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SAR for Forest Mapping
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SAR for Forest Mapping

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 27913

Special Issue Editors

Dr. Michele Martone

E-Mail Website
Guest Editor
Microwaves and Radar Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
Interests: forest mapping with SAR interferometry (InSAR); forest change detection; SAR raw data quantization; data volume reduction methods for future SAR systems
Special Issues, Collections and Topics in MDPI journals
Dr. Armando Marino

E-Mail Website
Guest Editor
Department of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, UK
Interests: processing of stacks of polarimetric synthetic aperture radar (PolSAR) images for environmental applications, with a special focus on target detection (e.g., ship and iceberg); change detection (e.g., deforestation), and classification (e.g., land cover)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a vital natural resource, forests are of extreme importance for all living beings on our planet. They play a key role in controlling climate change, represent an essential source of energy (such as biomass), food, jobs, and livelihoods, and serve as natural habitat to a large variety of animal species, which is essential for biodiversity preservation.

Forest ecosystems are constantly shaped and changed by physical and biological disturbances and eventual regeneration processes. Today, forest degradation is occurring at an alarming rate, often due to illegal anthropogenic activities, such as logging and fires, so that sensitive environments have been irreversibly damaged, with critical environmental and economic consequences at regional as well as at global scale. A precise and efficient assessment and monitoring of the forests resources, treatments, and recreational opportunities is therefore of crucial importance in order to develop early warning systems. In this scenario, synthetic aperture radar (SAR) remote sensing represents a unique technique for providing high-resolution images independently of daylight and almost any weather conditions. In the last few decades, SAR imaging has demonstrated its suitability for forest mapping applications. The combination of the polarimetric, interferometric, and/or tomographic information further increases its capabilities and the achievable product accuracy.

As Guest Editors, we would like to dedicate this Special Issue to documenting SAR-based methods for forest mapping. Well-prepared, unpublished submissions that address one or more of the following topics are solicited:

  • New methods and concepts for the quantitative assessment of forest biomass;
  • Combination of complementary SAR imaging methods (tomography, polarimetry, interferometry) to define novel approaches, concepts, and applications for forest mapping and monitoring;
  • Feasibility studies with new sensors, ranging from drones to spaceborne SAR systems, and their applications to forestry;
  • Combined use of multifrequency SAR imaging for forest applications;
  • Comparison and benchmarking studies using various sensors and/or processing methods for forestry;
  • New approaches for the detection of forest changes;
  • Potentials of artificial intelligence-based methods for forest information retrieval;
  • Novel methodologies considering the fusion of SAR data with data from other sources.

Mr. Michele Martone
Dr. Armando Marino
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

  • Drone/airborne/spaceborne synthetic aperture radar (SAR)
  • Biomass estimation
  • Forest mapping
  • Change detection
  • SAR polarimetry, interferometry, tomography
  • Artificial Intelligence for forest applications
  • Data fusion of SAR with other sensors

Related Special Issue

Published Papers (5 papers)

