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Special Issue "State-of-the-Art Remote Sensing in South America"

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 34299

Special Issue Editors

Dr. Carlos Souza Jr.
E-Mail Website
Guest Editor
Instituto do Homem e Meio Ambiente da Amazônia (Imazon), Belém 66055-200, Brazil
Interests: remote sensing; land cover/land use mapping; forest degradation; change detection; data science; spatial modeling; ecosystem services
Dr. Laerte Ferreira
E-Mail Website
Guest Editor
Image Processing and GIS Laboratory (LAPIG), Federal University of Goias (UFG), Goiania-GO 74001-97, Brazil
Interests: remote sensing; GIS; pasture mapping
Dr. Washington Rocha
E-Mail Website
Guest Editor
Universidade Estadual de Feira de Santana – Modelagem em Ciências da Terra e do Ambiente (PPGM), Av. Transnordestina, s/n - Novo Horizonte, Feira de Santana, CEP 44036-900, Brazil
Interests: data science; spatial modeling; remote sensing; time-series analysis; land cover/land use mapping; wildfire modelling; drylands degradation
Dr. Ane Alencar
E-Mail Website
Guest Editor
Instituto de Pesquisa Ambiental da Amazônia (IPAM), Brasília 70863-520, Brazil
Interests: remote sensing; land cover/land use mapping; forest degradation; change detection; spatial modeling; ecosystem services; climate change; fire science
Mg. Santiago Banchero
E-Mail Website
Guest Editor
Instituto de Clima y Agua - Instituto Nacional de Tecnología Agropecuaria (INTA), Nicolas Repetto y de los Reseros s/n (1686), Hurlingham, Buenos Aires
Interests: remote sensing; data science; land cover/land use; change detection

Special Issue Information

Dear Colleagues,

South America is a geo-diverse continent, home of several terrestrial biodiversity hot spots, and freshwater reservoirs with rich fishery biodiversity and vegetation varying from high-density tropical rainforest to grasslands. South American ecosystems are vulnerable to extreme climate change events, which can dramatically affect the forest, grasslands, surface water, glaciers, land productivity, and human wellbeing. Land use is another driver that modifies land cover on this continent. The interaction of land-use change with climate change on the land cover is not well understood in South America. The combined effect of climate change and land-use drivers can accelerate ecosystem tipping points, unlocking carbon from soil and vegetation to the atmosphere, altering the hydrological cycle, and lowering Net Primary Productive of aquatic and terrestrial ecosystems of this continent. Remote sensing has been vital to characterize land cover, land-use change, and climate change interactions in ecosystems of South America. In this Special Issue, we invite contributions that characterize the state-of-art of environmental remote sensing to further advance in the understanding of climate and land-use drivers on land cover. We invite submissions on the following topics:

  • Time-series analysis of ecosystem change.
  • Land cover/land use mapping.
  • Early signs of ecosystem tipping points.
  • Integrating long-term field and remotely sensed data.
  • Climate change impact on ecosystem services.
  • Interaction of climate and land use.
  • Cloud computing, deep learning and machine learning applications.
  • Multi-sensor integration for building long-term time-series.
  • Incorporation of remote sensing information on planning and policy making.
  • Future trends and gaps in remote sensing science in South America.

Dr. Carlos Souza Jr.
Dr. Laerte Ferreira
Dr. Washington Rocha
Dr. Ane Alencar
Mg. Santiago Banchero
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

  • Ecosystem service 
  • Climate change 
  • Change detection 
  • Time-series 
  • Tipping point
  • Land cover/land use
  • Fire modeling 
  • Urban mapping 
  • Biodiversity 
  • Desertification 
  • Cloud computing 
  • Machine learning 
  • Deep learning 
  • Data Science

Published Papers (9 papers)

