Relevance of aquatic environments for hominins: a case study from Trinil (Java, Indonesia)

Knowledge about dietary niche is key to understanding hominin evolution, since diet influences body proportions, brain size, cognition, and habitat preference. In this study we provide ecological context for the current debate on modernity (or not) of aquatic resource exploitation by hominins. We use the Homo erectus site of Trinil as a case study to investigate how research questions on possible dietary relevance of aquatic environments can be addressed. Faunal and geochemical analysis of aquatic fossils from Trinil Hauptknochenschicht (HK) fauna demonstrate that Trinil at ∼1.5 Ma contained near-coastal rivers, lakes, swamp forests, lagoons, and marshes with minor marine influence, laterally grading into grasslands. Trinil HK environments yielded at least eleven edible mollusc species and four edible fish species that could be procured with no or minimal technology. We demonstrate that, from an ecological point of view, the default assumption should be that omnivorous hominins in coastal habitats with catchable aquatic fauna could have consumed aquatic resources. The hypothesis of aquatic exploitation can be tested with taphonomic analysis of aquatic fossils associated with hominin fossils. We show that midden-like characteristics of large bivalve shell assemblages containing Pseudodon and Elongaria from Trinil HK indicate deliberate collection by a selective agent, possibly hominin.     

Summit-to-sea mapping and change detection using satellite imagery: tools for conservation and management of coral reefs

Continuous summit-to-sea maps showing both land features and shallow-water coral reefs have been completed in Puerto Rico and the U.S. Virgin Islands, using circa 2000 Landsat 7 Enhanced Thematic Mapper (ETM+) Imagery. Continuous land/sea terrain was mapped by merging Digital Elevation Models (DEM) with satellite-derived bathymetry. Benthic habitat characterizations were created by unsupervised classifications of Landsat imagery clustered using field data, and produced maps with an estimated overall accuracy of>75% (Tau coefficient >0.65). These were merged with Geocover-LC (land use/land cover) data to create continuous land/ sea cover maps. Image pairs from different dates were analyzed using Principle Components Analysis (PCA) in order to detect areas of change in the marine environment over two different time intervals: 2000 to 2001, and 1991 to 2003. This activity demonstrates the capabilities of Landsat imagery to produce continuous summit-to-sea maps, as well as detect certain changes in the shallow-water marine environment, providing a valuable tool for efficient coastal zone monitoring and effective management and conservation.     

A phytosociological study of a forest area in the humid tropics (Ujung Kulon,West Java,Indonesia)

A vegetation survey was carried out in the Ujung Kulon nature reserve (West Java, Indonesia). Particular attention was paid to the altitudinal zoning of the vegetation, to succession and to the applicability of the phytosociological approach in forests of the humid tropics. The altitudinal zoning provides a clear example of the telescope effect: low, isolated mountain complexes surrounded by sea reflect the vegetation zoning of higher mountains in a condensed, telescoped way. The pattern of primary forests, secondary forests and rattan-dominated shrublands can be explained by the former shifting cultivation practice and the impact of ash rains following the notorious eruption of the nearby volcano Krakatau in 1883. The arguments against the phytosociological approach in the humid tropics are found to be invalid. The minimal area concept does not cause great problems if used in a practical sense and if plants of all age classes and all terrestrial growth forms are included in the relevé.     

Modelling habitat and planning surveillance using Landsat imagery: a case study using Imported Red Fire ants

This paper presents a Bayesian mixture model approach for detecting areas of habitat that are suitable for S. invicta infestation, aiding the ongoing surveillance for early detection of these exotic pests. We show that Landsat imagery is an affordable and valuable tool to assist in determining an informed surveillance strategy. In this paper, we use Landsat band 3 (visible red), Landsat band 6 (mid infrared) and a soil brightness index, in various combinations, to assess the probability that the area associated with each pixel is habitable terrain, either in a multivariate analysis, or as a univariate spatial temporal model. The multivariate analysis allows researchers to create meaningful clusters that reflect the sometimes complex combinations of conditions of conditions that form suitable habitat, rather then relying on single derived indices.     

