Basic Arithmetical Skills of Students with Learning Disabilities in the Secondary Special Schools: An Exploratory Study covering Fifth to Ninth Grade

The mission of German special schools is to enhance the education of students with Special Educational Needs in the area of Learning (SEN-L). However, recent studies indicate that graduate students with SEN-L from special schools show difficulties in basic arithmetical operations, and the development of basic mathematical skills during secondary special school is not warranted. This study presents a newly developed test of basic arithmetical skills, based on already established tests. The test examines the arithmetical skills of students with SEN-L from fifth to ninth grade. The sample consisted of 110 students from three special schools in Munich. Testing took place in January and June 2013. The test shows to be an effective tool that reliably and precisely assesses students’ performance across different grades. The test items can be used without creating floor and ceiling effects among fifth to ninth grade students with SEN-L. The items’ conformity to the dichotomous Rasch model is demonstrated. The students’ skills turn out to be very heterogeneous, both overall and within grades. Many of the students do not even master basic arithmetical skills that are taught in primary school, although achievement improves in higher grades

Comparison of Several Methods for Determining the Internal Resistance of Lithium Ion Cells

The internal resistance is the key parameter for determining power, energy efficiency and lost heat of a lithium ion cell. Precise knowledge of this value is vital for designing battery systems for automotive applications. Internal resistance of a cell was determined by current step methods, AC (alternating current) methods, electrochemical impedance spectroscopy and thermal loss methods. The outcomes of these measurements have been compared with each other. If charge or discharge of the cell is limited, current step methods provide the same results as energy loss methods

Identification and characterization of urban trees using VHR remote sensing and auxiliary data

Trees play an important beneficial role within urban climate. Planning and maintenance of urban forest requires measurements of position, distribution, and characteristics of individual trees, which can be achieved with VHR remote sensing sensors. This work compares local maximum (LM) filtering and Laplacian of Gaussian (LoG) blob detector combined with marker-controlled watershed segmentation (MCWS), clustering with Voronoi-tessellation and region growing for individual tree detection and crown delineation (ITCD) in the urban area of Munich. Crown height model (CHM), lightness and distance transform of crown area from VHR aerial imagery are employed for ITCD. Region specific parameters are set with the inclusion of additional information of street green area. The trees are categorized by land-use into Street, Residential and Park trees. Tree height, crown area and diameter, and normalized difference vegetation index (NDVI) are measured for all delineated tree crowns. The ITCD result is validated against reference data from visual interpretation via stereophotogrammetry, then compared to a TLS street tree product and the tree classes of a VHR satellite landcover classification. The distribution and density of trees is analyzed over the urban area of Munich aggregated to 100 m INSPIRE GeoGitter, city districts and centrality. Tree detection with LM filtering on CHM with regional calibration based on street green yields highest results with an F-Score of 0.949, precision of 0.959, and recall of 0.939. Performance without region specific parameters decreases to an F-Score, precision, and recall of 0.900, 0.936 and 0.866 respectively. Tree crown delineation performs best with LM and CHM with overall accuracies between 73.5 % to 76.7 % for all delineation methods, regional restriction to street green resulted in overall accuracies of 86.9 % - 88.1 %. With less a priori information required, delineation with MCWS is preferred. The detection method LM CHM with MCWS results in 1.54 million trees with a combined crown area of 92.24 km2 for the administrative city area of Munich in 2017. Mean tree height and crown area of individual trees is 12.45 m and 60.3 m2 respectively. Categorization on land-use shows 9.1 %, 38.4 %, and 33.1 % of trees belonging to Street tree, Residential tree, and Park tree with relative crown area of 7.5 %, 30.4 %, and 45.5 %, respectively. The ITCD showed an accordance of 80.9 % of distinct trees with a TLS street tree product and high correlation to a VHR satellite landcover classification. The inner city of Munich displays low tree density compared to surrounding areas. The district Waldperlach has the highest tree density with an average of 83 trees per hectare. Crown coverage increases with distance to city center and levels off at a distance of 3 km with 30 % relative coverage

