Accuracy Assessment of Alos W3d30, Aster Gdem and Srtm30 Dem: A Case Study of Nigeria, West Africa

Apeh, Ojima Isaac; Uzodinma, Victus N.; Ebinne, ES; Moka, E.C.; Onah, E. U.

doi:10.4236/jgis.2019.112009

Cited by 15 publications

(6 citation statements)

References 32 publications

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“…Later, Google Earth images were also used for overcoming the drawbacks of the open-access global DEMs (AW3D30 and ASTER) acquired in different years. About 40 permanent building structures with no change over time were selected for comparing DSM values to overcome the difficulty of rapidly growing cities with temporal changes in building height [60,61]. Figure 4 illustrates a simplified flowchart of the methodology and processing of various data sources.…”

Section: Reference Datamentioning

confidence: 99%

Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

et al. 2020

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Spatial urban growth and its impact on land surface temperature (LST) is a high priority environmental issue for urban policy. Although the impact of horizontal spatial growth of cities on LST is well studied, the impact of the vertical spatial distribution of buildings on LST is under-investigated. This is particularly true for cities in sub-tropical developing countries. In this study, TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-XDEM), Advanced Spaceborne Thermal Emission and Reflection (ASTER)-Global Digital Elevation Model (GDEM), and ALOS World 3D-30m (AW3D30) based Digital Surface Model (DSM) data were used to investigate the vertical growth of the Dhaka Metropolitan Area (DMA) in Bangladesh. Thermal Infrared (TIR) data (10.6-11.2µm) of Landsat-8 were used to investigate the seasonal variations in LST. Thereafter, the impact of horizontal and vertical spatial growth on LST was studied. The result showed that: (a) TanDEM-X DSM derived building height had a higher accuracy as compared to other existing DSM that reveals mean building height of the Dhaka city is approximately 10 m, (b) built-up areas were estimated to cover approximately 94%, 88%, and 44% in Dhaka South City Corporation (DSCC), Dhaka North City Corporation (DNCC), and Fringe areas, respectively, of DMA using a Support Vector Machine (SVM) classification method, (c) the built-up showed a strong relationship with LST (Kendall tau coefficient of 0.625 in summer and 0.483 in winter) in comparison to vertical growth (Kendall tau coefficient of 0.156 in the summer and 0.059 in the winter), and (d) the ‘low height-high density’ areas showed high LST in both seasons. This study suggests that vertical development is better than horizontal development for providing enough open spaces, green spaces, and preserving natural features. This study provides city planners with a better understating of sustainable urban planning and can promote the formulation of action plans for appropriate urban development policies.

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Section: Reference Datamentioning

confidence: 99%

Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

et al. 2020

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“…A 30-m spatial resolution DEM was acquired from ALOS World 3D DEM (Tadono et al, 2016) and was resampled to 1,000 m by cubic resampling. ALOS World 3D was chosen because of its higher accuracy when compared to free DEM products of similar resolution (for example: SRTM and ASTER) (Santillan and Makinano-Santillan, 2016;Apeh et al, 2019 andCourty et al, 2019). The choice of the final spatial resolution of the DEM (1,000 m) and consequently, of the climate maps, represents a compromise between a resolution high enough to represent local topographic features (steep canyons and mountains) that would affect the climatic variables being mapped, and coarse enough to reduce the overfitting of the sparse network of weather stations.…”

Section: Topography Datamentioning

confidence: 99%

Terrain sensitive climate mapping for the Arequipa Department in Peru

Moraes

Bowling

Zeballos-Velarde

et al. 2022

Intl Journal of Climatology

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Climate is a powerful driver of agricultural and natural systems, and spatial climate datasets are currently in great demand. This is especially true in the Arequipa Department of Peru, a region with low seasonal precipitation, remarkable topographic variability, and significant water demand in a highly managed water system. This paper presents the Arequipa Climate Maps (ACM) datasets, a high resolution (1 km) spatial 30-year (1988-2017) climate dataset for the Arequipa Region, in Peru. Four interpolation methods, and combinations of those methods, were tested to produce 30 years of daily precipitation, maximum and minimum air temperature: Ordinary Kriging (OK), Thin Plate Splines (TPS), Regression Kriging (RK), and Regression Thin Plate Splines (RTPS). The mixed method RTPS-TPS and RTPS using locally fitted polynomial and potential regressions were found to best represent the spatial variability of precipitation and daily extreme temperatures, respectively, and helped compensate the bias resulting from the lack of weather stations at higher elevations. These methods were then selected to create the ACM dataset, which contains climate maps of 30-year annual and monthly climate normals (ACM-Normals) and 30 years of annual, monthly, and daily climate maps (ACM-YMD). In addition, insights on weather station gap filling in mountainous areas and bias corrections for avoidance of anomalous precipitation and to assure consistency between annual, monthly and daily data are presented, together with discussion about the quality and limitations of the dataset, and its comparison with other datasets.

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“…The reference condition, the most suitable available assessment of the ground condition, plays a necessary role in precision assessment and area estimation [4]. Precision assessment is a decisive step in processing remote sensing data [5], where digital elevation models (three-dimensional representations of the earth's surface) are the primary sources of height information that are greatly applied in many fields [6]. With advanced digital satellite remote sensing approaches, the necessity of performing an accurate assessment has received continued interest.…”

Section: Introductionmentioning

confidence: 99%

Supervised Classification Accuracy Assessment Using Remote Sensing and Geographic Information System

Abbas Al-Aarajy,

Zaeen,

Abood

2024

TEM Journal

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Assessing the accuracy of classification algorithms is paramount as it provides insights into reliability and effectiveness in solving real-world problems. Accuracy examination is essential in any remote sensing-based classification practice, given that classification maps consistently include misclassified pixels and classification misconceptions. In this study, two imaginary satellites for Duhok province, Iraq, were captured at regular intervals, and the photos were analyzed using spatial analysis tools to provide supervised classifications. Some processes were conducted to enhance the categorization, like smoothing. The classification results indicate that Duhok province is divided into four classes: vegetation cover, buildings, water bodies, and bare lands. During 2013-2022, vegetation cover increased from 63% in 2013 to 66% in 2022; buildings roughly increased by 1% to 3% yearly; water bodies showed a decrease of 2% to 1%; the amount of unoccupied land showed a decrease from 34% to 30%. Therefore, the classification accuracy was assessed using the approach of comparison with field data; the classification accuracy was about 85%.

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Accuracy Assessment of Alos W3d30, Aster Gdem and Srtm30 Dem: A Case Study of Nigeria, West Africa

Cited by 15 publications

References 32 publications

Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

Terrain sensitive climate mapping for the Arequipa Department in Peru

Supervised Classification Accuracy Assessment Using Remote Sensing and Geographic Information System

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