scholarly journals Drone technology for identification of healing forest spot at Kampung Cisamaya Mount Ciremai National Park

2021 ◽  
Vol 918 (1) ◽  
pp. 012040
Author(s):  
H Ramdan

Abstract Reconnecting people to nature through healing activities in the forest ecosystem is important. Various studies have shown that forest ecosystems dominated by tree vegetation have positive impacts on physical and psychological health. Not all locations in the forest ecosystem are suitable for healing forests (HF), but their suitability should be identified. Land slope, vegetation density, and easiness access to the site are some physical parameters of the land which are indicators for the development of HF site. Identification of suitable HF spots can be identified using drone technology and GIS. The research objective was the use of drones equipment in identifying potential sites for HF activities. The research site was Kampung Cisamaya in Mount Ciremai National Park. The type of drone used was the Phantom 4 Pro Obsidian equipped with a 20-megapixel RGB camera. The stages of research activities were data acquisition, processing, and analyzing from drone spatial data. Vegetation density was determined through GRVI (Green-Red Vegetation Index), while drone data analyzed the slope classification by DTM (Digital Terrain Model). The accessibility to the location was analyzed through data from the spatial map of the Kuningan Regency. The results found that the use of drones was effective in evaluating the suitability spots for HF activities. From this study can be concluded that the Cisamaya area was suitable for the development of HF activities due to physical parameters of flat to gentle slopes (0-15%), having dense vegetation, as well as the easiness access by people.

Author(s):  
J.-S. Lai ◽  
F. Tsai ◽  
S.-H. Chiang

This study implements a data mining-based algorithm, the random forests classifier, with geo-spatial data to construct a regional and rainfall-induced landslide susceptibility model. The developed model also takes account of landslide regions (source, non-occurrence and run-out signatures) from the original landslide inventory in order to increase the reliability of the susceptibility modelling. A total of ten causative factors were collected and used in this study, including aspect, curvature, elevation, slope, faults, geology, NDVI (Normalized Difference Vegetation Index), rivers, roads and soil data. Consequently, this study transforms the landslide inventory and vector-based causative factors into the pixel-based format in order to overlay with other raster data for constructing the random forests based model. This study also uses original and edited topographic data in the analysis to understand their impacts to the susceptibility modeling. Experimental results demonstrate that after identifying the run-out signatures, the overall accuracy and Kappa coefficient have been reached to be become more than 85 % and 0.8, respectively. In addition, correcting unreasonable topographic feature of the digital terrain model also produces more reliable modelling results.


2017 ◽  
Vol 21 (4) ◽  
pp. 197-204
Author(s):  
Maciej Góraj ◽  
Marcin Kucharski ◽  
Krzysztof Karsznia ◽  
Izabela Karsznia ◽  
Jarosław Chormański

AbstractThe main objective of this study is to evaluate the changes in the hydrographic network of Słowiński National Park. The authors analysed the changes occurring in the drainage network due to limited maintenance in this legally protected natural area. To accomplish this task, elaborations prepared on the basis of aerial photographs were used: an orthophoto map from 1996, hyperspectral imaging from June 2015, and a digital terrain model based on airborne laser scanning (ALS) from June 2015. These spatial data resources enabled the digitisation of the water courses for which selected hydro-morphological features had been defined. As a result of analysing the differences of these features, a quality map was elaborated which was then subjected to interpretation, and the identified changes were quantified in detail.


Author(s):  
J.-S. Lai ◽  
F. Tsai ◽  
S.-H. Chiang

This study implements a data mining-based algorithm, the random forests classifier, with geo-spatial data to construct a regional and rainfall-induced landslide susceptibility model. The developed model also takes account of landslide regions (source, non-occurrence and run-out signatures) from the original landslide inventory in order to increase the reliability of the susceptibility modelling. A total of ten causative factors were collected and used in this study, including aspect, curvature, elevation, slope, faults, geology, NDVI (Normalized Difference Vegetation Index), rivers, roads and soil data. Consequently, this study transforms the landslide inventory and vector-based causative factors into the pixel-based format in order to overlay with other raster data for constructing the random forests based model. This study also uses original and edited topographic data in the analysis to understand their impacts to the susceptibility modeling. Experimental results demonstrate that after identifying the run-out signatures, the overall accuracy and Kappa coefficient have been reached to be become more than 85 % and 0.8, respectively. In addition, correcting unreasonable topographic feature of the digital terrain model also produces more reliable modelling results.