Oil palm trees detection and counting on Microsoft Bing Maps Very High Resolution (VHR) satellite imagery and Unmanned Aerial Vehicles (UAV) data using image processing thresholding approach

Palm oil is one of the highest producing vegetable oil crops globally, with production increasing rapidly over the last 40 years from 5 million tonnes in 1980 to 74.5 million tonnes in 2019. This increase in production is in line with the high demand for vegetable oils worldwide. The accurate monitoring and statistics of oil palm plantation data are essential to support effective and efficient decision-making. However, the most commonly adopted data collection still uses conventional methods, i.e., field surveys, which are highly dependent on the massive amount of human resources, cost, processing time, and difficulty reaching remote areas. Remote sensing using satellite and UAV imagery can be an alternative in data collection due to its distinct advantages with a more efficient labor force, affordable cost, shorter time updates, and covering areas that are difficult to reach. In this work, we investigate the utilization of remote sensing data from Microsoft Bing Maps Very High Resolution (VHR) satellite imagery and Unmanned Aerial Vehicle (UAV) data using the image processing thresholding method for detecting and counting oil palm trees. The combination of Hue, Saturation, and Value (HSV) conversion, the Otsu segmentation thresholding, and contours detection and counting methods are used in our approach to enhance the accuracy of captured features of oil palm trees. The detection results are further categorized into best-case, average-case, and worst-case detection to comprehend the challenging real-world situation, based on the quality of captured imageries and model prediction results. Our proposed approach achieves better and more promising results when using UAV image data. This is indicated by an average true-positive rate (TPR) of 88 to 97% in best-case data input, 70 to 81% in average-case data input, 30 to 53% in worst-case data input, and 10% in estimation error. Meanwhile, Bing image data provides an average true-positive rate (TPR) of 9 to 13% in the best-case data input, 5 to 6% in the average-case data input, and 1 to 3% in the worst-case data input, with an average estimation error up to 78%. Overall, our proposed approach can perform the oil palm trees detection and counting. We also suggest that higher resolution imagery than Microsoft Bing maps or single utilization of UAV data can be considered input for better results. Our study could further be beneficial in providing more scalable and accurate plantation statistics.     

Comparison of different approaches for evaluation of the detection and quantitation limits of a purity method: a case study using a capillary isoelectrofocusing method for a monoclonal antibody

Several different techniques suggested by the International Conference on Harmonization (ICH) Q2R1 guideline were used to assess the signal and concentration at the limit of detection (LOD) and limit of quantitation (LOQ) for a purity method. These approaches were exemplified with a capillary isoelectrofocusing (cIEF) method, which has been developed to quantify the distribution of the charge isoforms of a monoclonal antibody. The charge isoforms are the result of incomplete posttranslational processing of C-terminal lysine residues of the heavy chain by carboxypeptidase. Results showed no significant discrepancy between LOD/LOQ obtained by the different techniques. Validation experiments corroborated the calculated LOQ. The results indicate that any single technique can provide meaningful values for the LOD and LOQ. Finally, important points to consider when applying these techniques to purity methods are discussed.     

Identification of soybean based on Sentinel-1/2 SAR and MSI imagery under a complex planting structure

Soybeans, as the major global oil seed crops, are among the large-scale agricultural products imported into China. In this way, the accurate identification of their growing areas is the basis for agricultural decision-making and planting structure adjustment, which are of great significance to the national food security. Currently, there is insufficient research on the identification of soybean, using remote sensing, under complex agricultural planting structures. In this paper, Guoyang County, a typical soybean-producing region in the north of Anhui Province, China, was selected as the study area, and then the multi-temporal Sentinel-1/2 (S-1/2) microwave and optical multispectral data were integrated to obtain the spatial distribution of the planting areas, via a stepwise hierarchical extraction strategy. Considering the Jeffries-Matusita (JM) distance, August 18 (i.e., the early pod setting stage) was correspondingly determined as more appropriate for soybean extraction, compared with other time phases. For this purpose, a set of rules were established to eliminate the non-cropland pixels, and the total distribution of the field vegetation was derived accordingly. In total, 30 candidate features (viz. 10 spectral bands, 11 vegetation indices, 4 texture features, and 5 microwave polarization features) were selected, and their importance was evaluated based on the ReliefF algorithm. With the support of the typical cover type samples, the weight assessment method for the ReliefF feature was further bundled with three machine-learning (ML) algorithms, namely, the random forest (RF), the back-propagation neural network (BPNN), and the support vector machine (SVM), to single out the optimum subset of the features for soybean identification. The results finally revealed that the ReliefF-RF model outperformed the other two. With nine optimum features, Kappa coefficient also reached up to 0.77–0.85. Furthermore, the extraction accuracy produced by the optimum subset of the features was significantly greater than the original 10 spectral bands (Kappa coefficient of 0.75–0.84), albeit slightly lower than the effect of the total 30 features (Kappa coefficient of 0.78–0.85).     