Detection of impervious soil area in multispectral remote sensing - a comparison

Urban areas are bound to huge changes to accommodate infrastructure, living- and workspaces. The monitoring of these changes plays a vital role in planning and managing sustainable land use and land cover. Impervious surface, although not attributable to the whole area of urban sprawl, influences local climate known as urban heat island (UHI) effect, distribution of watershed, water quality and many other factors which ultimately affect the inhabitants of an area. To approximate consequences, exact measurements of Impervious Surface Area (ISA) are needed, which have to be cost-effective and up to date. Current operational methods to detect ISA offer only low temporal resolutions, are mostly based upon costly airborne surveying techniques or are not freely available. In this study, an approach based on remote sensing satellite data for the mapping of impervious surface is evaluated on Munich, Bavaria for the year 2011 and 2017. With the use of very high spatially resolved WorldView-2 and WorldView-4 sensors as reference data, the Percentage of Impervious Surface (PIS) is estimated with a Support Vector Regression (SVR) for medium spatially resolved and freely available sensors Landsat 5, Landsat 8 and Sentinel 2. PIS calculation is done per-pixel on a spatial resolution of 30, 15 and 10 meters. Comparison is made for different inputs, which are the reflectance data of the sensor, Principal Component Analysis (PCA), spectral indices and combination of PCA and the Normalized Difference Vegetation Index (NDVI). Subsequently the needed amount of samples for an accurate result is analyzed, as well as the noise which results out of a randomized input sample selection. The results are compared to Copernicus’ imperviousness status maps and a municipal imperviousness study from 2011 by the city of Munich. Results show that PIS and extent of ISA can be predicted at 11.12% Mean Absolute Error (MAE) (16.15% Root Mean Square Error (RMSE)) for the city of Munich, with a slight loss in accuracy for higher spatial resolutions. Differences in accuracy related to spatial resolution are within 3% range in MAE and 6% RMSE for the same coherent Area Of Interest (AOI) as reference sample. A combination of PCA and the NDVI showed to deliver the most consistent results in the comparison. A sample size of 200 to 400 data points was preferred, given the amount of calculation time and reduced noise with more samples. The final comparison with the municipal imperviousness study for 2011 and Copernicus’ imperviousness maps for 2012 and 2015 showed MAE and RMSE values ranging from 14.23 to 19.92% and 20.64 to 29.33% for all sensors

Urban Trees – Detection, Delineation, Quantification, and Characterisation based on VHR Remote Sensing

Trees play a vital role in the urban ecosystem, providing benefits for society, ecology and economy. In particular in urban areas, trees mitigate the urban heat island effect, filter air pollution, regulate microclimate and hydrology, bond carbon dioxide, and provide spaces for recreation and leisure, among others. Despite these diverse positive effects, detailed information on the number, location, dimensions and other characteristics of urban trees remains scarce. For this reason, most cities in Germany currently aim to establish a tree information system for efficient and targeted management of their tree inventory. However, traditional terrestrial surveying is time-consuming and costly and therefore only suitable to a limited extent. In addition, the municipal tree cadastre usually only includes urban trees on public propertyand thus does not cover the complete stock. Against this background, remote sensing acquisitions with very-high spatial resolution (VHR) of less than one meter offer promising capabilities for area-wide detection, delineation, and characterization of urban trees. In this study, we use VHR aerial imagery as well as a derived canopy height model (CHM) for detection and delineation of urban trees. Different methods for individual tree detection using local maximum (LM) filtering andLaplacian of Gaussian (LoG) blob detectionare compared and evaluated. For tree crown delineation, marker-controlled watershed segmentation (MCWS), clustering using Voronoi tessellation, and region growing are implemented as segmentationtechniques. The detection of individual trees and delieation of tree crowns are validated against about1,000 reference trees from visual interpretation via stereophotogrammetry.In addition, we relate our results to street tree location data of Munich, which was derived from mobile terrestrial laser scanning (TLS).The characterization of urban trees is realized based on the 3-dimensional shape of individual tree segments as well as auxillary data sets of land use and building density. According to our analyses, there are 1.54 million trees in Munich.Compared to available reference trees, tree detection was evaluated with highest values of F-score, precision, and recall of 0.95, 0.99, and 0.94, respectively. Results of tree crown segmentation revealed an overall accuracy of 88.1 % compared to crowns of reference trees. Based on auxillary land use information, urban trees were categorized into street trees, (public) park trees, as well as trees in (private) residential gardens.In Munich, 9.1 % are characterized as street trees, 38.4 % are allocated in residential gardens and 33.1 % stand in public parks. The remaining 19.4 % oftree segments were found onother land use such as agricultural areas, parking lots, or along railroad tracks. According to these categories, the height and crown area of urban trees are analyzed and related to the distance to the city center. In a more general manner, this analysis was performed in relation to the building density in Munich. As expected, relatively few trees were found close to the city center and generally on areas with high building density. However, these areas are particularly associated with the greatest challenges in the context of sustainable and climate change-adapted urban development.In this study, we demonstrate that information derived from remote sensing contributes new spatial and quantitative knowledge on urban trees, providing the basis for sustainable management and informed decision-making in cities

Normalizing Sentinel-1 orbits for combined time series applications in forested areas

