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Institut
Redox-driven biogeochemical cycling of iron plays an integral role in the complex process network of ecosystems, such as carbon cycling, the fate of nutrients and greenhouse gas emissions. We investigate Fe-(hydr)oxide (trans)formation pathways from rhyolitic tephra in acidic topsoils of South Patagonian Andosols to evaluate the ecological relevance of terrestrial iron cycling for this sensitive fjord ecosystem. Using bulk geochemical analyses combined with micrometer-scale-measurements on individual soil aggregates and tephra pumice, we document biotic and abiotic pathways of Fe released from the glassy tephra matrix and titanomagnetite phenocrysts. During successive redox cycles that are controlled by frequent hydrological perturbations under hyper-humid climate, (trans)formations of ferrihydrite-organic matter coprecipitates, maghemite and hematite are closely linked to tephra weathering and organic matter turnover. These Fe-(hydr)oxides nucleate after glass dissolution and complexation with organic ligands, through maghemitization or dissolution-(re)crystallization processes from metastable precursors. Ultimately, hematite represents the most thermodynamically stable Fe-(hydr)oxide formed under these conditions and physically accumulates at redox interfaces, whereas the ferrihydrite coprecipitates represent a so far underappreciated terrestrial source of bio-available iron for fjord bioproductivity. The insights into Fe-(hydr)oxide (trans)formation in Andosols have implications for a better understanding of biogeochemical cycling of iron in this unique Patagonian fjord ecosystem.
This cumulative thesis encompass three studies focusing on the Weddell Sea region in the Antarctic. The first study produces and evaluates a high quality data set of wind measurements for this region. The second study produces and evaluates a 15 year regional climate simulation for the Weddell Sea region. And the third study produces and evaluates a climatology of low level jets (LLJs) from the simulation data set. The evaluations were done in the attached three publications and the produced data sets are published online.
In 2015/2016, the RV Polarstern undertook an Antarctic expedition in the Weddell Sea. We operated a Doppler wind lidar on board during that time running different scan patterns. The resulting data was evaluated, corrected, processed and we derived horizontal wind speed and directions for vertical profiles with up to 2 km height. The measurements cover 38 days with a temporal resolution of 10-15 minutes. A comparisons with other radio sounding data showed only minor differences.
The resulting data set was used alongside other measurements to evaluate temperature and wind of simulation data. The simulation data was produced with the regional climate model CCLM for the period of 2002 to 2016 for the Weddell Sea region. Only smaller biases were found except for a strong warm bias during winter near the surface of the Antarctic Plateau. Thus we adapted the model setup and were able to remove the bias in a second simulation.
This new simulation data was then used to derive a climatology of low level jets (LLJs). Statistics of occurrence frequency, height and wind speed of LLJs for the Weddell Sea region are presented along other parameters. Another evaluation with measurements was also performed in the last study.
Climate fluctuations and the pyroclastic depositions from volcanic activity both influence ecosystem functioning and biogeochemical cycling in terrestrial and marine environments globally. These controlling factors are crucial for the evolution and fate of the pristine but fragile fjord ecosystem in the Magellanic moorlands (~53°S) of southernmost Patagonia, which is considered a critical hotspot for organic carbon burial and marine bioproductivity. At this active continental margin in the core zone of the southern westerly wind belt (SWW), frequent Plinian eruptions and the extremely variable, hyper-humid climate should have efficiently shaped ecosystem functioning and land-to-fjord mass transfer throughout the Late Holocene. However, a better understanding of the complex process network defining the biogeochemical cycling at this land-to-fjord continuum principally requires a detailed knowledge of substrate weathering and pedogenesis in the context of the extreme climate. Yet, research on soils, the ubiquitous presence of tephra and the associated chemical weathering, secondary mineral (trans)formation and organic matter (OM) turnover processes is rare in this remote region. This complicates an accurate reconstruction of the ecosystem´s potentially sensitive response to past environmental impacts, including the dynamics of Late Holocene land-to-fjord fluxes as a function of volcanic activity and strong hydroclimate variability.