A chlorophyll-retrieval algorithm for satellite imagery (Medium Resolution Imaging Spectrometer) of inland and coastal waters

A chlorophyll (Chl) a retrieval algorithm, originally developedfor spectral subsurface irradiance reflectance determined fromabove-water shipboard measurements, was adapted for use withsatellite imagery to be acquired by the MERIS (Medium ResolutionImaging Spectrometer) instrument. This MERIS algorithm was calibratedfor Chl a concentrations in the range 3–185 mg m^-3 usingspectral reflectance calculated from shipboard measurementson the IJssel Lagoon (The Netherlands). Next, the algorithmwas validated for various inland and coastal waters coveringthis concentration range. Despite the lower spectral resolutionof MERIS as compared to the shipboard spectroradiometer, thestandard error of estimate is expected to be similar, i.e.     

The potential of modeling Prosopis Juliflora invasion using Sentinel-2 satellite data and environmental variables in the dryland ecosystem of Ethiopia

Earth observation data play a vital role for efficient modeling of invasive species. Particularly, optical Sentinel-2 (S2) data with its capability of providing high spatial, spectral and temporal resolutions creates ample opportunities. However, few studies so far evaluated the combined use of S2 derived variables and environmental variables for modeling the distribution of invasive species. This study aims to compare the performance of models using S2 derived variables with environmental variables and their integration for modeling invasive Prosopis juliflora in the lower Awash River basin of Ethiopia. A total of 680 field data were used to train and validate the Random Forest (RF) approach. Model performances were evaluated using True Skill Statistics (TSS), kappa index, correlation, area under the curve (ROC), sensitivity and specificity. Our results demonstrated that modeling using S2 vegetation indices and S2 spectral bands showed higher performance compared to topo-climatic based variables with TSS of 0.91, 0.89, and 0.74, respectively. The ROC also confirmed the higher accuracy of S2 vegetation indices, S2 spectral bands and combined models compared to a topo-climatic based modeling. Interestingly, models using the integration of S2 derived variables with topo-climatic variables showed even better performance than the individual models. Our study highlighted that S2 derived variables and their integration with topo-climatic variables are highly recommended for efficient monitoring of invasive species distribution.     

Freeze drying formulation using microscale and design of experiment approaches: a case study using granulocyte colony-stimulating factor

The lyophilization of proteins in microplates, to assess and optimise formulations rapidly, has been applied for the first time to a therapeutic protein and, in particular, one that requires a cell-based biological assay, in order to demonstrate the broader usefulness of the approach. Factorial design of experiment methods were combined with lyophilization in microplates to identify optimum formulations that stabilised granulocyte colony-stimulating factor during freeze drying. An initial screen rapidly identified key excipients and potential interactions, which was then followed by a central composite face designed optimisation experiment. Human serum albumin and Tween 20 had significant effects on maintaining protein stability. As previously, the optimum formulation was then freeze-dried in stoppered vials to verify that the microscale data is relevant to pilot scales. However, to validate the approach further, the selected formulation was also assessed for solid-state shelf-life through the use of accelerated stability studies. This approach allows for a high-throughput assessment of excipient options early on in product development, while also reducing costs in terms of time and quantity of materials required.     

