was mostly validated spatially, by splitting training and validation pixels for a given year. Though generalizing models temporally is potentially more difficult, it has been a recent trend to transfer models from one year to another, and therefore to validate temporally. The study argues that it is always important to check both, in order to generate models that are useful beyond the scope of the training data. It shows that convolutional neural networks have potential to generalize better than pixel based models, since they do not rely on phenological development alone, but can also consider object geometry and texture. The UNET classifier was able to achieve the highest F1 scores, averaging 0.61 in temporal validation samples, and 0.77 in spatial validation samples. The theoretical potential for overfitting geometry and just memorizing the shape of fields that are maize has been shown to be insignificant in practical applications. In conclusion, kernel based convolutions can offer a large contribution in making agricultural classification models more transferable, both to other regions and to other years.
Earth observation (EO) is a prerequisite for sustainable land use management, and the open-data Landsat mission is at the forefront of this development. However, increasing data volumes have led to a "digital-divide", and consequently, it is key to develop methods that account for the most data-intensive processing steps, then used for the generation and provision of analysis-ready, standardized, higher-level (Level 2 and Level 3) baseline products for enhanced uptake in environmental monitoring systems. Accordingly, the overarching research task of this dissertation was to develop such a framework with a special emphasis on the yet under-researched drylands of Southern Africa. A fully automatic and memory-resident radiometric preprocessing streamline (Level 2) was implemented. The method was applied to the complete Angolan, Zambian, Zimbabwean, Botswanan, and Namibian Landsat record, amounting 58,731 images with a total data volume of nearly 15 TB. Cloud/shadow detection capabilities were improved for drylands. An integrated correction of atmospheric, topographic and bidirectional effects was implemented, based on radiative theory with corrections for multiple scatterings, and adjacency effects, as well as including a multilayered toolset for estimating aerosol optical depth over persistent dark targets or by falling back on a spatio-temporal climatology. Topographic and bidirectional effects were reduced with a semi-empirical C-correction and a global set of correction parameters, respectively. Gridding and reprojection were already included to facilitate easy and efficient further processing. The selection of phenologically similar observations is a key monitoring requirement for multi-temporal analyses, and hence, the generation of Level 3 products that realize phenological normalization on the pixel-level was pursued. As a prerequisite, coarse resolution Land Surface Phenology (LSP) was derived in a first step, then spatially refined by fusing it with a small number of Level 2 images. For this purpose, a novel data fusion technique was developed, wherein a focal filter based approach employs multi-scale and source prediction proxies. Phenologically normalized composites (Level 3) were generated by coupling the target day (i.e. the main compositing criterion) to the input LSP. The approach was demonstrated by generating peak, end and minimum of season composites, and by comparing these with static composites (fixed target day). It was shown that the phenological normalization accounts for terrain- and land cover class-induced LSP differences, and the use of Level 2 inputs enables a wide range of monitoring options, among them the detection of within state processes like forest degradation. In summary, the developed preprocessing framework is capable of generating several analysis-ready baseline EO satellite products. These datasets can be used for regional case studies, but may also be directly integrated into more operational monitoring systems " e.g. in support of the Reducing Emissions from Deforestation and Forest Degradation (REDD) incentive. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Trier University's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.