Against this background, this PhD thesis aims to disentangle the controlling factors that modulate the terrigenous element mobilization and export mechanisms in the hyper-humid Patagonian Andes and assesses their significance for fjord primary productivity over the past 4.5 kyrs BP. For the first time, distinct biogeochemical characteristics of the regional weathering system serve as major criterion in paleoenvironmental reconstruction in the area. This approach includes broad-scale mineralogical and geochemical analyses of basement lithologies, four soil profiles, volcanic ash deposits, the non-karst stalagmite MA1 and two lacustrine sediment cores. In order to pay special attention to the possibly important temporal variations of pedosphere-atmosphere interaction and ecological consequences initiated by volcanic eruptions, the novel data were evaluated together with previously published reconstructions of paleoclimate and paleoenvironmental conditions.
The devastative high-tephra loading of a single eruption from Mt. Burney volcano (MB2 at 4.216 kyrs BP) sustainably transformed this vulnerable fjord ecosystem, while acidic peaty Andosols developed from ~2.5 kyrs BP onwards after the recovery from millennium-scale acidification. The special setting is dominated by most variable redox-pH conditions, profound volcanic ash weathering and intense OM turnover processes, which are closely linked and ultimately regulated by SWW-induced water-level fluctuations. Constant nutrient supply though sea spray deposition represents a further important control on peat accumulation and OM turnover dynamics. These extreme environmental conditions constrain the biogeochemical framework for an extended land-to-fjord export of leachates comprising various organic and inorganic colloids (i.e., Al-humus complexes and Fe-(hydr)oxides). Such tephra- and/or Andosol-sourced flux contains high proportions of terrigenous organic carbon (OCterr) and mobilized essential (micro)nutrients, e.g., bio-available Fe, that are beneficial for fjord bioproductivity. It can be assumed that this supply of bio-available Fe produced by specific Fe-(hydr)oxide (trans)formation processes from tephra components may outlast more than 6 kyrs and surpasses the contribution from basement rock weathering and glacial meltwaters. However, the land-to-fjord exports of OCterr and bio-available Fe occur mostly asynchronous and are determined by the frequency and duration of redox cycles in soils or are initiated by SWW-induced extreme weather events.
The verification of (crypto)tephra layers embedded stalagmite MA1 enabled the accurate dating of three smaller Late Holocene eruptions from Mt. Burney (MB3 at 2.291 kyrs BP and MB4 at 0.853 kyrs BP) and Aguilera (A1 at 2.978 kyrs BP) volcanoes. Irrespective of the improvement of the regional tephrochronology, the obtained precise 230Th/U-ages allowed constraints on the ecological consequences caused by these Plinian eruptions. The deposition of these thin tephra layers should have entailed a very beneficial short-term stimulation of fjord bioproductivity with bio-available Fe and other (micro)nutrients, which affected the entire area between 52°S and 53°S 30´, respectively. For such beneficial effects, the thickness of tephra deposited to this highly vulnerable peatland ecosystem should be below a threshold of 1 cm.
The Late Holocene element mobilization and land-to-fjord transport was mainly controlled by (i) volcanic activity and tephra thickness, (ii) SWW-induced and southern hemispheric climate variability and (iii) the current state of the ecosystem. The influence of cascading climate and environmental impacts on OCterr and Fe-(hydr)oxide fluxes to can be categorized by four individual, in part overlapping scenarios. These different scenarios take into account the previously specified fundamental biogeochemical mechanisms and define frequently recurring patterns of ecosystem feedbacks governing the land-to-fjord mass transfer in the hyper-humid Patagonian Andes on the centennial-scale. This PhD thesis provides first evidence for a primarily tephra-sourced, continuous and long-lasting (micro)nutrient fertilization for phytoplankton growth in South Patagonian fjords, which is ultimately modulated by variations in SWW-intensity. It highlights the climate sensitivity of such critical land-to-fjord element transport and particularly emphasizes the important but so far underappreciated significance of volcanic ash inputs for biogeochemical cycles at active continental margins.
Forest inventories provide significant monitoring information on forest health, biodiversity,
resilience against disturbance, as well as its biomass and timber harvesting potential. For this
purpose, modern inventories increasingly exploit the advantages of airborne laser scanning (ALS)
and terrestrial laser scanning (TLS).
Although tree crown detection and delineation using ALS can be seen as a mature discipline, the
identification of individual stems is a rarely addressed task. In particular, the informative value of
the stem attributes—especially the inclination characteristics—is hardly known. In addition, a lack
of tools for the processing and fusion of forest-related data sources can be identified. The given
thesis addresses these research gaps in four peer-reviewed papers, while a focus is set on the
suitability of ALS data for the detection and analysis of tree stems.