Forests are one of the largest above ground CO2 storage landcovers and therefore, as essential climate variables, an important asset to quantify and monitor. The current availability of more than 7 years of data from the Copernicus program is shifting analysis methods from single time steps to multitemporal and time-series studies with an unprecedented spatial and temporal resolution. In particular, the SAR sensor onboard of Sentinel-1 (S1) A and B enables the estimation of phenologically active phases within days and weeks [1], measure of seasonality of different forest types [2, 3, 5] or fallen trees [4] at regular interval through a weather and daylight independency. A high repetition rate is especially important in the detection of change points (beginning/end of growing season, or abrupt and permanent changes in land cover through, for example, logging). The possible resolution of changes in the observed area depends on the temporal sampling rate. S1 offers the possibility to increase the temporal sampling rate by using information from the twin satellites, reducing the repeat rate to 6 days over Europe. Additionally, overlapping orbits can be employed to increase data availability while including different viewing directions, resulting in one image roughly every 1.5 days. As recent examples of S1 time series studies, Soudani et al. [1] and Frison et al. [5] combined ascending and descending orbits for increased temporal sampling, relying on the fact that the sensor incidence angles of both orbits are similar throughout the study area. However, the combination of sensors and orbits as described above introduces systemic shifts in the data, if done without bias correction. We would like to highlight three mechanisms that result in such shifts

Potential of Sentinel-1 SAR to assess damage in drought-affected temperate deciduous broadleaf forests

Understanding forest decline under drought pressure is receiving research attention due to the increasing frequency of large-scale heat waves and massive tree mortality events. However, since assessing mortality on the ground is challenging and costly, this study explores the capability of satellite-borne Copernicus Sentinel-1 (S-1) C-band radar data for monitoring drought-induced tree canopy damage. As droughts cause water deficits in trees and eventually lead to early foliage loss, the S-1 radiometric signal and polarimetric indices are tested regarding their sensitivities to these effects, exemplified in a deciduous broadleaf forest. Due to the scattered nature of mortality in the study site, we employed a temporal-only time series filtering scheme that provides very high spatial resolution (10 m ×10 m) for measuring at the scale of single trees. Finally, the anomaly between heavily damaged and non-damaged tree canopy samples (n = 146 per class) was used to quantify the level of damage. With a maximum anomaly of −0.50 dB ± 1.38 for S-1 Span (VV+VH), a significant decline in hydrostructural scattering (moisture and geometry of scatterers as seen by SAR) was found in the second year after drought onset. By contrast, S-1 polarimetric indices (cross-ratio, RVI, Hα) showed limited capability in detecting drought effects. From our time series evaluation, we infer that damaged canopies exhibit both lower leaf-on and leaf-off backscatters compared to unaffected canopies. We further introduce an NDVI/Span hysteresis showing a lagged signal anomaly of Span behind NDVI (by ca. one year). This time-lagged correlation implies that SAR is able to add complementary information to optical remote sensing data for detecting drought damage due to its sensitivity to physiological and hydraulic tree canopy damage. Our study lays out the promising potential of SAR remote sensing information for drought impact assessment in deciduous broadleaf forests

The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets

The GHG-CCI project is one of several projects of the European Space Agency\u27s (ESA) Climate Change Initiative (CCI). The goal of the CCI is to generate and deliver data sets of various satellite-derived Essential Climate Variables (ECVs) in line with GCOS (Global Climate Observing System) requirements. The ECV Greenhouse Gases (ECV GHG) is the global distribution of important climate relevant gases - atmospheric CO2 and CH4 - with a quality sufficient to obtain information on regional CO2 and CH4 sources and sinks. Two satellite instruments deliver the main input data for GHG-CCI: SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT. The first order priority goal of GHG-CCI is the further development of retrieval algorithms for near-surface-sensitive column-averaged dry air mole fractions of CO2 and CH4, denoted XCO2 and XCH4, to meet the demanding user requirements. GHG-CCI focuses on four core data products: XCO2 from SCIAMACHY and TANSO and XCH4 from the same two sensors. For each of the four core data products at least two candidate retrieval algorithms have been independently further developed and the corresponding data products have been quality-assessed and inter-compared. This activity is referred to as Round Robin (RR) activity within the CCI. The main goal of the RR was to identify for each of the four core products which algorithms should be used to generate the Climate Research Data Package (CRDP). The CRDP will essentially be the first version of the ECV GHG. This manuscript gives an overview of the GHG-CCI RR and related activities. This comprises the establishment of the user requirements, the improvement of the candidate retrieval algorithms and comparisons with ground-based observations and models. The manuscript summarizes the final RR algorithm selection decision and its justification. Comparison with ground-based Total Carbon Column Observing Network (TCCON) data indicates that the breakthrough single measurement precision requirement has been met for SCIAMACHY and TANSO XCO2 (\u3c 3 ppm) and TANSO XCH4 (\u3c 17 ppb). The achieved relative accuracy for XCH4 is 3-15 ppb for SCIAMACHY and 2-8 ppb for TANSO depending on algorithm and time period. Meeting the 0.5 ppm systematic error requirement for XCO2 remains a challenge: approximately 1 ppm has been achieved at the validation sites but also larger differences have been found in regions remote from TCCON. More research is needed to identify the causes for the observed differences. In this context GHG-CCI suggests taking advantage of the ensemble of existing data products, for example, via the EnseMble Median Algorithm (EMMA)