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Special Issue "Assessing Changes in the Amazon and Cerrado Biomes by Remote Sensing"

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 March 2020) | Viewed by 23513

Special Issue Editors

Dr. Damien Arvor
E-Mail Website
Guest Editor
French National Centre for Scientific Research, Rennes 2 University, 35 043 Rennes, France
Interests: land use/land cover change; agricultural frontiers; Amazon; climate change; agrosystems; forest dynamics; remote sensing
Dr. Valéry Gond
E-Mail Website
Guest Editor
CIRAD
Interests: time series analysis; land cover change; forest degradation monitoring; Amazonia
Dr. Claudio Almeida
E-Mail Website
Guest Editor
National Institute for Space Research INPE
Interests: monitoring of Brazilian biomes; land use land cover change; landscape analysis
Dr. Mateus Batistella
E-Mail Website
Guest Editor
Brazilian Agricultural Research Corporation (Embrapa)
Interests: land use and land cover change; geospatial research and innovation for agriculture and the environment; landscape ecology and society

Special Issue Information

Dear Colleagues,

The Amazon and Cerrado (Brazilian savannah) biomes have long been affected by a colonization process that has impacted natural resources and local populations. In this regard, the scientific community has mainly pointed out the severe effects of deforestation and anthropization of major rivers (mainly through the construction of large hydroelectric dams) on biodiversity and climate change. This is especially true for the Amazon basin ecosystems, which are now considered to be in transition to a disturbance-dominated regime, including changing energy and water cycles (Davidson et al., 2012). In particular, the Southern and Eastern portions of the Amazon basin hold an intense agricultural frontier at the transition between the Amazon and Cerrado biomes. Large‐scale landscape modification in the Brazilian Cerrado is altering the hydrology and affecting carbon stocks and fluxes, as well as biodiversity (Klink and Machado 2005). Despite this, the last decade has also been characterized by important governance efforts to limit the environmental impacts of anthropogenic activities. For example, efficient command-and-control policies combined with market-oriented forms of regulation have enabled the rapid reduction of deforestation rates in the Amazon and Cerrado. Although these recent improvements may be put into question in a period of political and economic crisis, they testify to significant changes towards a development model that will conciliate economic development and environmental conservation. In this regard, specific regions on the Amazon and the Cerrado agricultural frontiers remain very dynamic.

Monitoring and understanding these new dynamics thus represents a challenge for the remote sensing community, which needs to adapt its practices and techniques to the detection of finer forms of change in the Amazon and the Cerrado. In particular, we wish to place special emphasis on (1) fine environmental changes related to forest degradation, the multiplication of dams, irrigation practices, infrastructure development and urbanization; and on (2) actions to reduce these environmental impacts through the adoption of new agricultural practices (e.g. crop–pasture–forestry integration, no-tillage, crop rotation, pasture restauration) and reforestation efforts (especially in riverine forests and private legal reserves under the frame of the implementation of the Brazil's National Environmental Registry of Rural Properties, CAR). In this regard, we will pay special attention to the role of landscapes in maintaining essential ecosystem services such as food or timber provision, soil fertility, biodiversity preservation and climate regulation.

To address these issues, experts can benefit from a wide range of data (from airborne to satellite images) produced by different sensors at various spatio-temporal scales and for diverse applications, e.g. land use mapping, climate and hydrology monitoring. In addition, various remote sensing-based land cover/use maps have also been released recently and their potential to monitor such changes remains unexplored to date.