2008 ◽  
Vol 1 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Kateřina Jačková ◽  
Dušan Romportl

The Relationship Between Geodiversity and Habitat Richness in Šumava National Park and Křivoklátsko PLA (Czech Republic): A Quantitative Analysis Approach This paper focuses on the development of a quantitative method for evaluating the relationship between abiotic heterogeneity and habitat richness at the landscape level. The study took place in the Křivoklátsko protected landscape area and Šumava national park (Czech Republic). Our initial hypothesis was that habitat richness should be high in areas with high abiotic heterogeneity, and vice versa. GIS vector layers of habitat were used for the formulation of habitat richness. A geological layer, a digital terrain model and hydrographic layers were used to determine abiotic heterogeneity. The study areas were overlain by a grid square and habitat richness and abiotic heterogeneity were assessed in each study cell. The data obtained were used in a statistical model (multiple spatial linear regression, with maximum credibility). The results of the statistical model indicated a significant influence of abiotic heterogeneity on habitat richness.


2012 ◽  
Vol 92 (4) ◽  
pp. 51-62
Author(s):  
Ivana Badnjarevic ◽  
Miro Govedarica ◽  
Dusan Jovanovic ◽  
Vladimir Pajic ◽  
Aleksandar Ristic

This research aims to describe the analysis of geoinformation technologies and systems and its usage in detection of terrain slope with reference to timely detection and mapping sites with a high risk of slope movement and activation of landslides. Special attention is referred to the remote sensing technology and data acquisition. In addition to acquisition, data processing is performed: the production of digital terrain model, calculating of the vegetation index NDVI (Normalized Difference Vegetation Index) based on satellite image and analyses of pedology maps. The procedures of processing the satellite images in order to identify locations of high risk of slope processes are described. Several factors and identifiers are analyzed and used as input values in automatic processing which is performed through a unique algorithm. Research results are presented in raster format. The direction of further research is briefly defined.


Author(s):  
В.К. Каличкин ◽  
Р.А. Корякин ◽  
К.Ю. Максимович ◽  
Р.Р. Галимов ◽  
Н.А. Чернецкая

