scholarly journals Logging Trail Segmentation via a Novel U-Net Convolutional Neural Network and High-Density Laser Scanning Data

2022 ◽  
Vol 14 (2) ◽  
pp. 349
Author(s):  
Omid Abdi ◽  
Jori Uusitalo ◽  
Veli-Pekka Kivinen
Keyword(s):  
Laser Scanning ◽  
High Density ◽  
Superior Performance ◽  
Surface Model ◽  
Digital Surface Model ◽  
Commercial Thinning ◽  
Digital Elevation ◽  
Main Components ◽  
Mature Stands ◽  
Height Model

Logging trails are one of the main components of modern forestry. However, spotting the accurate locations of old logging trails through common approaches is challenging and time consuming. This study was established to develop an approach, using cutting-edge deep-learning convolutional neural networks and high-density laser scanning data, to detect logging trails in different stages of commercial thinning, in Southern Finland. We constructed a U-Net architecture, consisting of encoder and decoder paths with several convolutional layers, pooling and non-linear operations. The canopy height model (CHM), digital surface model (DSM), and digital elevation models (DEMs) were derived from the laser scanning data and were used as image datasets for training the model. The labeled dataset for the logging trails was generated from different references as well. Three forest areas were selected to test the efficiency of the algorithm that was developed for detecting logging trails. We designed 21 routes, including 390 samples of the logging trails and non-logging trails, covering all logging trails inside the stands. The results indicated that the trained U-Net using DSM (k = 0.846 and IoU = 0.867) shows superior performance over the trained model using CHM (k = 0.734 and IoU = 0.782), DEMavg (k = 0.542 and IoU = 0.667), and DEMmin (k = 0.136 and IoU = 0.155) in distinguishing logging trails from non-logging trails. Although the efficiency of the developed approach in young and mature stands that had undergone the commercial thinning is approximately perfect, it needs to be improved in old stands that have not received the second or third commercial thinning.

scholarly journals APPLICATION OF DSM IN OBSTACLE CLEARANCE SURVEYING OF AERODROME

2016 ◽  
Vol XLI-B2 ◽  
pp. 227-233 ◽  
Author(s):  
X. Qiao ◽  
S. H. Lv ◽  
L. L. Li ◽  
X. J. Zhou ◽  
H. Y. Wang ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Satellite Imagery ◽  
Primary Objective ◽  
Considerable Improvement ◽  
Surface Model ◽  
Digital Surface Model ◽  
Horizontal Position ◽  
Obstacle Clearance ◽  
Digital Elevation ◽  
Elevation Model

Compared to the wide use of digital elevation model (DEM), digital surface model (DSM) receives less attention because that it is composed by not only terrain surface, but also vegetations and man-made objects which are usually regarded as useless information. Nevertheless, these objects are useful for the identification of obstacles around an aerodrome. The primary objective of the study was to determine the applicability of DSM in obstacle clearance surveying of aerodrome. According to the requirements of obstacle clearance surveying at QT airport, aerial and satellite imagery were used to generate DSM, by means of photogrammetry, which was spatially analyzed with the hypothetical 3D obstacle limitation surfaces (OLS) to identify the potential obstacles. Field surveying was then carried out to retrieve the accurate horizontal position and height of the obstacles. The results proved that the application of DSM could make considerable improvement in the efficiency of obstacle clearance surveying of aerodrome.

scholarly journals UAV AERIAL SURVEY: ACCURACY ESTIMATION FOR AUTOMATICALLY GENERATED DENSE DIGITAL SURFACE MODEL AND ORTHOTHOTO PLAN

2016 ◽  
Vol XLI-B6 ◽  
pp. 155-159
Author(s):  
M. A. Altyntsev ◽  
S. A. Arbuzov ◽  
R. A. Popov ◽  
G. V. Tsoi ◽  
M. O. Gromov
Keyword(s):  
Survey Data ◽  
Laser Scanning ◽  
Aerial Survey ◽  
Surface Model ◽  
Model Generation ◽  
Digital Surface Model ◽  
Reliable Criterion ◽  
Accuracy Estimation ◽  
Survey Accuracy ◽  
Digital Orthophoto