Energy transition strategies in Germany have led to an expansion of energy crop cultivation in landscape, with silage maize as most valuable feedstock. The changes in the traditional cropping systems, with increasing shares of maize, raised concerns about the sustainability of agricultural feedstock production regarding threats to soil health. However, spatially explicit data about silage maize cultivation are missing; thus, implications for soil cannot be estimated in a precise way. With this study, we firstly aimed to track the fields cultivated with maize based on remote sensing data. Secondly, available soil data were target-specifically processed to determine the site-specific vulnerability of the soils for erosion and compaction. The generated, spatially-explicit data served as basis for a differentiated analysis of the development of the agricultural biogas sector, associated maize cultivation and its implications for soil health. In the study area, located in a low mountain range region in Western Germany, the number and capacity of biogas producing units increased by 25 installations and 10,163 kW from 2009 to 2016. The remote sensing-based classification approach showed that the maize cultivation area was expanded by 16% from 7305 to 8447 hectares. Thus, maize cultivation accounted for about 20% of the arable land use; however, with distinct local differences. Significant shares of about 30% of the maize cultivation was done on fields that show at least high potentials for soil erosion exceeding 25 t soil ha−1 a−1. Furthermore, about 10% of the maize cultivation was done on fields that pedogenetically show an elevated risk for soil compaction. In order to reach more sustainable cultivation systems of feedstock for anaerobic digestion, changes in cultivated crops and management strategies are urgently required, particularly against first signs of climate change. The presented approach can regionally be modified in order to develop site-adapted, sustainable bioenergy cropping systems.
The main research question of this thesis was to set up a framework to allow for the identification of land use changes in drylands and reveal their underlying drivers. The concept of describing land cover change processes in a framework of global change syndrome was introduced by Schellnhuber et al. (1997). In a first step the syndrome approach was implemented for semi-natural areas of the Iberian Peninsula based on time series analysis of the MEDOKADS archive. In the subsequent study the approach was expanded and adapted to other land cover strata. Furthermore, results of an analysis of the relationship of annual NDVI and rainfall data were incorporated to designate areas that show a significant relationship indicating that at least a part of the variability found in NDVI time series was caused by precipitation. Additionally, a first step was taken towards the integration of socio-economic data into the analysis; population density changes between 1961 and 2008 were utilized to support the identification of processes related to land abandonment accompanied by cessation of agricultural practices on the one hand and urbanization on the other. The main findings of the studies comprise three major land cover change processes caused by human interaction: (i) shrub and woody vegetation encroachment in the wake of land abandonment of marginal areas, (ii) intensification of non-irrigated and irrigated, intensively used fertile regions and (iii) urbanization trends along the coastline caused by migration and the increase of mass tourism. Land abandonment of cultivated fields and the give-up of grazing areas in marginal mountainous areas often lead to the encroachment of shrubs and woody vegetation in the course of succession or reforestation. Whereas this cover change has positive effects concerning soil stabilization and carbon sequestration the increase of biomass involves also negative consequences for ecosystem goods and services; these include decreased water yield as a result of increased evapotranspiration, increasing fire risk, decreasing biodiversity due to landscape homogenization and loss of aesthetic value. Arable land in intensively used fertile zones of Spain was further intensified including the expansion of irrigated arable land. The intensification of agriculture has also generated land abandonment in these areas because less people are needed in the agricultural labour sector due to mechanization. Urbanization effects due to migration and the growth of the tourism sector were mapped along the eastern Mediterranean coast. Urban sprawl was only partly detectable by means of the MEDOKADS archive as the changes of urbanization are often too subtle to be detected by data with a spatial resolution of 1 km-². This is in line with a comparison of a Landsat TM time series and the NOAA AVHRR archive for a study area in the Greece that showed that small scale changes cannot be detected based on this approach, even though they might be of high relevance for local management of resources. This underlines the fact that land degradation processes are multi-scale problems and that data of several spatial and temporal scales are mandatory to build a comprehensive dryland observation system. Further land cover processes related to a decrease of greenness did not play an important role in the observation period. Thus, only few patches were identified, suggesting that no large-scale land degradation processes are taking place in the sense of decline of primary productivity after disturbances. Nevertheless, the land cover processes detected impact ecosystem functioning and using the example of shrub encroachment, bear risks for the provision of goods and services which can be valued as land degradation in the sense of a decline of important ecosystem goods and services. This risk is not only confined to the affected ecosystem itself but can also impact adjacent ecosystems due to inter-linkages. In drylands water availability is of major importance and the management of water resources is an important political issue. In view of climate change this topic will become even more important because aridity in Spain did increase within the last decades and is likely to further do so. In addition, the land cover changes detected by the syndrome approach could even augment water scarcity problems. Whereas the water yield of marginal areas, which often serve as headwaters of rivers, decreases with increasing biomass, water demand of agriculture and tourism is not expected to decline. In this context it will be of major importance to evaluate the trade-offs between different land uses and to take decisions that maintain the future functioning of the ecosystems for human well-being.
