scholarly journals Geomorphic analysis of soil erosion using digital surface model by multi-temporal aerial photographs in grazed pasture

2010 ◽  
Vol 49 (4) ◽  
pp. 219-226
Author(s):  
Kimihito SUZUKI ◽  
Eikichi SHIMA ◽  
Hiroshi SHIMADA ◽  
Katsuyuki TANAKA ◽  
Nagamitsu MAIE
Keyword(s):  
Soil Erosion ◽  
Aerial Photographs ◽  
Surface Model ◽  
Digital Surface Model ◽  
Grazed Pasture ◽  
Multi Temporal

scholarly journals THE POSSIBILITY OF USING IMAGES OBTAINED FROM THE UAS IN CADASTRAL WORKS

2016 ◽  
Vol XLI-B1 ◽  
pp. 909-915 ◽  
Author(s):  
Z. Kurczynski ◽  
K. Bakuła ◽  
M. Karabin ◽  
M. Kowalczyk ◽  
J. S. Markiewicz ◽  
...  
Keyword(s):  
Image Matching ◽  
Bundle Adjustment ◽  
Aerial Photographs ◽  
Surface Model ◽  
Digital Surface Model ◽  
Reference Field ◽  
Alternative Source ◽  
Dense Image ◽  
Low Altitude ◽  
Very High

Updating the cadastre requires much work carried out by surveying companies in countries that have still not solved the problem of updating the cadastral data. In terms of the required precision, these works are among the most accurate. This raises the question: to what extent may modern digital photogrammetric methods be useful in this process? The capabilities of photogrammetry have increased significantly after the introduction of digital aerial cameras and digital technologies. For the registration of cadastral objects, i.e., land parcels’ boundaries and the outlines of buildings, very high-resolution aerial photographs can be used. The paper relates an attempt to use an alternative source of data for this task - the development of images acquired from UAS platforms. Multivariate mapping of cadastral parcels was implemented to determine the scope of the suitability of low altitude photos for the cadastre. In this study, images obtained from UAS with the GSD of 3 cm were collected for an area of a few square kilometres. Bundle adjustment of these data was processed with sub-pixel accuracy. This led to photogrammetric measurements being carried out and the provision of an orthophotomap (orthogonalized with a digital surface model from dense image matching of UAS images). Geometric data related to buildings were collected with two methods: stereoscopic and multi-photo measurements. Data related to parcels’ boundaries were measured with monoplotting on an orthophotomap from low-altitude images. As reference field surveying data were used. The paper shows the potential and limits of the use of UAS in a process of updating cadastral data. It also gives recommendations when performing photogrammetric missions and presents the possible accuracy of this type of work.

scholarly journals IMPROVING THE ACCURACY OF ESTIMATED 3D POSITIONS USING MULTI-TEMPORAL ALOS/PRISM TRIPLET IMAGES

2015 ◽  
Vol II-3/W4 ◽  
pp. 223-230
Author(s):  
J. Susaki ◽  
H. Kishimoto
Keyword(s):  
Remote Sensing ◽  
Experimental Results ◽  
Polynomial Coefficient ◽  
Surface Model ◽  
Digital Surface Model ◽  
Rational Polynomial ◽  
Multi Temporal ◽  
Alos Prism

In this paper, we present a method to improve the accuracy of a digital surface model (DSM) by utilizing multi-temporal triplet images. The Advanced Land Observing Satellite (ALOS) / Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) measures triplet images in the forward, nadir, and backward view directions, and a DSM is generated from the obtained set of triplet images. To generate a certain period of DSM, multiple DSMs generated from individual triplet images are compared, and outliers are removed. Our proposed method uses a traditional surveying approach to increase observations and solves multiple observation equations from all triplet images via the bias-corrected rational polynomial coefficient (RPC) model. Experimental results from using five sets of PRISM triplet images taken of the area around Saitama, north of Tokyo, Japan, showed that the average planimetric and height errors in the coordinates estimated from multi-temporal triplet images were 3.26 m and 2.71 m, respectively, and that they were smaller than those generated by using each set of triplet images individually. As a result, we conclude that the proposed method is effective for stably generating accurate DSMs from multi-temporal triplet images.