This Special Issue, "Assessing changes in the Amazon and Cerrado biomes by remote sensing”, will call for original papers that demonstrate the potential of remote sensing data and remote sensing-based products to monitor fine changes in those biomes. The list below provides a general (but not exhaustive) overview of the topics that are solicited for this Special Issue:

  • Transdisciplinary studies focusing on the relationships between vegetation dynamics, climate, water and societies;
  • Transnational studies
  • Studies with a special interest in ecotone areas (e.g. transition between forest and Cerrado areas);
  • Studies focusing on solutions implemented to improve land use sustainability
  • Smart landscapes to maintain ecosystem services in the Amazon

Dr. Damien Arvor
Dr. Valéry Gond
Dr. Claudio Almeida
Dr. Mateus Batistella
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 2500 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

  • Land use and land cover change 
  • Climate change
  • Multi-source remote sensing 
  • Land use sustainability
  • Landscape approach

Published Papers (7 papers)

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Research

Article
Integrated Landscape Change Analysis of Protected Areas and their Surrounding Landscapes: Application in the Brazilian Cerrado
Remote Sens. 2020, 12(9), 1413; https://doi.org/10.3390/rs12091413 - 30 Apr 2020
Cited by 7 | Viewed by 2248
Abstract
Remote sensing tools have been long used to monitor landscape dynamics inside and around protected areas. Hereto, scientists have largely relied on land use and land cover (LULC) data to derive indicators for monitoring these dynamics, but these metrics do not capture changes [...] Read more.
Remote sensing tools have been long used to monitor landscape dynamics inside and around protected areas. Hereto, scientists have largely relied on land use and land cover (LULC) data to derive indicators for monitoring these dynamics, but these metrics do not capture changes in the state of vegetation surfaces that may compromise the ecological integrity of conservation areas’ landscapes. Here, we introduce a methodology that combines LULC change estimates with three Normalized Difference Vegetation Index-based proxy indicators of vegetation productivity, phenology, and structural change. We illustrate the utility of this methodology through a regional and local analysis of the landscape dynamics in the Cerrado Biome in Brazil in 2001 and 2016. Despite relatively little natural vegetation loss inside core protected areas and their legal buffer zones, the different indicators revealed significant LULC conversions from natural vegetation to farming land, general productivity loss, homogenization of natural forests, significant agricultural expansion, and a general increase in productivity. These results suggest an overall degradation of habitats and intensification of land use in the studied conservation area network, highlighting serious conservation inefficiencies in this region and stressing the importance of integrated landscape change analyses to provide complementary indicators of ecologically-relevant dynamics in these key conservation areas. Full article
(This article belongs to the Special Issue Assessing Changes in the Amazon and Cerrado Biomes by Remote Sensing)
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Article
Comparison of Cloud Cover Detection Algorithms on Sentinel–2 Images of the Amazon Tropical Forest
Remote Sens. 2020, 12(8), 1284; https://doi.org/10.3390/rs12081284 - 18 Apr 2020
Cited by 27 | Viewed by 3719
Abstract
Tropical forests regulate the global water and carbon cycles and also host most of the world’s biodiversity. Despite their importance, they are hard to survey due to their location, extent, and particularly, their cloud coverage. Clouds hinder the spatial and radiometric correction of [...] Read more.
Tropical forests regulate the global water and carbon cycles and also host most of the world’s biodiversity. Despite their importance, they are hard to survey due to their location, extent, and particularly, their cloud coverage. Clouds hinder the spatial and radiometric correction of satellite imagery and also diminishing the useful area on each image, making it difficult to monitor land change. For this reason, our purpose is to identify the cloud detection algorithm best suited for the Amazon rainforest on Sentinel–2 images. To achieve this, we tested four cloud detection algorithms on Sentinel–2 images spread in five areas of the Amazonia. Using more than eight thousand validation points, we compared four cloud detection methods: Fmask 4, MAJA, Sen2Cor, and s2cloudless. Our results point out that FMask 4 has the best overall accuracy on images of the Amazon region (90%), followed by Sen2Cor’s (79%), MAJA (69%), and S2cloudless (52%). We note the choice of method depends on the intended use. Since MAJA reduces the number of false positives by design, users that aim to improve the producer’s accuracy should consider its use. Full article
(This article belongs to the Special Issue Assessing Changes in the Amazon and Cerrado Biomes by Remote Sensing)
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Article
Evaluation of Deep Learning Techniques for Deforestation Detection in the Brazilian Amazon and Cerrado Biomes From Remote Sensing Imagery
Remote Sens. 2020, 12(6), 910; https://doi.org/10.3390/rs12060910 - 12 Mar 2020
Cited by 27 | Viewed by 2611
Abstract
Deforestation is one of the major threats to natural ecosystems. This process has a substantial contribution to climate change and biodiversity reduction. Therefore, the monitoring and early detection of deforestation is an essential process for preservation. Techniques based on satellite images are among [...] Read more.
Deforestation is one of the major threats to natural ecosystems. This process has a substantial contribution to climate change and biodiversity reduction. Therefore, the monitoring and early detection of deforestation is an essential process for preservation. Techniques based on satellite images are among the most attractive options for this application. However, many approaches involve some human intervention or are dependent on a manually selected threshold to identify regions that suffer deforestation. Motivated by this scenario, the present work evaluates Deep Learning-based strategies for automatic deforestation detection, namely, Early Fusion (EF), Siamese Network (SN), and Convolutional Support Vector Machine (CSVM) as well as Support Vector Machine (SVM), used as the baseline. The target areas are two regions with different deforestation patterns: the Amazon and Cerrado biomes in Brazil. The experiments used two co-registered Landsat 8 images acquired at different dates. The strategies based on Deep Learning achieved the best performance in our analysis in comparison with the baseline, with SN and EF superior to CSVM and SVM. In the same way, a reduction of the salt-and-pepper effect in the generated probabilistic change maps was noticed as the number of training samples increased. Finally, the work assesses how the methods can reduce the time invested in the visual inspection of deforested areas. Full article
(This article belongs to the Special Issue Assessing Changes in the Amazon and Cerrado Biomes by Remote Sensing)
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Article
Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks
Remote Sens. 2020, 12(6), 901; https://doi.org/10.3390/rs12060901 - 11 Mar 2020
Cited by 43 | Viewed by 3820
Abstract
Mapping deforestation is an essential step in the process of managing tropical rainforests. It lets us understand and monitor both legal and illegal deforestation and its implications, which include the effect deforestation may have on climate change through greenhouse gas emissions. Given that [...] Read more.
Mapping deforestation is an essential step in the process of managing tropical rainforests. It lets us understand and monitor both legal and illegal deforestation and its implications, which include the effect deforestation may have on climate change through greenhouse gas emissions. Given that there is ample room for improvements when it comes to mapping deforestation using satellite imagery, in this study, we aimed to test and evaluate the use of algorithms belonging to the growing field of deep learning (DL), particularly convolutional neural networks (CNNs), to this end. Although studies have been using DL algorithms for a variety of remote sensing tasks for the past few years, they are still relatively unexplored for deforestation mapping. We attempted to map the deforestation between images approximately one year apart, specifically between 2017 and 2018 and between 2018 and 2019. Three CNN architectures that are available in the literature—SharpMask, U-Net, and ResUnet—were used to classify the change between years and were then compared to two classic machine learning (ML) algorithms—random forest (RF) and multilayer perceptron (MLP)—as points of reference. After validation, we found that the DL models were better in most performance metrics including the Kappa index, F1 score, and mean intersection over union (mIoU) measure, while the ResUnet model achieved the best overall results with a value of 0.94 in all three measures in both time sequences. Visually, the DL models also provided classifications with better defined deforestation patches and did not need any sort of post-processing to remove noise, unlike the ML models, which needed some noise removal to improve results. Full article
(This article belongs to the Special Issue Assessing Changes in the Amazon and Cerrado Biomes by Remote Sensing)
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Article
Comparing Sentinel-2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon
Remote Sens. 2019, 11(8), 961; https://doi.org/10.3390/rs11080961 - 22 Apr 2019
