Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Microtopography in Geomorphology, Forest Sciences and Biomass Categorization
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Microtopography in Geomorphology, Forest Sciences and Biomass Categorization

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 8776

Special Issue Editors

Dr. Paavo Nevalainen

E-Mail Website
Guest Editor
Department of Information Technology, University of Turku, Turku, Finland
Interests: point clouds; natural resource open data; geometric data preprocessing
Dr. Fahimeh Farahnakian

E-Mail Website
Guest Editor
1. Department of Computing, University of Turku, Turku, Finland
2. Information Solutions, Geological Survey of Finland GTK, Espoo, Finland
Interests: computer vision; sensor fusion; autonomous systems; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Maarit Middleton

E-Mail Website
Guest Editor
Environmental Solutions, Geological Survey of Finland, Lähteentie 2, P.O.Box 77, 96101 Rovaniemi, Finland
Interests: biogeochemistry for mineral exploration; compositional data analysis; mineral prospectivity modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Jonne Pohjankukka

E-Mail Website
Guest Editor
Natural Resource Institute of Finland (LUKE), Finland
Interests: machine learning; data analysis; computer vision; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Light detection and ranging (LiDAR) data and photogrammetry from airborne (plane, UAVs) platforms can currently produce data of the ground surface and vegetation with a high level of detail. Meanwhile, several other remote sensing technologies, e.g., spaceborne synthetic aperture radar (SAR), have been developed and can provide information about, e.g., biomass and the ground surface and its movements over time. Each of these methods has different penetration through canopy and vegetation, resulting in different ground sample density and unit area costs of remote sensing. The research community needs to compare these data sources and their usefulness in various remote sensing applications.

These remote sensing technologies have revolutionized the interpretation of geomorphological interpretation, as smaller and smaller microtopographic features can be recognized from digital elevation models. Various natural geomorphological features and human-induced objects such as drainage structure, military installations, and historical formations on the ground surface can now be recognized with greater accuracy. Simultaneously, biomass inventory greatly benefits from increased spatial detail provided by these data sources.

Recognition of the microtopography may be based on histogram windows, point processes with parameterless definition of the local scope, etc. The character of a location can be described as a vector in a normed space in many ways. Several clustering approaches can be applied, and microtopography itself may be coupled to vegetation, water budget of the ground, etc. Meanwhile, neural network methods have become common, perhaps allowing analysis of dependencies between local microtopography, large- and medium-scale geomorphological features, and biome in a more unified format.

One constant problem with microtopography is a proper comparison of the accuracy and noise levels under different canopies or ground floor biomass. Ground hit densities have been one measure, but there is room for more sophisticated uncertainty or information measures, especially formalisms, which allow comparison of samples with different sample densities and scales. The topic of this Special Issue is to introduce novel application areas of the microtopography detected from remote sensing data sources and discuss the techniques and issues related to the interpretation of the data and sources of uncertainties.

Dr. Paavo Nevalainen
Dr. Fahimeh Farahnakian
Dr. Maarit Middleton
Dr. Jonne Pohjankukka
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Microtopography
  • Geomorphology
  • Ground model
  • TIN
  • LiDAR
  • SAR
  • Array programming
  • Photogrammetry
  • Point clouds
  • Moving window sampling

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 19696 KiB  
Article
Retrieval of DTM under Complex Forest Stand Based on Spaceborne LiDAR Fusion Photon Correction
by Bin Li, Guangpeng Fan, Tianzhong Zhao, Zhuo Deng and Yonghui Yu
Remote Sens. 2022, 14(1), 218; https://doi.org/10.3390/rs14010218 - 04 Jan 2022
Cited by 6 | Viewed by 2357
Abstract
The new generation of satellite-borne laser radar Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data has been successfully used for ground information acquisition. However, when dealing with complex terrain and dense vegetation cover, the accuracy of the extracted understory Digital Terrain Model (DTM) [...] Read more.
The new generation of satellite-borne laser radar Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data has been successfully used for ground information acquisition. However, when dealing with complex terrain and dense vegetation cover, the accuracy of the extracted understory Digital Terrain Model (DTM) is limited. Therefore, this paper proposes a photon correction data processing method based on ICESat-2 to improve the DTM inversion accuracy in complex terrain and high forest coverage areas. The correction value is first extracted based on the ALOS PALSAR DEM reference data to correct the cross-track photon data of ICESat-2. The slope filter threshold is then selected from the reference data, and the extracted possible ground photons are slope filtered to obtain accurate ground photons. Finally, the impacts of cross-track photon and slope filtering on fine ground extraction from the ICESat-2 data are discussed. The results show that the proposed photon correction and slope filtering algorithms help to improve the extraction accuracy of forest DTM in complex terrain areas. Compared with the forest DTM extracted without the photon correction and slope filtering methods, the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) are reduced by 51.90~57.82% and 49.37~53.55%, respectively. To the best of our knowledge, this is the first study demonstrating that photon correction can improve the terrain inversion ability of ICESat-2, while providing a novel method for ground extraction based on ICESat-2 data. It provides a theoretical basis for the accurate inversion of canopy parameters for ICESat-2. Full article
(This article belongs to the Special Issue Microtopography in Geomorphology, Forest Sciences and Biomass Categorization)
►▼ Show Figures