In addition to providing a novel post-processing strategy for geo-referencing forest inventory plots,
the thesis could show that ALS-based stem detections are very reliable and their positions are
accurate. In particular, the stems have shown to be suited to study prevailing trunk inclination
angles and orientations, while a species-specific down-slope inclination of the tree stems and a
leeward orientation of conifers could be observed.
Agricultural monitoring is necessary. Since the beginning of the Holocene, human agricultural
practices have been shaping the face of the earth, and today around one third of the ice-free land
mass consists of cropland and pastures. While agriculture is necessary for our survival, the
intensity has caused many negative externalities, such as enormous freshwater consumption, the
loss of forests and biodiversity, greenhouse gas emissions as well as soil erosion and degradation.
Some of these externalities can potentially be ameliorated by careful allocation of crops and
cropping practices, while at the same time the state of these crops has to be monitored in order
to assess food security. Modern day satellite-based earth observation can be an adequate tool to
quantify abundance of crop types, i.e., produce spatially explicit crop type maps. The resources to
do so, in terms of input data, reference data and classification algorithms have been constantly
improving over the past 60 years, and we live now in a time where fully operational satellites
produce freely available imagery with often less than monthly revisit times at high spatial
resolution. At the same time, classification models have been constantly evolving from
distribution based statistical algorithms, over machine learning to the now ubiquitous deep
learning.
In this environment, we used an explorative approach to advance the state of the art of crop
classification. We conducted regional case studies, focused on the study region of the Eifelkreis
Bitburg-Prüm, aiming to develop validated crop classification toolchains. Because of their unique
role in the regional agricultural system and because of their specific phenologic characteristics
we focused solely on maize fields.
In the first case study, we generated reference data for the years 2009 and 2016 in the study
region by drawing polygons based on high resolution aerial imagery, and used these in
conjunction with RapidEye imagery to produce high resolution maize maps with a random forest
classifier and a gaussian blur filter. We were able to highlight the importance of careful residual
analysis, especially in terms of autocorrelation. As an end result, we were able to prove that, in
spite of the severe limitations introduced by the restricted acquisition windows due to cloud
coverage, high quality maps could be produced for two years, and the regional development of
maize cultivation could be quantified.
In the second case study, we used these spatially explicit datasets to link the expansion of biogas
producing units with the extended maize cultivation in the area. In a next step, we overlayed the
maize maps with soil and slope rasters in order to assess spatially explicit risks of soil compaction
and erosion. Thus, we were able to highlight the potential role of remote sensing-based crop type
classification in environmental protection, by producing maps of potential soil hazards, which can
be used by local stakeholders to reallocate certain crop types to locations with less associated
risk.
In our third case study, we used Sentinel-1 data as input imagery, and official statistical records
as maize reference data, and were able to produce consistent modeling input data for four
consecutive years. Using these datasets, we could train and validate different models in spatially
iv
and temporally independent random subsets, with the goal of assessing model transferability. We
were able to show that state-of-the-art deep learning models such as UNET performed
significantly superior to conventional models like random forests, if the model was validated in a
different year or a different regional subset. We highlighted and discussed the implications on
modeling robustness, and the potential usefulness of deep learning models in building fully
operational global crop classification models.
We were able to conclude that the first major barrier for global classification models is the
reference data. Since most research in this area is still conducted with local field surveys, and only
few countries have access to official agricultural records, more global cooperation is necessary to
build harmonized and regionally stratified datasets. The second major barrier is the classification
algorithm. While a lot of progress has been made in this area, the current trend of many appearing
new types of deep learning models shows great promise, but has not yet consolidated. There is
still a lot of research necessary, to determine which models perform the best and most robust,
and are at the same time transparent and usable by non-experts such that they can be applied
and used effortlessly by local and global stakeholders.
