scholarly journals Global analysis of the slope of forest land

2020 ◽  
Author(s):  
Mikael Lundbäck ◽  
Henrik Persson ◽  
Carola Häggström ◽  
Tomas Nordfjell
Keyword(s):  
Large Scale ◽  
Forest Cover ◽  
Global Analysis ◽  
Forest Area ◽  
Forest Land ◽  
Digital Elevation ◽  
Total Forest Area ◽  
Cent Point ◽  
Elevation Model ◽  
Country Specific

Abstract Forests of the world constitute one-third of the total land area and are critical for e.g. carbon balance, biodiversity, water supply and as source for bio-based products. Although the terrain within forest land has a great impact on accessibility, there is a lack of knowledge about the distribution of its variation in slope. The aim was to address that knowledge gap and create a globally consistent dataset of the distribution and area of forest land within different slope classes. A Geographic Information System (GIS) analysis was performed using the open-source QGIS, GDAL and R software. The core of the analysis was a digital elevation model and a forest cover mask, both with a final resolution of 90 m. The total forest area according to the forest mask was 4.15 billion hectares whereof 82 per cent was on slope < 15°. The remaining 18 per cent was distributed over the following slope classes, with 6 per cent on a 15–20° slope, 8 per cent on a 20–30° slope and 4 per cent on a slope > 30°. Out of the major forestry countries, China had the largest proportion of forest steeper than 15° followed by Chile and India. A sensitivity analysis with 20 m resolution resulted in increased steep areas by 1 per cent point in flat Sweden and by 11 per cent points in steep Austria. In addition to country-specific and aggregated results of slope distribution and forest area, a global raster dataset is also made freely available to cover user-specific areas that are not necessarily demarcated by country borders. Apart from predicting the regional possibilities for different harvesting equipment, which was the original idea behind this study, the results can be used to relate geographical forest variables to slope. The results could also be used in strategic forest fire fighting and large-scale planning of forest conservation and management.

scholarly journals Fusion Analysis of Optical Satellite Images and Digital Elevation Model for Quantifying Volume in Debris Flow Disaster

2019 ◽  
Vol 11 (9) ◽  
pp. 1096 ◽  
Author(s):  
Hiroyuki Miura
Keyword(s):  
Debris Flow ◽  
Digital Elevation Model ◽  
Large Scale ◽  
Satellite Images ◽  
Erosion Depth ◽  
Digital Elevation ◽  
Elevation Model ◽  
Fusion Analysis ◽  
Optical Satellite Images ◽  
Debris Flow Disaster

Rapid identification of affected areas and volumes in a large-scale debris flow disaster is important for early-stage recovery and debris management planning. This study introduces a methodology for fusion analysis of optical satellite images and digital elevation model (DEM) for simplified quantification of volumes in a debris flow event. The LiDAR data, the pre- and post-event Sentinel-2 images and the pre-event DEM in Hiroshima, Japan affected by the debris flow disaster on July 2018 are analyzed in this study. Erosion depth by the debris flows is empirically modeled from the pre- and post-event LiDAR-derived DEMs. Erosion areas are detected from the change detection of the satellite images and the DEM-based debris flow propagation analysis by providing predefined sources. The volumes and their pattern are estimated from the detected erosion areas by multiplying the empirical erosion depth. The result of the volume estimations show good agreement with the LiDAR-derived volumes.

A new method for supporting interpretation of paleochannels in a large scale — Detrended Digital Elevation Model Interpretation

Geomorphology ◽  
2020 ◽  
Vol 369 ◽  
pp. 107374
Author(s):  
Shuyan Zhang ◽  
Yong Ma ◽  
Fu Chen ◽  
Jianbo Liu ◽  
Fulong Chen ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Large Scale ◽  
New Method ◽  
Model Interpretation ◽  
Digital Elevation ◽  
Elevation Model

scholarly journals Scale-Optimized Surface Roughness for Topographic Analysis

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 322 ◽  
Author(s):  
John B. Lindsay ◽  
Daniel R. Newman ◽  
Anthony Francioni
Keyword(s):  
Surface Roughness ◽  
Forest Cover ◽  
Agricultural Land ◽  
Spatial Scales ◽  
Grid Cell ◽  
Topographic Analysis ◽  
Digital Elevation ◽  
Locally Adaptive ◽  
Elevation Model