Arctic and Antarctic polynya systems are of high research interest since extensive new ice formation takes place in these regions. The monitoring of polynyas and the ice production is crucial with respect to the changing sea-ice regime. The thin-ice thickness (TIT) distribution within polynyas controls the amount of heat that is released to the atmosphere and has therefore an impact on the ice-production rates. This thesis presents an improved method to retrieve thermal-infrared thin-ice thickness distributions within polynyas. TIT with a spatial resolution of 1 km × 1 km is calculated using the MODIS ice-surface temperature and atmospheric model variables within the Laptev Sea polynya for the winter periods 2007/08 and 2008/09. The improvement of the algorithm is focused on the surface-energy flux parameterizations. Furthermore, a thorough sensitivity analysis is applied to quantify the uncertainty in the thin-ice thickness results. An absolute mean uncertainty of -±4.7 cm for ice below 20 cm of thickness is calculated. Furthermore, advantages and drawbacks using different atmospheric data sets are investigated. Daily MODIS TIT composites are computed to fill the data gaps arising from clouds and shortwave radiation. The resulting maps cover on average 70 % of the Laptev Sea polynya. An intercomparison of MODIS and AMSR-E polynya data indicates that the spatial resolution issue is essential for accurately deriving polynya characteristics. Monthly fast-ice masks are generated using the daily TIT composites. These fast-ice masks are implemented into the coupled sea-ice/ocean model FESOM. An evaluation of FESOM sea-ice concentrations is performed with the result that a prescribed high-resolution fast-ice mask is necessary regarding the accurate polynya location. However, for a more realistic simulation of other small-scale sea-ice features further model improvements are required. The retrieval of daily high-resolution MODIS TIT composites is an important step towards a more precise monitoring of thin sea ice and sea-ice production. Future work will address a combined remote sensing " model assimilation method to simulate fully-covered thin-ice thickness maps that enable the retrieval of accurate ice production values.
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.
In order to discuss potential sustainability issues of expanding silage maize cultivation in Rhineland-Palatinate, spatially explicit monitoring is necessary. Publicly available statistical records are often not a sufficient basis for extensive research, especially on soil health, where risk factors like erosion and compaction depend on variables that are specific to every site, and hard to generalize for larger administrative aggregates. The focus of this study is to apply established classification algorithms to estimate maize abundance for each independent pixel, while at the same time accounting for their spatial relationship. Therefore, two ways to incorporate spatial autocorrelation of neighboring pixels are combined with three different classification models. The performance of each of these modeling approaches is analyzed and discussed. Finally, one prediction approach is applied to the imagery, and the overall predicted acreage is compared to publicly available data. We were able to show that Support Vector Machine (SVM) classification and Random Forests (RF) were able to distinguish maize pixels reliably, with kappa values well above 0.9 in most cases. The Generalized Linear Model (GLM) performed substantially worse. Furthermore, Regression Kriging (RK) as an approach to integrate spatial autocorrelation into the prediction model is not suitable in use cases with millions of sparsely clustered training pixels. Gaussian Blur is able to improve predictions slightly in these cases, but it is possible that this is only because it smoothes out impurities of the reference data. The overall prediction with RF classification combined with Gaussian Blur performed well, with out of bag error rates of 0.5% in 2009 and 1.3% in 2016. Despite the low error rates, there is a discrepancy between the predicted acreage and the official records, which is 20% in 2009 and 27% in 2016.
