Full Day School Management at SMA As Safi'iyah Medan

This study aims to determine Full Day School Management in SMA As Syafi’iyah Medan, Jl. Karya Wisata Ii No.1,Medan Johor, Medan, North Sumatra Province. This research is a qualitative popoulasi study of the Full Day School teacher and management and students of SMA As Syafi’iyah Medan. In an effort to improve student character education in MAN 2, the terrain model includes stages, namely: planning Full Day School learning, implementing Full Day School learning and evaluating Full Day School learning. Learning planning is adapted to the curriculum adopted by SMA As Syafi’iyah Medan, namely the government curriculum, local curriculum and school curriculum and the formulation of syllabus and plan for implementing learning. The implementation of Full Day School learning consists of habituation activities, exemplary activities, nationalism and patriotism activities and student creativity activities. Evaluation Full Day School learning in general in Medan Model 2 MAN 2 in determining minimal completeness provides an assessment of three domains, namely cognitive, affective and domain psychomotor.

Retrieval of DTM under Complex Forest Stand Based on Spaceborne LiDAR Fusion Photon Correction

The new generation of satellite-borne laser radar Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data has been successfully used for ground information acquisition. However, when dealing with complex terrain and dense vegetation cover, the accuracy of the extracted understory Digital Terrain Model (DTM) is limited. Therefore, this paper proposes a photon correction data processing method based on ICESat-2 to improve the DTM inversion accuracy in complex terrain and high forest coverage areas. The correction value is first extracted based on the ALOS PALSAR DEM reference data to correct the cross-track photon data of ICESat-2. The slope filter threshold is then selected from the reference data, and the extracted possible ground photons are slope filtered to obtain accurate ground photons. Finally, the impacts of cross-track photon and slope filtering on fine ground extraction from the ICESat-2 data are discussed. The results show that the proposed photon correction and slope filtering algorithms help to improve the extraction accuracy of forest DTM in complex terrain areas. Compared with the forest DTM extracted without the photon correction and slope filtering methods, the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) are reduced by 51.90~57.82% and 49.37~53.55%, respectively. To the best of our knowledge, this is the first study demonstrating that photon correction can improve the terrain inversion ability of ICESat-2, while providing a novel method for ground extraction based on ICESat-2 data. It provides a theoretical basis for the accurate inversion of canopy parameters for ICESat-2.

How Can Remote Sensing Reduce Required Human Intervention in Robotic Forest Regeneration

In this article, we introduce an alternative solution for forest regeneration based on unmanned ground vehicles (UGV) and describe requirements for external data, which could significantly increase the level of automation. Over the past few decades, the global forested area has decreased, and there is a great need to restore and regenerate forests. Challenges such as the lack of labor and high costs demand innovative approaches for forest regeneration. Mechanization has shown satisfactory results in terms of time-efficient planting, although its usage is limited by high operational costs. Innovative technologies must be cost-efficient and profitable for large scale usage. Automation could make mechanized forest regeneration feasible. Forest regeneration operations can be automated using a purpose built unmanned platform. We developed a concept to automate forest planting operations based on mobility platform. The system requires external data for efficient mobility in clear-cut areas. We developed requirements for external data, analyzed available solutions, and experimented with the most promising option, the SfM (structure from motion) technique. Earth observation data are useful in the planning phase. A DEM (digital terrain model) for UGV planter operations can be constructed using ALS (airborne laser scanning), although it may be restricted by the cost. Low-altitude flights by drones equipped with digital cameras or lightweight laser scanners provided a usable model of the terrain. This model was precise (3–20 cm) enough for manually planning of the trajectory for the planting operation. This technique fulfilled the system requirements, although it requires further development and will have to be automated for operational use.

URBAN FLOOD IMPACTS, FLOOD WATER QUALITY AND RISK MAPPING OF OLODO AREA, IBADAN, NIGERIA

This study assessed urban flood impact, flood water quality and vulnerability around Olodo area of Ibadan region, Nigeria. The study employed remote sensing and GIS techniques in creating vulnerability and risk maps. Digital terrain model (DTM) was used to get the topography of the study area. Footprints of buildings along the Egberi riverbank and flood plain in Olodo were created in the GIS environment from high resolution satellite imagery. Buffering operation was conducted to classify the buildings into risk zones based on closeness to the riverbank using ArcGIS 10.0. The study revealed that 326 buildings were within the very vulnerable and vulnerable zones because they were less than 15.2m away from the riverbank. The characteristics of water quality change during the flood and non-flood periods. TSS, DO, NOD, and COD were all higher during the flood event. Microbial analysis showed that water quality levels in the floodwater exceeded water quality standards (e.g., the coliform excess from 10 to 10,000 times), and thus this may be a health risk for local people during flood events. Concentration of Escherichia coli (E. coli) ranged from 484 to 1290 cfu/100 mL during flooding compared to 192 to 295 cfu/100 mL after flood. Salmonella was found to be high ranging from 659 to 1840 cfu/100 mL during flooding compared to 530 to 1034 cfu/100 mL after flooding.

Možnosti srovnávací analýzy plužiny zaniklých středověkých vsí. Vypovídací hodnota vybraných lokalit a role digitálního modelu reliéfu z dat leteckého laserového skenování

The paper deals with the plowlands of deserted medieval villages (DMVs) representing a specific data source of medieval settlement research. Its basic priorities are based on the needs of archaeological heritage protection for a better definition of DMVs’ hinterlands, which are significantly less distinguishable in comparison with villages’ intravilans. At the same time, not much attention was paid to this area, even in known or well-surveyed sites. These issues are important especially in the context of what exactly we are looking for within the DMVs, how we define it and where we can find the best examples worthy of protection or further study. The basis of the presented work is the processing of a digital terrain model derived from airborne laser scanning data. The primary procedure consists of the ALS data processing into a DEM, its subsequent visualization, and classification of objects in DMVs’ hinterlands, which is further supplemented by selected examples of field verification. The informative value of the hinterlands is also discussed on the example of several differently preserved sites.