A dense digital surface model is one of the products generated by using UAV aerial survey data. Today more and more specialized software are supplied with modules for generating such kind of models. The procedure for dense digital model generation can be completely or partly automated. Due to the lack of reliable criterion of accuracy estimation it is rather complicated to judge the generation validity of such models. One of such criterion can be mobile laser scanning data as a source for the detailed accuracy estimation of the dense digital surface model generation. These data may be also used to estimate the accuracy of digital orthophoto plans created by using UAV aerial survey data. The results of accuracy estimation for both kinds of products are presented in the paper.

scholarly journals Assessment of Changes in a Viewshed in the Western Carpathians Landscape as a Result of Reforestation

Land ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 430
Author(s):  
Michał Sobala ◽  
Urszula Myga-Piątek ◽  
Bartłomiej Szypuła
Keyword(s):  
Digital Elevation Model ◽  
Forest Succession ◽  
Western Carpathians ◽  
Surface Model ◽  
Digital Surface Model ◽  
Mountainous Areas ◽  
Viewshed Analysis ◽  
Digital Elevation ◽  
Elevation Model ◽  
The Impact

A viewshed analysis is of great importance in mountainous areas characterized by high landscape values. The aim of this research was to determine the impact of reforestation occurring on former pasturelands on changes in the viewshed, and to quantify changes in the surface of glades. We combine a horizontal and a vertical approach to landscape analysis. The changes in non-forest areas and the viewshed from viewpoints located in glades were calculated using historical cartographic materials and a more recent Digital Elevation Model and Digital Surface Model. An analysis was conducted using a Visibility tool in ArcGIS. The non-forest areas decreased in the period 1848–2015. The viewshed in the majority of viewpoints also decreased in the period 1848–2015. In the majority of cases, the maximal viewsheds were calculated in 1879/1885 and 1933 (43.8% of the analyzed cases), whereas the minimal ones were calculated in 2015 (almost 57.5% of analyzed cases). Changes in the viewshed range from 0.2 to 23.5 km2 with half the cases analyzed being no more than 1.4 km2. The results indicate that forest succession on abandoned glades does not always cause a decline in the viewshed. Deforestation in neighboring areas may be another factor that has an influence on the decline.

scholarly journals Comparison of Digital Elevation Models by Visibility Analysis in Landscape

2016 ◽  
Vol 19 (2) ◽  
pp. 28-31
Author(s):  
Jozef Sedláček ◽  
Ondřej Šesták ◽  
Miroslava Sliacka
Keyword(s):  
Field Survey ◽  
Reference Model ◽  
Digital Terrain Model ◽  
Surface Model ◽  
Digital Surface Model ◽  
Visibility Analysis ◽  
Terrain Model ◽  
Digital Terrain ◽  
Model Average ◽  
Digital Elevation

Abstract The paper investigates suitability of digital surface model for visibility analysis in GIS. In experiment there were analysed viewsheds from 14 observer points calculated on digital surface model, digital terrain model and its comparison to field survey. Data sources for the investigated models were LiDAR digital terrain model and LiDAR digital surface model with vegetation distributed by the Czech Administration for Land Surveying and Cadastre. The overlay method was used for comparing accuracy of models and the reference model was LiDAR digital surface model. Average equalities in comparison with LiDAR digital terrain model, ZABAGED model and field survey were 15.5 %, 17.3% and 20.9%, respectively.

scholarly journals TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 161-169 ◽  
Author(s):  
K. Bakuła ◽  
P. Kupidura ◽  
Ł. Jełowicki
Keyword(s):  
Land Cover ◽  
Laser Scanning ◽  
Near Infrared ◽  
Airborne Laser Scanning ◽  
Surface Model ◽  
Digital Surface Model ◽  
Airborne Laser ◽  
Laser Reflectance ◽  
Elevation Data ◽  
Intensity Images

Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return numbers slightly improved the results.

scholarly journals TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 161-169 ◽  
Author(s):  
K. Bakuła ◽  
P. Kupidura ◽  
Ł. Jełowicki
Keyword(s):  
Land Cover ◽  
Laser Scanning ◽  
Near Infrared ◽  
Airborne Laser Scanning ◽  
Surface Model ◽  
Digital Surface Model ◽  
Airborne Laser ◽  
Laser Reflectance ◽  
Elevation Data ◽  
Intensity Images

Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return numbers slightly improved the results.

scholarly journals Urban Feature Extraction from Merged Airborne LiDAR Data and Digital Camera Data

2021 ◽  
Vol 7 (2) ◽  
pp. 57-74
Author(s):  
Lamyaa Gamal EL-Deen Taha ◽  
A. I. Ramzi ◽  
A. Syarawi ◽  
A. Bekheet
Keyword(s):  
Neural Network ◽  
Digital Camera ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Surface Model ◽  
Digital Surface Model ◽  
3D Point Clouds ◽  
Digital Elevation ◽  
Surface Models ◽  
Elevation Model

Until recently, the most highly accurate digital surface models were obtained from airborne lidar. With the development of a new generation of large format digital photogrammetric aerial camera, a fully digital photogrammetric workflow became possible. Digital airborne images are sources for elevation extraction and orthophoto generation. This research concerned with the generation of digital surface models and orthophotos as applications from high-resolution images.  In this research, the following steps were performed. A Benchmark data of LIDAR and digital aerial camera have been used.  Firstly, image orientation, AT have been performed. Then the automatic digital surface model DSM generation has been produced from the digital aerial camera. Thirdly true digital ortho has been generated from the digital aerial camera also orthoimage will be generated using LIDAR digital elevation model (DSM). Leica Photogrammetric Suite (LPS) module of Erdsa Imagine 2014 software was utilized for processing. Then the resulted orthoimages from both techniques were mosaicked. The results show that automatic digital surface model DSM that been produced from digital aerial camera method has very high dense photogrammetric 3D point clouds compared to the LIDAR 3D point clouds. It was found that the true orthoimage produced from the second approach is better than the true orthoimage produced from the first approach. The five approaches were tested for classification of the best-orthorectified image mosaic using subpixel based (neural network) and pixel-based ( minimum distance and maximum likelihood).Multicues were extracted such as texture(entropy-mean),Digital elevation model, Digital surface model ,normalized digital surface model (nDSM) and intensity image. The contributions of the individual cues used in the classification have been evaluated. It was found that the best cue integration is intensity (pan) +nDSM+ entropy followed by intensity (pan) +nDSM+mean then intensity image +mean+ entropy after that DSM )image and two texture measures (mean and entropy) followed by the colour image. The integration with height data increases the accuracy. Also, it was found that the integration with entropy texture increases the accuracy. Resulted in fifteen cases of classification it was found that maximum likelihood classifier is the best followed by minimum distance then neural network classifier. We attribute this to the fine resolution of the digital camera image. Subpixel classifier (neural network) is not suitable for classifying aerial digital camera images. 

scholarly journals Accuracy assessment of TanDEM-X IDEM using airborne LiDAR on the area of Poland

2017 ◽  
Vol 66 (1) ◽  
pp. 137-148 ◽  
Author(s):  
Małgorzata Woroszkiewicz ◽  
Ireneusz Ewiak ◽  
Paulina Lulkowska
Keyword(s):  
Digital Elevation Model ◽  
Laser Scanning ◽  
Accuracy Assessment ◽  
Shuttle Radar Topography Mission ◽  
Reference Points ◽  
Airborne Lidar ◽  
Surface Model ◽  
Digital Elevation ◽  
Elevation Model ◽  
German Aerospace