scholarly journals Updating Geospatial Data by Creating a High Resolution Digital Surface Model

2018 ◽  
Vol 8 (2) ◽  
pp. 51-58 ◽  
Author(s):  
Iuliana Adriana Cuibac Picu
Keyword(s):  
Laser Scanning ◽  
Smart Cities ◽  
Geographic Information ◽  
Geospatial Data ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Planning System ◽  
Surface Model ◽  
Digital Surface Model ◽  
Digital Terrain

Abstract Smart Cities are no longer just an aspiration, they are a necessity. For a city to be smart, accurate data collection or improvement the existing ones is needed, also an infrastructure that allows the integration of heterogeneous geographic information and sensor networks at a common technological point. Over the past two decades, laser scanning technology, also known as LiDAR (Light Detection and Ranging), has become a very important measurement method, providing high accuracy data and information on land topography, vegetation, buildings, and so on. Proving to be a great way to create Digital Terrain Models. The digital terrain model is a statistical representation of the terrain surface, including in its dataset the elements on its surface, such as construction or vegetation. The data use in the following article is from the LAKI II project “Services for producing a digital model of land by aerial scanning, aerial photographs and production of new maps and orthophotomaps for approximately 50 000 sqKm in 6 counties: Bihor, Arad, Hunedoara, Alba, Mures, Harghita including the High Risk Flood Zone (the border area with the Republic of Hungary in Arad and Bihor)”, which are obtained through LiDAR technology with a point density of 8 points per square meter. The purpose of this article is to update geospatial data with a higher resolution digital surface model and to demonstrate the differences between a digital surface models obtain by aerial images and one obtain by LiDAR technology. The digital surface model will be included in the existing geographic information system of the city Marghita in Bihor County, and it will be used to help develop studies on land use, transport planning system and geological applications. It could also be used to detect changes over time to archaeological sites, to create countur lines maps, flight simulation programs, or other viewing and modelling applications.

scholarly journals OBJECT BOUNDARIES REGULARIZATION USING THE DYNAMIC POLYLINE COMPRESSION ALGORITHM

2018 ◽  
Vol XLII-4 ◽  
pp. 541-546
Author(s):  
O. Saud Azeez ◽  
B. Kalantar ◽  
H. A. H. Al-Najjar ◽  
A. A. Halin ◽  
N. Ueda ◽  
...  
Keyword(s):  
3D Models ◽  
Compression Algorithm ◽  
Aerial Photographs ◽  
Average Error ◽  
Surface Model ◽  
Digital Surface Model ◽  
Spectral Difference ◽  
Image Objects ◽  
Image Layer ◽  
Quality Image

<p><strong>Abstract.</strong> This study presents a regularization approach to refine object boundaries for the purpose of buildings 3D modelling and reconstruction. Specifically, the derivative Normalized Digital Surface model (nDSM) image layer is firstly segmented using the classical multi-resolution segmentation followed by spectral difference segmentation. As the segmentation results can contain quite a number of boundary artefacts in the form geometrical distortions, the Dynamic Polyline Compression algorithm (DCPA) is applied as a regularization step in order to refine the outer boundaries, which removes the distortions. This results in higher quality image objects for the purpose of 3D models reconstruction. Experimental results after comparing between automatically extracted buildings and manually digitized aerial photographs indicate high completeness scores of 94%&amp;ndash;97% and correctness of 93%&amp;ndash;96%. Overall average error is minimized with very low Root Mean Square (RMS) and Overlay errors.</p>

scholarly journals THE POSSIBILITY OF USING IMAGES OBTAINED FROM THE UAS IN CADASTRAL WORKS

2016 ◽  
Vol XLI-B1 ◽  
pp. 909-915
Author(s):  
Z. Kurczynski ◽  
K. Bakuła ◽  
M. Karabin ◽  
M. Kowalczyk ◽  
J. S. Markiewicz ◽  
...  
Keyword(s):  
Image Matching ◽  
Bundle Adjustment ◽  
Aerial Photographs ◽  
Surface Model ◽  
Digital Surface Model ◽  
Reference Field ◽  
Alternative Source ◽  
Dense Image ◽  
Low Altitude ◽  
Very High