Bioclimatic characterisation of an urban area: a case study in Bologna (Italy)

Summer bioclimatic discomfort is a significant public health problem. Bioclimatic characterisations of populations living in urban areas are usually very poor, although the risks are relatively higher in cities because of the phenomenon known as the “urban heat island”. We compared airport, rural, and urban bioclimatic conditions in terms of apparent temperature, Thom index, and temperature alone in several sites within a radius of approximately 25 km from the city of Bologna (Italy). The comparison between meteorological monitoring stations within and near the urban area showed the large impact of the urban heat island effect. Nighttime data showed the largest differences among the investigated sites. Minimum apparent temperatures at rural stations were about 3.5°C lower than the urban 30 m reference station, and 6°C lower than the 2 m urban site. The 2 m apparent temperature values within the urban area were several degrees higher (typically 2°C) than those taken above the roof, both for minimum and maximum values. Temporal trends in the different sites were highly correlated (generally above 0.90), but regression residuals were sometimes quite large. Finally, epidemiological implications are briefly addressed.     

Mapping species density of trees,shrubs and vines in a tropical forest,using field measurements,satellite multiespectral imagery and spatial interpolation

We estimated the number of species in a tropical forest landscape in Quintana Roo, Mexico, based on the relationship between reflectance values of satellite imagery and field measurements of plant species density (mean number of species per plot). Total species density as well as that of tree, shrub and vine species were identified from 141 sampling quadrats (16543 individuals sampled). Spatial prediction of plant diversity was performed using universal kriging. This approach considered the linear relationship between plant species density and reflectance values of Thematic Mapper™, as well as the spatial dependence of the observations. We explored the linear relationships between spectral properties of TM bands and the species density of trees, shrubs and vines, using regression analysis. We employed Akaike Information Criterion (AIC) to select a set of candidate models. Based on Akaike weights, we calculated model-averaged parameters. Linear regression between number of species and reflectance values of TM bands yielded regression residuals. We used variogram analysis to analyze the spatial structure of these residuals. Results show that accounting for spatial autocorrelation in the residual variation improved model R² from 0.57 to 0.66 for number of all species, from 0.58 to 0.65 for number of tree species, from 0.26 to 0.41 for number of shrub species and from 0.13 to 0.17 for species density of vines. The empirical models we developed can be used to predict landscape-level species density in the Yucatan Peninsula, helping to guide and evaluate management and conservation strategies.     

Assessing native desert vegetation recovery in a war‐affected area using multispectral and hyperspectral imagery: a case study of the Sabah Al‐Ahmad Nature Reserve,Kuwait

The primary aim of this project was to assess vegetation changes in the Sabah Al‐Ahmad Nature Reserve, Kuwait, which is a war‐affected area following the Iraqi invasion in 1990. After the liberation in 1991, several portions of the reserve were under a restoration program. Remote sensing has been used as a tool to assess vegetation and land cover changes. We studied the feasibility of three common methods—the Mahalanobis distance (MD), maximum likelihood (ML), and support vector machine (SVM)—for classification of the multispectral imagery (Landsat) and hyperspectral (Hyperion). The reserve was also compared to the demilitarized zone (DMZ) located at Umm Nigga at the northern border of Kuwait, as it had recovered naturally, to distinguish between an autogenic recovery and a restored area. We discovered that the location was damaged during the military occupation, but a rapid recovery of the vegetation was then recorded in the reserve after the war from less than 1% measured in 1991 to 42% in 1998. Then, the vegetation cover significantly decreased in 2002 (26%) and slightly increased in 2013 (28%). We found that similar rapid increase in vegetation cover occurred in most parts of the reserve that was under the restoration program, and in the DMZ, which was naturally recovered. We concluded that remote sensing technologies are helpful tools in understanding the process of vegetation recovery as it provides information on location and timing of recovery, particularly where optimal condition exists.     

Quantitative estimation of area parameters (with representatives of genus rana as a case study)

A quantitative method of species "point" area analysis is considered that provides the interpolation of species distribution to the whole territory on the basis of its relationships with climatic and relief variables. It is shown that application of standart statistical interpolation techniques is incorrect. The proposed approach is based on interpolation onto the whole territory the species-specific relations with environmental variables detected in single "points". The basic method for the task proves to be the factor analysis. Within the scope of the study, we have considered the methods for quantitative representation of species relationships with climatic and relief variables. The analysis efficiency is demonstrated by an example of three species of brown frogs: Rana temporaria, R. arvalis u R. amurensis.     

