scholarly journals Multiscale Landforms Classification Based on UAV Datasets

2018 ◽  
Vol 3 (2) ◽  
pp. 128
Author(s):  
Jean A. Doumit
Keyword(s):  
Unmanned Aerial Vehicles ◽  
High Spatial Resolution ◽  
Topographic Position Index ◽  
Topographic Slope ◽  
Morphometric Features ◽  
Surface Models ◽  
Very High Spatial Resolution ◽  
Position Index ◽  
Very High

<p><em>The advance uses of Unmanned Aerial Vehicles (UAV) in geosciences by producing very high spatial resolution Digital Surface Models (DSMs), the various UAV flight altitudes led to different scales DSM. In this paper, we analyzed terrain forms using Topographic Position Index (TPI), landforms extracted by Iwahashi and Pike method and morphometric features</em><em> </em><em>of three different spatial resolutions DSM processed from different UAV flights height datasets of the same study area.</em></p><p><em>Topographic </em><em>P</em><em>osition </em><em>I</em><em>ndex (TPI) is an algorithm for measuring topographic slope positions and to automate landform classi?cations,</em><em> </em><em>Iwahashi and Pike had developed an unsupervised method for classification of Landforms and we have used the techniques developed by Peuker and Douglas, a method classifying terrain surfaces into 7 classes.</em><em></em></p><em><em>Landforms extracted from the three indices listed above at the three flight heights of 120, 240 and 360 meters and compared with each other to understand the generalization of different scale and to highlight which landforms are more affected by the scale changes.</em><br /></em>

scholarly journals Mapping Soil Burn Severity at Very High Spatial Resolution from Unmanned Aerial Vehicles

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 179
Author(s):  
David Beltrán-Marcos ◽  
Susana Suárez-Seoane ◽  
José Manuel Fernández-Guisuraga ◽  
Víctor Fernández-García ◽  
Rayo Pinto ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Burn Severity ◽  
Spectral Indices ◽  
Aerial Vehicles ◽  
Soil Burn Severity ◽  
Debris Cover ◽  
Very High Spatial Resolution ◽  
Very High

The evaluation of the effect of burn severity on forest soils is essential to determine the impact of wildfires on a range of key ecological processes, such as nutrient cycling and vegetation recovery. The main objective of this study was to assess the potentiality of different spectral products derived from RGB and multispectral imagery collected by unmanned aerial vehicles (UAVs) at very high spatial resolution for discriminating spatial variations in soil burn severity after a heterogeneous wildfire. In the case study, we chose a mixed-severity fire that occurred in the northwest (NW) of the Iberian Peninsula (Spain) in 2019 that affected 82.74 ha covered by three different types of forests, each dominated by Pinus pinaster, Pinus sylvestris, and Quercus pyrenaica. We evaluated soil burn severity in the field 1 month after the fire using the Composite Burn Soil Index (CBSI), as well as a pool of five individual indicators (ash depth, ash cover, fine debris cover, coarse debris cover, and unstructured soil depth) of easy interpretation. Simultaneously, we operated an unmanned aerial vehicle to obtain RGB and multispectral postfire images, allowing for deriving six spectral indices. Then, we explored the relationship between spectral indices and field soil burn severity metrics by means of univariate proportional odds regression models. These models were used to predict CBSI categories, and classifications were validated through confusion matrices. Results indicated that multispectral indices outperformed RGB indices when assessing soil burn severity, being more strongly related to CBSI than to individual indicators. The Normalized Difference Water Index (NDWI) was the best-performing spectral index for modelling CBSI (R2cv = 0.69), showing the best ability to predict CBSI categories (overall accuracy = 0.83). Among the individual indicators of soil burn severity, ash depth was the one that achieved the best results, specifically when it was modelled from NDWI (R2cv = 0.53). This work provides a useful background to design quick and accurate assessments of soil burn severity to be implemented immediately after the fire, which is a key factor to identify priority areas for emergency actions after forest fires.

Land use/cover classification of a complex agricultural landscape using single-dated very high spatial resolution satellite-sensed imagery

2010 ◽  
Vol 36 (6) ◽  
pp. 722-736 ◽  
Author(s):  
Satoru Okubo ◽  
Parikesit ◽  
Dendi Muhamad ◽  
Koji Harashina ◽  
Kazuhiko Takeuchi ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Resolution ◽  
Agricultural Landscape ◽  
High Spatial Resolution ◽  
Very High Spatial Resolution ◽  
Very High
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