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Research

23 pages, 6867 KiB  
Article
Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries
by Javier Ruiz-Ramos, Armando Marino, Carl Boardman and Juan Suarez
Remote Sens. 2020, 12(18), 3061; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12183061 - 18 Sep 2020
Cited by 21 | Viewed by 6459
Abstract
Forest degradation is recognized as a major environmental threat on a global scale. The recent rise in natural and anthropogenic destruction of forested ecosystems highlights the need for developing new, rapid, and accurate remote sensing monitoring systems, which capture forested land transformations. In [...] Read more.
Forest degradation is recognized as a major environmental threat on a global scale. The recent rise in natural and anthropogenic destruction of forested ecosystems highlights the need for developing new, rapid, and accurate remote sensing monitoring systems, which capture forested land transformations. In spite of the great technological advances made in airborne and spaceborne sensors over the past decades, current Earth observation (EO) change detection methods still need to overcome numerous limitations. Optical sensors have been commonly used for detecting land use and land cover changes (LULCC), however, the requirement of certain technical and environmental conditions (e.g., sunlight, not cloud-coverage) restrict their use. More recently, synthetic aperture radar (SAR)-based change detection approaches have been used to overcome these technical limitations, but they commonly rely on static detection approaches (e.g., pre and post disturbance scenario comparison) that are slow to monitor change. In this context, this paper presents a novel approach for mapping forest structural changes in a continuous and near-real-time manner using dense Sentinel-1 image time-series. Our cumulative sum–spatial mean corrected (CUSU-SMC) algorithm approach is based on cumulative sum statistical analysis, which allows the continuous monitoring of radar signal variations, derived from forest structural change. Taking advantage of the high data availability offered by the Sentinel-1 (S-1) C-band constellation, we used an S-1 ground range detected (GRD) dual (VV, VH) polarization timeseries, formed by a total of 84 images, to monitor clear-cutting operations carried out in a Scottish forest during 2019. The analysis showed a user’s accuracy of 82% for the (conservative) detection approach. The use of a post-processing neighbor filter increased the detection performance to a user’s accuracy of 86% with an overall accuracy of 77% for areas of a minimum extent of 0.4 ha. To further validate the detection performance of the method, the CUSU-SMC change detector was tested against commonly-used pairwise change detection approaches for the same period. These results emphasize the capabilities of dense SAR time-series for environmental monitoring and provide a useful tool for optimizing national forest inventories. Full article
(This article belongs to the Special Issue SAR for Forest Mapping)
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23 pages, 9001 KiB  
Article
Investigating the Impact of Digital Elevation Models on Sentinel-1 Backscatter and Coherence Observations
by Ignacio Borlaf-Mena, Maurizio Santoro, Ludovic Villard, Ovidiu Badea and Mihai Andrei Tanase
Remote Sens. 2020, 12(18), 3016; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12183016 - 16 Sep 2020
Cited by 11 | Viewed by 4490
Abstract
Spaceborne remote sensing can track ecosystems changes thanks to continuous and systematic coverage at short revisit intervals. Active remote sensing from synthetic aperture radar (SAR) sensors allows day and night imaging as they are not affected by cloud cover and solar illumination and [...] Read more.
Spaceborne remote sensing can track ecosystems changes thanks to continuous and systematic coverage at short revisit intervals. Active remote sensing from synthetic aperture radar (SAR) sensors allows day and night imaging as they are not affected by cloud cover and solar illumination and can capture unique information about its targets. However, SAR observations are affected by the coupled effect of viewing geometry and terrain topography. The study aims to assess the impact of global digital elevation models (DEMs) on the normalization of Sentinel-1 backscattered intensity and interferometric coherence. For each DEM, we analyzed the difference between orbit tracks, the difference with results obtained with a high-resolution local DEM, and the impact on land cover classification. Tests were carried out at two sites located in mountainous regions in Romania and Spain using the SRTM (Shuttle Radar Topography Mission, 30 m), AW3D (ALOS (Advanced Land Observation Satellite) World 3D, 30 m), TanDEM-X (12.5, 30, 90 m), and Spain national ALS (aerial laser scanning) based DEM (5 m resolution). The TanDEM-X DEM was the global DEM most suitable for topographic normalization, since it provided the smallest differences between orbital tracks, up to 3.5 dB smaller than with other DEMs for peak landform, and 1.4–1.9 dB for pit and valley landforms. Full article
(This article belongs to the Special Issue SAR for Forest Mapping)
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22 pages, 4754 KiB  
Article
Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery
by Mateo Gašparović and Dino Dobrinić
Remote Sens. 2020, 12(12), 1952; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12121952 - 17 Jun 2020
Cited by 49 | Viewed by 5829
Abstract
