Analysis of Digital Elevation Model and LNDSAT Data Using Geographic Information System for Soil Mapping in Urban Areas

Mohamed, Mohamed Ali

doi:10.4236/nr.2017.812047

Cited by 3 publications

(4 citation statements)

References 48 publications

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“…Adanya kesamaan tersebut menandakan ketiga metode ini berpotensi untuk diintegrasikan dalam rangka pengelolaan SDAL. Open data menyediakan beragam data dasar yang dapat diolah lebih lanjut untuk berbagai keperluan, misalnya citra satelit multi-spektral (Landsat, MODIS, CBERS, Sentinel), Synthetic Aperture Radar (SAR), Digital Elevation Model (DEM), jaringan jalan, batimetri, peta topografi (rupa bumi), polusi udara, kependudukan, kejahatan dan lain sebagainya (Samanta, et al, 2011;Dede, et al, 2017;Mohamed, 2017;. Di tangan pihak yang mampu melakukan beragam analisis konvensional dan spasial, open data dapat menghasilkan banyak data maupun informasi berharga untuk beragam keperluan pengambilan keputusan (Hu, et al, 2017;Rusli et al, 2014).…”

Section: Keterbatasan Integrasi Dan Solusinya Dalam Pengelolaan Sdalunclassified

INTEGRASI OPEN DATA, URUN DAYA, DAN PEMETAAN PARTISIPATIF DALAM PENGELOLAAN SUMBER DAYA ALAM DAN LINGKUNGAN HIDUP (Integration of Open Data, Crowdsourcing, and Participatory Mapping for Natural Resources and Environmental Management)

Dede

Mulyadi

Widiawaty

2020

J SIG

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Data and information availability area main obstacles in the natural resources and environmental (SDAL) management in developing countries. Technology development in society should be able to support SDAL management to be more efficient and effective while empowering the local human resources through open data, crowdsourcing, and participatory mapping. This article aims to explain the integration scheme of open data, crowdsourcing, and participatory mapping in SDAL management. The integration can be initiated by utilizing open data freely available through many institution websites as basic data and information. The essential shortcoming of open data in terms of accuracy and updating can be overcome through crowdsourcing. Voluntary involvement in collecting data and information is a key characteristic of crowdsourcing. The results of the crowdsourcing activities are collected online through a social media platform or organizer page. The quality of the data and information is very influenced by the socialization of organizers, assistive media installation, and devices used by society. More detailed data and information needs can be obtained through participatory mapping where the implementation must begin with technical training for society and parties involved. Open data, participatory mapping, and participatory mapping can be integrated using GIS or non-spatial analysis because of similarities such as a passion for sharing, community empowerment, giving more value to data and information, cost and time efficiency, and openness.

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Section: Keterbatasan Integrasi Dan Solusinya Dalam Pengelolaan Sdalunclassified

INTEGRASI OPEN DATA, URUN DAYA, DAN PEMETAAN PARTISIPATIF DALAM PENGELOLAAN SUMBER DAYA ALAM DAN LINGKUNGAN HIDUP (Integration of Open Data, Crowdsourcing, and Participatory Mapping for Natural Resources and Environmental Management)

Dede

Mulyadi

Widiawaty

2020

J SIG

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“…The processing and analysis of these spatial data according to specific rules allows for a quantitative prediction of the distribution of soil forms or soil types within landscapes [11,12]. There are several previous studies that dealt with the topic of digital mapping and survey of the soil [3,5,[13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…Digital elevation models (DEMs) have also been increasingly used in recent years in geomorphological and pedological research [25]. Thus, soil-landform classifications based on geomorphometric parameters (digital terrain data) derived from DEMs are important inputs to map soil and to predict soil characteristics [3,10,19,23,[26][27][28][29][30][31][32]. From a geomorphological terms of view, quantified geomorphometric parameters can be understood as a continuous numeric description of the topographical surfaces or landforms.…”

