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Remote Sensing
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Mapping Forest Extent and Disturbances with Dense SAR Time Series...
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Mapping Forest Extent and Disturbances with Dense SAR Time Series Data

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

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 12633

Special Issue Editors

Dr. Alexandre Bouvet

E-Mail Website
Guest Editor
Centre d’Etudes Spatiales de la BIOsphère (CESBIO), Toulouse 31401, France
Interests: SAR remote sensing; vegetation monitoring; forestry; agriculture; near-real time forest loss detection
Dr. Juan Doblas

E-Mail Website
Guest Editor
Amazon Environmental Research Institute (IPAM), Cuiabá 78043-435, MT, Brazil
Interests: synthetic aperture radar (SAR); change detection; earth observation; radar signal processing; spatial analysis; mapping; vegetation mapping; SAR; environment

Special Issue Information

Dear Colleagues,

Forest areas have been lost globally at an alarming rate through the last few decades, predominantly because of anthropogenic factors. Remote sensing constitutes a unique tool to monitor the extent and intensity of forest losses. Synthetic aperture radar (SAR) sensors have unique properties when compared to optical, passive sensors, such as their sensitivity to the amount of vegetation and (almost) all-weather coverage capabilities. Recently, several SAR satellites (Sentinel-1A/B, SAOCOM-1, ALOS-2/PALSAR-2, TanDEM-X, etc.) have been deployed, aiming to acquire frequent images of the Earth surface. The consistency and density of the time series created by these sensors enables the development of near-real time forest monitoring applications. As the continuity of Sentinel-1 data is guaranteed until at least 2030, and other sensors are planned for launch in the near future, especially at L-band (NISAR, ALOS-4/PALSAR-3, Tandem-L, ROSE-L) and P-band (BIOMASS), methods that rely on the use of dense time-series of SAR data for forest extent and forest loss mapping are, more than ever, highly relevant.

This issue aims to investigate the state of the art on SAR time-series analysis over forests. Forest loss detection is the main objective, but most forest loss detection methods rely on a forest map to mask out non-forest areas, and therefore, methods that allow an accurate mapping of forest extent using time series analysis will be considered of interest as well.

This issue will welcome papers dealing with SAR time-series data processing, classification, and interpretation over tropical, temperate, or boreal forested landscapes. A broad range of subtopics may be considered, such as operational approaches to near-real time forest disturbance monitoring, experimental deep learning methods to analyze time series, or PolInSAR change detection.

Dr. Alexandre Bouvet
Dr. Juan Doblas
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

  • time-series analysis
  • deforestation detection
  • forest mapping
  • SAR
  • forest extent
  • change detection

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Published Papers (3 papers)