Рассмотрен процесс создания последовательностей при описании предметных областей на формально-логическом языке UML. Использование последовательностей основано на понятии «источник данных», введённом авторами на основе предыдущего этапа концептуализации предметной области «агроэкологические свойства земель» – диаграммы классов. В классе начала связи выбирается один из комплектов атрибутов, в классе конца связи – один из методов (запрос), соответствующий этому комплекту. Многократно применяя этот подход при различных значениях атрибутов центрального класса, получается массив данных (в том числе пространственных). Атрибуты являются связующим звеном между создаваемой моделью, методами, потоками данных и запросов системы, так как, с одной стороны, они входят в состав классов, участвующих в сценариях диаграмм последовательностей, а с другой – принадлежат к внешней оболочке модели. На примерах движения информации, необходимой для расчетов гидротермического коэффициента Селянинова и степени проявления эрозии для рабочего участка, построены диаграммы последовательностей «ГидротермическийКоэффициент» и «СтепеньПроявленияЭрозии». Данные для диаграмм последовательностей формируются с помощью геоинформационных систем (географические координаты рабочего участка, цифровая модель рельефа) и справочно-информационного портала «Погода и климат». Предлагаемый подход даёт возможность автоматического построения баз знаний на основе двух концептуальных понятий: «источники данных» и «последовательности». Структурирование и формализация знаний позволяет осуществить переход от набора информации к знаниям и последующему их графическому отображению. Визуализация помогает наглядно отобразить связи между классами, которые могут быть не очевидны. Становится доступной возможность последующей оценки жизнеспособности модели, ее проектирования в симбиозе с использованием инструментов для имитационного моделирования, а также математических методов анализа и обработки информации. Данные диаграммы используются для построения и верификации созданных подсистем в процессе прямого и обратного проектирования аграрной интеллектуальной системы. The process of creating sequences while describing subdicipline in the formal-logical language UML is considered. The sequences usage is based on the concept of a "data source". It was deduced by the authors on the basis of the previous step of subdicipline conceptualization «agroecological lands properties» - class diagrams. In the beginning link's class, one of the attribute set is selected, in the ending class - one of the adequate to this set methods (query). The result of repeated application this approach, with different values of the attributes of the central class, is a database (including spatial data). Attributes mediate the created model, methods, data streams and system requests, as, on the one hand, they are among the classes involved in sequence diagrams scripting, and on the other - belong to the outer shell of the model. Sequences diagrams were constructed by the examples of the information flow necessary for calculating the Selyaninov hydrothermal index and the degree of erosion for the working land area. These diagrams are "HydrothermalIndexQuery" and "ErosionDegreeQuery". Data for sequence diagrams is generated by Geological Information System (geographic coordinates of the working land area, digital terrain model) and the reference-information gateway “Weather and Climate". The proposed approach makes it possible to build knowledge bases with the scope of two concepts: "data sources" and "sequence" automatically. Knowledge structuralizasion and formalization allows produce a shift from collecting information to knowledge and its subsequent graphical image. Visualization helps to demonstrably provide insight into classes' connections that may occur not to be obvious. The possibility of subsequent estimate of model consistency, its creation process using simulation modeling tools, as well as mathematical analysis methods and processing of data becomes more accessible. Diagrams' data is used for sybsystem construction and verification. These parts of a whole system were created in the process of forward and reverse engineering agricultural intelligence system.


Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 433 ◽  
Author(s):  
Maleika Wojciech

The paper presents an optimized method of digital terrain model (DTM) estimation based on modified kriging interpolation. Many methods are used for digital terrain model creation; the most popular methods are: inverse distance weighing, nearest neighbour, moving average, and kriging. The latter is often considered to be one of the best methods for interpolation of non-uniform spatial data, but the good results with respect to model’s accuracy come at the price of very long computational time. In this study, the optimization of the kriging method was performed for the purpose of seabed DTM creation based on millions of measurement points obtained from a multibeam echosounder device (MBES). The purpose of the optimization was to significantly decrease computation time, while maintaining the highest possible accuracy of created model. Several variants of kriging method were analysed (depending on search radius, minimum of required points, fixed number of points, and used smoothing method). The analysis resulted in a proposed optimization of the kriging method, utilizing a new technique of neighbouring points selection throughout the interpolation process (named “growing radius”). Experimental results proved the new kriging method to have significant advantages when applied to DTM estimation.


