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Special Issue "Remote Sensing of Forest Cover Change"

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

Deadline for manuscript submissions: closed (28 September 2018)

Special Issue Editors

Guest Editor
Dr. Joao Carreiras

National Centre for Earth Observation, University of Sheffield, Sheffield S3 7RH, United Kingdom
Website | E-Mail
Interests: optical and microwave remote sensing; tropical forest dynamics; above-ground biomass estimation
Guest Editor
Dr. Pedro Rodríguez-Veiga

National Centre for Earth Observation, University of Leicester, Leicester LE1 7RH, United Kingdom
Website | E-Mail
Interests: optical and microwave remote sensing; biomass; forest ecology; spatial & temporal analisys; forest monitoring; machine learning

Special Issue Information

Dear Colleagues,

Forests are an important sink of carbon and biodiversity worldwide, and they cover all major land masses, from boreal to tropical regions. Therefore, it is of the utmost importance to have a good understanding of all processes leading to forest cover change, such as deforestation, degradation, afforestation and regeneration. Data obtained from Earth Observation (EO) platforms are critical in providing a systematic and temporally resolved assessment of those changes. The current availability of long-term Landsat sensor data and the launch of Sentinel-1A/1B and -2A/2B are fostering the development of new approaches to better characterize temporal changes of forests. Furthermore, advances on high performance and cloud computing, machine learning, high quality temporal datasets (e.g., Landsat collection 1), as well as the development of datacube formats, are increasingly facilitating the analysis of forest cover change and the temporal dynamics of forest biophysical parameters.

This Special Issue seeks to improve our understanding of current methods and datasets to characterise forest cover dynamics worldwide. Therefore, submissions covering the following (non-exhaustive) topics in the scope of forest cover change are very welcome:

  • Interoperability of optical datasets (e.g., Landsat and Sentinel-2)
  • Data fusion from optical and microwave sensors
  • Analysis of long time-series
  • Representing uncertainty
  • Big data approaches
  • (Near) real-time applications
  • Temporal dynamics of forest biophysical parameters
  • Forest biomass/carbon change
Dr. Joao Carreiras
Dr. Pedro Rodríguez-Veiga
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 monthly 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 1800 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 cover change
  • remote sensing
  • temporal analysis
  • early-warning systems

Published Papers (7 papers)