Updating the cadastre requires much work carried out by surveying companies in countries that have still not solved the problem of updating the cadastral data. In terms of the required precision, these works are among the most accurate. This raises the question: to what extent may modern digital photogrammetric methods be useful in this process? The capabilities of photogrammetry have increased significantly after the introduction of digital aerial cameras and digital technologies. For the registration of cadastral objects, i.e., land parcels’ boundaries and the outlines of buildings, very high-resolution aerial photographs can be used. The paper relates an attempt to use an alternative source of data for this task - the development of images acquired from UAS platforms. Multivariate mapping of cadastral parcels was implemented to determine the scope of the suitability of low altitude photos for the cadastre. In this study, images obtained from UAS with the GSD of 3 cm were collected for an area of a few square kilometres. Bundle adjustment of these data was processed with sub-pixel accuracy. This led to photogrammetric measurements being carried out and the provision of an orthophotomap (orthogonalized with a digital surface model from dense image matching of UAS images). Geometric data related to buildings were collected with two methods: stereoscopic and multi-photo measurements. Data related to parcels’ boundaries were measured with monoplotting on an orthophotomap from low-altitude images. As reference field surveying data were used. The paper shows the potential and limits of the use of UAS in a process of updating cadastral data. It also gives recommendations when performing photogrammetric missions and presents the possible accuracy of this type of work.

scholarly journals END-TO-END BUILDING CHANGE DETECTION MODEL IN AERIAL IMAGERY AND DIGITAL SURFACE MODEL BASED ON NEURAL NETWORKS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1239-1246
Author(s):  
X. Lian ◽  
W. Yuan ◽  
Z. Guo ◽  
Z. Cai ◽  
X. Song ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Change Detection ◽  
Surface Model ◽  
Digital Surface Model ◽  
Model Framework ◽  
Detection Techniques ◽  
Model Based ◽  
Multi Temporal ◽  
End To End

Abstract. Multi-temporal building change detection is one of the most essential major issues of photogrammetry and remote sensing at current stage, which is of great significance for wide applications as offering real estate indicators as well as monitoring urban environment. Although current photogrammetry methodologies could be applicated to 2-D remote sensing imagery for rectification with sensor parameters, multi-temporal aerial or satellite imagery is not adequate to offer spectral and textual features for building change detection. Alongside recent development of Dense Image Matching (DIM) technology, the acquisition of 3-D point cloud and Digital Surface Model (DSM) has been generally realized, which could be combined with imagery, making building change detection more effective with greater spatial structure and texture information. Over the past years, scholars have put forward vast change detection techniques including traditional and model-based solutions. Nevertheless, existing appropriate methodology combined with Neural Networks (NN) for accurate building change detection with multi-temporal imagery and DSM remains to be of great research focus currently due to the inevitable limitations and omissions of existing NN-based methods, which is of great research prospect. This study proposed a novel end-to-end model framework based on deep learning for pixel-level building change detection from high-spatial resolution aerial ortho imagery and corresponding DSM sharing same resolution, which is from the dataset of Tokyo whole area.

EVALUASI KETELITIAN HORIZONTAL DARI CITRA TEGAK SATELIT RESOLUSI SANGAT TINGGI MENGGUNAKAN SRTM DAN TERRASAR-X UNTUK PEMETAAN SKALA BESAR

2018 ◽  
Vol 2 ◽  
pp. 535
Author(s):  
Maundri Prihanggo
Keyword(s):  
Control Point ◽  
Ground Control ◽  
Surface Model ◽  
Digital Surface Model ◽  
Check Point ◽  
Ground Control Point