2014 ◽  
Vol 1 (1) ◽  
pp. 52-69
Author(s):  
S.O. Ogedegbe

This study examines the effectiveness and accuracy of SPOT-5 and ASTER LiDAR data satellite images, Global Pos1t1on1ng System (GPS), Digital Terrain Model (DTM), and Geographic Information System (GIS) in carrying out a revision of Nigerian topographic maps at the scale of 1:50,000. The data for the study were collected by extraction of relevant spatial data from the 1964 topographic map, delineation and interpretation of 2009 SPOT-5 data, and field surveys. The landscape changes extracted from SPOT- 5 were used to update the topographic base map and to determine the nature and direction of changes that have taken place in the study area. The findings revealed that changes have occurred in both cultural and relief features over time. The coefficient of correlation and t-test was calculated to show that changes in point, linear and areal features are significant. Also significant were the planh11etric and height accuracies of the revised map. The study shows that satellite data especially SPOT-5 is useful for the revision of topographic maps at scales of 1:50,000 and even larger. And, high-resolution remote sensing at Sm and ASTER data (30m) with GPS (±1.9m) can be used to c.reate a digital elevation model (DEM) on the map which is an essential dataset for complete revision. Cette étude examine l'efficacité et la précision des images satellites de données SPOT-5 et ASTER LiDAR, du système de positionnement global (GPS), du modèle numérique de terrain (MNT) et du système d'information géographique (SIG) pour effectuer une révision des cartes topographiques nigérianes au échelle de 1:50 000. Les données de l'étude ont été recueillies par extraction de données spatiales pertinentes à partir de la carte topographique de 1964, délimitation et interprétation des données SPOT-5 de 2009 et relevés de terrain. Les changements de paysage extraits de SPOT-5 ont été utilisés pour mettre à jour le fond de carte topographique et pour déterminer la nature et la direction des changements qui ont eu lieu dans la zone d'étude. Les résultats ont révélé que des changements se sont produits dans les caractéristiques culturelles et du relief au fil du temps. Le coefficient de corrélation et le test t ont été calculés pour montrer que les changements dans les caractéristiques ponctuelles, linéaires et aréales sont significatifs. Les précisions planimétriques et altimétriques de la carte révisée étaient également importantes. L'étude montre que les données satellitaires, en particulier SPOT-5, sont utiles pour la révision des cartes topographiques à des échelles de 1:50 000 et même plus. De plus, la télédétection haute résolution aux données Sm et ASTER (30 m) avec GPS (± 1,9 m) peut être utilisée pour créer un modèle d'élévation numérique (DEM) sur la carte qui est un ensemble de données essentiel pour une révision complète.


2021 ◽  
Vol 13 (14) ◽  
pp. 7969
Author(s):  
Grzegorz Budzik ◽  
Piotr Krajewski

In an era of significant growth in the availability of spatial data and continued advances in computing technologies, opportunities for new interpretations and solutions to the landscape research problems posed worldwide are emerging. This paper presents different possibilities of applying digital terrain model (DTM) data in research of various aspects of landscape. For this purpose, two different methods were proposed. The first was to identify a set of components of the Jelenia Góra city landscape character on the basis of the topographic position index and spatial distribution of land cover, while the second was to assess the landscape of Jelenia Góra city in terms of the possibility of adopting new elements, using the author’s scenic absorptivity method. The results indicate the structure of the components of the landscape character of Jelenia Góra city together with its spatial distribution, which also allowed for the delineation of landscape units. The scenic absorptivity analysis showed that there are isolated areas within Jelenia Góra city that are capable of accommodating significant size elements that would not adversely affect the city landscape. In conclusion, DTM data are able to significantly improve research methods in landscape studies.


2018 ◽  
Vol 8 (2) ◽  
pp. 59-64
Author(s):  
Iuliana Maria Pârvu ◽  
F. Remondino ◽  
E. Ozdemir

Abstract The VOLTA project is a RISE Marie-Curie action designed to realize Research & Innovation (R&I) among intersectoral partners to exchange knowledge, methods and workflows in the geospatial field. To accomplish its objectives, the main R&I activities of VOLTA are divided in four interlinked Work Packages with two transversal ones responsible for knowledge transfer & training as well as dissemination of the project results. The research activities and knowledge transfer are performed with a series of secondments between partners. The consortium is composed of 13 partners from academic & research institutions, industrial partners and national mapping agencies. The Romanian National Center of Cartography is part of this research project and in this article the achievements of the secondment at Bruno Kessler Foundation in Trento (Italy) are given. The main goal of the exchange was to generate level of detail - LOD2 building models in an automated manner from photogrammetric point clouds and without any ancillary data. To benchmark existing commercial solutions for the realization of LOD2 building models, we tested Building Reconstruction. This program generates LOD2 models starting from building footprints, digital terrain model (DTM) and digital surface model (DSM). The presented work examined a research and a commercial-based approach to reconstruct LOD2 building models from point clouds. The full paper will report all technical details of the work with insight analyses and comparisons.


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