Phylogeographic analyses point to long-term survival on the spot in micro-endemic Lycian salamanders
(2020)
Lycian salamanders (genus Lyciasalamandra) constitute an exceptional case of microendemism of an amphibian species on the Asian Minor mainland. These viviparous salamanders are confined to karstic limestone formations along the southern Anatolian coast and some islands. We here study the genetic differentiation within and among 118 populations of all seven Lyciasalamandra species across the entire genus’ distribution. Based on circa 900 base pairs of fragments of the mitochondrial 16SrDNA and ATPase genes, we analysed the spatial haplotype distribution as well as the genetic structure and demographic history of populations. We used 253 geo-referenced populations and CHELSA climate data to infer species distribution models which we projected on climatic conditions of the Last Glacial Maximum (LGM). Within all but one species, distinct phyloclades were identified, which only in parts matched current taxonomy. Most haplotypes (78%) were private to single populations. Sometimes population genetic parameters showed contradicting results, although in several cases they indicated recent population expansion of phyloclades. Climatic suitability of localities currently inhabited by salamanders was significantly lower during the LGM compared to recent climate. All data indicated a strong degree of isolation among Lyciasalamandra populations, even within phyloclades. Given the sometimes high degree of haplotype differentiation between adjacent populations, they must have survived periods of deteriorated climates during the Quaternary on the spot. However, the alternative explanation of male biased dispersal combined with a pronounced female philopatry can only be excluded if independent nuclear data confirm this result.
Because EU water quality policy can result in infrastructure creation or adaptation at the local level across member states, compliance cases are worth examining critically from a sustainable spatial planning perspective. In this study, the 2000 EU Water Framework Directive’s (WFD) reach to local implementation efforts in average towns and cities is shown through the case study of nonconforming household wastewater infrastructure in the German state of Rhineland Palatinate. Seeing wastewater as a socio-technical infrastructure, we ask how the WFD implementation can be understood in the context of local infrastructure development, sustainability, and spatial planning concepts. In particular, this study examines what compliance meant for the centralization or decentralization of local wastewater infrastructure systems—and the sustainability implications for cities
from those choices.
Background: Increasing exposure to engineered inorganic nanoparticles takes actually place in both terrestric and aquatic ecosystems worldwide. Although we already know harmful effects of AgNP on the soil bacterial community, information about the impact of the factors functionalization, concentration, exposure time, and soil texture on the AgNP effect expression are still rare. Hence, in this study, three soils of different grain size were exposed for up to 90 days to bare and functionalized AgNP in concentrations ranging from 0.01 to 1.00 mg/kg soil dry weight. Effects on soil microbial community were quantified by various biological parameters, including 16S rRNA gene, photometric, and fluorescence analyses.
Results: Multivariate data analysis revealed significant effects of AgNP exposure for all factors and factor combinations investigated. Analysis of individual factors (silver species, concentration, exposure time, soil texture) in the unifactorial ANOVA explained the largest part of the variance compared to the error variance. In depth analysis of factor combinations revealed even better explanation of variance. For the biological parameters assessed in this study, the matching of soil texture and silver species, and the matching of soil texture and exposure time were the two most relevant factor combinations. The factor AgNP concentration contributed to a lower extent to the effect expression compared to silver species, exposure time and physico–chemical composition of soil.
Conclusions: The factors functionalization, concentration, exposure time, and soil texture significantly impacted the effect expression of AgNP on the soil microbial community. Especially long-term exposure scenarios are strongly needed for the reliable environmental impact assessment of AgNP exposure in various soil types.
The trophic niche is a life trait that identifies the consumer’s position in a local food web. Several factors, such as ontogeny, competitive ability and resource availability contribute in shaping species trophic niches. To date, information on the diet of European Hydromantes salamanders are only available for a limited number of species, no dietary studies have involved more than one species of the genus at a time, and there are limited evidences on how multiple factors interact in determining diet variation. In this study we examined the diet of multiple populations of six out of the eight European cave salamanders, providing the first data on the diet for five of them. In addition, we assessed whether these closely related generalist species show similar diet and, for each species, we tested whether season, age class or sex influence the number and the type of prey consumed. Stomach condition (empty/full) and the number of prey consumed were strongly related to seasonality and to the activity level of individuals. Empty stomachs were more frequent in autumn, in individuals far from cave entrance and in juveniles. Diet composition was significantly different among species. Hydromantes imperialis and H. supramontis were the most generalist species; H. flavus and H. sarrabusensis fed mostly on Hymenoptera and Coleoptera Staphylinidae, while H. genei and H. ambrosii mostly consumed Arachnida and Endopterygota larvae. Furthermore, we detected seasonal shifts of diet in the majority of the species examined. Conversely, within each species, we did not find diet differences between females, males and juveniles. Although being assumed to have very similar dietary habits, here Hydromantes species were shown to be characterized by a high divergence in diet composition and in the stomach condition of individuals.