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Research

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Article
Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America
Remote Sens. 2022, 14(16), 4005; https://doi.org/10.3390/rs14164005 - 17 Aug 2022
Cited by 1 | Viewed by 1004
Abstract
The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal [...] Read more.
The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal map consistency as a post-classification endeavor. However, we argue that time can be better modeled as codependent during the model classification stage to produce more consistent land cover estimates over long time periods and gradual change events. To produce temporally-dependent land cover estimates—meaning land cover is predicted over time in connected sequences as opposed to predictions made for a given time period without consideration of past land cover—we use structured learning with conditional random fields (CRFs), coupled with a land cover augmentation method to produce time series training data and bi-weekly Landsat imagery over 20 years (1999–2018) across the Southern Cone region of South America. A CRF accounts for the natural dependencies of land change processes. As a result, it is able to produce land cover estimates over time that better reflect real change and stability by reducing pixel-level annual noise. Using CRF, we produced a twenty-year dataset of land cover over the region, depicting key change processes such as cropland expansion and tree cover loss at the Landsat scale. The augmentation and CRF approach introduced here provides a more temporally consistent land cover product over traditional mapping methods. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in South America)
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Article
Mapping Roads in the Brazilian Amazon with Artificial Intelligence and Sentinel-2
Remote Sens. 2022, 14(15), 3625; https://doi.org/10.3390/rs14153625 - 28 Jul 2022
Cited by 1 | Viewed by 5932
Abstract
This study presents our efforts to automate the detection of unofficial roads (herein, roads) in the Brazilian Amazon using artificial intelligence (AI). In this region, roads are built by loggers, goldminers, and unauthorized land settlements from existing official roads, expanding over pristine forests [...] Read more.
This study presents our efforts to automate the detection of unofficial roads (herein, roads) in the Brazilian Amazon using artificial intelligence (AI). In this region, roads are built by loggers, goldminers, and unauthorized land settlements from existing official roads, expanding over pristine forests and leading to new deforestation and fire hotspots. Previous research used visual interpretation, hand digitization, and vector editing techniques to create a thorough Amazon Road Dataset (ARD) from Landsat imagery. The ARD allowed assessment of the road dynamics and impacts on deforestation, landscape fragmentation, and fires and supported several scientific and societal applications. This research used the existing ARD to train and model a modified U-Net algorithm to detect rural roads in the Brazilian Amazon using Sentinel-2 imagery from 2020 in the Azure Planetary Computer platform. Moreover, we implemented a post-AI detection protocol to connect and vectorize the U-Net road detected to create a new ARD. We estimated the recall and precision accuracy using an independent ARD dataset, obtaining 65% and 71%, respectively. Visual interpretation of the road detected with the AI algorithm suggests that the accuracy is underestimated. The reference dataset does not include all roads that the AI algorithm can detect in the Sentinel-2 imagery. We found an astonishing footprint of roads in the Brazilian Legal Amazon, with 3.46 million km of roads mapped in 2020. Most roads are in private lands (~55%) and 25% are in open public lands under land grabbing pressure. The roads are also expanding over forested areas with 41% cut or within 10 km from the roads, leaving 59% of the 3.1 million km2 of the remaining original forest roadless. Our AI and post-AI models fully automated road detection in rural areas of the Brazilian Amazon, making it possible to operationalize road monitoring. We are using the AI road map to understand better rural roads’ impact on new deforestation, fires, and landscape fragmentation and to support societal and policy applications for forest conservation and regional planning. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in South America)
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Article
Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning
Remote Sens. 2022, 14(11), 2510; https://doi.org/10.3390/rs14112510 - 24 May 2022
Cited by 2 | Viewed by 3812
Abstract
Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial [...] Read more.
Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in South America)
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Article
Mapping Three Decades of Changes in the Tropical Andean Glaciers Using Landsat Data Processed in the Earth Engine
Remote Sens. 2022, 14(9), 1974; https://doi.org/10.3390/rs14091974 - 20 Apr 2022
Viewed by 4230
Abstract
The fast retreat of the tropical Andean glaciers (TAGs) is considered an important indicator of climate change impact on the tropics, since the TAGs provide resources to highly vulnerable mountain populations. This study aims to reconstruct the glacier coverage of the TAGs, using [...] Read more.
The fast retreat of the tropical Andean glaciers (TAGs) is considered an important indicator of climate change impact on the tropics, since the TAGs provide resources to highly vulnerable mountain populations. This study aims to reconstruct the glacier coverage of the TAGs, using Landsat time-series images from 1985 to 2020, by digitally processing and classifying satellite images in the Google Earth Engine platform. We used annual reductions of the Normalized Difference Snow Index (NDSI) and spectral bands to capture the pixels with minimum snow cover. We also implemented temporal and spatial filters to have comparable maps at a multitemporal level and reduce noise and temporal inconsistencies. The results of the multitemporal analysis of this study confirm the recent and dramatic recession of the TAGs in the last three decades, in base to physical and statistical significance. The TAGs reduced from 2429.38 km2 to 1409.11 km2 between 1990 and 2020, representing a loss of 42% of the total glacier area. In addition, the time-series analysis showed more significant losses at altitudes below 5000 masl, and differentiated changes by slope, latitude, and longitude. We found a more significant percentage loss of glacier areas in countries with less coverage. The multiannual validation showed accuracy values of 92.81%, 96.32%, 90.32%, 97.56%, and 88.54% for the metrics F1 score, accuracy, kappa, precision, and recall, respectively. The results are an essential contribution to understanding the TAGs and guiding policies to mitigate climate change and the potential negative impact of freshwater shortage on the inhabitants and food production in the Andean region. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in South America)
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Article
Assessing the Wall-to-Wall Spatial and Qualitative Dynamics of the Brazilian Pasturelands 2010–2018, Based on the Analysis of the Landsat Data Archive
Remote Sens. 2022, 14(4), 1024; https://doi.org/10.3390/rs14041024 - 20 Feb 2022
Cited by 4 | Viewed by 1828