Surface roughness is a terrain parameter that has been widely applied to the study of geomorphological processes. One of the main challenges in studying roughness is its highly scale-dependent nature. Determining appropriate mapping scales in topographically heterogenous landscapes can be difficult. A method is presented for estimating multiscale surface roughness based on the standard deviation of surface normals. This method utilizes scale partitioning and integral image processing to isolate scales of surface complexity. The computational efficiency of the method enables high scale sampling density and identification of maximum roughness for each grid cell in a digital elevation model (DEM). The approach was applied to a 0.5 m resolution LiDAR DEM of a 210 km2 area near Brantford, Canada. The case study demonstrated substantial heterogeneity in roughness properties. At shorter scales, tillage patterns and other micro-topography associated with ground beneath forest cover dominated roughness scale signatures. Extensive agricultural land-use resulted in 35.6% of the site exhibiting maximum roughness at micro-topographic scales. At larger spatial scales, rolling morainal topography and fluvial landforms, including incised channels and meander cut banks, were associated with maximum surface roughness. This method allowed for roughness mapping at spatial scales that are locally adapted to the topographic context of each individual grid cell within a DEM. Furthermore, the analysis revealed significant differences in roughness characteristics among soil texture categories, demonstrating the practical utility of locally adaptive, scale-optimized roughness.

scholarly journals A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images

2020 ◽  
Vol 12 (3) ◽  
pp. 561 ◽  
Author(s):  
Bruno Adriano ◽  
Naoto Yokoya ◽  
Hiroyuki Miura ◽  
Masashi Matsuoka ◽  
Shunichi Koshimura
Keyword(s):  
Disaster Response ◽  
Large Scale ◽  
Reference Data ◽  
Mass Movement ◽  
Recovery Planning ◽  
Additional Information ◽  
Digital Elevation ◽  
Before And After ◽  
Intensity Images ◽  
Elevation Model

The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively.

scholarly journals Morphological and Soil Determinants of Forest Cover Changes in Świętokrzyski National Park and Its Buffer Zone in the last 200 Years

2015 ◽  
Vol 34 (1) ◽  
pp. 55-63 ◽  
Author(s):  
Tadeusz Ciupa ◽  
Roman Suligowski ◽  
Grzegorz Wałek
Keyword(s):  
National Park ◽  
Forest Cover ◽  
Buffer Zone ◽  
Digital Elevation ◽  
Świętokrzyski National Park ◽  
Forest Cover Changes ◽  
Gis Methods ◽  
Elevation Model ◽  
The 19Th Century ◽  
Source Materials

Abstract The research described in the paper utilized GIS methods and comparative cartography in order to analyze changes in forest cover in the period 1800-2011 in the Świętokrzyski National Park (76.26 km²) and its buffer zone (207.86 km²). The research was done for predefined elevation intervals, slope gradients, and genetic soil types. Source materials included historical maps as well as a digital elevation model. Changes in forest cover were noted in spatial and temporal terms and were usually linked to morphology and soil type. While the 19th century was characterized by intense deforestation, this process reversed starting in the early 20th century. Nevertheless, forest cover in the study area has still not returned to its state from 1800.

Food Submergence Calculation and Analysis Using Digital Elevation Model and Geographical Information System

2014 ◽  
Vol 571-572 ◽  
pp. 792-795
Author(s):  
Xiao Qing Zhang ◽  
Kun Hua Wu
Keyword(s):  
Information System ◽  
Digital Elevation Model ◽  
Flood Control ◽  
Large Scale ◽  
Geographical Information ◽  
Economic Losses ◽  
Model Data ◽  
Digital Elevation ◽  
Elevation Model ◽  
Digital Elevation Model Data