Cited by 36 | Viewed by 6084
Abstract
Imagery from medium resolution satellites, such as Landsat, have long been used to map forest disturbances in the tropics. However, the Landsat spatial resolution (30 m) has often been considered too coarse for reliably mapping small-scale selective logging. Imagery from the recently launched [...] Read more.
Imagery from medium resolution satellites, such as Landsat, have long been used to map forest disturbances in the tropics. However, the Landsat spatial resolution (30 m) has often been considered too coarse for reliably mapping small-scale selective logging. Imagery from the recently launched Sentinel-2 sensor, with a resampled 10 m spatial resolution, may improve the detection of forest disturbances. This study compared the performance of Landsat 8 and Sentinel-2 data for the detection of selective logging in an area located in the Brazilian Amazon. Logging impacts in seven areas, which had governmental authorization for harvesting timber, were mapped by calculating the difference of a self-referenced normalized burn ratio (ΔrNBR) index over corresponding time periods (2016–2017) for imagery of both satellite sensors. A robust reference dataset was built using both high- and very-high-resolution imagery. It was used to define optimum ΔrNBR thresholds for forest disturbance maps, via a bootstrapping procedure, and for estimating accuracies and areas. A further assessment of our approach was also performed in three unlogged areas. Additionally, field data regarding logging infrastructure were collected in the seven study sites where logging occurred. Both satellites showed the same performance in terms of accuracy, with area-adjusted overall accuracies of 96.7% and 95.7% for Sentinel-2 and Landsat 8, respectively. However, Landsat 8 mapped 36.9% more area of selective logging compared to Sentinel-2 data. Logging infrastructure was better detected from Sentinel-2 (43.2%) than Landsat 8 (35.5%) data, confirming its potential for mapping small-scale logging. We assessed the impacted area by selective logging with a regular 300 m × 300 m grid over the pixel-based results, leading to 1143 ha and 1197 ha of disturbed forest on Sentinel-2 and Landsat 8 data, respectively. No substantial differences in terms of accuracy were found by adding three unlogged areas to the original seven study sites. Full article
(This article belongs to the Special Issue Assessing Changes in the Amazon and Cerrado Biomes by Remote Sensing)
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Article
Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR
Remote Sens. 2019, 11(6), 709; https://doi.org/10.3390/rs11060709 - 24 Mar 2019
Cited by 22 | Viewed by 2632
Abstract
Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. [...] Read more.
Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6–7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of −4.14 ± 0.76 MgC ha−1 y−1. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data. Full article
(This article belongs to the Special Issue Assessing Changes in the Amazon and Cerrado Biomes by Remote Sensing)
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Article
Object-Based Change Detection in the Cerrado Biome Using Landsat Time Series
Remote Sens. 2019, 11(5), 570; https://doi.org/10.3390/rs11050570 - 08 Mar 2019
Cited by 13 | Viewed by 1947
Abstract
Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, [...] Read more.
Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, few studies have explored Landsat’s shortwave infrared channel (SWIR 2) to discriminate between seasonal changes and land use/land cover changes (LULCC). Here, we explored the effectiveness of Operational Land Imager (OLI) spectral bands and vegetation indices for detecting deforestation in highly seasonal areas of Brazilian savannas. We adopted object-based image analysis (OBIA), applying a multidate segmentation to an OLI time series to generate input data for discrimination of deforestation from seasonal changes using the Random Forest (RF) algorithm. We found adequate separability between deforested objects and seasonal changes using SWIR 2. Using spectral indices computed from SWIR 2, the RF algorithm generated a change map with an overall accuracy of 88.3%. For deforestation, the producer’s accuracy was 88.0% and the user’s accuracy was 84.6%. The SWIR 2 channel as well as the mid-infrared burn index presented the highest importance among spectral variables computed by the RF average impurity decrease measure. Our results give support to further change detection studies regarding to suitable spectral channels and provided a useful foundation for savanna change detection using an object-based method applied to Landsat time series. Full article
(This article belongs to the Special Issue Assessing Changes in the Amazon and Cerrado Biomes by Remote Sensing)
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