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Remote Sensing of the Amazon Region

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 32135

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Special Issue Editors

Dr. Beatriz M. Funatsu

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Guest Editor

CNRS, Université de Nantes, UMR 6554 LETG, 44312 Nantes, France
Interests: atmospheric dynamics; regional climate and climate change (particularly in the Amazon); stratosphere; satellite remote sensing applications for climate studies

Dr. Sergio Bernardes

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Guest Editor

Center for Geospatial Research, Department of Geography, College of Arts and Sciences, The University of Georgia, Athens, GA, USA
Interests: remote sensing; unmanned aerial systems; development and applications of innovative methods for geospatial analysis; spectral bio-indicators; vegetation–climate interactions; time series analysis; geography
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Special Issue Information

Dear Colleagues,

The Amazon region is at the center of the debate on global climate change. It encompasses a large area over eight South American countries and the French Guiana territory, and comprises extraordinary ecosystems. It plays a key role in the climatic system, acting as an energy source for the large-scale atmospheric circulation in the tropical region. The region faces challenges brought by both global climate change and anthropic pressures, which has caused drastic land transformation through deforestation for agriculture, cattle grazing, and mining purposes. After some progress in deforestation control over the Brazilian territory since 2004, the plight of Amazon forest conservation has again been in the spotlight in recent years: Between August 2019 and July 2020, deforestation in the Brazilian Legal Amazon—estimated to be more than 11,000 km²—was 9.5% higher than the previous year, and the highest since 2008. The increase in particulate matter due to forest burning over large areas raises questions on aerosols’ impact on health, air quality, cloudiness and precipitation, among others. Deforestation is one of the drivers of changing energy, water, and carbon cycles in the southern and eastern portions of the Amazon basin, but local changes in land cover and use have impacts that propagate in time and space. Investigating land cover changes (forest fragmentation, forest health) and land use subsequent to deforestation is important for forest management, and for understanding the underlying societal and economical dynamics in these areas.

Due to the large territorial extension of the Amazon region, remote sensing is the most efficient tool to provide comprehensive spatial monitoring of its biogeosphere, including atmospheric components (particulates, water vapor, ozone, etc.), forest dynamics, land cover changes, and land use. The plethora of atmospheric and environmental sensing instruments onboard several platforms, some of those spanning several decades, combined with in-situ measurements and the development of new, powerful analysis techniques (e.g., deep learning) and collaborative tools (e.g., MapBiomas) allows for long-term monitoring, perspective analysis, and new insights for this rapidly evolving environment.

We invite papers (including reviews) dealing with all aspects of the environmental remote sensing of the Amazon, with the aim of diagnosing and elucidating the direct and indirect impacts of anthropic pressures on the Amazon region environment, including but not limited to:

Vegetation and ecosystem dynamics: ecosystem fragmentation, disturbance and recovery, degradation, vegetation growth, biodiversity, changes in species composition and vulnerability;
Biogeophysics monitoring and changes (energy, water, carbon fluxes, biomass estimates, rivers discharge, water resources);
Atmospheric components: particulate, water vapor, ozone, CH₄, etc.;
Land cover changes and land use over deforested and frontier regions;
Feedbacks between climate and deforestation;
New techniques for the remote sensing of tropical forests and savannas.

Dr. Beatriz M. Funatsu
Dr. Sergio Bernardes
Guest Editors

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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.

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Keywords

Amazon forest
tropical savannas
forest and savanna ecosystem dynamics
climatic disturbances
biogeophysical cycles
atmospheric components
LCLU

Published Papers (9 papers)

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19 pages, 7596 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Magnitude of the Amazonian Forest Blowdowns and Post-Disturbance Recovery Using Landsat-8 and Time Series of PlanetScope Satellite Constellation Data

by Dazhou Ping, Ricardo Dalagnol, Lênio Soares Galvão, Bruce Nelson, Fabien Wagner, David M. Schultz and Polyanna da C. Bispo

Remote Sens. 2023, 15(12), 3196; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15123196 - 20 Jun 2023

