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The argan woodlands of South Morocco represent an open-canopy dryland forest with traditional silvopastoral usage that includes browsing by goats, sheep and camels, oil production as well as agricultural use. In the past, these forests have undergone extensive clearing, but are now protected by the state. However, the remaining argan woodlands are still under pressure from intensive grazing and illegal firewood collection. Although the argan-forest area seems to be overall decreasing due to large forest clearings for intensive agriculture, little quantitative data is available on the dynamics and overall state of the remaining argan forest. To determine how the argan woodlands in the High Atlas and the Anti-Atlas had changed in tree-crown cover from 1972 to 2018 we used historical black and white HEXAGON satellite images as well as recent WorldView satellite images (see Part A of our study). Because tree shadows can oftentimes not be separated from the tree crown on panchromatic satellite images, individual trees were mapped in three size categories to determine if trees were unchanged, had decreased/increased in crown size or had disappeared or newly grown. The current state of the argan trees was evaluated by mapping tree architectures in the field. Tree-cover changes varied highly between the test sites. Trees that remained unchanged between 1972 and 2018 were in the majority, while tree mortality and tree establishment were nearly even. Small unchanged trees made up 48.4% of all remaining trees, of these 51% showed degraded tree architectures. 40% of small (re-) grown trees were so overbrowsed that they only appeared as bushes, while medium (3–7 m crown diameter) and large trees (>7 m) showed less degraded trees regardless if they had changed or not. Approaches like grazing exclusion or cereal cultivation lead to a positive influence on tree architecture and less tree-cover decrease. Although the woodland was found to be mostly unchanged 1972–2018, the analysis of tree architecture reveals that a lot of (mostly small) trees remained stable but in a degraded state. This stability might be the result of the small trees’ high degradation status and shows the heavy pressure on the argan forest.
Using a dendrochronological approach, we determined the resistance, recovery and resilience of the radial stem increment towards episodes of growth decline, and the accompanying variation of 13C discrimination against atmospheric CO2 (Δ13C) in tree rings of two palaeotropical pine species. These species co-occur in the mountain ranges of south–central Vietnam (1500–1600 m a.s.l.), but differ largely in their areas of distribution (Pinus kesiya from northeast India to the Philippines; P. dalatensis only in south and central Vietnam and in some isolated populations in Laos). For P. dalatensis, a robust growth chronology covering the past 290 years could be set up for the first time in the study region. For P. kesiya, the 140-year chronology constructed was the longest that could be established to date in that region for this species. In the first 40 years of the trees’ lives, the stem diameter increment was significantly larger in P. kesiya, but levelled off and even decreased after 100 years, whereas P. dalatensis exhibited a continuous growth up to an age of almost 300 years. Tree-ring growth of P. kesiya was negatively related to temperature in the wet months and season of the current year and in October (humid transition period) of the preceding year and to precipitation in August (monsoon season), but positively to precipitation in December (dry season) of the current year. The P. dalatensis chronologies exhibited no significant correlation with temperature or precipitation. Negative correlations between BAI and Δ13C indicate a lack of growth impairment by drought in both species. Regression analyses revealed a lower resilience of P. dalatensis upon episodes of growth decline compared to P. kesiya, but, contrary to our hypothesis, mean values of the three sensitivity parameters did not differ significantly between these species. Nevertheless, the vigorous growth of P. kesiya, which does not fall behind that of P. dalatensis even at the margin of its distribution area under below-optimum edaphic conditions, is indicative of a relatively high plasticity of this species towards environmental factors compared to P. dalatensis, which, in tendency, is less resilient upon environmental stress even in the “core” region of its occurrence.
In 2014/2015 a one-year field campaign at the Tiksi observatory in the Laptev Sea area was carried out using Sound Detection and Ranging/Radio Acoustic Sounding System (SODAR/RASS) measurements to investigate the atmospheric boundary layer (ABL) with a focus on low-level jets (LLJ) during the winter season. In addition to SODAR/RASS-derived vertical profiles of temperature, wind speed and direction, a suite of complementary measurements at the Tiksi observatory was available. Data of a regional atmospheric model were used to put the local data into the synoptic context. Two case studies of LLJ events are presented. The statistics of LLJs for six months show that in about 23% of all profiles LLJs were present with a mean jet speed and height of about 7 m/s and 240 m, respectively. In 3.4% of all profiles LLJs exceeding 10 m/s occurred. The main driving mechanism for LLJs seems to be the baroclinicity, since no inertial oscillations were found. LLJs with heights below 200 m are likely influenced by local topography.
Properties Evaluation of Composite Materials Based on Gypsum Plaster and Posidonia Oceanica Fibers
(2023)
Estimating the amount of material without significant losses at the end of hybrid casting is a problem addressed in this study. To minimize manufacturing costs and improve the accuracy of results, a correction factor (CF) was used in the formula to estimate the volume percent of the material in order to reduce material losses during the sample manufacturing stage, allowing for greater confidence between the approved blending plan and the results obtained. In this context, three material mixing schemes of different sizes and shapes (gypsum plaster, sand (0/2), gravel (2/4), and Posidonia oceanica fibers (PO)) were created to verify the efficiency of CF and more precisely study the physico-mechanical effects on the samples. The results show that the use of a CF can reduce mixing loss to almost 0%. The optimal compressive strength of the sample (S1B) with the lowest mixing loss was 7.50 MPa. Under optimal conditions, the addition of PO improves mix volume percent correction (negligible), flexural strength (5.45%), density (18%), and porosity (3.70%) compared with S1B. On the other hand, the addition of PO thermo-chemical treatment by NaOH increases the compressive strength (3.97%) compared with PO due to the removal of impurities on the fiber surface, as shown by scanning electron microscopy. We then determined the optimal mixture ratio (PO divided by a mixture of plaster, sand, and gravel), which equals 0.0321 because Tunisian gypsum contains small amounts of bassanite and calcite, as shown by the X-ray diffraction results.
Addition of Phosphogypsum to Fire-Resistant Plaster Panels:
A Physic–Mechanical Investigation
(2023)
Gypsum (GPS) has great potential for structural fire protection and is increasingly used in construction due to its high-water retention and purity. However, many researchers aim to improve its physical and mechanical properties by adding other organic or inorganic materials such as fibers, recycled GPS, and waste residues. This study used a novel method to add non-natural GPS from factory waste (phosphogypsum (PG)) as a secondary material for GPS. This paper proposes to mix these two materials to properly study the effect of PG on the physico-mechanical properties and fire performance of two Tunisian GPSs (GPS1 and GPS2). PG initially replaced GPS at 10, 20, 30, 40, and 50% weight percentage (mixing plan A). The PGs were then washed with distilled water several times. Two more mixing plans were run when the pH of the PG was equal to 2.4 (mixing plan B), and the pH was equal to 5 (mixing plan C). Finally, a comparative study was conducted on the compressive strength, flexural strength, density, water retention, and mass loss levels after 90 days of drying, before/after incineration of samples at 15, 30, 45, and 60 min. The results show that the mixture of GPS1 and 30% PG (mixing plan B) obtained the highest compressive strength (41.31%) and flexural strength (35.03%) compared to the reference sample. The addition of 10% PG to GPS1 (mixing plan A) improved fire resistance (33.33%) and the mass loss (17.10%) of the samples exposed to flame for 60 min compared to GPS2. Therefore, PG can be considered an excellent insulating material, which can increase physico-mechanical properties and fire resistance time of plaster under certain conditions.
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.
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.
Both water scarcity and flood risk are increasingly turning into safety concerns for many urban dwellers and, consequently, become increasingly politicised. This development involves a reconfiguration of the academic land- scape around urban risk, vulnerability and adaptation to climate change research. This paper is a literature assessment of concepts on disaster risk, vulnerability and adaptation and their applicability to the context of studying water in an African city. An overview on water-related risk in African cities is presented and concepts and respective disciplinary backgrounds reviewed. Recent debates that have emerged from the application of risk, vulnerability and adaptation concepts in research and policy practice are presented. Finally the applicability of these concepts as well as the relevance and implications of recent debates for studying water in African cities is discussed. ‘Riskscape’ is proposed as a conceptual frame for close and integrated analysis of water related risk in an African city.
GIS – what can and what can’t it say about social relations in adaptation to urban flood risk?
(2017)
Urban flooding cannot be avoided entirely and in all areas, particularly in coastal cities. Therefore adaptation to the growing risk is necessary. Geographical Information Systems (GIS) based knowledge on risk informs location-based approach to adaptation to climate risk. It allows managing city- wide coordination of adaptation measures, reducing adverse impacts of local strategies on neighbouring areas to the minimum. Quantitative assessments dominate GIS applications in flood risk management, for instance to demonstrate the distribution of people and assets in a flood prone area. Qualitative, participatory approaches to GIS are on the rise but have not been applied in the context of flooding yet. The overarching research question of this working paper is: what can GIS, and what can it not say about relationships / social relations in adaptation to urban flood risk? The use of GIS in risk mapping has exposed environmental injustices. Applications of GIS further allow model- ling future flood risk in function of demographic and land use changes, and combining it with decision support systems (DSS). While such GIS applications provide invaluable information for urban planners steering adaptation they however fall short on revealing the social relations that shape individual and household adaptation decisions. The relevance of networked social relations in adaptation to flood risk has been demonstrated in case studies, and extensively in the literature on organizational learning and adaptation to change. The purpose of this literature review is to identify the type of social relations that shape adaptive capacities towards urban flood risk which can- not be identified in a conventional GIS application.
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.