Section: Introductionmentioning

confidence: 99%

Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany

Mohamed

2020

Land

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In this study, a knowledge-based fuzzy classification method was used to classify possible soil-landforms in urban areas based on analysis of morphometric parameters (terrain attributes) derived from digital elevation models (DEMs). A case study in the city area of Berlin was used to compare two different resolution DEMs in terms of their potential to find a specific relationship between landforms, soil types and the suitability of these DEMs for soil mapping. Almost all the topographic parameters were obtained from high-resolution light detection and ranging (LiDAR)-DEM (1 m) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-DEM (30 m), which were used as thresholds for the classification of landforms in the selected study area with a total area of about 39.40 km2. The accuracy of both classifications was evaluated by comparing ground point samples as ground truth data with the classification results. The LiDAR-DEM based classification has shown promising results for classification of landforms into geomorphological (sub)categories in urban areas. This is indicated by an acceptable overall accuracy of 93%. While the classification based on ASTER-DEM showed an accuracy of 70%. The coarser ASTER-DEM based classification requires additional and more detailed information directly related to soil-forming factors to extract geomorphological parameters. The importance of using LiDAR-DEM classification was particularly evident when classifying landforms that have narrow spatial extent such as embankments and channel banks or when determining the general accuracy of landform boundaries such as crests and flat lands. However, this LiDAR-DEM classification has shown that there are categories of landforms that received a large proportion of the misclassifications such as terraced land and steep embankments in other parts of the study area due to the increased distance from the major rivers and the complex nature of these landforms. In contrast, the results of the ASTER-DEM based classification have shown that the ASTER-DEM cannot deal with small-scale spatial variation of soil and landforms due to the increasing human impacts on landscapes in urban areas. The application of the approach used to extract terrain parameters from the LiDAR-DEM and their use in classification of landforms has shown that it can support soil surveys that require a lot of time and resources for traditional soil mapping.

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“…Also, soil types in an area control the amount of water that can infiltrate into the soil which becomes as flow (Nicholls and Wong 1990). The soil distribution can be derived from the spectral characteristics of remotely sensed data based on its spectra (Boettinger et al 2008;Mohamed 2017). Several studies were conducted to map the soil distribution using IRS-1C, LISS III, and PAN data at 1:25,000 to 1:12,500 scales, which could be generated through the combination of these data (Kudrat et al 2000).…”

Section: Introductionmentioning

confidence: 99%

Flood hazard mapping using geospatial techniques and satellite images—a case study of coastal district of Tamil Nadu

Thirumurugan

Krishnaveni

2019

Environ Monit Assess

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In recent years, extreme rainfall events have resulted in several devastating floods especially in the east coast of Tamil Nadu, India. The main aim of this study is to identify the flood hazard zones in Cuddalore district by integrating spatial distribution of extreme rainfall events, slope, drainage density, soil, and geomorphology. Moderate Resolution Imaging Spectroradiometer satellite data was used to delineate the flood inundation area for the flood event which occurred in 2010 to validate the derived flood hazard zones by using geographical information system (GIS) and satellite images. The ground truth points were collected from the field to validate flood hazard zones, and it was identified that 92% of results were matched with hazard zones. Highly vulnerable blocks including Cuddalore, Kattumannarkoil, Keerapalayam, Kumaratchi, Kurinjipadi, Melbhunagiri, and Parangipettai were identified in Cuddalore district. From this study, it was also identified that nearly 45% of the total area of 3678 km 2 were inundated during the flood time. This present study will be a very useful tool and a resource for the policy planners and coastal planners to make effective decisions towards mitigation measures in floodprone areas in the coastal districts.

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Analysis of Digital Elevation Model and LNDSAT Data Using Geographic Information System for Soil Mapping in Urban Areas

Cited by 3 publications

References 48 publications

INTEGRASI OPEN DATA, URUN DAYA, DAN PEMETAAN PARTISIPATIF DALAM PENGELOLAAN SUMBER DAYA ALAM DAN LINGKUNGAN HIDUP (Integration of Open Data, Crowdsourcing, and Participatory Mapping for Natural Resources and Environmental Management)

INTEGRASI OPEN DATA, URUN DAYA, DAN PEMETAAN PARTISIPATIF DALAM PENGELOLAAN SUMBER DAYA ALAM DAN LINGKUNGAN HIDUP (Integration of Open Data, Crowdsourcing, and Participatory Mapping for Natural Resources and Environmental Management)

Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany

Flood hazard mapping using geospatial techniques and satellite images—a case study of coastal district of Tamil Nadu

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