Special Issue "Optimizing the Usages of High-Spatial Resolution Remote Sensing Data: from Precision Resources Inventory to Operational Forestry"

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

Deadline for manuscript submissions: 30 July 2021.

Special Issue Editors

Prof. Dr. Krzysztof Stereńczak
Guest Editor
Department of Geomatics, Forest Research Institute, Braci Leśnej 3 Street, Sękocin Stary, 05-090 Raszyn, Poland
Interests: remote sensing; laser scanning; precision forestry; forest management; forest health
Dr. Hooman Latifi
Website SciProfiles
Guest Editor
(1) Dept. of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University of Technology
(2) Dept. of Remote Sensing, University of Würzburg, Würzburg
Interests: ecosystem monitoring; vegetation health; time series remote sensing; LiDAR
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

High-spatial resolution remote sensing embraces a broad range of data, including airborne, mobile, and terrestrial laser scanning, aerial imagery, unmanned aerial vehicles (UAV), airborne synthetic aperture radar (SAR), and aerial/terrestrial spectroscopy, which, in turn, entail development and adoption of appropriate methodological approaches. All in all, the bundle of those data and methods, together with relevant sampling designs and field surveys for small-scale domains, form the framework for so-called “precision forestry”, with the main objective to maximize information extraction and analysis, mostly based on individual objects in forest ecosystems, for research, monitoring, and management purposes. Nevertheless, the majority of practical applications and monitoring programs entail medium- to large-scale information, mostly on levels of sample plot, parcel or other management units on regular repetition rates. In the context of remote sensing, this would mean shifting, but not necessarily downgrading, from smaller, but high-precision domain (single objects and individuals) to more generalized (pixel or segment) spatial domains while not notably compensating information accuracy. The main questions around this include those concerning:

  • The spatial extrapolation methods, sampling design, and error propagation studies;
  • Multidimensional, multiscale, multilevel, and multitemporal RS, especially LIDAR and UAV data analysis for forest management and monitoring purposes;
  • Implementation of RS, especially LIDAR and UAV-based products in precision forestry.

In this Special Issue of Remote Sensing, we will pursue these and other related issues by hosting contributions presenting state-of-the-art data and methods with a special focus on the applications of remotely-sensed methods in precision and operational forestry. Thus, we invite all colleagues from different parts of the world to contribute to this Special Issue by submitting high-quality relevant works. We particularly welcome submissions in which uncommon methodical approaches have been developed and results were implemented in practical forest management at various scales. This call is also possibly open to communications, meta-analyses, and reviews, provided they are relevant and the detailed structure in which transfer from RS data analysis to operational precision forestry is addressed.  

PD Dr. Hooman Latifi
Prof. Krzysztof Stereńczak
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 papers will be 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 2200 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.


  • Precision forestry
  • Operational forestry
  • High-spatial resolution data
  • Multitemporal data
  • Sampling strategy
  • Error prediction and propagation

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


Open AccessArticle
Digital Aerial Photogrammetry (DAP) and Airborne Laser Scanning (ALS) as Sources of Information about Tree Height: Comparisons of the Accuracy of Remote Sensing Methods for Tree Height Estimation
Remote Sens. 2020, 12(11), 1808; https://doi.org/10.3390/rs12111808 - 03 Jun 2020
The rapid developments in the field of digital aerial photogrammetry (DAP) in recent years have increased interest in the application of DAP data for extracting three-dimensional (3D) models of forest canopies. This technology, however, still requires further investigation to confirm its reliability in [...] Read more.
The rapid developments in the field of digital aerial photogrammetry (DAP) in recent years have increased interest in the application of DAP data for extracting three-dimensional (3D) models of forest canopies. This technology, however, still requires further investigation to confirm its reliability in estimating forest attributes in complex forest conditions. The main purpose of this study was to evaluate the accuracy of tree height estimation based on a crown height model (CHM) generated from the difference between a DAP-derived digital surface model (DSM) and an airborne laser scanning (ALS)-derived digital terrain model (DTM). The tree heights determined based on the DAP-CHM were compared with ground-based measurements and heights obtained using ALS data only (ALS-CHM). Moreover, tree- and stand-related factors were examined to evaluate the potential influence on the obtained discrepancies between ALS- and DAP-derived heights. The obtained results indicate that the differences between the means of field-measured heights and DAP-derived heights were statistically significant. The root mean square error (RMSE) calculated in the comparison of field heights and DAP-derived heights was 1.68 m (7.34%). The results obtained for the CHM generated using only ALS data produced slightly lower errors, with RMSE = 1.25 m (5.46%) on average. Both ALS and DAP displayed the tendency to underestimate tree heights compared to those measured in the field; however, DAP produced a higher bias (1.26 m) than ALS (0.88 m). Nevertheless, DAP heights were highly correlated with the heights measured in the field (R2 = 0.95) and ALS-derived heights (R2 = 0.97). Tree species and height difference (the difference between the reference tree height and mean tree height in a sample plot) had the greatest influence on the differences between ALS- and DAP-derived heights. Our study confirms that a CHM computed based on the difference between a DAP-derived DSM and an ALS-derived DTM can be successfully used to measure the height of trees in the upper canopy layer. Full article
Show Figures

Graphical abstract

Back to TopTop