Cited by 2 | Viewed by 6948

Abstract

Blowdown events are a major natural disturbance in the central Amazon Forest, but their impact and subsequent vegetation recovery have been poorly understood. This study aimed to track post-disturbance regeneration after blowdown events in the Amazon Forest. We analyzed 45 blowdown sites identified after September 2020 at Amazonas, Mato Grosso, and Colombia jurisdictions using Landsat-8 and PlanetScope NICFI satellite imagery. Non-photosynthetic vegetation (NPV), green vegetation (GV), and shade fractions were calculated for each image and sensor using spectral mixture analysis in Google Earth Engine. The results showed that PlanetScope NICFI data provided more regular and higher-spatial-resolution observations of blowdown areas than Landsat-8, allowing for more accurate characterization of post-disturbance vegetation recovery. Specifically, NICFI data indicated that just four months after the blowdown event, nearly half of ΔNPV, which represents the difference between the NPV after blowdown and the NPV before blowdown, had disappeared. ΔNPV and GV values recovered to pre-blowdown levels after approximately 15 months of regeneration. Our findings highlight that the precise timing of blowdown detection has huge implications on quantification of the magnitude of damage. Landsat data may miss important changes in signal due to the difficulty of obtaining regular monthly observations. These findings provide valuable insights into vegetation recovery dynamics following blowdown events. Full article

(This article belongs to the Special Issue Remote Sensing of the Amazon Region)

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28 pages, 6591 KiB

Open AccessArticle

Changes in Carbon Dioxide Balance Associated with Land Use and Land Cover in Brazilian Legal Amazon Based on Remotely Sensed Imagery

by Patrícia Monique Crivelari-Costa, Mendelson Lima, Newton La Scala Jr., Fernando Saragosa Rossi, João Lucas Della-Silva, Ricardo Dalagnol, Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro, Gabriel de Oliveira, José Francisco de Oliveira Junior and Carlos Antonio da Silva Junior

Remote Sens. 2023, 15(11), 2780; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112780 - 26 May 2023

Cited by 5 | Viewed by 2093

Abstract

The Amazon region comprises the largest tropical forest on the planet and is responsible for absorbing huge amounts of CO₂ from the atmosphere. However, changes in land use and cover have contributed to an increase in greenhouse gas emissions, especially CO₂, and in endangered indigenous lands and protected areas in the region. The objective of this study was to detect changes in CO₂ emissions and removals associated with land use and land cover changes in the Brazilian Legal Amazon (BLA) through the analysis of multispectral satellite images from 2009 to 2019. The Gross Primary Production (GPP) and CO₂Flux variables were estimated by the MODIS sensor onboard Terra and Aqua satellite, representing carbon absorption by vegetation during the photosynthesis process. Atmospheric CO₂ concentration was estimated from the GOSAT satellite. The variables GPP and CO₂Flux showed the effective flux of carbon in the BLA to atmosphere, which were weakly correlated with precipitation (r = 0.191 and 0.133). The forest absorbed 211.05 TgC annually but, due to its partial conversion to other land uses, the loss of 135,922.34 km² of forest area resulted in 5.82 TgC less carbon being absorbed. Pasture and agriculture, which comprise the main land conversions, increased by 100,340.39 km² and absorbed 1.32 and 3.19 TgC less, and emitted close to twice more, than forest in these areas. Atmospheric CO₂ concentrations increased from 2.2 to 2.8 ppm annually in BLA, with hotspots observed in the southeast Amazonia, and CO₂ capture by GPP showed an increase over the years, mainly after 2013, in the north and west of the BLA. This study brings to light the carbon dynamics, by GPP and CO₂Flux models, as related to the land use and land cover in one of the biggest world carbon reservoirs, the Amazon, which is also important to fulfillment of international agreements signed by Brazil to reduce greenhouse gas emissions and for biodiversity conservation and other ecosystem services in the region. Full article

(This article belongs to the Special Issue Remote Sensing of the Amazon Region)

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21 pages, 19365 KiB

Open AccessArticle

Mapping Tropical Forest Cover and Deforestation with Planet NICFI Satellite Images and Deep Learning in Mato Grosso State (Brazil) from 2015 to 2021

by Fabien H. Wagner, Ricardo Dalagnol, Celso H. L. Silva-Junior, Griffin Carter, Alison L. Ritz, Mayumi C. M. Hirye, Jean P. H. B. Ometto and Sassan Saatchi