Graphical abstract

18 pages, 4939 KiB  
Article
Underlying Topography Inversion Using TomoSAR Based on Non-Local Means for an L-Band Airborne Dataset
by Xing Peng, Youjun Wang, Shilin Long, Xiong Pan, Qinghua Xie, Yanan Du, Haiqiang Fu, Jianjun Zhu and Xinwu Li
Remote Sens. 2021, 13(15), 2926; https://doi.org/10.3390/rs13152926 - 26 Jul 2021
Cited by 4 | Viewed by 1940
Abstract
The underlying topography is an important part of the three-dimensional structure of forests, and is used for a variety of applications, such as hydrology and water resource management, civil engineering projects, and forest resource surveying. Due to the three-dimensional imaging ability and strong [...] Read more.
The underlying topography is an important part of the three-dimensional structure of forests, and is used for a variety of applications, such as hydrology and water resource management, civil engineering projects, and forest resource surveying. Due to the three-dimensional imaging ability and strong penetration, the tomographic synthetic aperture radar (TomoSAR) with a long wavelength has been shown to be a useful tool to estimate the underlying topography. At present, most of the current methods use the local means method to estimate the sample covariance matrix, in which the vertical backscattering power is estimated. However, these methods cannot easily obtain high-precision underlying topography, and often lose some detailed information. In this paper, to solve this problem, a non-local means method is introduced to estimate the optimal covariance matrix by combining weighted neighborhood pixels. To validate the feasibility and effectiveness of this proposed method, a BioSAR 2008 campaign L-band dataset acquired from the northern forests of Sweden was used to inverse the underlying topography. The results show that the accuracy of the underlying topography retrieved by the proposed method is improved by more than 30% when compared with the traditional method. Full article
(This article belongs to the Special Issue Microtopography in Geomorphology, Forest Sciences and Biomass Categorization)
►▼ Show Figures

Graphical abstract

15 pages, 5096 KiB  
Article
Sensitivity of C-Band SAR Polarimetric Variables to the Directionality of Surface Roughness Parameters
by Zohreh Alijani, John Lindsay, Melanie Chabot, Tracy Rowlandson and Aaron Berg
Remote Sens. 2021, 13(11), 2210; https://doi.org/10.3390/rs13112210 - 05 Jun 2021
Cited by 4 | Viewed by 3600
Abstract
Surface roughness is an important factor in many soil moisture retrieval models. Therefore, any mischaracterization of surface roughness parameters (root mean square height, RMSH, and correlation length, ʅ) may result in unreliable predictions and soil moisture estimations. In many environments, but particularly in [...] Read more.
Surface roughness is an important factor in many soil moisture retrieval models. Therefore, any mischaracterization of surface roughness parameters (root mean square height, RMSH, and correlation length, ʅ) may result in unreliable predictions and soil moisture estimations. In many environments, but particularly in agricultural settings, surface roughness parameters may show different behaviours with respect to the orientation or azimuth. Consequently, the relationship between SAR polarimetric variables and surface roughness parameters may vary depending on measurement orientation. Generally, roughness obtained for many SAR-based studies is estimated using pin profilers that may, or may not, be collected with careful attention to orientation to the satellite look angle. In this study, we characterized surface roughness parameters in multi-azimuth mode using a terrestrial laser scanner (TLS). We characterized the surface roughness parameters in different orientations and then examined the sensitivity between polarimetric variables and surface roughness parameters; further, we compared these results to roughness profiles obtained using traditional pin profilers. The results showed that the polarimetric variables were more sensitive to the surface roughness parameters at higher incidence angles (θ). Moreover, when surface roughness measurements were conducted at the look angle of RADARSAT-2, more significant correlations were observed between polarimetric variables and surface roughness parameters. Our results also indicated that TLS can represent more reliable results than pin profiler in the measurement of the surface roughness parameters. Full article
(This article belongs to the Special Issue Microtopography in Geomorphology, Forest Sciences and Biomass Categorization)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop