Rapid urbanization in African metropolises like the Greater Asmara Area, Eritrea, poses numerous environmental challenges, including soil sealing, loss of vegetation cover, threats to protected natural areas, and climate change, among others. Mapping and assessing ecosystem services, particularly analyzing their spatial and temporal distribution is crucial for sustainable spatial planning. This study aims at mapping and analyzing ecosystem services hotspots and coldspots dynamics in the Greater Asmara Area to identify recent trends and opportunities for enhancing ecosystem services supply. Utilizing remote sensing images, we produced land cover maps for 2009 and 2020 and mapped six ecosystem services through a lookup table approach. The study includes provisioning, regulating and maintenance, and cultural ecosystem services. We analyzed their spatio-temporal variations, identifying ecosystem services hotspots and coldspots and their changes over time. Results show that overall ecosystem services potential in the Greater Asmara Area remains low but stable, with some improvements. By 2020, areas with no ecosystem services potential decreased in southern regions like Gala Nefhi and Berik, and new hotspots and coldspots emerged in central Gala Nefhi. This pilot study demonstrates the feasibility and key challenges of the ecosystem services hotspots and coldspots approach for sustainable spatial planning in rapidly urbanizing African metropolitan regions. Despite limitations, the study offers valuable insights into ecosystem services potentials, and related hotspots and coldspots dynamics, raising awareness and paving the way for further research and application.
- Klíčová slova
- East Africa, IPBES, Land cover change analysis, Matrix approach, Sustainable cities and communities, Urban planning,
- MeSH
- ekosystém * MeSH
- klimatické změny MeSH
- technologie dálkového snímání MeSH
- urbanizace MeSH
- zachování přírodních zdrojů * metody MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Eritrea MeSH
A wide range of portable chlorophyll meters are increasingly being used to measure leaf chlorophyll content as an indicator of plant performance, providing reference data for remote sensing studies. We tested the effect of leaf anatomy on the relationship between optical assessments of chlorophyll (Chl) against biochemically determined Chl content as a reference. Optical Chl assessments included measurements taken by four chlorophyll meters: three transmittance-based (SPAD-502, Dualex-4 Scientific, and MultispeQ 2.0), one fluorescence-based (CCM-300), and vegetation indices calculated from the 400-2500 nm leaf reflectance acquired using an ASD FieldSpec and a contact plant probe. Three leaf types with different anatomy were included: dorsiventral laminar leaves, grass leaves, and needles. On laminar leaves, all instruments performed well for chlorophyll content estimation (R2 > 0.80, nRMSE < 15%), regardless of the variation in their specific internal structure (mesomorphic, scleromorphic, or scleromorphic with hypodermis), similarly to the performance of four reflectance indices (R2 > 0.90, nRMSE < 16%). For grasses, the model to predict chlorophyll content across multiple species had low performance with CCM-300 (R2 = 0.45, nRMSE = 11%) and failed for SPAD. For Norway spruce needles, the relation of CCM-300 values to chlorophyll content was also weak (R2 = 0.45, nRMSE = 11%). To improve the accuracy of data used for remote sensing algorithm development, we recommend calibration of chlorophyll meter measurements with biochemical assessments, especially for species with anatomy other than laminar dicot leaves. The take-home message is that portable chlorophyll meters perform well for laminar leaves and grasses with wider leaves, however, their accuracy is limited for conifer needles and narrow grass leaves. Species-specific calibrations are necessary to account for anatomical variations, and adjustments in sampling protocols may be required to improve measurement reliability.
- Klíčová slova
- Chlorophyll, Leaf pigments, Leaf structure, Leaf with hypodermis, Remote sensing, Vegetation index,
- MeSH
- chlorofyl * analýza metabolismus MeSH
- lipnicovité MeSH
- listy rostlin * anatomie a histologie chemie MeSH
- technologie dálkového snímání metody MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- chlorofyl * MeSH
Unraveling the intricate spatial and temporal complexities of vegetation represents a crucial key to understanding ecosystem functioning. Drones, as cutting-edge technology, hold immense potential in bridging the gap between on-ground measurements and satellite remote sensing data. Nonetheless, a multitude of challenges still looms, with one of the foremost being the nuanced identification of scales that strike a balance between capturing maximum complexity while minimizing measurement errors. To explore how current research deals with the above-mentioned challenges, we carried out a literature survey on research studies employing drones to characterize natural and semi-natural vegetation. We selected papers related to the role of spatial and/or temporal complexity in ecosystem state, function and/or services. Our result showed that most studies focused on ecosystem state, whereas function and services were barely addressed. Similarly, the effects of spatial or temporal scales on vegetation heterogeneity (complexity) are rarely studied even though drone technology seems ideal for this task. Since heterogeneity differs between ecosystems and its comprehension is greatly influenced by the features of the survey, careful design is important to maximize the efficiency and the range of complexity captured by the survey. However, in reality, most studies do not follow any specific planning of the drone survey according to the case study characteristics. In fact, we found a positive trend between spatial resolution and extent of the study area, and no significant relationship between spatial resolution and accuracy, regardless of the characteristics of the given ecosystem type. Specifically designed studies need to be carried out to further explore the effects of changing spatial and temporal resolution on complexity captured across ecosystem gradients, and establish the optimal resolution for different ecosystem types to assure transferability and operational use in land management. Despite the mentioned challenges and research gaps, drones represent a powerful and effective tool to explore vegetation complexity in new ways and dimensions. Nevertheless, there is an urgent need to define the appropriate methods for each scope.