Remote Sens. 2023, 15(2), 521; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020521 - 16 Jan 2023

Cited by 12 | Viewed by 5068

Abstract

Monitoring changes in tree cover for assessment of deforestation is a premise for policies to reduce carbon emission in the tropics. Here, a U-net deep learning model was used to map monthly tropical tree cover in the Brazilian state of Mato Grosso between 2015 and 2021 using 5 m spatial resolution Planet NICFI satellite images. The accuracy of the tree cover model was extremely high, with an F1-score >0.98, further confirmed by an independent LiDAR validation showing that 95% of tree cover pixels had a height >5 m while 98% of non-tree cover pixels had a height <5 m. The biannual map of deforestation was then built from the monthly tree cover map. The deforestation map showed relatively consistent agreement with the official deforestation map from Brazil (67.2%) but deviated significantly from Global Forest Change (GFC)’s year of forest loss, showing that our product is closest to the product made by visual interpretation. Finally, we estimated that 14.8% of Mato Grosso’s total area had undergone clear-cut logging between 2015 and 2021, and that deforestation was increasing, with December 2021, the last date, being the highest. High-resolution imagery from Planet NICFI in conjunction with deep learning techniques can significantly improve the mapping of deforestation extent in tropical regions. Full article

(This article belongs to the Special Issue Remote Sensing of the Amazon Region)

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16 pages, 2989 KiB

Open AccessArticle

Drought Propagation in Brazilian Biomes Revealed by Remote Sensing

by Júlia Brusso Rossi, Anderson Ruhoff, Ayan Santos Fleischmann and Leonardo Laipelt

Remote Sens. 2023, 15(2), 454; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020454 - 12 Jan 2023

Cited by 4 | Viewed by 1986

Abstract

Drought events have been reported in all Brazilian regions every year, evolving slowly over time and large areas, and largely impacting agriculture, hydropower production, and water supplies. In the last two decades, major drought events have occurred over the country, such as the 2010 and 2015 events in the Amazon, the 2012 event in the Pampa, and the 2014 event in the Cerrado biome. This research aimed to understand drought propagation and patterns over these biomes through joint analysis of hydrological, climatic, and vegetation indices based on remote sensing data. To understand the drought cascade propagation patterns, we assessed precipitation, evapotranspiration, soil moisture (at surface and sub-surface), terrestrial water storage, land surface temperature, enhanced vegetation index, and gross primary productivity. Similar drought patterns were observed in the 2015 Amazon and 2012 Pampa droughts, with meteorological and agricultural droughts followed by a hydrological drought, while the 2014 event in the Cerrado was more associated with a hydrological drought. Moreover, the 2015 Amazon drought showed a different pattern than that of 2010, with higher anomalies in precipitation and lower anomalies in evapotranspiration. Thus, drought propagation behaves differently in distinct Brazilian biomes. Our results highlight that terrestrial water storage anomalies were able to represent the hydrological drought patterns over the country. Our findings reveal important aspects of drought propagation using remote sensing in a heterogenous country largely affected by such events. Full article

(This article belongs to the Special Issue Remote Sensing of the Amazon Region)

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28 pages, 43916 KiB

Open AccessArticle

Use of Airborne Radar Images and Machine Learning Algorithms to Map Soil Clay, Silt, and Sand Contents in Remote Areas under the Amazon Rainforest

by Ana Carolina de S. Ferreira, Marcos B. Ceddia, Elias M. Costa, Érika F. M. Pinheiro, Mariana Melo do Nascimento and Gustavo M. Vasques

Remote Sens. 2022, 14(22), 5711; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225711 - 11 Nov 2022