In the present study a non-motion-stabilized scanning Doppler lidar was operated on board of RV Polarstern in the Arctic (June 2014) and Antarctic (December 2015– January 2016). This is the first time that such a system measured on an icebreaker in the Antarctic. A method for a motion correction of the data in the post-processing is presented.
The wind calculation is based on vertical azimuth display (VAD) scans with eight directions that pass a quality control. Additionally a method for an empirical signal-tonoise ratio (SNR) threshold is presented, which can be calculated for individual measurement set-ups. Lidar wind profiles are compared to total of about 120 radiosonde profiles and also to wind measurements of the ship.
The performance of the lidar measurements in comparison with radio soundings generally shows small root mean square deviation (bias) for wind speed of around 1ms-1(0.1ms-1) and for wind direction of around 10 (1). The post-processing of the non-motion-stabilized data shows comparably high quality to studies with motion-stabilized systems.
Two case studies show that a flexible change in SNR threshold can be beneficial for special situations. Further the studies reveal that short-lived low-level jets in the atmospheric boundary layer can be captured by lidar measurements with a high temporal resolution in contrast to routine radio soundings. The present study shows that a non-motionstabilized Doppler lidar can be operated successfully on an
icebreaker. It presents a processing chain including quality control tests and error quantification, which is useful for further measurement campaigns.
In the context of accelerated global socio-environmental change, the Water-Energy-Food Nexus has received increasing attention within science and international politics by promoting integrated resource governance. This study explores the scientific nexus debates from a discourse analytical perspective to reveal knowledge and power relations as well as geographical settings of nexus research. We also investigate approaches to socio-nature relations that influence nexus research and subsequent political implications. Our findings suggest that the leading nexus discourse is dominated by natural scientific perspectives and a neo-Malthusian framing of environmental challenges. Accordingly, the promoted cross-sectoral nexus approach to resource governance emphasizes efficiency, security, future sustainability, and poverty reduction. Water, energy, and food are conceived as global trade goods that require close monitoring, management and control, to be achieved via quantitative assessments and technological interventions. Within the less visible discourse, social scientific perspectives engage with the social, political, and normative elements of the Water-Energy-Food Nexus. These perspectives criticize the dominant nexus representation for itsmanagerial, neoliberal, and utilitarian approach to resource governance. The managerial framing is critiqued for masking power relations and social inequalities, while alternative framings acknowledge the political nature of resource governance and socio-nature relations. The spatial dimensions of the nexus debate are also discussed. Notably, the nexus is largely shaped by western knowledge, yet applied mainly in specific regions of the Global South. In order for the nexus to achieve integrative solutions for sustainability, the debate needs to overcome its current discursive and spatial separations. To this end, we need to engage more closely with alternative nexus discourses, embrace epistemic pluralism and encourage multi-perspective debates about the socio-nature relations we actually intend to promote.
Dry tropical forests are facing massive conversion and degradation processes and they are the most endangered forest type worldwide. One of the largest dry forest types are Miombo forests that stretch across the Southern African subcontinent and the proportionally largest part of this type can be found in Angola. The study site of this thesis is located in south-central Angola. The country still suffers from the consequences of the 27 years of civil war (1975-2002) that provides a unique socio-economic setting. The natural characteristics are a representative cross section which proved ideal to study underlying drivers as well as current and retrospective land use change dynamics. The major land change dynamic of the study area is the conversion of Miombo forests to cultivation areas as well as modification of forest areas, i.e. degradation, due to the extraction of natural resources. With future predictions of population growth, climate change and large scale investments, land pressure is expected to further increase. To fully understand the impacts of these dynamics, both, conversion and modification of forest areas were assessed. By using the conceptual framework of ecosystem services, the predominant trade-off between food and timber in the study area was analyzed, including retrospective dynamics and impacts. This approach accounts for products that contribute directly or indirectly to human well-being. For this purpose, data from the Landsat archive since 1989 until 2013 was applied in different study area adapted approaches. The objectives of these approaches were (I) to detect underlying drivers and their temporal and spatial extent of impact, (II) to describe modification and conversion processes that reach from times of armed conflicts over the ceasefire and the post-war period and (III) to provide an assessment of drivers and impacts in a comparative setting. It could be shown that major underlying drivers for the conversion processes are resettlement dynamics as well as the location and quality of streets and settlements. Furthermore, forests that are selectively used for resource extraction have a higher chance of being converted to a field. Drivers of forest degradation are on one hand also strongly connected to settlement and infrastructural structures. But also to a large extent to fire dynamics that occur mostly in more remote and presumably undisturbed forest areas. The loss of woody biomass as well as its slow recovery after the abandonment of fields could be quantified and stands in large contrast to the amount of potentially cultivated food that is necessarily needed. The results of the thesis support the fundamental understanding of drivers and impacts in the study area and can thus contribute to a sustainable resource management.