Nově objevené hradiště Čerťák u obce Sovolusky v kontextu halštatského osídlení regionu

This paper presents a new hillfort site which is situated on top of „Čerťák“ Hill (651 m n. m.), Sovolusky municipality, Karlovy Vary district. It has been identified with the help of a digital terrain model based on Airborne Laser Scanning (LiDAR). Two separate lines of stone ramparts have been confirmed on top of the Čerťák Hill, formed by a significant right bank meander in the upper course of the river Střela. The inner area reaches 1.4 ha and the external enclosed area spreads to 2.3 ha. Subsequent field research yielded a collection of more than 500 pottery fragments from the Late Hallstatt period. The dispersion of finds shows relatively intensive settlement. The paper also discusses other sites in the surrounding region which date to the same period. The Hallstatt settlement seems to have been a structurally connected complex in the presented area.

Application of Supervised Machine Learning Technique on LiDAR Data for Monitoring Coastal Land Evolution

Machine Learning (ML) techniques are now being used very successfully in predicting and supporting decisions in multiple areas such as environmental issues and land management. These techniques have also provided promising results in the field of natural hazard assessment and risk mapping. The aim of this work is to apply the Supervised ML technique to train a model able to classify a particular gravity-driven coastal hillslope geomorphic model (slope-over-wall) involving most of the soft rocks of Cilento (southern Italy). To train the model, only geometric data have been used, namely morphometric feature maps computed on a Digital Terrain Model (DTM) derived from Light Detection and Ranging (LiDAR) data. Morphometric maps were computed using third-order polynomials, so as to obtain products that best describe landforms. Not all morphometric parameters from literature were used to train the model, the most significant ones were chosen by applying the Neighborhood Component Analysis (NCA) method. Different models were trained and the main indicators derived from the confusion matrices were compared. The best results were obtained using the Weighted k-NN model (accuracy score = 75%). Analysis of the Receiver Operating Characteristic (ROC) curves also shows that the discriminating capacity of the test reached percentages higher than 95%. The model, resulting more accurate in the training area, will be extended to similar areas along the Tyrrhenian coastal land.

Multiscale analysis of surface roughness for the improvement of natural hazard modelling

Surface roughness influences the release of avalanches and the dynamics of rockfall, avalanches and debris flow, but it is often not objectively implemented in natural hazard modelling. For two study areas, a treeline ecotone and a windthrow-disturbed forest landscape of the European Alps, we tested seven roughness algorithms using a photogrammetric digital surface model (DSM) with different resolutions (0.1, 0.5 and 1 m) and different moving-window areas (9, 25 and 49 m2). The vector ruggedness measure roughness algorithm performed best overall in distinguishing between roughness categories relevant for natural hazard modelling (including shrub forest, high forest, windthrow, snow and rocky land cover). The results with 1 m resolution were found to be suitable to distinguish between the roughness categories of interest, and the performance did not increase with higher resolution. In order to improve the roughness calculation along the hazard flow direction, we tested a directional roughness approach that improved the reliability of the surface roughness computation in channelised paths. We simulated avalanches on different elevation models (lidar-based) to observe a potential influence of a DSM and a digital terrain model (DTM) using the simulation tool Rapid Mass Movement Simulation (RAMMS). In this way, we accounted for the surface roughness based on a DSM instead of a DTM, which resulted in shorter simulated avalanche runouts by 16 %–27 % in the two study areas. Surface roughness above a treeline, which in comparison to the forest is not represented within the RAMMS, is therefore underestimated. We conclude that using DSM-based surface roughness in combination with DTM-based surface roughness and considering the directional roughness is promising for achieving better assessment of terrain in an alpine landscape, which might improve the natural hazard modelling.

The Geological History of the Chang’e-5 Sample Return Region

Chang’e-5 (CE-5), China’s first sample-return mission, has successfully landed in Oceanus Procellarum near Mons Rümker. It is important to have a detailed study of the geological evolution of the CE-5 sample return region. This work aims to study the geological background, topography, geomorphology, major chemical composition, mineralogy, and chronology of the landing site region. First, we used the map of topography obtained by the Kaguya TC merged Digital Terrain Model (DTM) to analyze the topographic characteristics. Then, we used the Kaguya Multiband Imager (MI) reflectance data to derive FeO and TiO2 abundance and the hyperspectral data of the Moon Mineralogy Mapper (M3) onboard the Chandrayaan-1 spacecraft to study the mineralogy of the landing site region. Later, we defined and dated the geological units of the landing area using the crater size–frequency distribution (CSFD) method. Finally, we conducted a detailed analysis of the volcanism and tectonism that occurred in the CE-5 landing area. The study region has experienced multi-stage magmatic activities (~3.36 Ga to ~1.22 Ga) and formed multiple mare units with different chemical and mineral compositions. The relationship between the wrinkle ridges cut by small impact craters suggests that the U7/Em5 has experienced Copernican aged tectonism recently ~320 Ma. The U7/Em5 unit where the Chang’e-5 sample return mission landed is dominantly composed of mature pyroxene and the basalts are mainly high-iron and mid-titanium basalts. Additionally, the analysis of pure basalt in the U7/Em5 suggests that the samples returned by the CE-5 mission may contain the ejecta and ray materials of young craters, including sharp B, Harding, Copernicus, and Aristarchus.