Cited by 1 | Viewed by 1530

Abstract

Soil texture has a great influence on the physical–hydric and chemical behavior of soils. In the Amazon regions, due to the presence of dense forest cover and limited access to roads, carrying out surveys and mapping of soils is challenging. When data exist, they are relatively sparse and the distribution is quite uneven. In this context, machine learning algorithms (ML) associated with remote sensor covariates offer a framework to derive digital maps of soil attributes. The objective of this study was to produce maps of surface and subsurface soil clay, silt, and sand contents in a 13.440 km² area in the Amazon. The specific objectives were to (a) evaluate the gain in prediction accuracy when using the P-band of airborne radar as a covariate; (b) evaluate two sampling approaches (Reference Area—RA and Total Area—TA); and (c) evaluate the transferability and performance of three ML algorithms: regression tree (RT), random forest (RF), and support vector machine (SVM). The study site was divided into three blocks, called Urucu, Araracanga, and Juruá, respectively. The soil dataset consisted of 151 surface and subsurface sand, silt, and clay observations and 21 covariates (20 relief variables and the backscattering coefficient from the P-band). Both the RA and TA sampling approach used 114 observations for training the prediction models (75%) and 37 for validation (25%). The RA approach was better for the development of sand and silt models. Overall, RF derived the most accurate predictions for all variables. The effect of introducing the P-band backscattering coefficient improved the sand prediction accuracy at the surface and subsurface in Araracanga, which had the highest sand content, with relative improvements (RI) of the R², root mean square error (RMSE), and mean absolute error (MAE) of 46%, 3%, and 4% at the surface, respectively, and 66.7%, 4.4%, and 5.2% at the subsurface, respectively. For silt, the P-band improved the predictions at the surface in Araracanga, which had the lowest silt contents among the blocks. For clay, adding the P-band improved the RF predictions at the subsurface, with RI of the R², RMSE, and MAE of 29%, 5%, and 5%, respectively. Despite the low observation density, inherently hindered by the low accessibility of the area and high costs of sampling thereof, the results showed the potential of ML algorithms boosted by airborne radar P-band to map soil clay, silt, and sand contents in the Amazon. Full article

(This article belongs to the Special Issue Remote Sensing of the Amazon Region)

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13 pages, 4381 KiB

Open AccessCommunication

Mining Is a Growing Threat within Indigenous Lands of the Brazilian Amazon

by Guilherme Mataveli, Michel Chaves, João Guerrero, Elton Vicente Escobar-Silva, Katyanne Conceição and Gabriel de Oliveira

Remote Sens. 2022, 14(16), 4092; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14164092 - 21 Aug 2022

Cited by 13 | Viewed by 5580 | Correction

Abstract

Conserving tropical forests is crucial for the environment and future of our climate. Tropical rainforests worldwide, including the Brazilian Legal Amazon (BLA), offer exceptional ecosystem services. However, the disturbances that have been occurring more frequently within them are endangering their key role in tackling climate change. An alternative approach for preserving the intact forests that remain in the BLA is the delimitation of Indigenous Lands (ILs), which can, additionally, ensure the well-being of the traditional peoples inhabiting there. An increase in deforestation rates of the BLA in recent years, due to the weakening of the Brazilian environmental policy, is not confined to unprotected areas but is also occurring within ILs. Under this scenario, mining, not allowed in ILs, is a growing threat in these protected areas. Thus, using the freely available MapBiomas dataset, we have quantified for the first time the total mining area within ILs of the BLA from 1985 to 2020. Such activity jumped from 7.45 km² in 1985 to 102.16 km² in 2020, an alarming increase of 1271%. Three ILs (Kayapó, Mundurukú, and Yanomami) concentrated 95% of the mining activity within ILs in 2020 and, therefore, they require closer monitoring. Most of the mining in ILs in 2020 (99.5%) was related to gold extraction. A total of 25 of the 31 ILs of the BLA where mining activity was detected in at least one of 36 years analyzed (~81% of them) had a statistically significant increasing trend according to the Mann–Kendall test at 5%. The datasets used or cited in this study (MapBiomas, PRODES, and DETER) enable the monitoring of the current status of ILs, and the identification of emerging trends related to illegal activities. Therefore, they are critical tools for legal authorities. Full article

(This article belongs to the Special Issue Remote Sensing of the Amazon Region)

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19 pages, 15720 KiB

Open AccessArticle

Fires Drive Long-Term Environmental Degradation in the Amazon Basin

by Carlos Antonio da Silva Junior, Mendelson Lima, Paulo Eduardo Teodoro, José Francisco de Oliveira-Júnior, Fernando Saragosa Rossi, Beatriz Miky Funatsu, Weslei Butturi, Thaís Lourençoni, Aline Kraeski, Tatiane Deoti Pelissari, Francielli Aloisio Moratelli, Damien Arvor, Iago Manuelson dos Santos Luz, Larissa Pereira Ribeiro Teodoro, Vincent Dubreuil and Vinicius Modolo Teixeira