Evapotranspiration (ET) is one of the most important variables in hydrological studies. In the ET process, energy exchange and water transfer are involved. ET consists of transpiration and evaporation. The amount of plants transpiration dominates in ET. Especially in the forest regions, the ratio of transpiration to ET is in general 80-90 %. Meteorological variables, vegetation properties, precipitation and soil moisture are critical influence factors for ET generation. The study area is located in the forest area of Nahe catchment (Rhineland-Palatinate, Germany). The Nahe catchment is highly wooded. About 54.6 % of this area is covered by forest, with deciduous forest and coniferous forest are two primary types. A hydrological model, WaSiM-ETH, was employed for a long-term simulation from 1971-2003 in the Nahe catchment. In WaSiM-ETH, the potential evapotranspiration (ETP) was firstly calculated by the Penman-Monteith equation, and subsequently reduced according to the soil water content to obtain the actual evapotranspiration (ETA). The Penman-Monteith equation has been widely used and recommended for ETP estimation. The difficulties in applying this equation are the high demand of ground-measured meteorological data and the determination of surface resistance. A method combined remote sensing images with ground-measured meteorological data was also used to retrieve the ETA. This method is based on the surface properties such as surface albedo, fractional vegetation cover (FVC) and land surface temperature (LST) to obtain the latent heat flux (LE, corresponding to ETA) through the surface energy balance equation. LST is a critical variable for surface energy components estimation. It was retrieved from the TM/ETM+ thermal infrared (TIR) band. Due to the high-quality and cloudy-free requirements for TM/ETM+ data selection as well as the overlapping cycle of TM/ETM+ sensor is 16 days, images on only five dates are available during 1971-2003 (model ran) " May 15, 2000, July 05, 2001, July 19, August 04 and September 21 in 2003. It is found that the climate conditions of 2000, 2001 and 2003 are wet, medium wet and dry, respectively. Therefore, the remote sensing-retrieved observations are noncontinuous in a limited number over time but contain multiple climate conditions. Aerodynamic resistance and surface resistance are two most important parameters in the Penman-Monteith equation. However, for forest area, the aerodynamic resistance is calculated by a function of wind speed in the model. Since transpiration and evaporation are separately calculated by the Penman-Monteith equation in the model, the surface resistance was divided into canopy surface resistance rsc and soil surface resistance rse. rsc is related to the plants transpiration and rse is related to the bare soil evaporation. The interception evaporation was not taken into account due to its negligible contribution to ET rate under a dry-canopy (no rainfall) condition. Based on the remote sensing-retrieved observations, rsc and rse were calibrated in the WaSiM-ETH model for both forest types: for deciduous forest, rsc = 150 sm−1, rse = 250 sm−1; for coniferous forest, rsc = 300 sm−1, rse = 650 sm−1. We also carried out sensitivity analysis on rsc and rse. The appropriate value ranges of rsc and rse were determined as (annual maximum): for deciduous forest, [100,225] sm−1 for rsc and [50,450] sm−1 for rse; for coniferous forest, [225,375] sm−1 for rsc and [350,1200] sm−1 for rse. Due to the features of the observations that are in a limited number but contain multiple climate conditions, the statistical indices for model performance evaluation are required to be sensitive to extreme values. In this study, boxplots were found to well exhibit the model performance at both spatial and temporal scale. Nush-Sutcliffe efficiency (NSE), RMSE-observations standard deviation ratio (RSR), percent bias (PBIAS), mean bias error (MBE), mean variance of error distribution (S2d), index of agreement (d), root mean square error (RMSE) were found as appropriate statistical indices to provide additional evaluation information to the boxplots. The model performance can be judged as satisfactory if NSE > 0.5, RSR ≤ 0.7, PBIAS < -±12, MBE < -±0.45, S2d < 1.11, d > 0.79, RMSE < 0.97. rsc played a more important role than rse in ETP and ETA estimation by the Penman-Monteith equation, which is attributed to the fact that transpiration dominates in ET. The ETP estimation was found the most correlated to the relative humidity (RH), followed by air temperature (T), relative sunshine duration (SSD) and wind speed (WS). Under wet or medium wet climate conditions, ETA estimation was found the most correlated to T, followed by RH, SSD and WS. Under a water-stress condition, there were very small correlations between ETA and each meteorological variable.