Floods usually cause large-scale loss of human life and wide spread damage to properties. Determining flood zone is the core of flood damage assessment and flood control decision. The aim of this paper is to delineate the flood inundation area and estimate economic losses arising from flood using the digital elevation model data and geographic information system techniques. Flood extent estimation showed that digital elevation model data is very precious to model inundation, however, in order to be spatially explicit flood model, high resolution DEM is necessary. Finally, Analyses for the submergence area calculation accuracy.

scholarly journals CHINA FOREST COVER EXTRACTION BASED ON GOOGLE EARTH ENGINE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 855-862 ◽  
Author(s):  
Y. T. Guo ◽  
X. M. Zhang ◽  
T. F. Long ◽  
W. L. Jiao ◽  
G. J. He ◽  
...  
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Forest Cover ◽  
Survey Methods ◽  
Coniferous Forest ◽  
Google Earth ◽  
Forest Area ◽  
Landsat 8 ◽  
Forest Change ◽  
Google Earth Engine

Abstract. Forest cover rate is the principal indice to reflect the forest acount of a nation and region. In view of the difficulty of accurately calculating large-scale forest area by traditional statistical survey methods, it is proposed to extract China forest area based on Google Earth Engine platform. Trained by the enough samples selected through the Google Earth software, there are nine different random forest classifiers applicable to their corresponding zones. Using Landsat 8 surface reflectance data of 2018 year and the modified forest partition map, China forest cover is generated on the Google Earth Engine platform. The accuracy of China's forest coverage achieves 89.08%, while the accuracy of Global Forest Change datasets of Maryland university and Japan’s ALOS Forest/Non-Forest forest product reach 87.78% and 84.57%. Besides, the precision of tropical/subtropical forest, temperate coniferous forest as well as nonforest region are 83.25%, 87.94% and 97.83%, higher than those of other’s accuracy. Our results show that by means of the random forest algorithm and enough samples, tropical and subtropical broadleaf forest, temperate coniferous forest and nonforest partition can be extracted more accurately. Through the computation of forest cover, our result shows that China has a area of 220.42 million hectare in 2018.

scholarly journals Morphotectonic analysis of Kozani Basin (Western Macedonia, Greece)

2017 ◽  
Vol 47 (2) ◽  
pp. 657
Author(s):  
E. Simou ◽  
V. Karagkouni ◽  
G. Papantoniou ◽  
D. Papanikolaou ◽  
P. Nomikou
Keyword(s):  
Large Scale ◽  
Drainage Pattern ◽  
Tectonic Structure ◽  
Tectonic Structures ◽  
River Terraces ◽  
Digital Elevation ◽  
Combined Use ◽  
Digital Modelling ◽  
Elevation Model ◽  
Topographic Relief

Kozani Basin is located in northern-central Greece and constitutes the southernmost of the Plio-Pleistocene basins of western Macedonia. Quantitative and qualitative analysis of morphological slope values, as well as the analysis of the drainage pattern in Kozani Basin confirms that the current topographic relief reflects intense neotectonic activity. Synthetic Morphotectonic Map of the under study area was carried out by means of the combined use of: (a) Digital Elevation Model (DEM), (b) Slope Distribution Map, (c) Morphological Slope Map and (d) Drainage Pattern Map. The composition of the digital modelling in conjunction with the regional geological setting, allows the identification of the main morphological discontinuities and lineaments that result from morphotectonic interpretation. The high morphological slope values indicate well-defined morphotectonic features, which mainly trend NE - SW and, secondarily, NW - SE. Distinct tectonic structures are mostly recognized in the SE margin of Kozani Basin, which is characterized by intense topographic relief. The main large-scale tectonic structure trends NE - SW and corresponds to the major Aliakmonas marginal fault zone that bounds the Kozani basin to the south. On the other hand, the NW margin’s features are indiscernible; thus, the criteria for their recognition are based on the existence of the river terraces, which reflect the tectonic control. The results of our studies are presented on the Morphotectonic Map, which is followed by our 3D model of Kozani Basin.