- Klíčová slova
- Drone, Ecosystem, Heterogeneity, Optimal resolution, Scale, Spatial and temporal patterns, UAS, UAV, Vegetation complexity, survey efficiency,
- MeSH
- ekosystém * MeSH
- monitorování životního prostředí * metody MeSH
- rostliny * MeSH
- technologie dálkového snímání * MeSH
- zachování přírodních zdrojů MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Cyanobacteria blooms in fishponds, driven by climate change and anthropogenic activities, have become a critical concern for aquatic ecosystems worldwide. The diversity in fishpond sizes and fish densities further complicates their monitoring. This study addresses the challenge of accurately predicting cyanobacteria concentrations in turbid waters via remote sensing, hindered by optical complexities and diminished light signals. A comprehensive dataset of 740 sampling points was compiled, encompassing water quality metrics (cyanobacteria levels, total chlorophyll, turbidity, total cell count) and spectral data obtained through AlgaeTorch, alongside Sentinel-2 reflectance data from three Třeboň fishponds (UNESCO Man and Biosphere Reserve) in the Czech Republic over 2022-2023. Partial Least Squares Regression (PLSR) and three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were developed based on seasonal and annual data volumes. The SVM algorithm demonstrated commendable performance on the one-year data validation dataset from the Svět fishpond for the prediction of cyanobacteria, reflected by the key performance indicators: R2 = 0.88, RMSE = 15.07 μg Chl-a/L, and RPD = 2.82. Meanwhile, SVM displayed steady results in the unified one-year validation dataset from Naděje, Svět, and Vizír fishponds, with metrics showing R2 = 0.56, RMSE = 39.03 μg Chl-a/L, RPD = 1.50. Thus, Sentinel data proved viable for seasonal cyanobacteria monitoring across different fishponds. Overall, this study presents a novel approach for enhancing the precision of cyanobacteria predictions and long-term ecological monitoring in fishponds, contributing significantly to the water quality management strategies in the Třeboň region.
- Klíčová slova
- Cyanobacteria, Fishponds, Machine learning, Remote sensing, Water quality inversion,
- MeSH
- eutrofizace MeSH
- kvalita vody MeSH
- monitorování životního prostředí * metody MeSH
- sinice * MeSH
- strojové učení * MeSH
- support vector machine MeSH
- technologie dálkového snímání * MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Česká republika MeSH
The growing effects of climate change on Malaysia's coastal ecology heighten worries about air pollution, specifically caused by urbanization and industrial activity in the maritime sector. Trucks and vessels are particularly noteworthy for their substantial contribution to gas emissions, including nitrogen dioxide (NO2), which is the primary gas released in port areas. The application of advanced analysis techniques was spurred by the air pollution resulting from the combustion of fossil fuels such as fuel oil, natural gas and gasoline in vessels. The study utilized satellite photos captured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5P satellite to evaluate the levels of NO2 gas pollution in Malaysia's port areas and exclusive economic zone. Before the COVID-19 pandemic, unrestricted gas emissions led to persistently high levels of NO2 in the analyzed areas. The temporary cessation of marine industry operations caused by the pandemic, along with the halting of vessels to prevent the spread of COVID-19, resulted in a noticeable decrease in NO2 gas pollution. In light of these favourable advancements, it is imperative to emphasize the need for continuous investigation and collaborative endeavours to further alleviate air contamination in Malaysian port regions, while simultaneously acknowledging the wider consequences of climate change on the coastal ecology. The study underscores the interdependence of air pollution, maritime activities and climate change. It emphasizes the need for comprehensive strategies that tackle both immediate environmental issues and the long-term sustainability and resilience of coastal ecosystems in the context of global climate challenges.