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Research

Open AccessArticle Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series
Remote Sens. 2018, 10(8), 1250; https://doi.org/10.3390/rs10081250
Received: 22 June 2018 / Revised: 20 July 2018 / Accepted: 6 August 2018 / Published: 8 August 2018
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Abstract
To detect deforestation using Earth Observation (EO) data, widely used methods are based on the detection of temporal changes in the EO measurements within the deforested patches. In this paper, we introduce a new indicator of deforestation obtained from synthetic aperture radar (SAR)
[...] Read more.
To detect deforestation using Earth Observation (EO) data, widely used methods are based on the detection of temporal changes in the EO measurements within the deforested patches. In this paper, we introduce a new indicator of deforestation obtained from synthetic aperture radar (SAR) images, which relies on a geometric artifact that appears when deforestation happens, in the form of a shadow at the border of the deforested patch. The conditions for the appearance of these shadows are analyzed, as well as the methods that can be employed to exploit them to detect deforestation. The approach involves two steps: (1) detection of new shadows; (2) reconstruction of the deforested patch around the shadows. The launch of Sentinel-1 in 2014 has opened up opportunities for a potential exploitation of this approach in large-scale applications. A deforestation detection method based on this approach was tested in a 600,000 ha site in Peru. A detection rate of more than 95% is obtained for samples larger than 0.4 ha, and the method was found to perform better than the optical-based UMD-GLAD Forest Alert dataset both in terms of spatial and temporal detection. Further work needed to exploit this approach at operational levels is discussed. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Cover Change)
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Open AccessArticle Identifying Establishment Year and Pre-Conversion Land Cover of Rubber Plantations on Hainan Island, China Using Landsat Data during 1987–2015
Remote Sens. 2018, 10(8), 1240; https://doi.org/10.3390/rs10081240
Received: 13 June 2018 / Revised: 26 July 2018 / Accepted: 31 July 2018 / Published: 7 August 2018
PDF Full-text (5522 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Knowing the stand age of rubber tree (Hevea brasiliensis) plantations is vitally important for best management practices, estimations of rubber latex yields, and carbon cycle studies (e.g., biomass, carbon pools, and fluxes). However, the stand age (as estimated from the establishment
[...] Read more.
Knowing the stand age of rubber tree (Hevea brasiliensis) plantations is vitally important for best management practices, estimations of rubber latex yields, and carbon cycle studies (e.g., biomass, carbon pools, and fluxes). However, the stand age (as estimated from the establishment year of rubber plantation) is not available across large regions. In this study, we analyzed Landsat time series images from 1987–2015 and developed algorithms to identify (1) the establishment year of rubber plantations; and (2) the pre-conversion land cover types, such as old rubber plantations, evergreen forests, and cropland. Exposed soil during plantation establishment and linear increases in canopy closure during non-production periods (rubber seedling to mature plantation) were used to identify the establishment year of rubber plantations. Based on the rubber plantation map for 2015 (overall accuracy = 97%), and 1981 Landsat images since 1987, we mapped the establishment year of rubber plantations on Hainan Island (R2 = 0.85/0.99, and RMSE = 2.34/0.54 years at pixel/plantation scale). The results show that: (1) significant conversion of croplands and old rubber plantations to new rubber plantations has occurred substantially in the northwest and northern regions of Hainan Island since 2000, while old rubber plantations were mainly distributed in the southeastern inland strip; (2) the pattern of rubber plantation expansion since 1987 consisted of fragmented plantations from smallholders, and there was no tendency to expand towards a higher altitude and steep slope regions; (3) the largest land source for new rubber plantations since 1988 was old rubber plantations (1.26 × 105 ha), followed by cropland (0.95 × 105 ha), and evergreen forests (0.68 × 105 ha). The resultant algorithms and maps of establishment year and pre-conversion land cover types are likely to be useful in plantation management, and ecological assessments of rubber plantation expansion in China. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Cover Change)
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Open AccessArticle An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States
Remote Sens. 2018, 10(8), 1184; https://doi.org/10.3390/rs10081184
Received: 24 May 2018 / Revised: 10 July 2018 / Accepted: 18 July 2018 / Published: 27 July 2018
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Abstract
The Operational Remote Sensing (ORS) program leverages Landsat and MODIS data to detect forest disturbances across the conterminous United States (CONUS). The ORS program was initiated in 2014 as a collaboration between the US Department of Agriculture Forest Service Geospatial Technology and Applications
[...] Read more.
The Operational Remote Sensing (ORS) program leverages Landsat and MODIS data to detect forest disturbances across the conterminous United States (CONUS). The ORS program was initiated in 2014 as a collaboration between the US Department of Agriculture Forest Service Geospatial Technology and Applications Center (GTAC) and the Forest Health Assessment and Applied Sciences Team (FHAAST). The goal of the ORS program is to supplement the Insect and Disease Survey (IDS) and MODIS Real-Time Forest Disturbance (RTFD) programs with imagery-derived forest disturbance data that can be used to augment traditional IDS data. We developed three algorithms and produced ORS forest change products using both Landsat and MODIS data. These were assessed over Southern New England and the Rio Grande National Forest. Reference data were acquired using TimeSync to conduct an independent accuracy assessment of IDS, RTFD, and ORS products. Overall accuracy for all products ranged from 71.63% to 92.55% in the Southern New England study area and 63.48% to 79.13% in the Rio Grande National Forest study area. While the accuracies attained from the assessed products are somewhat low, these results are similar to comparable studies. Although many ORS products met or exceeded the overall accuracy of IDS and RTFD products, the differences were largely statistically insignificant at the 95% confidence interval. This demonstrates the current implementation of ORS is sufficient to provide data to augment IDS data. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Cover Change)