Sentinel-1 InSAR data by LiCSAR system

2021 ◽  
Author(s):  
Milan Lazecky ◽  
Yasser Maghsoudi Mehrani ◽  
Scott Watson ◽  
Yu Morishita ◽  
John Elliott ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Atmospheric Correction ◽  
East African ◽  
Digital Elevation ◽  
African Rift ◽  
Earth Observation Satellites ◽  
Elevation Model ◽  
Data Coherence ◽  
Standard Frame

<p>Looking Into the Continents from Space with Synthetic Aperture Radar (LiCSAR) is a system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR automatically produces geocoded wrapped and unwrapped interferograms combining every acquisition epoch with four preceding epochs, and complementary data (coherence, amplitude, line-of-sight unit vectors, digital elevation model, metadata, and atmospheric phase screen estimates by the Generic Atmospheric Correction Online Service, GACOS).</p><p>The LiCSAR products are generated in frame units where a standard frame covers ~220x250 km, at 0.001° resolution (WGS-84 coordinate system). Frames are continuously updated for tectonic and volcanic priority areas. In 2020, the LiCSAR system covered about 1,500 global frames in which we have processed over 89,000 Sentinel-1 acquisitions and generated over 300,000 interferograms. Among these, 470 frames cover 1,024 global volcanoes. We aim to cover the global seismic mask defined by the Committee on Earth Observation Satellites (CEOS), but focus initially on the Alpine-Himalayan belt and East African Rift.</p><p>We serve the products as open and freely accessible through our web portal: https://comet.nerc.ac.uk/comet-lics-portal and aim to provide them to shared infrastructures as the European Plate Observing System (EPOS). We also generate rapid response coseismic interferograms for earthquakes with moment magnitude (Mw)> 5.5  a few hours after the postseismic data become available, and we update frames covering active volcanoes twice per day.</p><p>Our products can be directly converted to displacement time series and velocities using  the LiCSBAS time series analysis software. We present solutions implemented in LiCSAR, and show several case studies that use LiCSAR and LiCSBAS products to measure tectonic and volcanic deformation.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.1c122b867cff59390830161/sdaolpUECMynit/12UGE&app=m&a=0&c=02895a62108de9393057db6a355e3b06&ct=x&pn=gnp.elif&d=1" alt=""></p>

scholarly journals VO-SMOG: A VERSATILE, SMOOTH SEGMENT-BASED GROUND FILTER FOR POINT CLOUDS VIA MULTI-SCALE VOXELIZATION

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 59-66
Author(s):  
E. Che ◽  
A. Senogles ◽  
M. J. Olsen
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Mean Squared Error ◽  
Point Clouds ◽  
Process Data ◽  
Multi Scale ◽  
Digital Elevation ◽  
Real World Datasets ◽  
Aircraft System ◽  
Elevation Model

Abstract. Point clouds acquired by light detection and ranging (lidar) and photogrammetry technology (e.g., structure from motion/multi-view stereo-SfM/MVS) are widely used for various applications such topographic mapping due to their high resolution and accuracy. To generate a digital elevation model (DEM) or extract other features in the data, the ground points and non-ground points usually need to be separated first. This process, called ground filtering, can be tedious and time consuming as it requires substantial manual effort for high quality results. Although many have developed automated ground filtering algorithms, very few have the versatility to process data acquired from different scenes and systems. In this paper, we propose a versatile ground filter based on multi-scale voxelization and smooth segments, named Vo-SmoG. The proposed method introduces a novel voxelization approach, followed by isolated voxel filtering, lowest point filtering, local smooth filtering, and ground clustering. The result of the Vo-SmoG ground filtering is a classified point cloud. The effectiveness and efficiency of our method are demonstrated qualitatively and quantitatively. The quantitative evaluation consists of both point-wise and grid-wise comparisons. The recall, precision, and F1-score are over 97% in terms of classification while the root mean squared error (RMSE) of the DEM is within 0.1 m, which is on par with the reported vertical accuracy of the tested data. We further demonstrate the versatility of the Vo-SmoG via large-scale, real-world datasets collected from different environments with mobile laser scanning, airborne laser scanning, terrestrial laser scanning, uncrewed aircraft system (UAS)-SfM, and UAS-lidar.

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