- Klíčová slova
- Nitrogen dioxide (NO(2)), Sentinel 5P, air pollution, climate change,
- MeSH
- COVID-19 epidemiologie MeSH
- klimatické změny * MeSH
- látky znečišťující vzduch * analýza MeSH
- lodě MeSH
- monitorování životního prostředí * metody MeSH
- oxid dusičitý * analýza MeSH
- satelitní snímkování * MeSH
- výfukové emise vozidel analýza MeSH
- znečištění ovzduší * analýza MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Malajsie MeSH
- Názvy látek
- látky znečišťující vzduch * MeSH
- oxid dusičitý * MeSH
- výfukové emise vozidel MeSH
To enhance our understanding of forest carbon sequestration, climate change mitigation and drought impact on forest ecosystems, the availability of high-resolution annual forest growth maps based on tree-ring width (TRW) would provide a significant advancement to the field. Site-specific characteristics, which can be approximated by high-resolution Earth observation by satellites (EOS), emerge as crucial drivers of forest growth, influencing how climate translates into tree growth. EOS provides information on surface reflectance related to forest characteristics and thus can potentially improve the accuracy of forest growth models based on TRW. Through the modelling of TRW using EOS, climate and topography data, we showed that species-specific models can explain up to 52 % of model variance (Quercus petraea), while combining different species results in relatively poor model performance (R2 = 13 %). The integration of EOS into models based solely on climate and elevation data improved the explained variance by 6 % on average. Leveraging these insights, we successfully generated a map of annual TRW for the year 2021. We employed the area of applicability (AOA) approach to delineate the range in which our models are deemed valid. The calculated AOA for the established forest-type models was 73 % of the study region, indicating robust spatial applicability. Notably, unreliable predictions predominantly occurred in the climate margins of our dataset. In conclusion, our large-scale assessment underscores the efficacy of combining climate, EOS and topographic data to develop robust models for mapping annual TRW. This research not only fills a critical void in the current understanding of forest growth dynamics but also highlights the potential of integrated data sources for comprehensive ecosystem assessments.
- Klíčová slova
- NDMI, NDRE, Random forest, Sentinel-1, Sentinel-2, Tree rings,
- MeSH
- ekosystém * MeSH
- klimatické změny MeSH
- lesy MeSH
- stromy MeSH
- technologie dálkového snímání * MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Evropa MeSH
- východní Evropa MeSH
Cerebral perfusion pressure (CPP) is the net pressure gradient that drives oxygen delivery to cerebral tissue. It is the difference between the mean arterial pressure (MAP) and the intracranial pressure (ICP). As CPP is a calculated value, MAP and ICP must be measured simultaneously. In research models, anesthetized and acute monitoring is incapable of providing a realistic picture of the relationship between ICP and MAP under physiological and/or pathophysiological conditions. For long-term monitoring of both pressures, the principle of telemetry can be used. The aim of this study was to map changes in CPP and spontaneous behavior using continuous pressure monitoring and video recording for 7 days under physiological conditions (group C - 8 intact rats) and under altered brain microenvironment induced by brain edema (group WI - 8 rats after water intoxication) and neuroprotection with methylprednisolone - MP (group WI+MP - 8 rats with MP 100 mg/kg b.w. applicated intraperitoneally during WI). The mean CPP values in all three groups were in the range of 40-60 mm Hg. For each group of rats, the percentage of time that the rats spent during the 7 days in movement pattern A (standard movement stereotype) or B (atypical movement) was defined. Even at very low CPP values, the standard movement stereotype (A) clearly dominated over the atypical movement (B) in all rats. There was no significant difference between control and experimental groups. Chronic CPP values with correlated behavioral type may possibly answer the question of whether there is a specific, universal, optimal CPP at all.
In view of the challenges faced by organizations and departments concerned with agricultural capacity observations, we collected In-Situ data consisting of diverse crops (More than 11 consumable vegetation types) in our pilot region of Harichand Charsadda, Khyber Pakhtunkhwa (KP), Pakistan. Our proposed Long Short-Term Memory based Deep Neural network model was trained for land cover land use statistics generation using the acquired ground truth data, for a synergy between Planet-Scope Dove and European Space Agency's Sentinel-2. Total of 4 bands from both sentinel-2 and planet scope including Red, Green, Near-Infrared (NIR) and Normalised Difference Vegetation Index (NDVI) were used for classification purpose. Using short temporal frame of Sentinel-2 comprising 5 date images, we propose an realistic and implementable procedure for generating accurate crop statistics using remote sensing. Our self collected data-set consists of a total number of 107,899 pixels which was further split into 70% and 30% for training and testing purpose of the model respectively. The collected data is in the shape of field parcels, which has been further split for training, validation and test sets, to avoid spatial auto-correlation. To ensure the quality and accuracy 15% of the training data was left out for validation purpose, and 15% for testing. Prediction was also performed on our trained model and visual analysis of the area from the image showed significant results. Further more a comparison between Sentinel-2 time series is performed separately from the fused Planet-Scope and Sentinel-2 time-series data sets. The results achieved shows a weighted average of 93% for Sentinel-2 time series and 97% for fused Planet-Scope and Sentinel-2 time series.
