Program documentation: B-average Bhattacharyya distance

The total Bhattacharyya distance is calculated using all N channels. The output of this subroutine is used to construct the B matrix using one or two Householder transformations. FORTRAN calling sequences are given

Feature combinations and the Bhattacharyya criterion

A procedure for calculating a kxn rank k matrix B for data compression using the Bhattacharyya bound on the probability of error and an iterative construction using Householder transformation was developed. Two sets of remotely sensed agricultural data are used to demonstrate the application of the procedure. The results of the applications gave some indication of the extent to which the Bhattacharyya bound on the probability of error is affected by such transformations for multivariate normal populations

The Spaces of Social Services as Social Infrastructure: Insights From a Policy‐Innovation Project in Milan

The spatial organisation of social services has long been residual for both urban planning and social welfare policies in Italian cities. This often results in randomly chosen locations and poor design arrangements, which ignore the role that space might play in fostering social life and inclusion. The scarce relevance given to the topic both in research and implementation is connected to the historical evolution of social services in the country and the scant resources devoted to their provision. Basing itself on the debate on welfare spaces and social infrastructures and drawing on a collaborative‐research experience within an experimental policy‐innovation project developed in Milan, this article tackles the role of space in social services provision following three directions. Firstly, it analyses how, at the urban level, welfare innovations and the interplay between urban planning and welfare policies might contribute to reshaping the traditional physical structures of social services and their map to favour more inclusive patterns of access to local welfare. Secondly, it investigates the role of social services as social infrastructures in increasing accessibility, reducing stigmatisation, and interpreting in a more inclusive way the complex public‐private partnerships that allow welfare implementation nowadays. Finally, it discusses how, in the face of contemporary trends in the activation of welfare spaces, traditional urban planning tools are challenged in monitoring their increasingly dynamic distribution in the city. This highlights the need to develop innovative urban planning strategies and tools to effectively support decision‐making and design

Water vapour absorption effects on solar radiation in an Apennine valley from hygrometric measurements of precipitable water taken at various altitudes

Hygrometric ratio measurements were simultaneously taken on six autumn clear-sky days of 1981 and 1982 by employing four Volz sun-photometers and the FISBAT sun-photometer at five stations located at different altitudes along the western slope of the Leo Valley, in the Apennines (Italy). Due to the solar heating of ground, intense upslope breezes forming during the early morning caused the vertical transport of more humid air from the bottom of the valley toward the ridge of the mountain chain. Precise calibration curves of the hygrometric ratio were defined on the basis of criteria suggested by the atmospheric infrared hygrometry technique and using the calibration constants found through an accurate intercomparison procedure. Examining the sun-photometric measurements by means of these calibration curves, precipitable water was determined at all stations, with the frequency of one measurement every 15 minutes from the early morning to one hour after noon. Daily homogeneous time-patterns of precipitable water were defined at the various stations, showing that this quantity varies appreciably during the morning at all stations, sometimes presenting daily increases of more than 40% at the lower stations. Average values of absolute humidity were then determined within the four atmospheric layers defined by the station altitudes, finding that the convective transport of humid air along the valley slopes can produce important variations within the atmospheric layer below the 1.6 km height. For these moisture conditions of the atmosphere, calculations of the time-variations caused by water vapour absorption in the downwelling flux Φ1 of global solar radiation reaching the ground were made at the various stations, as well as of those in the upwelling flux Φ of solar radiation at the top-level of the atmosphere. The results indicate that: i) flux Φ1 can appreciably decrease due to water vapour absorption, by 10 to 20 W m −2 at the highest station of Mt. Cimone and by 70 to 80 W m −2 at the lowest station situated on the bottom of the Leo Valley, and ii) the changes caused by water vapour absorption in the upwelling flux Φ were estimated to range usually between about 5 W m−2 at the Mt. Cimone station and more than 25 W m−2 at the lowest station. In particular, as a consequence of the time-variations in both precipitable water and solar elevation angle, the change ΔΦ caused by water vapour in the instantaneous outgoing flux of solar radiation at noon was found to increase almost linearly as a function of precipitable water throughout the range from 0.8 to 1.8 g cm−2, with an average slope coefficient equal to 12.5 W m−2 per unit variation of precipitable water

Evaluation of the achievement development program of the dki jakarta province softball sports

This research aims to obtain facts, data, and information about the results of the evaluation of the achievement coaching program for the women's softball sports of DKI Jakarta Province. This study used evaluative methods with the CIPP model. Data collection techniques use questionnaires, documentation, observations, and interviews. The subjects of this study are The Core Board, Coach, and Athlete. The results revealed that the context component of the Pengprov Perbasasi DKI Jakarta already has a long-term, medium, and short-term plan. However, the DKI Jakarta Provincial Women's Softball Team was only able to achieve the goal of the achievement coaching program at the 2018 Softball National Tournament. The input components, facilities, equipment, and equipment provided are enough to meet the needs of the DKI Jakarta Provincial Women's Softball Team. However, the need for supplements and vitamins for new athletes ahead of the implementation of PON. In the process component, the coaching staff implements a walking selection system and promotes and degrades athletes based on batting average data, left on base, running base in, accumulation of errors, & success in sacrifice. The training process was carried out face-to-face and virtually during the Covid-19 pandemic. In the product component, the results obtained by the DKI Jakarta Provincial Women's Softball Team during the period 2018 to 2022 were finalists in the 2018 Softball National Tournament. However, in the 2019 PON Qualification and PON XX Papua in 2021, the DKI Jakarta Provincial Women's Softball Team was only able to rank fift

