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Near Real Time Forest Inventory with Remote Sensing: Novel Techniques...
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Near Real Time Forest Inventory with Remote Sensing: Novel Techniques and Applications

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 December 2021) | Viewed by 6335

Special Issue Editors

Dr. Oleg Antropov

E-Mail Website
Guest Editor
VTT Technical Research Centre of Finland, Espoo, Finland
Interests: forest; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Erkki Tomppo

E-Mail Website
Guest Editor
1. Department of Electronics and Nanoengineering, Aalto University, Aalto, Finland
2. Department of Forest Sciences, University of Helsinki, Helsinki, Finland
Interests: forest inventory; forest remote sensing; statistical methods
Special Issues, Collections and Topics in MDPI journals
Dr. Ronald E. McRoberts

E-Mail Website
Guest Editor
1. Department of Forest Resources, University of Minnesota, Saint Paul, MN, USA
2. Raspberry Ridge Analytics, Hugo, MN, USA
Interests: forest inventory; forest remote sensing; uncertainty assessment; model-based inference
Dr. Jaan Praks

E-Mail Website
Guest Editor
Department of Electronics and Nanoengineering, Aalto University, FI-00076 AALTO, Finland
Interests: small satellite missions; space technology; microwave earth observation; SAR remote sensing; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest inventory programs aim to produce timely and accurate information for a wide range of forest parameters for a large variety of users and applications. Users include forest owners and forest owner groups, from private to government, national and regional authorities, forest industry, forest, environmental and climate research communities, development banks, as well as non-governmental and conservation organizations. Critical constraints in forest inventories are timeliness, processing costs, and the accuracy and precision of estimated parameters.  Many of the recent innovations involve remotely sensed data and related statistical estimation methods. Field data-based inventories with statistical sampling have a long history in producing estimates and uncertainty estimates for large areas. While airborne laser scanning with field observations facilitates accurate small area estimation, space-borne optical and SAR data appear to be effective information sources for producing large area forest resources estimates and mapping with frequent updates.

Further progress in the framework of forest resources mensuration are expected in the areas of novel imaging sensor geometries (particularly advanced SAR techniques), multi-sensor fusion, improved modeling techniques, big data and AI methodologies, advanced time series analysis, development of operational mapping applications and services and implementing software-as-a-service platforms. 

This Special Issue will highlight both new methods and applications that represent fundamental advances in the use of remotely sensed data for forest inventory applications and new uses of forest inventory data and estimates. All manuscripts must address validation and uncertainty assessment methods.

Submissions to the Special Issue are welcome until 31 June 2021.

Dr. Oleg Antropov

Dr. Erkki Tomppo

Dr. Ronald E. McRoberts

Dr. Jaan Praks

Guest Editor

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

  • forest inventory
  • above ground biomass
  • model-based inference
  • SAR remote sensing
  • airborne laser scanning
  • image time series
  • data fusion
  • machine learning
  • uncertainty assessment

Published Papers (2 papers)

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Research

20 pages, 16234 KiB  
Article
Improved LSTM Model for Boreal Forest Height Mapping Using Sentinel-1 Time Series
by Shaojia Ge, Weimin Su, Hong Gu, Yrjö Rauste, Jaan Praks and Oleg Antropov
Remote Sens. 2022, 14(21), 5560; https://doi.org/10.3390/rs14215560 - 04 Nov 2022
Cited by 8 | Viewed by 2281
Abstract
Time series of SAR imagery combined with reference ground data can be suitable for producing forest inventories. Copernicus Sentinel-1 imagery is particularly interesting for forest mapping because of its free availability to data users; however, temporal dependencies within SAR time series that can [...] Read more.
Time series of SAR imagery combined with reference ground data can be suitable for producing forest inventories. Copernicus Sentinel-1 imagery is particularly interesting for forest mapping because of its free availability to data users; however, temporal dependencies within SAR time series that can potentially improve mapping accuracy are rarely explored. In this study, we introduce a novel semi-supervised Long Short-Term Memory (LSTM) model, CrsHelix-LSTM, and demonstrate its utility for predicting forest tree height using time series of Sentinel-1 images. The model brings three important modifications to the conventional LSTM model. Firstly, it uses a Helix-Elapse (HE) projection to capture the relationship between forest temporal patterns and Sentinel-1 time series, when time intervals between datatakes are irregular. A skip-link based LSTM block is introduced and a novel backbone network, Helix-LSTM, is proposed to retrieve temporal features at different receptive scales. Finally, a novel semisupervised strategy, Cross-Pseudo Regression, is employed to achieve better model performance when reference training data are limited. CrsHelix-LSTM model is demonstrated over a representative boreal forest site located in Central Finland. A time series of 96 Sentinel-1 images are used in the study. The developed model is compared with basic LSTM model, attention-based bidirectional LSTM and several other established regression approaches used in forest variable mapping, demonstrating consistent improvement of forest height prediction accuracy. At best, the achieved accuracy of forest height mapping was 28.3% relative root mean squared error (rRMSE) for pixel-level predictions and 18.0% rRMSE on stand level. We expect that the developed model can also be used for modeling relationships between other forest variables and satellite image time series. Full article
(This article belongs to the Special Issue Near Real Time Forest Inventory with Remote Sensing: Novel Techniques and Applications)
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16 pages, 18521 KiB  
Article
Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series
by Oleg Antropov, Yrjö Rauste, Jaan Praks, Frank Martin Seifert and Tuomas Häme
Remote Sens. 2021, 13(4), 740; https://doi.org/10.3390/rs13040740 - 17 Feb 2021
Cited by 8 | Viewed by 3038
Abstract
Dense time series of stripmap RADARSAT-2 data acquired in the Multilook Fine mode were used for detecting and mapping the extent of selective logging operations in the tropical forest area in the northern part of the Republic of the Congo. Due to limited [...] Read more.
Dense time series of stripmap RADARSAT-2 data acquired in the Multilook Fine mode were used for detecting and mapping the extent of selective logging operations in the tropical forest area in the northern part of the Republic of the Congo. Due to limited radiometric sensitivity to forest biomass variation at C-band, basic multitemporal change detection approach was supplemented by spatial texture analysis to separate disturbed forest from intact. The developed technique primarily uses multi-temporal aggregation of orthorectified synthetic aperture radar (SAR) imagery that are acquired before and after the logging operations. The actual change analysis is based on textural features of the log-ratio image calculated using two SAR temporal composites compiled of SAR scenes acquired before and after the logging operations. Multitemporal aggregation and filtering of SAR scenes decreased speckle and made the extracted textural features more prominent. The overall detection accuracy was around 80%, with some underestimation of the area of forest disturbance compared to reference based on optical data. The user’s accuracy for disturbed forest varied from 76.7% to 94.9% depending on the accuracy assessment approach. We conclude that change detection utilizing RADARSAT-2 time series represents a useful instrument to locate areas of selective logging in tropical forests. Full article
(This article belongs to the Special Issue Near Real Time Forest Inventory with Remote Sensing: Novel Techniques and Applications)
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