The Hindukush-Karakoram-Himalaya (HKH) mountain ranges are the sources of Asia's most important river systems, which provide fresh water to 1.4 billion inhabitants in the region. Environmental and socioeconomic conditions are affected in many ways by climate change. Globally, climate change has received widespread attention, especially regarding seasonal and annual temperatures. Snow cover is vulnerable to climate warming, particularly temperature variations. By employing Moderate Resolution Imaging Spectroradiometer (MODIS) datasets and observed data, this study investigated the seasonal and interannual variability using snow cover, vegetation and land surface temperature (LST), and their spatial and temporal trend on different elevations from 2001 to 2020 in these variables in Gilgit Baltistan (GB), northern Pakistan. The study region was categorized into five elevation zones extending from < 2000 to > 7000 masl. Non-parametric Mann-Kendall trend tests and Sen's slope estimates indicate snow cover increases throughout the winter, but decreases significantly between June and July. In contrast, GB has an overall increasing annual LST trend. Pearson correlation coefficient (PCC) reveals a significant positive relationship between vegetation and LST (PCC = 0.73) and a significant negative relationship between LST and snow cover (PCC = - 0.74), and vegetation and snow cover (PCC = - 0.78). Observed temperature data and MODIS LST have a coefficient of determination greater than 0.59. Snow cover decreases at 3000-2000 masl elevations while increases at higher 5000 masl elevations.The vegetation in low and mid-elevation < 4000 masl zones decreases significantly annually. The temperature shows a sharply increasing trend at lower 2000-3000 masl elevations in the autumn, indicating the shifting of the winter seasons at this elevation zone. These findings better explain the spatiotemporal variations in snow cover, vegetation, and LST at various elevation zones and the interactions between these parameters at various elevations across the HKH region.
- Klíčová slova
- Climate change, Land surface temperature, MODIS, Snow cover area, Trend analysis, Vegetation cover area,
- MeSH
- klimatické změny MeSH
- roční období MeSH
- satelitní snímkování * MeSH
- sníh * MeSH
- teplota MeSH
- Publikační typ
- časopisecké články MeSH
In the field of species conservation, the use of unmanned aerial vehicles (UAV) is increasing in popularity as wildlife observation and monitoring tools. With large datasets created by UAV-based species surveying, the need arose to automate the detection process of the species. Although the use of computer learning algorithms for wildlife detection from UAV-derived imagery is an increasing trend, it depends on a large amount of imagery of the species to train the object detector effectively. However, there are alternatives like object-based image analysis (OBIA) software available if a large amount of imagery of the species is not available to develop a computer-learned object detector. The study tested the semi-automated detection of reintroduced Arabian Oryx (O. leucoryx), using the specie's coat sRGB-colour profiles as input for OBIA to identify adult O. leucoryx, applied to UAV acquired imagery. Our method uses lab-measured spectral reflection of hair sample values, collected from captive O. leucoryx as an input for OBIA ruleset to identify adult O. leucoryx from UAV survey imagery using semi-automated supervised classification. The converted mean CIE Lab reflective spectrometry colour values of n = 50 hair samples of adult O. leucoryx to 8-bit sRGB-colour profiles of the species resulted in the red-band value of 157.450, the green-band value of 151.390 and blue-band value of 140.832. The sRGB values and a minimum size permitter were added as the input of the OBIA ruleset identified adult O. leucoryx with a high degree of efficiency when applied to three UAV census datasets. Using species sRGB-colour profiles to identify re-introduced O. leucoryx and extract location data using a non-invasive UAV-based tool is a novel method with enormous application possibilities. Coat refection sRGB-colour profiles can be developed for a range of species and customised to autodetect and classify the species from remote sensing data.
- Klíčová slova
- Aerial imagery, Arabian oryx, Automated detection, Drone, UAV, Wildlife management, sRGB-colour profiles,
- MeSH
- algoritmy * MeSH
- divoká zvířata MeSH
- počítačové zpracování obrazu MeSH
- software MeSH
- spektrální analýza MeSH
- technologie dálkového snímání * metody MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