Mapping of green vegetation in urban areas using remote sensing techniques can be used as a tool for integrated spatial planning to deal with urban challenges. In this context, multitemporal (MT) synthetic aperture radar (SAR) data have not been equally investigated, as compared [...] Read more.
Mapping of green vegetation in urban areas using remote sensing techniques can be used as a tool for integrated spatial planning to deal with urban challenges. In this context, multitemporal (MT) synthetic aperture radar (SAR) data have not been equally investigated, as compared to optical satellite data. This research compared various machine learning methods using single-date and MT Sentinel-1 (S1) imagery. The research was focused on vegetation mapping in urban areas across Europe. Urban vegetation was classified using six classifiers—random forests (RF), support vector machine (SVM), extreme gradient boosting (XGB), multi-layer perceptron (MLP), AdaBoost.M1 (AB), and extreme learning machine (ELM). Whereas, SVM showed the best performance in the single-date image analysis, the MLP classifier yielded the highest overall accuracy in the MT classification scenario. Mean overall accuracy (OA) values for all machine learning methods increased from 57% to 77% with speckle filtering. Using MT SAR data, i.e., three and five S1 imagery, an additional increase in the OA of 8.59% and 13.66% occurred, respectively. Additionally, using three and five S1 imagery for classification, the F1 measure for forest and low vegetation land-cover class exceeded 90%. This research allowed us to confirm the possibility of MT C-band SAR imagery for urban vegetation mapping. Full article
(This article belongs to the Special Issue SAR for Forest Mapping)
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17 pages, 7271 KiB  
Article
Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping
by Andrea Pulella, Rodrigo Aragão Santos, Francescopaolo Sica, Philipp Posovszky and Paola Rizzoli
Remote Sens. 2020, 12(5), 847; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050847 - 06 Mar 2020
Cited by 33 | Viewed by 4474
Abstract
This paper reports recent advancements in the field of Synthetic Aperture Radar (SAR) for forest mapping by using interferometric short-time-series. In particular, we first present how the interferometric capabilities of the Sentinel-1 satellites constellation can be exploited for the monthly mapping of the [...] Read more.
This paper reports recent advancements in the field of Synthetic Aperture Radar (SAR) for forest mapping by using interferometric short-time-series. In particular, we first present how the interferometric capabilities of the Sentinel-1 satellites constellation can be exploited for the monthly mapping of the Amazon rainforest. Indeed, the evolution in time of the interferometric coherence can be properly modeled as an exponential decay and the retrieved interferometric parameters can be used, together with the backscatter, as input features to the machine learning Random Forests classifier. Furthermore, we present an analysis on the benefits of the use of textural information, derived from Sentinel-1 backscatter, in order to enhance the classification accuracy. These textures are computed through the Sum And Difference Histograms methodology and the final classification accuracy, resulting by adding them to the aforementioned features, is a thematic map that exceeds an overall agreement of 85 % , when validated using the optical external reference Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) map. The experiments presented in the final part of the paper are enriched with a further analysis and discussion on the selected scenes using updated multispectral Sentinel-2 acquisitions. Full article
(This article belongs to the Special Issue SAR for Forest Mapping)
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18 pages, 10412 KiB  
Article
TanDEM-X Forest Mapping Using Convolutional Neural Networks
by Antonio Mazza, Francescopaolo Sica, Paola Rizzoli and Giuseppe Scarpa
Remote Sens. 2019, 11(24), 2980; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11242980 - 12 Dec 2019
Cited by 33 | Viewed by 3877
Abstract
In this work, we face the problem of forest mapping from TanDEM-X data by means of Convolutional Neural Networks (CNNs). Our study aims to highlight the relevance of domain-related features for the extraction of the information of interest thanks to their joint nonlinear [...] Read more.
In this work, we face the problem of forest mapping from TanDEM-X data by means of Convolutional Neural Networks (CNNs). Our study aims to highlight the relevance of domain-related features for the extraction of the information of interest thanks to their joint nonlinear processing through CNN. In particular, we focus on the main InSAR features as the backscatter, coherence, and volume decorrelation, as well as the acquisition geometry through the local incidence angle. By using different state-of-the-art CNN architectures, our experiments consistently demonstrate the great potential of deep learning in data fusion for information extraction in the context of synthetic aperture radar signal processing and specifically for the task of forest mapping from TanDEM-X images. We compare three state-of-the-art CNN architectures, such as ResNet, DenseNet, and U-Net, obtaining a large performance gain over the baseline approach for all of them, with the U-Net solution being the most effective one. Full article
(This article belongs to the Special Issue SAR for Forest Mapping)
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