LIDAR DERIVED SALT MARSH TOPOGRAPHY AND BIOMASS: DEFINING ACCURACY AND SPATIAL PATTERNS OF UNCERTAINTY

As valuable and vulnerable blue carbon ecosystems, salt marshes require adaptable and robust monitoring methods that span a range of spatiotemporal scales. The application of unmanned aerial vehicle (UAV) based remote sensing is a key tool in achieving this goal. Due to the particular characteristics of tidal wetlands, however, there are challenges in obtaining research and management relevant data with the requisite level of accuracy. In this study, the spatial patterns in uncertainty stemming from scan angle, binning method, vegetation structure and platform surface morphology are examined in the context of UAV light detection and ranging (LiDAR) derived digital elevation models (DEM). The results demonstrate that overlapping the UAV flight paths sufficiently to avoid sole reliance on LIDAR data with scan angles exceeding 15 degrees is advisable. Furthermore, the spatial arrangement of halophyte species and marsh morphology has a clear influence on DEM accuracy. The largest errors were associated with sudden structural transitions at the marsh channel boundaries. The DEMmean was found to be the most accurate for bare ground, while the DEMmin was the most accurate for channels and the middle to high marsh vegetation (MAEs = −0.01m). For the low to middle vegetation, all the trialled DEMs returned a similar magnitude of mean error (MAE = ± 0.03m). The accuracy difference between the two vegetation associations examined appears to be connected to variations in coverage, height and biomass. Overall, these findings reinforce the link between salt marsh biogeomorphic complexity and the spatial distribution and magnitude of LiDAR DEM erro

LiDAR mapping of tidal marshes for ecogeomorphological modelling in the TIDE project

The European research project TIDE (Tidal Inlets Dynamics and Environment) is developing and validating coupled models describing the morphological, biological and ecological evolution of tidal environments. The interactions between the physical and biological processes occurring in these regions requires that the system be studied as a whole rather than as separate parts. Extensive use of remote sensing including LiDAR is being made to provide validation data for the modelling. This paper describes the different uses of LiDAR within the project and their relevance to the TIDE science objectives. LiDAR data have been acquired from three different environments, the Venice Lagoon in Italy, Morecambe Bay in England, and the Eden estuary in Scotland. LiDAR accuracy at each site has been evaluated using ground reference data acquired with differential GPS. A semi-automatic technique has been developed to extract tidal channel networks from LiDAR data either used alone or fused with aerial photography. While the resulting networks may require some correction, the procedure does allow network extraction over large areas using objective criteria and reduces fieldwork requirements. The networks extracted may subsequently be used in geomorphological analyses, for example to describe the drainage patterns induced by networks and to examine the rate of change of networks. Estimation of the heights of the low and sparse vegetation on marshes is being investigated by analysis of the statistical distribution of the measured LiDAR heights. Species having different mean heights may be separated using the first-order moments of the height distribution

Toward coherent space-time mapping of seagrass cover from satellite data: An example of a Mediterranean lagoon

Seagrass meadows are a highly productive and economically important shallow coastal habitat. Their sensitivity to natural and anthropogenic disturbances, combined with their importance for local biodiversity, carbon stocks, and sediment dynamics, motivate a frequent monitoring of their distribution. However, generating time series of seagrass cover from field observations is costly, and mapping methods based on remote sensing require restrictive conditions on seabed visibility, limiting the frequency of observations. In this contribution, we examine the effect of accounting for environmental factors, such as the bathymetry and median grain size (D50) of the substrate as well as the coordinates of known seagrass patches, on the performance of a random forest (RF) classifier used to determine seagrass cover. Using 148 Landsat images of the Venice Lagoon (Italy) between 1999 and 2020, we trained an RF classifier with only spectral features from Landsat images and seagrass surveys from 2002 and 2017. Then, by adding the features above and applying a time-based correction to predictions, we created multiple RF models with different feature combinations. We tested the quality of the resulting seagrass cover predictions from each model against field surveys, showing that bathymetry, D50, and coordinates of known patches exert an influence that is dependent on the training Landsat image and seagrass survey chosen. In models trained on a survey from 2017, where using only spectral features causes predictions to overestimate seagrass surface area, no significant change in model performance was observed. Conversely, in models trained on a survey from 2002, the addition of the out-of-image features and particularly coordinates of known vegetated patches greatly improves the predictive capacity of the model, while still allowing the detection of seagrass beds absent in the reference field survey. Applying a time-based correction eliminates small temporal variations in predictions, improving predictions that performed well before correction. We conclude that accounting for the coordinates of known seagrass patches, together with applying a time-based correction, has the most potential to produce reliable frequent predictions of seagrass cover. While this case study alone is insufficient to explain how geographic location information influences the classification process, we suggest that it is linked to the inherent spatial auto-correlation of seagrass meadow distribution. In the interest of improving remote-sensing classification and particularly to develop our capacity to map vegetation across time, we identify this phenomenon as warranting further research