Remote Sens. 2022, 14(2), 338; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020338 - 12 Jan 2022

Cited by 14 | Viewed by 3709

Abstract

The Amazon Basin is undergoing extensive environmental degradation as a result of deforestation and the rising occurrence of fires. The degradation caused by fires is exacerbated by the occurrence of anomalously dry periods in the Amazon Basin. The objectives of this study were: (i) to quantify the extent of areas that burned between 2001 and 2019 and relate them to extreme drought events in a 20-year time series; (ii) to identify the proportion of countries comprising the Amazon Basin in which environmental degradation was strongly observed, relating the spatial patterns of fires; and (iii) examine the Amazon Basin carbon balance following the occurrence of fires. To this end, the following variables were evaluated by remote sensing between 2001 and 2019: gross primary production, standardized precipitation index, burned areas, fire foci, and carbon emissions. During the examined period, fires affected 23.78% of the total Amazon Basin. Brazil had the largest affected area (220,087 fire foci, 773,360 km² burned area, 54.7% of the total burned in the Amazon Basin), followed by Bolivia (102,499 fire foci, 571,250 km² burned area, 40.4%). Overall, these fires have not only affected forests in agricultural frontier areas (76.91%), but also those in indigenous lands (17.16%) and conservation units (5.93%), which are recognized as biodiversity conservation areas. During the study period, the forest absorbed 1,092,037 Mg of C, but emitted 2908 Tg of C, which is 2.66-fold greater than the C absorbed, thereby compromising the role of the forest in acting as a C sink. Our findings show that environmental degradation caused by fires is related to the occurrence of dry periods in the Amazon Basin. Full article

(This article belongs to the Special Issue Remote Sensing of the Amazon Region)

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20 pages, 3476 KiB

Open AccessArticle

Optical Classification of Lower Amazon Waters Based on In Situ Data and Sentinel-3 Ocean and Land Color Instrument Imagery

by Aline de M. Valerio, Milton Kampel, Vincent Vantrepotte, Nicholas D. Ward and Jeffrey E. Richey

Remote Sens. 2021, 13(16), 3057; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163057 - 04 Aug 2021

Cited by 3 | Viewed by 2668

Abstract

Optical water types (OWTs) were identified from an in situ dataset of concomitant biogeochemical and optical parameters acquired in the Amazon River and its tributaries, in the Lower Amazon region, at different hydrological conditions from 2014 to 2017. A seasonal bio-optical characterization was [...] Read more.

r_{n} (λ)

), allowing the identification of four OWTs. An optical index method was also applied to the

r_{n} (λ)

defining the thresholds of the OWTs. Next, level-3 Sentinel-3 Ocean and Land Color Instrument images representative of the seasonal discharge conditions were classified using the identified in situ OWTs as reference. The differences between Amazon River and clearwater tributary OWTs were dependent on the hydrological dynamics of the Amazon River, also showing a strong seasonal variability. Each OWT was associated with a specific bio-optical and biogeochemical environment assessed from the corresponding absorption coefficient values of colored dissolved organic matter (a_CDOM) and particulate matter (a_p), chlorophyll-a and suspended particulate matter (SPM) concentrations, and a_CDOM/a_p ratio. The rising water season presented a unique OWT with high SPM concentration and high relative contribution of a_p to total absorption compared to the other OWTs. This bio-optical characterization of Lower Amazon River waters represents a first step for developing remote sensing inversion models adjusted to the optical complexity of this region. Full article

(This article belongs to the Special Issue Remote Sensing of the Amazon Region)

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Open AccessCorrection

Correction: Mataveli et al. Mining Is a Growing Threat within Indigenous Lands of the Brazilian Amazon. Remote Sens. 2022, 14, 4092

by Guilherme Mataveli, Michel Chaves, João Guerrero, Elton Vicente Escobar-Silva, Katyanne Conceição and Gabriel de Oliveira

Remote Sens. 2023, 15(11), 2809; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112809 - 29 May 2023

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In the original publication [...] Full article

(This article belongs to the Special Issue Remote Sensing of the Amazon Region)

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