Water-deficit stress, usually shortened to water- or drought stress, is one of the most critical abiotic stressors limiting plant growth, crop yield and quality concerning food production. Today, agriculture consumes about 80-90% of the global freshwater used by humans and about two thirds are used for crop irrigation. An increasing world population and a predicted rise of 1.0-2.5-°C in the annual mean global temperature as a result of climate change will further increase the demand of water in agriculture. Therefore, one of the most challenging tasks of our generation is to reduce the amount water used per unit yield to satisfy the second UN Sustainable Development Goal and to ensure global food security. Precision agriculture offers new farming methods with the goal to improve the efficiency of crop production by a sustainable use of resources. Plant responses to water stress are complex and co-occur with other environmental stresses under natural conditions. In general, water stress causes plant physiological and biochemical changes that depend on the severity and the duration of the actual plant water deficit. Stomatal closure is one of the first responses to plant water stress causing a decrease in plant transpiration and thus an increase in plant temperature. Prolonged or severe water stress leads to irreversible damage to the photosynthetic machinery and is associated with decreasing chlorophyll content and leaf structural changes (e.g., leaf rolling). Since a crop can already be irreversibly damaged by only mild water deficit, a pre-visual detection of water stress symptoms is essential to avoid yield loss. Remote sensing offers a non-destructive and spatio-temporal method for measuring numerous physiological, biochemical and structural crop characteristics at different scales and thus is one of the key technologies used in precision agriculture. With respect to the detection of plant responses to water stress, the current state-of-the-art hyperspectral remote sensing imaging techniques are based on measurements of thermal infrared emission (TIR; 8-14 -µm), visible, near- and shortwave infrared reflectance (VNIR/SWIR; 0.4-2.5 -µm), and sun-induced fluorescence (SIF; 0.69 and 0.76 -µm). It is, however, still unclear how sensitive these techniques are with respect to water stress detection. Therefore, the overall aim of this dissertation was to provide a comparative assessment of remotely sensed measures from the TIR, SIF, and VNIR/SWIR domains for their ability to detect plant responses to water stress at ground- and airborne level. The main findings of this thesis are: (i) temperature-based indices (e.g., CWSI) were most sensitive for the detection of plant water stress in comparison to reflectance-based VNIR/SWIR indices (e.g., PRI) and SIF at both, ground- and airborne level, (ii) for the first time, spectral emissivity as measured by the new hyperspectral TIR instrument could be used to detect plant water stress at ground level. Based on these findings it can be stated that hyperspectral TIR remote sensing offers great potential for the detection of plant responses to water stress at ground- and airborne level based on both TIR key variables, surface temperature and spectral emissivity. However, the large-scale application of water stress detection based on hyperspectral TIR measures in precision agriculture will be challenged by several problems: (i) missing thresholds of temperature-based indices (e.g., CWSI) for the application in irrigation scheduling, (ii) lack of current TIR satellite missions with suitable spectral and spatial resolution, (iii) lack of appropriate data processing schemes (including atmosphere correction and temperature emissivity separation) for hyperspectral TIR remote sensing at airborne- and satellite level.
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.
This paper describes the concept of the hyperspectral Earth-observing thermal infrared (TIR) satellite mission HiTeSEM (High-resolution Temperature and Spectral Emissivity Mapping). The scientific goal is to measure specific key variables from the biosphere, hydrosphere, pedosphere, and geosphere related to two global problems of significant societal relevance: food security and human health. The key variables comprise land and sea surface radiation temperature and emissivity, surface moisture, thermal inertia, evapotranspiration, soil minerals and grain size components, soil organic carbon, plant physiological variables, and heat fluxes. The retrieval of this information requires a TIR imaging system with adequate spatial and spectral resolutions and with day-night following observation capability. Another challenge is the monitoring of temporally high dynamic features like energy fluxes, which require adequate revisit time. The suggested solution is a sensor pointing concept to allow high revisit times for selected target regions (1"5 days at off-nadir). At the same time, global observations in the nadir direction are guaranteed with a lower temporal repeat cycle (>1 month). To account for the demand of a high spatial resolution for complex targets, it is suggested to combine in one optic (1) a hyperspectral TIR system with ~75 bands at 7.2"12.5 -µm (instrument NEDT 0.05 K"0.1 K) and a ground sampling distance (GSD) of 60 m, and (2) a panchromatic high-resolution TIR-imager with two channels (8.0"10.25 -µm and 10.25"12.5 -µm) and a GSD of 20 m. The identified science case requires a good correlation of the instrument orbit with Sentinel-2 (maximum delay of 1"3 days) to combine data from the visible and near infrared (VNIR), the shortwave infrared (SWIR) and TIR spectral regions and to refine parameter retrieval.
Dry tropical forests undergo massive conversion and degradation processes. This also holds true for the extensive Miombo forests that cover large parts of Southern Africa. While the largest proportional area can be found in Angola, the country still struggles with food shortages, insufficient medical and educational supplies, as well as the ongoing reconstruction of infrastructure after 27 years of civil war. Especially in rural areas, the local population is therefore still heavily dependent on the consumption of natural resources, as well as subsistence agriculture. This leads, on one hand, to large areas of Miombo forests being converted for cultivation purposes, but on the other hand, to degradation processes due to the selective use of forest resources. While forest conversion in south-central rural Angola has already been quantitatively described, information about forest degradation is not yet available. This is due to the history of conflicts and the therewith connected research difficulties, as well as the remote location of this area. We apply an annual time series approach using Landsat data in south-central Angola not only to assess the current degradation status of the Miombo forests, but also to derive past developments reaching back to times of armed conflicts. We use the Disturbance Index based on tasseled cap transformation to exclude external influences like inter-annual variation of rainfall. Based on this time series, linear regression is calculated for forest areas unaffected by conversion, but also for the pre-conversion period of those areas that were used for cultivation purposes during the observation time. Metrics derived from linear regression are used to classify the study area according to their dominant modification processes.rnWe compare our results to MODIS latent integral trends and to further products to derive information on underlying drivers. Around 13% of the Miombo forests are affected by degradation processes, especially along streets, in villages, and close to existing agriculture. However, areas in presumably remote and dense forest areas are also affected to a significant extent. A comparison with MODIS derived fire ignition data shows that they are most likely affected by recurring fires and less by selective timber extraction. We confirm that areas that are used for agriculture are more heavily disturbed by selective use beforehand than those that remain unaffected by conversion. The results can be substantiated by the MODIS latent integral trends and we also show that due to extent and location, the assessment of forest conversion is most likely not sufficient to provide good estimates for the loss of natural resources.
Avoiding aerial microfibre contamination of environmental samples is essential for reliable analyses when it comes to the detection of ubiquitous microplastics. Almost all laboratories have contamination problems which are largely unavoidable without investments in clean-air devices. Therefore, our study supplies an approach to assess background microfibre contamination of samples in the laboratory under particle-free air conditions. We tested aerial contamination of samples indoor, in a mobile laboratory, within a laboratory fume hood and on a clean bench with particles filtration during the examining process of a fish. The used clean bench reduced aerial microfibre contamination in our laboratory by 96.5%. This highlights the value of suitable clean-air devices for valid microplastic pollution data. Our results indicate, that pollution levels by microfibres have been overestimated and actual pollution levels may be many times lower. Accordingly, such clean-air devices are recommended for microplastic laboratory applications in future research work to significantly lower error rates.