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Open AccessEditor’s ChoiceArticle Landsat-Based Land Use Change Assessment in the Brazilian Atlantic Forest: Forest Transition and Sugarcane Expansion
Remote Sens. 2018, 10(7), 996; https://doi.org/10.3390/rs10070996
Received: 27 March 2018 / Revised: 19 May 2018 / Accepted: 23 May 2018 / Published: 22 June 2018
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Abstract
In this study, we examine the hypothesis of a forest transition in an area of early expansion of the agricultural frontier over the Brazilian Atlantic Forest in the south-central part of the State of São Paulo. Large scale land use/cover changes were assessed
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In this study, we examine the hypothesis of a forest transition in an area of early expansion of the agricultural frontier over the Brazilian Atlantic Forest in the south-central part of the State of São Paulo. Large scale land use/cover changes were assessed by integrating Landsat imagery, census data, and landscape metrics. Two Landsat multi-temporal datasets were assembled for two consecutive periods—1995–2006 and 2006–2013—to assess changes in forest cover according to four classes: (i) transition from non-forest cover to planted forest (NF-PF); (ii) transition from non-forest to secondary (successional) forest (NF-SF); (iii) conservation of planted forest (PF) and (iv) conservation of forest remnants (REM). Data from the two most recent, 1995/96 and 2006 agricultural censuses were analyzed to single out major changes in agricultural production. The total area of forest cover, including primary, secondary, and planted forest, increased 30% from 1995 to 2013, whereas forest planted in non-forest areas (NF-PF) and conservation of planted forest (PF) accounted for 14.1% and 19.6%, respectively, of the total forest area by 2013. Such results showed a relatively important forest transition that would be explained mostly by forest plantations though. Analysis of the landscape metrics indicated an increase in connectivity among forest fragments during the period of study, and revealed that nearly half of the forest fragments were located within 50 m from riverbeds, possibly suggesting some level of compliance with environmental laws. Census data showed an increase in both the area and productivity of sugarcane plantations, while pasture and citrus area decreased by a relatively important level, suggesting that sugarcane production has expanded at the expense of these land uses. Both satellite and census data helped to delineate the establishment of two major production systems, the first one dominated by sugarcane plantations approximately located in the NE part of the study area, and a second one concentrating most of the forest plantations in the SW portion of the study area, where most of the forest transition could be observed. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Cover Change)
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Open AccessArticle SAR-Based Estimation of Above-Ground Biomass and Its Changes in Tropical Forests of Kalimantan Using L- and C-Band
Remote Sens. 2018, 10(6), 831; https://doi.org/10.3390/rs10060831
Received: 6 April 2018 / Revised: 18 May 2018 / Accepted: 24 May 2018 / Published: 25 May 2018
Cited by 4 | PDF Full-text (2958 KB) | HTML Full-text | XML Full-text
Abstract
Kalimantan poses one of the highest carbon emissions worldwide since its landscape is strongly endangered by deforestation and degradation and, thus, carbon release. The goal of this study is to conduct large-scale monitoring of above-ground biomass (AGB) from space and create more accurate
[...] Read more.
Kalimantan poses one of the highest carbon emissions worldwide since its landscape is strongly endangered by deforestation and degradation and, thus, carbon release. The goal of this study is to conduct large-scale monitoring of above-ground biomass (AGB) from space and create more accurate biomass maps of Kalimantan than currently available. AGB was estimated for 2007, 2009, and 2016 in order to give an overview of ongoing forest loss and to estimate changes between the three time steps in a more precise manner. Extensive field inventory and LiDAR data were used as reference AGB. A multivariate linear regression model (MLR) based on backscatter values, ratios, and Haralick textures derived from Sentinel-1 (C-band), ALOS PALSAR (Advanced Land Observing Satellite’s Phased Array-type L-band Synthetic Aperture Radar), and ALOS-2 PALSAR-2 polarizations was used to estimate AGB across the country. The selection of the most suitable model parameters was accomplished considering VIF (variable inflation factor), p-value, R2, and RMSE (root mean square error). The final AGB maps were validated by calculating bias, RMSE, R2, and NSE (Nash-Sutcliffe efficiency). The results show a correlation (R2) between the reference biomass and the estimated biomass varying from 0.69 in 2016 to 0.77 in 2007, and a model performance (NSE) in a range of 0.70 in 2016 to 0.76 in 2007. Modelling three different years with a consistent method allows a more accurate estimation of the change than using available biomass maps based on different models. All final biomass products have a resolution of 100 m, which is much finer than other existing maps of this region (>500 m). These high-resolution maps enable identification of even small-scaled biomass variability and changes and can be used for more precise carbon modelling, as well as forest monitoring or risk managing systems under REDD+ (Reducing Emissions from Deforestation, forest Degradation, and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks) and other programs, protecting forests and analyzing carbon release. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Cover Change)