Identification of most spectrally distinguishable phenological stage of invasive Phramites australis in Lake Erie wetlands (Canada) for accurate mapping using multispectral satellite imagery

Phragmites australis (Cav.) Trin. ex Steudel subspecies australis is one of the worst plant invaders in wetlands of North America. Remote sensing is the most cost-effective method to track its spread given its widespread distribution and rapid colonization rate. We hypothesize that the morphological and/or physiological features associated with different phenological states of Phragmites can influence their reflectance signal and thus affect mapping accuracies. We tested this hypothesis by comparing classification accuracies of cloud-free images acquired by Landsat 7, Landsat 8, and Sentinel 2 at roughly monthly intervals over a calendar year for two wetlands in southern Ontario. We used the Support Vector Machines classification and employed field observations and image acquired from unmanned aerial vehicle (8 cm) to perform accuracy assessments. The highest Phragmites producer’s, user’s, and overall accuracy (96.00, 91.11, and 88.56% respectively) were provided by images acquired in late summer and fall period. During this period, green, Near Infrared, and Short-Wave Infrared bands generated more unique reflectance signals for Phragmites. Both Normalized Difference Vegetation Index and Normalized Difference Water Index showed significant difference between Phragmites and the most confused classes (cattail; Typha latifolia L., and meadow marsh) during the late summer and fall period. Since meadow marsh separated out best from Phragmites and cattail in the February image, we used it to mask the meadow marsh in the July image to reduce confusion. The unique reflectance signal of Phragmites in late summer and fall is likely due to prolonged greenness of Phragmites when compared to other wetland vegetation, large, distinct inflorescence, and the water content of Phragmites during this period.

     

Automatic detection of gait events: a case study using inductive learning techniques

One of the problems which occurs in the development of a control system for functional electrical stimulation of the lower limbs is to detect accurately specific events within the gait cycle. We present a method for the classification of phases of the gait cycle using the artificial intelligence technique of inductive learning. Both the terminology of inductive learning and the algorithm used for the analyses are fully explained. Given a set of examples of sensor data from the gait events that are to be delected, the inductive learning algorithm is able to produce a decision tree (or set of rules) which classify the data using a minimum number of sensors. The nature of the redundancy of the sensor set is examined by progressively removing combinations of sensors and noting the effect on both the size of the decision trees produced and their classification accuracy on ‘unseen’ testing data. Since the algorithm is able to calculate which sensors are more important (informative), comparisons with the intuitive appreciation of sensor importance of five researchers in the fields were made, revealing that those sensors which appear intuitively most informative may, in fact, provide the least information. Comparison results with the standard statistical classification technique of linear discriminant analysis are also presented, showing the relative simplicity of the inductively derived rules together with their good classification accuracy. In addition to the control of FES, such techniques are also applicable to automatic gait analysis and the construction of expert systems for diagnosis of gait pathologies.     

The influence of climate on the distribution of lichens: a case study in a borderline area (Liguria,NW Italy)

The purposes of this article are to quantify the relationship between epiphytic lichen distribution and macroclimatic variables in the study area and to provide a case study for evaluating the predictive role of epiphytic lichens as bioclimatic indicators. The study was carried out in the Liguria region (NW-Italy), a small (5432 km²) borderline area, where phytoclimatic features range from the dry Mediterranean to the Alpine in a few kilometers. Epiphytic lichen diversity was sampled using a standardized protocol [Asta et al (2002) In: Nimis et al (eds) Monitoring with lichens: monitoring lichens. Kluwer, Dordrecht]. Abundance of the species in the sampling sites was related to macroclimatic parameters (yearly average temperature and rainfall) and non-parametric multivariate models were calculated to find significative relationships among predictive and response variables. A total of 59 species showed highly significant relation with macroclimatic parameters. Four groups were selected, by means of a cluster analysis, related to four climatic niches (warm-humid, cold-humid, mesothermic-humid, warm-dry). Distributional pattern of the groups in the survey area showed a good correspondence with the bioclimatic units of Liguria region described by Nimis [(2003) Checklist of the Lichens of Italy 3.0. University of Trieste, Dept of Biology. http://www.dbiodbs.univ.trieste.it. Cited 1 Jun 2006]. A significant subset of epiphytic lichen species in the study area have been proved to be efficient bioclimatic indicator and it is supposed to give good results to monitor climatic changes, in a long-term perspective.