In recent decades, the Arctic has been undergoing a wide range of fast environmental changes. The sea ice covering the Arctic Ocean not only reacts rapidly to these changes, but also influences and alters the physical properties of the atmospheric boundary layer and the underlying ocean on various scales. In that regard, polynyas, i.e. regions of open water and thin ice within thernclosed pack ice, play a key role as being regions of enhanced atmosphere-ice-ocean interactions and extensive new ice formation during winter. A precise long-term monitoring and increased efforts to employ long-term and high-resolution satellite data is therefore of high interest for the polar scientific community. The retrieval of thin-ice thickness (TIT) fields from thermal infrared satellite data and atmospheric reanalysis, utilizing a one-dimensional energy balance model, allows for the estimation of the heat loss to the atmosphere and hence, ice-production rates. However, an extended application of this approach is inherently connected with severe challenges that originate predominantly from the disturbing influence of clouds and necessary simplifications in the model set-up, which all need to be carefully considered and compensated for. The presented thesis addresses these challenges and demonstrates the applicability of thermal infrared TIT distributions for a long-term polynya monitoring, as well as an accurate estimation of ice production in Arctic polynyas at a relatively high spatial resolution. Being written in a cumulative style, the thesis is subdivided into three parts that show the consequent evolution and improvement of the TIT retrieval, based on two regional studies (Storfjorden and North Water (NOW) polynya) and a final large-scale, pan-Arctic study. The first study on the Storfjorden polynya, situated in the Svalbard archipelago, represents the first long-term investigation on spatial and temporal polynya characteristics that is solely based on daily TIT fields derived from MODIS thermal infrared satellite data and ECMWF ERA-Interim atmospheric reanalysis data. Typical quantities such as polynya area (POLA), the TIT distribution, frequencies of polynya events as well as the total ice production are derived and compared to previous remote sensing and modeling studies. The study includes a first basic approach that aims for a compensation of cloud-induced gaps in daily TIT composites. This coverage-correction (CC) is a mathematically simple upscaling procedure that depends solely on the daily percentage of available MODIS coverage and yields daily POLA with an error-margin of 5 to 6 %. The NOW polynya in northern Baffin Bay is the main focus region of the second study, which follows two main goals. First, a new statistics-based cloud interpolation scheme (Spatial Feature Reconstruction - SFR) as well as additional cloud-screening procedures are successfully adapted and implemented in the TIT retrieval for usage in Arctic polynya regions. For a 13-yr period, results on polynya characteristics are compared to the CC approach. Furthermore, an investigation on highly variable ice-bridge dynamics in Nares Strait is presented. Second, an analysis of decadal changes of the NOW polynya is carried out, as the additional use of a suite of passive microwave sensors leads to an extended record of 37 consecutive winter seasons, thereby enabling detailed inter-sensor comparisons. In the final study, the SFR-interpolated daily TIT composites are used to infer spatial and temporal characteristics of 17 circumpolar polynya regions in the Arctic for 2002/2003 to 2014/2015. All polynya regions combined cover an average thin-ice area of 226.6 -± 36.1 x 10-³ km-² during winter (November to March) and yield an average total wintertime accumulated ice production of about 1811 -± 293 km-³. Regional differences in derived ice production trends are noticeable. The Laptev Sea on the Siberian shelf is presented as a focus region, as frequently appearing polynyas along the fast-ice edge promote high rates of new ice production. New affirming results on a distinct relation to sea-ice area export rates and hence, the Transpolar Drift, are shown. This new high-resolution pan-Arctic data set can be further utilized and build upon in a variety of atmospheric and oceanographic applications, while still offering room for further improvements such as incorporating high-resolution atmospheric data sets and an optimized lead-detection.
Determining the exact position of a forest inventory plot—and hence the position of the sampled trees—is often hampered by a poor Global Navigation Satellite System (GNSS) signal quality beneath the forest canopy. Inaccurate geo-references hamper the performance of models that aim to retrieve useful information from spatially high remote sensing data (e.g., species classification or timber volume estimation). This restriction is even more severe on the level of individual trees. The objective of this study was to develop a post-processing strategy to improve the positional accuracy of GNSS-measured sample-plot centers and to develop a method to automatically match trees within a terrestrial sample plot to aerial detected trees. We propose a new method which uses a random forest classifier to estimate the matching probability of each terrestrial-reference and aerial detected tree pair, which gives the opportunity to assess the reliability of the results. We investigated 133 sample plots of the Third German National Forest Inventory (BWI, 2011"2012) within the German federal state of Rhineland-Palatinate. For training and objective validation, synthetic forest stands have been modeled using the Waldplaner 2.0 software. Our method has achieved an overall accuracy of 82.7% for co-registration and 89.1% for tree matching. With our method, 60% of the investigated plots could be successfully relocated. The probabilities provided by the algorithm are an objective indicator of the reliability of a specific result which could be incorporated into quantitative models to increase the performance of forest attribute estimations.
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.