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Open AccessArticle Rohingya Refugee Crisis and Forest Cover Change in Teknaf, Bangladesh
Remote Sens. 2018, 10(5), 689; https://doi.org/10.3390/rs10050689
Received: 6 March 2018 / Revised: 25 April 2018 / Accepted: 25 April 2018 / Published: 30 April 2018
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Abstract
Following a targeted campaign of violence by Myanmar military, police, and local militias, more than half a million Rohingya refugees have fled to neighboring Bangladesh since August 2017, joining thousands of others living in overcrowded settlement camps in Teknaf. To accommodate this mass
[...] Read more.
Following a targeted campaign of violence by Myanmar military, police, and local militias, more than half a million Rohingya refugees have fled to neighboring Bangladesh since August 2017, joining thousands of others living in overcrowded settlement camps in Teknaf. To accommodate this mass influx of refugees, forestland is razed to build spontaneous settlements, resulting in an enormous threat to wildlife habitats, biodiversity, and entire ecosystems in the region. Although reports indicate that this rapid and vast expansion of refugee camps in Teknaf is causing large scale environmental destruction and degradation of forestlands, no study to date has quantified the camp expansion extent or forest cover loss. Using remotely sensed Sentinel-2A and -2B imagery and a random forest (RF) machine learning algorithm with ground observation data, we quantified the territorial expansion of refugee settlements and resulting degradation of the ecological resources surrounding the three largest concentrations of refugee camps—Kutupalong–Balukhali, Nayapara–Leda and Unchiprang—that developed between pre- and post-August of 2017. Employing RF as an image classification approach for this study with a cross-validation technique, we obtained a high overall classification accuracy of 94.53% and 95.14% for 2016 and 2017 land cover maps, respectively, with overall Kappa statistics of 0.93 and 0.94. The producer and user accuracy for forest cover ranged between 92.98–98.21% and 96.49–92.98%, respectively. Results derived from the thematic maps indicate a substantial expansion of refugee settlements in the three refugee camp study sites, with an increase of 175 to 1530 hectares between 2016 and 2017, and a net growth rate of 774%. The greatest camp expansion is observed in the Kutupalong–Balukhali site, growing from 146 ha to 1365 ha with a net increase of 1219 ha (total growth rate of 835%) in the same time period. While the refugee camps’ occupancy expanded at a rapid rate, this gain mostly occurred by replacing the forested land, degrading the forest cover surrounding the three camps by 2283 ha. Such rapid degradation of forested land has already triggered ecological problems and disturbed wildlife habitats in the area since many of these makeshift resettlement camps were set up in or near corridors for wild elephants, causing the death of several Rohingyas by elephant trampling. Hence, the findings of this study may motivate the Bangladesh government and international humanitarian organizations to develop better plans to protect the ecologically sensitive forested land and wildlife habitats surrounding the refugee camps, enable more informed management of the settlements, and assist in more sustainable resource mobilization for the Rohingya refugees. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Cover Change)
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Open AccessArticle Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests: A Test Case in Continental Southeast Asia
Remote Sens. 2018, 10(4), 544; https://doi.org/10.3390/rs10040544
Received: 23 February 2018 / Revised: 19 March 2018 / Accepted: 26 March 2018 / Published: 2 April 2018
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Abstract
This study presents an approach to forest canopy disturbance monitoring in evergreen forests in continental Southeast Asia, based on temporal differences of a modified normalized burn ratio (NBR) vegetation index. We generate NBR values from each available Landsat 8 scene of a given
[...] Read more.
This study presents an approach to forest canopy disturbance monitoring in evergreen forests in continental Southeast Asia, based on temporal differences of a modified normalized burn ratio (NBR) vegetation index. We generate NBR values from each available Landsat 8 scene of a given period. A step of ‘self-referencing’ normalizes the NBR values, largely eliminating illumination/topography effects, thus maximizing inter-comparability. We then create yearly composites of these self-referenced NBR (rNBR) values, selecting per pixel the maximum rNBR value over each observation period, which reflects the most open canopy cover condition of that pixel. The ΔrNBR is generated as the difference between the composites of two reference periods. The methodology produces seamless and consistent maps, highlighting patterns of canopy disturbances (e.g., encroachment, selective logging), and keeping artifacts at minimum level. The monitoring approach was validated within four test sites with an overall accuracy of almost 78% using very high resolution satellite reference imagery. The methodology was implemented in a Google Earth Engine (GEE) script requiring no user interaction. A threshold is applied to the final output dataset in order to separate signal from noise. The approach, capable of detecting sub-pixel disturbance events as small as 0.005 ha, is transparent and reproducible, and can help to increase the credibility of monitoring, reporting and verification (MRV), as required in the context of reducing emissions from deforestation and forest degradation (REDD+). Full article
(This article belongs to the Special Issue Remote Sensing of Forest Cover Change)
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