<p>Saat ini, citra satelit resolusi sangat tinggi digunakan dalam berbagai macam aplikasi, terutama pemetaan skala besar. Sebelum dapat digunakan, citra satelit tersebut harus diorthorektifikasi terlebih dahulu. Data <em>Digital Surface Model </em>(DSM) dan <em>Ground Control Point</em> (GCP) adalah dua data utama yang diperlukan saat melakukan orthorektifikasi. Perbedaan data DSM yang digunakan akan menghasilkan perbedaan nilai ketelitian horizontal pada kedua citra tegak hasil orthorektifikasi. Pada penelitian ini digunakan dua jenis DSM yaitu SRTM dan Terrasar-X. Ketelitian vertikal dari SRTM adalah 90 m sedangkan ketelitian vertikal dari Terrasar-X adalah 12,5 m. Penelitian ini berlokasi di Wilayah Buli, Kabupaten Halmahera Timur, Provinsi Maluku. Terdapat tiga sensor citra satelit yang digunakan yaitu Pleiades, Quickbird dan Worldview-2 yang digunakan pada lokasi penelitian. Total GCP yang digunakan adalah 33 titik, tiap titiknya diukur dengan melakukan pengamatan geodetik dan memiliki ketelitian horizontal ≤15 cm dan ketelitian vertikal ≤30 cm. Ketelitian horizontal dari citra tegak satelit resolusi sangat tinggi diperoleh dengan melakukan uji terhadap Independent Check Point (ICP). Total ICP yang digunakan adalah 12 titik, tiap titik ICP diukur dengan metode dan standar yang sama dengan titik GCP. Ketelitian horizontal dengan Circular Error (CE 90) dari citra tegak satelit menggunakan data SRTM adalah 18,856 m sedangkan ketelitian horizontal dengan Circular Error (CE 90) dari citra tegak satelit menggunakan data Terrasar-X adalah 2.168 m . Hasil dari penelitian ini membuktikan bahwa ketelitian vertikal data DSM yang digunakan memberikan pengaruh pada citra tegak satelit hasil orthorektifikasi tersebut. Mengacu pada Peraturan Kepala BIG nomor 15 tahun 2014, citra tegak satelit hasil orthorektifikasi menggunakan data Terrasar-X sebagai DSM memenuhi ketelitian horizontal peta dasar kelas 3 skala 1:5.000 sedangkan citra tegak satelit hasil orthorektifikasi menggunakan data SRTM sebagai DSM tidak dapat memenuhi ketelitian horizontal peta dasar skala besar.</p><p><strong>Kata kunci:</strong> orthorektifikasi, DSM, ketelitian horizontal</p>

Measuring historical flooding and erosion in Goodnews Bay using datasets commonly available to Alaska communities

Shore & Beach ◽  
2020 ◽  
pp. 3-13
Author(s):  
Richard Buzard ◽  
Christopher Maio ◽  
David Verbyla ◽  
Nicole Kinsman ◽  
Jacquelyn Overbeck
Keyword(s):  
Rural Communities ◽  
Coastal Communities ◽  
Aerial Imagery ◽  
Coastal Hazards ◽  
Surface Model ◽  
Digital Surface Model ◽  
Wave Impact ◽  
Information Gap ◽  
Quantitative Analyses ◽  
Gnss Measurements

Coastal hazards are of increasing concern to many of Alaska’s rural communities, yet quantitative assessments remain absent over much of the coast. To demonstrate how to fill this critical information gap, an erosion and flood analysis was conducted for Goodnews Bay using an assortment of datasets that are commonly available to Alaska coastal communities. Measurements made from orthorectified aerial imagery from 1957 to 2016 show the shoreline eroded 0 to 15.6 m at a rate that posed no immediate risk to current infrastructure. Storm surge flood risk was assessed using a combination of written accounts, photographs of storm impacts, GNSS measurements, hindcast weather models, and a digital surface model. Eight past storms caused minor to major flooding. Wave impact hour calculations showed that the record storm in 2011 doubled the typical annual wave impact hours. Areas at risk of erosion and flooding in Goodnews Bay were identified using publicly available datasets common to Alaska coastal communities; this work demonstrates that the data and tools exist to perform quantitative analyses of coastal hazards across Alaska.

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