Abstract
Brazilian livestock is predominantly extensive, with approximately 90% of the production being sustained on pasture, which occupies around 20% of the territory. It is estimated that more than half of Brazilian pastures have some level of degradation. In this study, we mapped and [...] Read more.
Brazilian livestock is predominantly extensive, with approximately 90% of the production being sustained on pasture, which occupies around 20% of the territory. It is estimated that more than half of Brazilian pastures have some level of degradation. In this study, we mapped and evaluated the spatiotemporal dynamics of pasture quality in Brazil, between 2010 and 2018, considering three classes of degradation: Absent (D0), Intermediate (D1), and Severe (D2). There was no variation in the total area occupied by pastures in the evaluated period, in spite of the accentuated spatial dynamics. The percentage of non-degraded pastures increased by ~12%, due to the recovery of degraded areas and the emergence of new pasture areas. However, about 44 Mha of the pasture area is currently severely degraded. The dynamics in pasture quality were not homogeneous in property size classes. We observed that in the approximately 2.68 million properties with livestock activity, the proportion with quality gains was twice as low in small properties compared with large ones, and the proportion with losses was three times greater, showing an increase in inequality between properties with more and fewer resources (large and small properties, respectively). The areas occupied by pastures in Brazil present a unique opportunity to increase livestock production and make areas available for agriculture, without the need for new deforestation in the coming decades. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in South America)
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Article
A Large-Scale Deep-Learning Approach for Multi-Temporal Aqua and Salt-Culture Mapping
Remote Sens. 2021, 13(8), 1415; https://doi.org/10.3390/rs13081415 - 07 Apr 2021
Cited by 5 | Viewed by 2661
Abstract
Aquaculture and salt-culture are relevant economic activities in the Brazilian Coastal Zone (BCZ). However, automatic discrimination of such activities from other water-related covers/uses is not an easy task. In this sense, convolutional neural networks (CNN) have the advantage of predicting a given pixel’s [...] Read more.
Aquaculture and salt-culture are relevant economic activities in the Brazilian Coastal Zone (BCZ). However, automatic discrimination of such activities from other water-related covers/uses is not an easy task. In this sense, convolutional neural networks (CNN) have the advantage of predicting a given pixel’s class label by providing as input a local region (named patches or chips) around that pixel. Both the convolutional nature and the semantic segmentation capability provide the U-Net classifier with the ability to access the “context domain” instead of solely isolated pixel values. Backed by the context domain, the results obtained show that the BCZ aquaculture/saline ponds occupied ~356 km2 in 1985 and ~544 km2 in 2019, reflecting an area expansion of ~51%, a rise of 1.5× in 34 years. From 1997 to 2015, the aqua-salt-culture area grew by a factor of ~1.7, jumping from 349 km2 to 583 km2, a 67% increase. In 2019, the Northeast sector concentrated 93% of the coastal aquaculture/salt-culture surface, while the Southeast and South sectors contained 6% and 1%, respectively. Interestingly, despite presenting extensive coastal zones and suitable conditions for developing different aqua-salt-culture products, the North coast shows no relevant aqua or salt-culture infrastructure sign. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in South America)
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Article
Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products
Remote Sens. 2020, 12(24), 4033; https://doi.org/10.3390/rs12244033 - 09 Dec 2020
Cited by 31 | Viewed by 7715
Abstract
Recently, remote sensing image time series analysis has being widely used to investigate the dynamics of environments over time. Many studies have combined image time series analysis with machine learning methods to improve land use and cover change mapping. In order to support [...] Read more.
Recently, remote sensing image time series analysis has being widely used to investigate the dynamics of environments over time. Many studies have combined image time series analysis with machine learning methods to improve land use and cover change mapping. In order to support image time series analysis, analysis-ready data (ARD) image collections have been modeled and organized as multidimensional data cubes. Data cubes can be defined as sets of time series associated with spatially aligned pixels. Based on lessons learned in the research project e-Sensing, related to national demands for land use and cover monitoring and related to state-of-the-art studies on relevant topics, we define the requirements to build Earth observation data cubes for Brazil. This paper presents the methodology to generate ARD and multidimensional data cubes from remote sensing images for Brazil. We describe the computational infrastructure that we are developing in the Brazil Data Cube project, composed of software applications and Web services to create, integrate, discover, access, and process the data sets. We also present how we are producing land use and cover maps from data cubes using image time series analysis and machine learning techniques. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in South America)
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Article
Open Data and Machine Learning to Model the Occurrence of Fire in the Ecoregion of “Llanos Colombo–Venezolanos”
Remote Sens. 2020, 12(23), 3921; https://doi.org/10.3390/rs12233921 - 29 Nov 2020
Cited by 7 | Viewed by 2959
Abstract
A fire probability map is an important tool for landscape management, providing better identification of areas prone to fires and helping optimize the allocation of limited resources for fire prevention, control, and management. In this study, the random forest machine learning algorithm was [...] Read more.
A fire probability map is an important tool for landscape management, providing better identification of areas prone to fires and helping optimize the allocation of limited resources for fire prevention, control, and management. In this study, the random forest machine learning algorithm was applied to model the probability of fire occurrence in the Colombian-Venezuelan plains (llanos) ecoregion in South America. Information on burned areas was collected using Moderate Resolution Imaging Spectroradiometer (MODIS) Product MCD64A1 for the period 2015–2019. We also used spatial information of related factors that were grouped into four levels of information: topography, human presence, vegetation, and climate-related variables. The model had an accuracy of 94%, which indicates the performance of the model was excellent. The cartography generated from the model can be used as base information in the context of fire management in the region, to identify areas for prioritizing efforts and attention. The probability of occurrence zoning results indicates that the very low category covers the largest area (28.2%), followed by low (23.2%), very high (17.6%), moderate (17.2%), and high (13.8%). Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in South America)
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Review