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Research

24 pages, 21258 KiB  
Article
Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon
by Harry Carstairs, Edward T. A. Mitchard, Iain McNicol, Chiara Aquino, Eric Chezeaux, Médard Obiang Ebanega, Anaick Modinga Dikongo and Mathias Disney
Remote Sens. 2022, 14(17), 4233; https://doi.org/10.3390/rs14174233 - 27 Aug 2022
Cited by 7 | Viewed by 2744
Abstract
Selective logging is a major cause of forest degradation in the tropics, but its precise scale, location and timing are not known as wide-area, automated remote sensing methods are not yet available at this scale. This limits the abilities of governments to police [...] Read more.
Selective logging is a major cause of forest degradation in the tropics, but its precise scale, location and timing are not known as wide-area, automated remote sensing methods are not yet available at this scale. This limits the abilities of governments to police illegal logging, or monitor (and thus receive payments for) reductions in degradation. Sentinel-1, a C-band Synthetic Aperture Radar satellite mission with a 12-day repeat time across the tropics, is a promising tool for this due to the known appearance of shadows in images where canopy trees are removed. However, previous work has relied on optical satellite data for calibration and validation, which has inherent uncertainties, leaving unanswered questions about the minimum magnitude and area of canopy loss this method can detect. Here, we use a novel bi-temporal LiDAR dataset in a forest degradation experiment in Gabon to show that canopy gaps as small as 0.02 ha (two 10 m × 10 m pixels) can be detected by Sentinel-1. The accuracy of our algorithm was highest when using a timeseries of 50 images over 20 months and no multilooking. With these parameters, canopy gaps in our study site were detected with a false alarm rate of 6.2%, a missed detection rate of 12.2%, and were assigned disturbance dates that were a good qualitative match to logging records. The presence of geolocation errors and false alarms makes this method unsuitable for confirming individual disturbances. However, we found a linear relationship (r2=0.74) between the area of detected Sentinel-1 shadow and LiDAR-based canopy loss at a scale of 1 hectare. By applying our method to three years’ worth of imagery over Gabon, we produce the first national scale map of small-magnitude canopy cover loss. We estimate a total gross canopy cover loss of 0.31 Mha, or 1.3% of Gabon’s forested area, which is a far larger area of change than shown in currently available forest loss alert systems using Landsat (0.022 Mha) and Sentinel-1 (0.019 Mha). Our results, which are made accessible through Google Earth Engine, suggest that this approach could be used to quantify the magnitude and timing of degradation more widely across tropical forests. Full article
(This article belongs to the Special Issue Mapping Forest Extent and Disturbances with Dense SAR Time Series Data)
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21 pages, 50766 KiB  
Article
DETER-R: An Operational Near-Real Time Tropical Forest Disturbance Warning System Based on Sentinel-1 Time Series Analysis
by Juan Doblas, Mariane S. Reis, Amanda P. Belluzzo, Camila B. Quadros, Douglas R. V. Moraes, Claudio A. Almeida, Luis E. P. Maurano, André F. A. Carvalho, Sidnei J. S. Sant’Anna and Yosio E. Shimabukuro
Remote Sens. 2022, 14(15), 3658; https://doi.org/10.3390/rs14153658 - 30 Jul 2022
Cited by 26 | Viewed by 4843
Abstract
Continuous monitoring of forest disturbance on tropical forests is a fundamental tool to support proactive preservation actions and to stop further destruction of native vegetation. Currently most of the monitoring systems in operation are based on optical imagery, and thus are flaw-prone on [...] Read more.
Continuous monitoring of forest disturbance on tropical forests is a fundamental tool to support proactive preservation actions and to stop further destruction of native vegetation. Currently most of the monitoring systems in operation are based on optical imagery, and thus are flaw-prone on areas with frequent cloud cover. As this, several Synthetic Aperture Radar (SAR)-based systems have been developed recently, aiming all-weather disturbance detection. This article presents the main aspects and the results of the first year of operation of the SAR based Near Real-Time Deforestation Detection System (DETER-R), an automated deforestation detection system focused on the Brazilian Amazon. DETER-R uses the Google Earth Engine platform to preprocess and analyze Sentinel-1 SAR time series. New images are treated and analyzed daily. After the automated analysis, the system vectorizes clusters of deforested pixels and sends the corresponding polygons to the environmental enforcement agency. After 12 months of operational life, the system has produced 88,572 forest disturbance warnings. Human validation of the warning polygons showed a extremely low rate of misdetections, with less than 0.2% of the detected area corresponding to false positives. During the first year of operation, DETER-R provided 33,234 warnings of interest to national monitoring agencies which were not detected by its optical counterpart DETER in the same period, corresponding to an area of 105,238.5 ha, or approximately 5% of the total detections. During the rainy season, the rate of additional detections increased as expected, reaching 8.1%. Full article
(This article belongs to the Special Issue Mapping Forest Extent and Disturbances with Dense SAR Time Series Data)
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19 pages, 12784 KiB  
Article
Improving Deforestation Detection on Tropical Rainforests Using Sentinel-1 Data and Convolutional Neural Networks
by Mabel Ortega Adarme, Juan Doblas Prieto, Raul Queiroz Feitosa and Cláudio Aparecido De Almeida
Remote Sens. 2022, 14(14), 3290; https://doi.org/10.3390/rs14143290 - 8 Jul 2022
Cited by 9 | Viewed by 3857
Abstract
Detecting early deforestation is a fundamental process in reducing forest degradation and carbon emissions. With this procedure, it is possible to monitor and control illegal activities associated with deforestation. Most regular monitoring projects have been recently proposed, but most of them rely on [...] Read more.
Detecting early deforestation is a fundamental process in reducing forest degradation and carbon emissions. With this procedure, it is possible to monitor and control illegal activities associated with deforestation. Most regular monitoring projects have been recently proposed, but most of them rely on optical imagery. In addition, these data are seriously restricted by cloud coverage, especially in tropical environments. In this regard, Synthetic Aperture Radar (SAR) is an attractive alternative that can fill this observational gap. This work evaluated and compared a conventional method based on time series and a Fully Convolutional Network (FCN) with bi-temporal SAR images. These approaches were assessed in two regions of the Brazilian Amazon to detect deforestation between 2019 and 2020. Different pre-processing techniques, including filtering and stabilization stages, were applied to the C-band Sentinel-1 images. Furthermore, this study proposes to provide the network with the distance map to past-deforestation as additional information to the pair of images being compared. In our experiments, this proposal brought up to 4% improvement in average precision. The experimental results further indicated a clear superiority of the DL approach over a time series-based deforestation detection method used as a baseline in all experiments. Finally, the study proved the benefits of pre-processing techniques when using detection methods based on time series. On the contrary, the analysis revealed that the neural network could eliminate noise from the input images, making filtering innocuous and, therefore, unnecessary. On the other hand, the stabilization of the input images brought non-negligible accuracy gains to the DL approach. Full article
(This article belongs to the Special Issue Mapping Forest Extent and Disturbances with Dense SAR Time Series Data)
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