Abstract The TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) mission launched in 2010 is another programme – after the Shuttle Radar Topography Mission (SRTM) in 2000 – that uses space-borne radar interferometry to build a global digital surface model. This article presents the accuracy assessment of the TanDEM-X intermediate Digital Elevation Model (IDEM) provided by the German Aerospace Center (DLR) under the project “Accuracy assessment of a Digital Elevation Model based on TanDEM-X data” for the southwestern territory of Poland. The study area included: open terrain, urban terrain and forested terrain. Based on a set of 17,498 reference points acquired by airborne laser scanning, the mean errors of average heights and standard deviations were calculated for areas with a terrain slope below 2 degrees, between 2 and 6 degrees and above 6 degrees. The absolute accuracy of the IDEM data for the analysed area, expressed as a root mean square error (Total RMSE), was 0.77 m.

Morphological House Extraction from Digital Surface Model and Imagery of High-Density Residential Areas

2016 ◽  
Vol 82 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Yan Li ◽  
Lin Zhu ◽  
Kikuo Tachibana ◽  
Hideki Shimamura ◽  
Manchun Li
Keyword(s):  
High Density ◽  
Surface Model ◽  
Digital Surface Model ◽  
Residential Areas

scholarly journals Konversi DSM Menjadi DTM Menggunakan Filter Berbasis Kelerengan Untuk Pemetaan Genangan Banjir Rob Di Kecamatan Tirto

2020 ◽  
Author(s):  
Trida Ridho Fariz ◽  
Nur Rokhayati
Keyword(s):  
Digital Terrain Model ◽  
Surface Model ◽  
Digital Surface Model ◽  
Model Data ◽  
Alos Palsar ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Elevation Model ◽  
Digital Elevation Model Data

Salah satu data penginderaan jauh yang penting adalah DEM (Digital Elevation Model). Data DEM memberikan informasi ketinggian suatu permukaan bumi dimana dikelompokkan menjadi 2 yaitu DSM (Digital Surface Model) yang menyajikan informasi ketinggian permukaan tutupan lahan dan DTM (Digital Terrain Model) yang menyajikan informasi ketinggian tanah. Pemetaan banjir rob secara umum menggunakan data DTM. Tetapi untuk mendapatkan data DTM sangatlah sulit. Salah satu data DEM yang tersedia secara gratis adalah data DEM terkoreksi hasil ekstraksi dari ALOS PALSAR yang memiliki resolusi spasial 12,5 meter, tidak terlalu bagus untuk digunakan sebagai data untuk pemetaan genangan banjir rob mengingat itu hanyalah DSM. Sedangkan menggunakan data titik ketinggian yang di interpolasi tidak terlalu merepresentatifkan kondisi ketinggian medan suatu wilayah kecuali jika jumlah titiknya banyak. Penelitian ini menggunakan metode slope based filtering untuk mengkonversi data DEM dari ALOS PALSAR menjadi DTM.Hasil dari metode ini dilakukan uji statistik berupa korelasi dengan data titik ketinggian dan mempunyai nilai korelasi yang sangat tinggi yaitu sebesar 0,80 dan nilai RMSE sebesar 1,402. Selanjutnya dibuat pemodalan spasial genangan banjir rob dari DTM. Hasil pemodelan spasial genanngan banjir rob kemudin diuji akurasi dengan uji statistik korelasi dan penghitungan RMSE dengan data hasil survey lapangan. Hasil pemodelan memiliki korelasi sebesar 0,78 dengan nilai RMSE tinggi genangan banjir rob sebesar 0,763. Yang berarti bahwa rata-rata selisih nilai ketinggian genangan banjir rob dari peta dan dilapangan adalah sebesar 0,763m. Wilayah genangan banjir rob meliputi Desa Jeruksari, Desa Tegaldowo, Desa Mulyorejo dan Desa Karangjompo.

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