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Review
Mapping South America’s Drylands through Remote Sensing—A Review of the Methodological Trends and Current Challenges
Remote Sens. 2022, 14(3), 736; https://doi.org/10.3390/rs14030736 - 04 Feb 2022
Viewed by 2268
Abstract
The scientific grasp of the distribution and dynamics of land use and land cover (LULC) changes in South America is still limited. This is especially true for the continent’s hyperarid, arid, semiarid, and dry subhumid zones, collectively known as drylands, which are under-represented [...] Read more.
The scientific grasp of the distribution and dynamics of land use and land cover (LULC) changes in South America is still limited. This is especially true for the continent’s hyperarid, arid, semiarid, and dry subhumid zones, collectively known as drylands, which are under-represented ecosystems that are highly threatened by climate change and human activity. Maps of LULC in drylands are, thus, essential in order to investigate their vulnerability to both natural and anthropogenic impacts. This paper comprehensively reviewed existing mapping initiatives of South America’s drylands to discuss the main knowledge gaps, as well as central methodological trends and challenges, for advancing our understanding of LULC dynamics in these fragile ecosystems. Our review centered on five essential aspects of remote-sensing-based LULC mapping: scale, datasets, classification techniques, number of classes (legends), and validation protocols. The results indicated that the Landsat sensor dataset was the most frequently used, followed by AVHRR and MODIS, and no studies used recently available high-resolution satellite sensors. Machine learning algorithms emerged as a broadly employed methodology for land cover classification in South America. Still, such advancement in classification methods did not yet reflect in the upsurge of detailed mapping of dryland vegetation types and functional groups. Among the 23 mapping initiatives, the number of LULC classes in their respective legends varied from 6 to 39, with 1 to 14 classes representing drylands. Validation protocols included fieldwork and automatic processes with sampling strategies ranging from solely random to stratified approaches. Finally, we discussed the opportunities and challenges for advancing research on desertification, climate change, fire mapping, and the resilience of dryland populations. By and large, multi-level studies for dryland vegetation mapping are still lacking. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in South America)
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